In Silico Drug Design: Repurposing Techniques and Methodologies [1 ed.] 0128161256, 9780128161258

Table of contents :
Front Matter
Copyright
Dedication
Contributors
About the Editor
Preface
Drug Repositioning: New Opportunities for Older Drugs
Introduction
The Fundamentals for Drug Repositioning
Different Approaches to the Development of New Indications for Old Drugs
Serendipity and Text Mining
Observation of Unexpected Side Effects
Detection of a New Role for the Existing Targets
Identification of New Drug-Target Interactions
Drug Repositioning for Specific Disease Phenotypes
Conclusions
Acknowledgment
References
Computational Drug Design Methods-Current and Future Perspectives
Introduction
Overview of Current Approaches Used in Computer-Aided Drug Design
Classification of Computer-Aided Drug Design Methods
Structure-Based Methods
Ligand-Based Methods
Hybrid Methods and Methods Based on End-Points
Main Applications of Computer-Aided Drug Design
Hit Finding
Lead Optimization
Case Studies: Successful Applications of Computer-Aided Drug Design
Trending Concepts and Technologies
Big Data
Web Servers
Workflows
Machine Learning
Applications of Machine Learning in Drug Discovery
Deep Learning
Artificial Intelligence
Molecular Dynamics
General Aspects of Molecular Dynamics
Applications of Molecular Dynamics in Drug Discovery
Challenges and Emerging Problems in Computer-Aided Drug Design
Integration With Other Techniques
Absorption, Distribution, Metabolism, and Excretion, and Toxicity Prediction
Difficult and Emerging Targets
Neglected Diseases
Chemical Space
Advance Multitarget Drug Discovery and Polypharmacology
Training, Teaching, and Divulgation
Conclusions
Acknowledgments
References
Further Reading
In Silico Drug Design Methods for Drug Repurposing
Drug Repurposing
Computational Approaches for Drug Repositioning
Target-Based Methods
Knowledge-Based Methods
Signature-Based Methods
Network-Based Methods
Target Mechanism-Based Methods
Examples of Successful Drug Repositioning
Opportunities and Limitations of In Silico Drug Repositioning
References
Further Reading
Computational Drug Repurposing for Neurodegenerative Diseases
Neurodegenerative Diseases
Drug Repurposing
Activity-Based Drug Repurposing
Phenotypic Screening Approach
Target-Based Screening
Computational Drug Repurposing
Structure-Based Virtual Screening (Molecular Docking)
Ligand-Based Methods
Pharmacophore Model Method
Quantitative Structure-Activity Relationship Methods
Reverse Docking Methods
Transcriptomic-Based Methods
Genome-Wide Association Studies-Based Methods
Network-Based Methods
Integration of Methods
Machine Learning-Based Approaches
Literature-Based Discovery Methods
Drug Repurposing Challenges
Validation of Methods
Drug Combination
Discussion and Conclusion
References
Further Reading
Repurposed Molecules: A New Hope in Tackling Neglected Infectious Diseases
Introduction
Part I: Current Knowledge and Challenges in Neglected Infectious Diseases
Eukaryotic Neglected Infectious Diseases
Leishmaniases
Chagas Disease
Human African Trypanosomiasis
Human Taeniasis
Echinococcoses
Schistosomiasis
Food-Borne Trematode Infections
Lymphatic Filariasis
Onchocerciasis
Soil-Transmitted Helminthiasis
Dracunculiasis
Bacterial NIDs
Buruli Ulcer
Leprosy (Hansen's Disease)
Trachoma
Yaws
Viral Diseases
Dengue
Chikungunya
Rabies
PART II: Tools and Strategies
Useful Databases for Drug Repurposing in Neglected Infectious Diseases
Computational Tools For Drug Repurposing in Neglected Infectious Diseases: A Virtual Translational Reality From Comp ...
Ligand-Based Approaches
Target-Based Approaches
Integration of Databases and Computational Tools for Drug Repurposing in Neglected Infectious Diseases
Perspectives and Concluding Remarks
Acknowledgments
References
Further Reading
Molecular Docking: A Structure-Based Approach for Drug Repurposing
Introduction
Molecular Docking
Basic Requirements for Molecular Docking
Structural Data
Ligand Representation
Receptor Representation
Role of Water in Docking Studies
Types of Docking Methodology
Types of Interactions
Approaches of Molecular Docking
Shape Complementarity Approach
Simulation Approach
Mechanism of Docking
Search Algorithms
Scoring Functions
Force Field-Based Scoring Function
Empirical-Based Scoring Function
Knowledge-Based Scoring Function
Target-Specific Scoring Function
Estimating Binding Affinity With Scoring Functions
Exploring the Energy Landscape in Docking
Protein Flexibility and Binding Affinity Prediction
Postdocking Analysis
Applications of Molecular Docking
Limitations of Molecular Docking
Available Software for Docking
Drug Repurposing
Types of Drug Repurposing
Blind Search or Screening Method
Target-Based Method
Knowledge-Based Methods
Signature-Based Methods
Network- or Pathway-Based Method
Targeted Mechanism-Based Method
Case Studies
Repurposing of Antipsychotic Drugs for Alzheimer's Disease
Repurposed Drug of G-Protein Coupled Receptor Inhibitor
Repurposed Drugs as Modulators of Protein-Protein Interactions
Repurposed Drug as a Nuclear Receptor Antagonist
Repurposed Drug as a Stabilizer of G-Quadruplex DNA
Success of Private-Public Partnership for Drug Repurposing
Limitations of Drug Repurposing
Intellectual Property Considerations
Conclusion
References
Further Reading
Data Science Driven Drug Repurposing for Metabolic Disorders
Introduction
Overview of Metabolic Disorders
Metabolism
Metabolic Disorders and Metabolic Syndrome
Different Components Underlying Metabolic Syndrome
Classification of Metabolic Disorders
General Classification
Disorders in Protein Metabolism
Due to the Inability to Metabolize Some Amino Acids
Due to Organic Acid Disorders
Due to the Urea Cycle Defect Which Is Caused by Any Defect in or Absence of an Enzyme or Cofactors in the Urea Cycle
Disorders in Lipid Metabolism
Disorders in Carbohydrate Metabolism
Diabetes Mellitus
Disorders in Hormone Metabolism
Lysosomal Storage Disorders
Mitochondrial Storage Disorders
Pathophysiological Classification
Metabolic Disorders That Give Rise to Intoxication
Phenylketonuria
Metabolic Disorders Involving Energy Metabolism
Hypoglycemia
Metabolic Disorder Involving Complex Molecules
Lysosomal Storage Disorders
Computational Approaches
Next Generation Sequencing
Personalized Medicine
Big-Data Approaches
Diabetic Retinopathy Screening Using Artificial Intelligence
Drug Repurposing Based on -Omics-Data Mining
Molecular Property Diagnostic Suite
Genetics and -Omics Toolkit to Analyse Gene Function
Functional Annotation of the Human Genome
Classification of Common Human Diseases
Immune Metabolic Interactions
Metabolic System and Immune System
Leptin
IL-6
TNF-α
Dietary Restriction in Metabolic Health
Different Molecular Mechanisms Associated With Metabolic Disorders
Epigenetics, Genetics and Transcriptomics
Host Epigenetic Response in Immune-Metabolic Interactions
Role of Genetics and Transcriptomics in Metabolic Disorders
Structural Bioinformatics
Mutation and Single Nucleotide Polymorphism-Based Structural Analysis
Variation Databases
Deleterious Mutation Prediction
Role of Structural Bioinformatics in Single Nucleotide Polymorphisms Analysis
Protein Secondary Structure Prediction
Molecular Docking and Virtual Screening
Molecular Dynamics Simulation
Importance of Modeling, Informatics, Simulation, and Data Analytics in Drug Discovery
Drug Repurposing
Different Methods for Drug Repurposing
Repurposed Drugs for Metabolic Disorders
Outlook
References
Data-Driven Systems Level Approaches for Drug Repurposing: Combating Drug Resistance in Priority Pathogens
Introduction
High-Throughput Data-Driven Drug Repurposing Platforms
Genomics-Based Approaches for Drug Repurposing
Network-Based Approaches for Drug Repurposing
Structural Proteomics-Based Approaches for Drug Repurposing
Exploring Extended Target Space
Pathogen Specific Essential Genes
Capturing Genetic Complexity in Resistant Strains: Pan Genome Approach
Drug-Resistance Mechanisms to Drive Drug-Repurposing Strategies
Network-Based Target Exploration
Biological Big Data and Network Analyses for Target Identification
Protein-Protein Interaction Networks for Identifying Novel Drug Targets
Metabolic Networks for Drug Target Prediction
Gene Regulatory Networks for Identifying Novel Drug Targets
Extended Chemical Space
Conclusion
References
In Silico Repurposing of Cell Cycle Modulators for Cancer Treatment
Targeting the Cell Cycle in the Treatment of Cancer
Drug Repurposing in Cancer Drug Discovery
In Silico Cell Cycle Modulator Repurposing
Ligand-Based Drug Repurposing
Target-Based Drug Repurposing
Expression-Based Drug Repurposing
Phenotype-Based Drug Repurposing
Challenges and Future Direction
Conclusion
Acknowledgments
References
Proteochemometric Modeling for Drug Repositioning
Overview of Drug Discovery
Polypharmacology and Drug Repositioning
Computational Drug Discovery
Biological and Chemical Repositories
Computational Drug Discovery Approaches
Extending QSAR to Simultaneously Consider Biology and Chemistry via PCM
Case Studies on the Use of PCM for Drug Repositioning
Conclusion
Acknowledgments
References
Drug Repurposing From Transcriptome Data: Methods and Applications
Introduction
Methods
Similarity-Based Methods
Gene Set Enrichment Analysis
Co-Expressed Gene-Set Enrichment Analysis
Other Metrics
Machine-Learning Algorithms
Network-Based Approaches
Matrix Factorization Models
Supervised Classifiers
Applications
Drug-Disease Connections
Disease-Disease Similarities
Drug-Drug Connections
Drug-Target Connections
Drug-Drug Combinations
Databases and Tools
NCBI GEO
ArrayExpress
LINCS
DSigDB
PharmGKB
DrugMatrix
CTD
CDA
CMap
Clue
MANTRA
MarQ
NFFinder
Cogena
ksRepo
GOpredict
Integrity
Gene2Drug
GeneExpressionSignature
DvD
DeSigN
PDOD
Conclusions
References
Omics-Driven Knowledge-Based Discovery of Anthelmintic Targets and Drugs
Background: Parasitic Worms and Anthelmintics Used to Treat Them
Omics Knowledge Expansion to Facilitate New Target/Drug Discovery
Genomes
Transcriptomes
Proteomes
Metabolomes
Species-Centric, -Omics-Based, Drug-Target Identification
Pan-Phylum, -Omics-Based, Drug-Target Identification
Chemogenomic Screening for Identification of Drug-Like Compounds
Experimental Screening, Confirmation, and Validation
Future Considerations
References
Further Reading
Analysis of Chemical Spaces: Implications for Drug Repurposing
Introduction
Available Chemical Data
Network-Based Representations
Chemical Space Networks for Drugs
Chemical Space Networks for Bioactive Compounds
Dimensionality Reduction Methods
Principal Component Analysis
Self-Organizing Maps
Generative Topographic Mapping
Learned Latent Representations of Chemical Space
Scaffold-Based Representation
Privileged Fragments Approach
Hierarchical Clustering Approach
Scaffold-Based Exploration of the Chemical Space
Conclusion
Acknowledgments
References
Examples and Case Studies
Drug Repurposing in Search of Anti-Infectives: Need of the Hour in the Multidrug Resistance&s
Introduction
Computational Drug Repurposing Methods: A Brief Overview
Drug Repurposing for Antibacterial Indication
Infections Caused by Gram-Positive Bacteria
Infections Caused by Gram-Negative Bacteria
Infections Caused by Mycobacterium tuberculosis
Drug Repurposing for Antiviral Indication
Drug Repurposing for Neglected Tropical Diseases
Current Treatments and Drug Development for Neglected Tropical Diseases
Drug Repurposing for Antifungal Indication
References
Further Reading
Application of In Silico Drug Repurposing in Infectious Diseases
Introduction
Why Is Drug Purposing Needed?
No Drug Is Ever Understood Completely
Why This Field of Drug Discovery Is Encouraging: Financial Benefits
Rational Drug Design Pipeline and the Challenges Arise
Who Is the Beneficiary
Neglected Tropical Diseases
Rare or Orphan Diseases
Cancer
Infectious Diseases
Emerging Disease Like Zika Virus
Identifying Repurposing Opportunities: Methods
High-Throughput Screening Based Approach
Computational Approach
Knowledge Mining
Clinical Indications
Novel Target
Target-Based Screening
Challenges of Selectivity by In Silico
Chemoinformatics-Based Methods
Structure-Based and Simulation
Genomics and Proteomics Driven Target Identification (Only for Repurposing)
Genetic Analysis Methods
Drug-Target and Disease-Drug Network/Association
Web-Based Analysis Tools
Machine Learning and Artificial Intelligence
Other Challenges Like Sharing Information and Risk
Summary and Future
References
In Silico Modeling of FDA-Approved Drugs for Discovery of Anticandida Agents: A Drug-Repurposing Approach
Introduction
Methodology
Dataset
Chem-Bioinformatics Approach to Identify Potential Drug-Target Associations in C. albicans
Sequence Analyses
Structural Analyses
Augmenting Chemical Space within the Domain of Approved Drugs
Proof of Concept
Results and Discussion
Identification of Potential Repurposable Drugs
Identification of Potential Drug Targets in Candida albicans
Case Study 1: Cetrimonium-A Potential Repurposable Drug Candidate Against MTS1
Case Study 2: Fusidic Acid-A Potential Repurposable Drug Candidate Against MEF1 and MEF2
Case Study 3: Nitrofural-A Potential Repurposable Drug Candidate Against Multiple Metabolic Enzymes in C. albicans
Other Shortlisted Cases
Aminoglycosidic Antibiotics-Potential Repurposable Anti-Candida Agents
Mupirocin-A Potential Repurposable Anti-Candida Agent
Gatifloxacin-A Potential Repurposable Anti-Candida Agent
Tetracycline Analogues-Potential Repurposable Anti-Candida Agents
Conclusion
Acknowledgments
References
In Silico Modeling of FDA-Approved Drugs for Discovery of Anticancer Agents: A Drug-Repurposing Approach
Introduction
Examples of Structure-Based Virtual Ligand Screening of FDA-Approved Drugs for Discovery of Anticancer Agents
Reprogramming Energy Metabolism
Inducing Angiogenesis
Evading Growth Suppressors
Sustaining Proliferative Signaling
Resisting Cell Death
An Enabling Characteristic: Tumor-Promoting Inflammation
Conclusion
References
Tackling Lung Cancer Drug Resistance Using Integrated Drug-Repurposing Strategy
Introduction: Historical Perspective, Targets and Therapies of Lung Cancer
Emergence, Cause and Statistics
Lung Cancer Targets
ErbB/HER Family of Proteins
Anaplastic Lymphoma Kinase
Mesenchymal Epithelial Transition Factor
BRAF
KRAS
Rearranged During Transfection
ROS1
Fibroblast Growth Factor Receptors
Discoidin Domain Receptor 2
Phosphatidyl 3-Kinase
Vascular Endothelial Growth Factor
Therapies for Lung Cancer
Drug Resistance Pattern in Lung Cancer
Drug Resistance Towards Chemotherapy
Drug Transporters
Drug Inactivation
DNA-Repair Pathways
Loss of Intracellular Death Mechanisms
Drug Resistance Towards Small Molecule Inhibitors
Primary Resistance
Acquired Resistance
Resources for Drug Repurposing in Lung Cancer
Drug Repositioning: A Case Study With HER Proteins of NSCLC
Materials and Methods
Results and Discussion
Central Nervous System Activity and Human Oral Absorption Analysis
Molecular Docking
MCS and PASS Analysis
Protein-Ligand Interaction Analysis
Conclusion
Acknowledgments
References
Further Reading
In Silico Modeling of FDA-Approved Drugs for Discovery of Anti-Cancer Agents: A Drug-Repurposing Approach
Introduction
Basic In Silico Repurposing Strategy Workflow
Big Data Collection and Integration
Different Algorithms for Drug Repurposing
Scientific Literature Mining Strategy
Case Studies
Transcriptional Signatures-Based Predictions for Anticancer Drug Repurposing
Case Studies
Network-Based Drug Repurposing for Anti-Cancer Drug Discovery
Case Studies
Ligand-Based Approach to Drug Repurposing for Anti-Cancer Drug Discovery
Case Studies
Structure-Based Approaches in Drug Repurposing
Case Studies
Multiple In Silico Strategy-Based Approach
Case Studies
Validation Techniques for In Silico Repurposing Study
Summary and Future Prospectives
Acknowledgments
References
Drug Repurposing by Connectivity Mapping and Structural Modeling
Introduction
Genomic C-MAPPING of the Bisphosphonate Gene Signature
In Vitro Validation of C-MAP Hits
Computational Modeling of Bisphosphonate-EGFR Interactions
In Vitro Confirmation of Anticancer Effects
Implications of In Silico Drug Repurposing
Acknowledgments
References
In Silico Modeling of FDA-Approved Drugs for Discovery of Therapies Against Neglected Diseases: A Drug Repurposing Approach
Introduction
Cheminformatics and NTD-Oriented Drug Repurposing
Virtual Screening
The Similarity Ensemble Approach
Molecular Topology and Promiscuity Determinants as Predictors of Drug Repurposing
Bioinformatics and NTD-Oriented Drug Repurposing
High-Throughput Literature Analysis
Network Analysis
Conclusion
Acknowledgments
References
Ascorbic Acid Is a Potential Inhibitor of Collagenases-In Silico and In Vitro Biological Studies
Introduction
Drug Repositioning
Molecular Modeling in Drug Discovery
Molecular Docking
Evaluation of Docking Results
Matrix Metalloproteinases
Inhibitors of MMPs
Zinc-Binding Groups of MMP Inhibitors
Experimental Procedures
Molecular Docking
Ligand Preparation
Ligand Docking
Molecular Dynamic Simulations
Rheumatoid Synovial Fibroblast Culture for MMP-8 Conditioned Medium
Fluorogenic MMP Activity Measurements
MMP-8 Enzyme Assay
Collagen Zymography
Statistical Analyses
Results
Docking Studies
Molecular Dynamic Simulations
Simulation Analysis of 3DNG Protein With GM6001
Simulation Analysis of 3DNG Protein With Vitamin C
Inhibition of MMP-8 Activity Against Collagen by Ascorbic Acid
Discussion
Acknowledgments
References
Bioinformatic Approaches for Repurposing and Repositioning Antibiotics, Antiprotozoals, and Antivirals
Introduction
DRR Framework
Genomics and DRR
Identify Essential Genes/Metabolic Pathways in Pathogens for DRR
Identify Alternative Pathways to Achieve an Essential Function for Effective DRR
Genomic Analysis to Reduce Drug Resistance and Environmental Impact
Transcriptomics and DRR: A Word of Caution
Structural Biology and DRR
Phenotypic Screening, Big Data, and Deep Learning
Conventional Phenotypic Screening in DRR
Problems With Phenotypic Screening and Its Deep Learning Solution
Acknowledgments
References
Further Reading
In Silico Databases and Tools for Drug Repurposing
Introduction
Databases for Drug Repurposing
ADReCS-Target
BindingDB
BioGRID
ChEMBL
ChemProt 3.0
Comparative Toxicogenomics Database
CTD2 Dashboard
DGIdb 3.0
DrugBank 5.0
DrugCentral
ECOdrug
IntAct
KEGG Databases
Pharos
PDBBind
PDID
PharmGKB
STITCH
SuperDRUG2
SuperTarget
IUPHAR/BPS Guide to Pharmacology
Therapeutic Target Database
ChemSpider
ZINC 15
SWEETLEAD
Side Effect Resource (SIDER)
ClinicalTrials.gov
DrugPath
Connectivity Map
ArrayExpress
Gene Expression Omnibus
PubChem
DailyMed
Encyclopedia of Rare Disease Annotation for Precision Medicine
Orphanet
repoDB
Repurposed Drug Database
FAERS
Offsides
ACToR
WITHDRAWN
Web-Based and Stand-Alone Tools for Drug Repurposing
Cogena
DIGEP-Pred
Drug vs. Disease
Galahad
Gene2drug
ksRepo
MANTRA
NFFinder
Drug Repurposing Recommendation System
DrugNet
GeneDiseaseRepositioning
Hetionet and Project Rephetio
PDOD
PROMISCUOUS
ChemMine Tools
C-SPADE
ChemTreeMap
DeSigN
SuperPred
BalestraWeb
DASPFind
DDI-CPI
DINIES
DPDR-CPI and DRAR-CPI
DT-Web and DT-Hybrid
HitPick
iDrugTarget
Polypharmacology Browser
SEA
SwissTargetPrediction
DR. PRODIS
DrugQuest
MeSHDD
PolySearch2
LimTox
Drug Repurposing Hub
RE:fine Drugs
Conclusion
Acknowledgments
References
An Overview of Computational Methods, Tools, Servers, and Databases for Drug Repurposing
Drug Repurposing
Phenotype-Based (Blinded) Screening
Knowledge and Database Methods
Target-Based Methods
Signature-Based Methods
Pathway or Network-Based Methods
Computer-Aided Drug Design
CADD Techniques
Virtual Screening
Docking-Based Virtual Screening
Structure-Based Pharmacophore Screening
Molecular Dynamics Simulation
Target-Based Drug Repurposing Using CADD Techniques
Drug/Small Molecule Databases
Drug Target Databases
Prediction of 3D Structure of Drug Targets
Homology Modeling/Comparative Modeling
Identification of Template Structure
Alignment of Target Sequence to Template
Model Building
Model Optimization and Validation
Binding Site Identification
Virtual Screening for Drug Repurposing
Drug Repurposing for Protein-Protein Interactions
Databases and Tools for Drug Repurposing Using Gene Expression Signatures
Drug Repurposing for Aurora Kinase C Target Using CADD (A Case Study)
Aurora Kinases C Model Building and Validation
Binding Site Identification and Virtual Screening of DrugBank Compounds
Summary
References
In Silico Drug Repurposing for MDR Bacteria: Opportunities and Challenges
Introduction
Computational Methods and Drug Discovery
National Microbial Pathogen Data Resource
Functional Antibiotic Resistant Metagenomic Element Database (FARME-DB)
MEGARes Database
ARGs-OAP (Antibiotic Resistance Gene-Online Analysis Pipeline)
NFFinder Database
Connectivity Map
Network-Based Drug Repurposing
Adverse Drug Reactions and Drug Repurposing
Comprehensive Antibiotic Resistance Database
DeepARG Database
Antibiotic Resistance Genes Database
BacMet
Antibiotic Resistance Gene-ANNOTation (ARG-ANNOT)
Database for Multidrug Resistance Genes (DbMDR)
Challenges for In Silico Drug Discovery
Conclusion
References
Further Reading
Drug Repositioning Strategies to Explore New Candidates Treating Prostate Cancer
Introduction
Evolution of Pharmacology and the Drug Discovery Process
Drug Repositioning as a Promising Strategy in Drug Discovery
Challenges in Drug Discovery for Prostate Cancer
A Conceptual Summary of Drug Repositioning Approaches
Publicly Available Web-Based Tools for Drug Repositioning
Connectivity Map
DeSigN
BalestraWeb
PROMISCUOUS
STITCH
RE:fine Drugs
DSigDB
DRUGSURV
canSAR
geneXpharma
Web-Based Tools as a Reference for Drug Repositioning Studies
RepurposeDB
repoDB
Repurposed Noncancer Drugs Via Web-Based Tools for Prostate Cancer Treatment
Determination of Inputs as Prostate Cancer Signatures
Queries Based on Genes
Queries Based on Differentially Expressed Genes or Proteins
Evaluation of the Outputs from Web-Based Tool Aiming at Drug Repositioning
Future Directions and Concluding Remarks
Acknowledgment
References
PDID: Database of Experimental and Putative Drug Targets in Human Proteome
Introduction
Development and Outline of the PDID Database
eFindSite
SMAP
ILbind
Predictive Quality of ILbind, eFindSite, and SMAP
Content of the PDID Database
Use of the PDID Database
Summary
Acknowledgment
References
Index
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C
D
E
F
G
H
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Citation preview

IN SILICO DRUG DESIGN

IN SILICO DRUG DESIGN Repurposing Techniques and Methodologies Edited by

KUNAL ROY

Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1650, San Diego, CA 92101, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom # 2019 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility. To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein. Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library ISBN 978-0-12-816125-8 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Andre Gerhard Wolff Acquisition Editor: Erin Hill-Parks Editorial Project Manager: Tracy Tufaga Production Project Manager: Sreejith Viswanathan Cover Designer: Miles Hitchens Typeset by SPi Global, India

Dedication

For Aatreyi, Arpit, and Chaitali.

v

Contributors Bashir Akhlaq Akhoon Discovery Informatics Division, CSIR—Indian Institute of Integrative Medicine, Jammu, India Marta E. Alarco´n-Riquelme Medical Genomics, GENYO: Centre for Genomics and Oncological Research, Pfizer/University of Granada/ Andalusian Regional Government, Granada; Unit of Inflammatory Chronic Diseases, Institute of Environmental Medicine, Karolinska Institutet, Solna, Sweden Lucas N. Alberca Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, University of La Plata (UNLP); Argentinean National Council of Scientific and Technical Research (CONICET), Buenos Aires, Argentina Juan I. Alice Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, University of La Plata (UNLP), Buenos Aires, Argentina Nuttapat Anuwongcharoen Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand Kazim Yalcin Arga Department of Bioengineering, Marmara University, Istanbul, Turkey Carolina L. Belllera Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, University of La Plata (UNLP); Argentinean National Council of Scientific and Technical Research (CONICET), Buenos Aires, Argentina Vladimir P. Berishvili Department of Chemistry, Lomonosov Moscow State University, Moscow, Russia

Anshu Bhardwaj CSIR-Institute of Microbial Technology, Chandigarh; Council of Scientific and Industrial Research – Academy of Scientific and Innovative Research, Chennai, India Vijaya Lakshmi Bodiga Department of Molecular Biology, Institute of Genetics & Hospital for Genetic Diseases, Begumpet, Osmania University, Hyderabad, India Sreedhar Bodiga Department of Biochemistry, Kakatiya University, Warangal, India Michal Brylinski Department of Biological Sciences; Center for Computation & Technology, Louisiana State University, Baton Rouge, LA, United States Pedro Carmona-Sa´ez Bioinformatics Unit, GENYO: Centre for Genomics and Oncological Research, Pfizer/University of Granada/ Andalusian Regional Government, Granada, Spain Sohini Chakraborti Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India Vaishali Chaudhry Department of Biotechnology, Graphic Era (deemed to be University), Dehradun, India Lixia Chen Wuya College of Innovation, School of Traditional Chinese Materia Medica, Key Laboratory of Structure-Based Drug Design & Discovery, Ministry of Education, Shenyang Pharmaceutical University, Shenyang, China Mohane Selvaraj Coumar Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India Daniel Toro-Domı´nguez Bioinformatics Unit; Medical Genomics, GENYO: Centre for Genomics and Oncological Research, Pfizer/ University of Granada/Andalusian Regional Government, Granada, Spain

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CONTRIBUTORS

Nikolas Dietis Medical School, University of Cyprus, Nicosia, Cyprus

Bani Jolly CSIR-Institute of Microbial Technology, Chandigarh, India

Noelie Douanne Pathology and Microbiology Department, University of Montreal, SaintHyacinthe, QC, Canada

Prashant S. Kharkar Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM’s NMIMS, Mumbai, India

Dmitry Druzhilovskiy Institute of Biomedical Chemistry, Moscow, Russia K. Eurı´dice Jua´rez-Mercado Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico Christopher Ferna´ndez-Prada Pathology and Microbiology Department, University of Montreal, Saint-Hyacinthe, QC, Canada Pankaj Gautam Department of Life Sciences, Graphic Era (deemed to be University), Dehradun, India Indira Ghosh School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India C. Gopi Mohan Bioinformatics and Computational Biology Lab, Center for Nanosciences and Molecular Medicine, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Kochi, India Shozeb Haider School of Pharmacy, University College, London, United Kingdom Li Hua Hubei Key Laboratory of Natural Medicinal Chemistry and Resource Evaluation, School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan; Wuya College of Innovation, School of Traditional Chinese Materia Medica, Key Laboratory of StructureBased Drug Design & Discovery, Ministry of Education, Shenyang Pharmaceutical University, Shenyang, China Jameel Iqbal Department of Medicine, Mount Sinai School of Medicine; Department of Pathology, James J. Peters VA Medical Center, New York, NY, United States Nivya James Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, India

Se-Min Kim Department of Medicine, Mount Sinai School of Medicine, New York, NY, United States Shivani Kumar University School of Biotechnology, GGS Indraprastha University, Dwarka, New Delhi, India Suresh Kumar University School of Biotechnology, GGS Indraprastha University, Dwarka, New Delhi, India Pawan Kumar School of Computational and Integrative Sciences, Jawaharlal Nehru University, New Delhi, India Lukasz Kurgan Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States Yu-Chen Lo Department of Bioengineering, Stanford University, Stanford, CA, United States Edgar Lo´pez-Lo´pez Medicinal Chemistry Laboratory, University of Veracruz, Veracruz, Mexico Janvhi S. Machhar Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM’s NMIMS, Mumbai, India K. Manzoor Bioinformatics and Computational Biology Lab, Center for Nanosciences and Molecular Medicine, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Kochi, India Jos e L. Medina-Franco Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico Anu R. Melge Bioinformatics and Computational Biology Lab, Center for Nanosciences and Molecular Medicine, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Kochi, India

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CONTRIBUTORS

George Minadakis Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus Aida Minguez-Menendez Pathology and Microbiology Department, University of Montreal, Saint-Hyacinthe, QC, Canada Makedonka Mitreva McDonnell Genome Institute, Washington University in St. Louis; Department of Medicine, Division of Infectious Diseases, Washington University School of Medicine, St. Louis, MO, United States Rubens L. Monte-Neto Rene Rachou Institute, Belo Horizonte, Brazil Gurusamy Muneeswaran Centre for Molecular Modeling, CSIR-Indian Institute of Chemical Technology, Hyderabad, India Selvaraman Nagamani Centre for Molecular Modeling, CSIR-Indian Institute of Chemical Technology, Hyderabad, India Shantikumar V. Nair Bioinformatics and Computational Biology Lab, Center for Nanosciences and Molecular Medicine, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Kochi, India Chanin Nantasenamat Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand G. Narahari Sastry Centre for Molecular Modeling, CSIR-Indian Institute of Chemical Technology, Hyderabad, India Amit Nargotra Discovery Informatics Division, CSIR—Indian Institute of Integrative Medicine, Jammu, India Anastasia A. Nikitina Department of Chemistry, Lomonosov Moscow State University; Institute of Poliomyelitis and Viral Encephalitides, FSBSI Chumakov FSC R&D IBP RAS, Moscow, Russia Alexey A. Orlov Department of Chemistry, Lomonosov Moscow State University; Institute of Poliomyelitis and Viral Encephalitides, FSBSI Chumakov FSC R&D IBP RAS, Moscow, Russia

Dmitry I. Osolodkin Department of Chemistry, Lomonosov Moscow State University; Institute of Poliomyelitis and Viral Encephalitides, FSBSI Chumakov FSC R&D IBP RAS, Moscow, Russia Anastasis Oulas Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus Manoj Kumar Pal Department of Life Sciences, Graphic Era (deemed to be University), Dehradun, India Vladimir A. Palyulin Department of Chemistry, Lomonosov Moscow State University, Moscow, Russia Ashma Pandya India

Delhi Public School, New Delhi,

Anurag Passi CSIR-Institute of Microbial Technology, Chandigarh; Council of Scientific and Industrial Research – Academy of Scientific and Innovative Research, Chennai, India Joan Pena Pathology and Microbiology Department, University of Montreal, SaintHyacinthe, QC, Canada Chuleeporn Phanus-umporn Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand Douglas E.V. Pires Rene Rachou Institute, Belo Horizonte, Brazil Vladimir Poroikov Institute of Biomedical Chemistry, Moscow, Russia Fernando D. Prieto-Martı´nez Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico Eugene V. Radchenko Department of Chemistry, Lomonosov Moscow State University, Moscow, Russia Gayatri Ramakrishnan Molecular Biophysics Unit; Indian Institute of Science Mathematics Initiative, Indian Institute of Science, Bangalore, India Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States

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CONTRIBUTORS

K. Ramanathan Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, India

Kleitos Sokratous Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus

Bruce A. Rosa McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, United States

George M. Spyrou Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus

Rosaleen Sahoo Centre for Molecular Modeling, CSIR-Indian Institute of Chemical Technology, Hyderabad, India

Narayanaswamy Srinivasan Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India

Niteshkumar U. Sahu Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM’s NMIMS, Mumbai, India

Nagaya Sriwanichpoom Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok; Mahidol University International College, Nakhon Pathom, Thailand

Pemra Ozbek Sarica Department of Bioengineering, Faculty of Engineering, Marmara University, I˙stanbul, Turkey Sailu Sarvagalla Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India Kyriaki Savva Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus Marı´a L. Sbaraglini Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, University of La Plata (UNLP), Buenos Aires, Argentina Nalini Schaduangrat Center of Data Mining and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok, Thailand Onur Serc¸ inog˘lu Department of Bioengineering, Faculty of Engineering, Marmara University, I˙stanbul, Turkey Chetan P. Shah Shobhaben Pratapbhai Patel School of Pharmacy and Technology Management, SVKM’s NMIMS, Mumbai, India V. Shanthi Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, India Tina Sharma CSIR-Institute of Microbial Technology, Chandigarh; Council of Scientific and Industrial Research – Academy of Scientific and Innovative Research, Chennai, India

Li Sun Department of Medicine, Mount Sinai School of Medicine, New York, NY, United States Safiulla Basha Syed Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India Alan Talevi Laboratory of Bioactive Research and Development (LIDeB), Department of Biological Sciences, Faculty of Exact Sciences, University of La Plata (UNLP); Argentinean National Council of Scientific and Technical Research (CONICET), Buenos Aires, Argentina Harshita Tiwari Discovery Informatics Division, CSIR—Indian Institute of Integrative Medicine, Jammu, India Jorge Z. Torres Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, CA, United States Luiza G. Tunes Rene Rachou Institute, Belo Horizonte, Brazil Beste Turanli Department of Bioengineering, Istanbul Medeniyet University; Department of Bioengineering, Marmara University, Istanbul, Turkey Rahul Tyagi McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO, United States Chen Wang Department of Computer Science, Virginia Commonwealth University, Richmond, VA, United States

CONTRIBUTORS

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Jarl E.S. Wikberg Department of Pharmaceutical Biosciences, BMC, Uppsala University, Uppsala, Sweden

Samir Zaidi Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States

Xuhua Xia Department of Biology, University of Ottawa; Ottawa Institute of Systems Biology, Ottawa, ON, Canada

Mone Zaidi Department of Medicine, Mount Sinai School of Medicine, New York, NY, United States

Tony Yuen Department of Medicine, Mount Sinai School of Medicine, New York, NY, United States

Alberta Zallone Department of Medicine, Mount Sinai School of Medicine, New York, NY, United States

Margarita Zachariou Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus

Mengzhu Zheng Hubei Key Laboratory of Natural Medicinal Chemistry and Resource Evaluation, School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Neeha Zaidi Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States

About the Editor Dr. Kunal Roy is a professor in the Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India. He has been a recipient of the Commonwealth Academic Staff Fellowship (University of Manchester, 2007) and the Marie Curie International Incoming Fellowship (University of Manchester, 2013). His research interests lie in the fields of QSAR and molecular modeling with applications in drug design and ecotoxicological modeling. Dr. Roy has published more than 280 research articles in refereed journals (current SCOPUS h index 38). He has also coauthored two QSARrelated books, edited three QSAR books, and published more than 10 book chapters. He is Co-Editor-in-Chief of Molecular Diversity (Springer Nature) and Editor-in-Chief of International Journal of Quantitative Structure-Property Relationships (IJQSPR) (IGI Global). He also serves as a member on editorial boards of several international journals.

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Preface In spite of a substantial increase in R&D spending of pharmaceutical companies, the number of new drugs being brought to the market has gone down. In this scenario, drug repurposing has recently become an important branch of drug discovery. The strategies for repositioning can actually yield more preclinical compounds than those with similar internal R&D investments. The major advantages of a drug-repurposing approach are that the preclinical, pharmacokinetic, pharmacodynamic, and toxicity data of the drug are already known, thus reducing the risk of compound development. The compound can then rapidly translate into later phases of clinical studies, resulting in a decreased development cost, a better return on investment, and an accelerated development time. Over the years, biological and chemical information has been generated at an everincreasing pace, marking the entrance into the so-called “big data” era. This offers the scientific community new opportunities to link drugs to diseases, although this relationship is indirect and relies on complex mechanisms of action. Chem/bioinformatics techniques offer an unprecedented opportunity to exercise a rational and exhaustive exploration of all possible repositioning opportunities for most drugs based on available data sources. Compared with experimental repositioning techniques, in silico methods allow a faster repurposing process at a reduced cost, as they apply various strategies to systematically retrieve, integrate, and analyze datasets from diverse sources.

There are two general approaches to drug repositioning: discovering new indications for an existing drug (drug-centric) and identifying effective drugs for a disease (diseasecentric). In the drug-centric space, pharmaceutical companies mainly focus on drug candidates demonstrated to be safe in early phases of clinical trials but that have failed owing to efficacy issues in subsequent clinical trials. In the disease-centric space, repositioning studies usually focus on specific diseases, particularly those chronic diseases that lack safe and effective therapeutic options for long-term treatment and disease stabilization. The underlying hypotheses in drug-centric and disease-centric repositioning are different. The former hypothesizes that “similar drugs” have the same therapeutic effects and are equally effective for a disease, whereas the latter assumes that “similar diseases” need the same therapies and can thus be treated with the same drugs. Both drug-centric and disease-centric strategies encounter the challenge of assessing “similarity” between drugs or between diseases. Different bioinformatics and cheminformatics approaches have been explored for this purpose, including 3D structurebased methods (molecular docking, molecular dynamics), similarity-based methods (quantitative structure-activity relationship or QSAR, similarity ensemble approach), inference-based methods (employing emerging approaches such as network pharmacology and systems biology), and machinelearning algorithms. Network modeling has

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a predominant presence in drug repositioning. Most disease-centric repositioning discovery approaches use genome-wide metrics to assess the similarity between diseases. Finally, an algorithm for drug repurposing should undergo a procedure (validation) to assess its ability to make relevant and accurate predictions. This book provides an introduction to drug repurposing (Section 1), theoretical background and methodologies of useful techniques of cheminformatics and bioinformatics that can be applied for drug repurposing (Section 2), appropriate examples of applications of in silico drug repurposing and case studies (Section 3), along with descriptions of different databases and tools (Section 4). However, some chapters have overlapping information for the sake of completeness, and a chapter might have information relevant to more than one section. Thus, these have been arranged as considered best for ease of reading. Chapter 1 (Drug Repositioning: New Opportunities for Older Drugs) gives an introduction to the topic of drug repurposing. In this chapter, the authors have taken an indepth look at the current state, possibilities, and limitations of further progress in the field of drug repositioning. Chapter 2 (Computational Drug Design Methods—Current and Future Perspectives) offers an introduction to computer-aided drug design methods and examples of their recent applications to drugs that have advanced in clinical trials or that have been approved for clinical use. Chapter 3 (In Silico Drug Design Methods for Drug Repurposing) provides a comprehensive view of various in silico methods, their applicability, advantages, and disadvantages for drug repurposing. Chapter 4 (Computational Drug Repurposing for Neurodegenerative Diseases) provides a comprehensive review of the various

computational drug repurposing methods including structure-based virtual screening, ligand-based, transcriptomics-based, GWASbased, network-based, integration-based, machine learning-based and literature-based methods along with examples of their applications in neurodegenerative diseases. Chapter 5 (Repurposed Molecules: A New Hope in Tackling Neglected Infectious Diseases) discusses the current situation of neglected infectious diseases globally as well as different drug-repurposing strategies— with a particular focus on in silico approaches that can be implemented in current and future pipelines aiming to develop more effective drugs for such diseases. Chapter 6 (Molecular Docking: A Structure-Based Approach for Drug Repurposing) describes the application of molecular docking tools in drug repurposing. Chapter 7 (Data Science Driven Drug Repurposing for Metabolic Disorders) presents different drug discovery approaches directed towards the metabolic disorders with an emphasis on roles of structural bioinformatics and drug repurposing. Chapter 8 (Data-Driven Systems Level Approaches for Drug Repurposing: Combating Drug Resistance in Priority Pathogens) discusses the available high-throughput chemical-biology integrative data platforms including network-based approaches for drug repurposing. Chapter 9 (In Silico Repurposing of Cell Cycle Modulators for Cancer Treatment) provides an overview of the broad spectrum state-of-the-art computational techniques for drug repositioning using cheminformatics, structural bioinformatics, bioactivity profiling, and phenotype analysis. The authors further discuss several challenges of current in silico drug repositioning approaches and suggest future directions to guide further development of this emerging field in the context of cancer drug discovery and research.

PREFACE

Chapter 10 (Proteochemometric Modeling for Drug Repositioning) briefly explores the concepts of polypharmacology and computational drug discovery and finally examines the use of proteochemometrics, which extends QSAR to an extra dimension by allowing several target proteins to be considered in a single model, for drug repositioning. Chapter 11 (Drug Repurposing From Transcriptome Data: Methods and Applications) describes in detail the methods, applications and computational resources for drug repositioning from gene expression signature data. Chapter 12 (Omics-driven Knowledge Based Discovery of Anthelmintic Targets and Drugs) discusses multiomics datadriven large-scale searches for novel targets against helminths. Chapter 13 (Analysis of Chemical Spaces: Implications for Drug Repurposing) presents a review of approaches to chemical space analysis and discusses their potential application in the field of drug repurposing. Chapter 14 (Drug Repurposing in Search of Antiinfectives: Need of the Hour in the Multidrug Resistance Era!) discusses drug repurposing investigations, with a particular emphasis on the computational aspects, against bacterial, viral, antiparasitic (with particular emphasis on neglected tropical diseases), and fungal infections over the last 3–5 years. Chapter 15 (Application of In Silico Drug Repurposing in Infectious Diseases) discusses the opportunities to achieve selectivity using computational pipeline in the repurposing strategy as applied to, especially, infectious diseases. Chapter 16 (In Silico Modeling of FDAapproved Drugs for Discovery of AntiCandida Agents: A Drug Repurposing Approach) describes an in silico drugrepurposing strategy to identify potential

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anti-Candida agents from the existing repertoire of FDA-approved drugs by means of comparative analyses of targets of approved drugs and proteins of C. albicans. Chapter 17 (In Silico Modeling of FDAApproved Drugs for Discovery of Anticancer Agents: A Drug Repurposing Approach) provides a brief overview of recent advances in in silico modeling of FDA-approved drugs for discovery of anticancer agents by using of various computational approaches. Chapter 18 (Tackling Lung Cancer Drug Resistance Using Integrated Drug Repurposing Strategy) presents various drug repurposing repositories that may help in the designing of novel inhibitors in lung cancer. Further, the authors employed in silico drug repurposing strategy to identify novel inhibitors of EGFR and HER4 proteins. Chapter 19 (In Silico Modeling of FDAApproved Drugs for Discovery of Anticancer Agents: A Drug Repurposing Approach) provides a comprehensive understanding of the processes involved in in silico drug repurposing in cancer along with their respective case studies describing in detail the computational trends adopted for effective drug repurposing. Chapter 20 (Drug Repurposing by Connectivity Mapping and Structural Modeling) describes a combinatorial approach for drug discovery using two technologies—genomic connectivity mapping (C-MAPping) and structural modeling—to repurpose drugs, using bisphosphonates as a paradigm. Chapter 21 (In Silico Modeling of FDAApproved Drugs for Discovery of Therapies Against Neglected Diseases: A Drug Repurposing Approach) presents an overview of cheminformatic, bioinformatic, text mining, and network-based approaches that are currently applied to guide drug repurposing, including virtual screening, target fishing, estimation of molecular determinants of promiscuity, binding site similarity

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predictions, the similarity ensemble approach, among others. Specific applications of such techniques within the field of neglected conditions are also discussed. Chapter 22 (Ascorbic Acid Is a Potential Inhibitor of Collagenases—In Silico and In Vitro Biological Studies) analyzes the potential inhibitory action of ascorbic acid with proven bioavailability against collagenases by computational docking studies. Chapter 23 (Bioinformatic Approaches for Repurposing and Repositioning Antibiotics, Antiprotozoals, and Antivirals) presents a detailed comparison of transcriptomic results from different programs to highlight the point that many claimed findings obtained from analyzing transcriptomic data may be false discoveries. Chapter 24 (In Silico Databases and Tools for Drug Repurposing) provides an overview of the existing online drug-related databases and tools that can be used in a drug repurposing study. Chapter 25 (An Overview of Computational Methods, Tools, Servers, and Databases for Drug Repurposing) discusses the common computer-aided drug design techniques (involving both ligand- and structure-based approaches) with a particular emphasis on the open source in silico

tools, databases, and servers available for this purpose. Chapter 26 (In Silico Drug Repurposing for MDR Bacteria: Opportunities and Challenges) provides a synopsis of useful online databases for drug repurposing against antibiotic resistance strains and briefly mentions the challenges of computational analysis for future improvements. Chapter 27 (Drug Repositioning Strategies to Explore New Candidates Treating Prostate Cancer) discusses different publicly available web-based tools for drug repositioning, and then provides a comprehensive analysis on the repurposed noncancer drugs against prostate cancer through these tools. Chapter 28 (PDID: Database of Experimental and Putative Drug Targets in Human Proteome) describes the Protein-Drug Interaction Database (PDID), a new resource that comprehensively covers experimental and putative drug-protein interactions. I am confident that this collection of 28 chapters will be useful to the researchers working in the field of drug repurposing and drug discovery in general. Kunal Roy Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India

C H A P T E R

1 Drug Repositioning: New Opportunities for Older Drugs Vladimir Poroikov, Dmitry Druzhilovskiy Institute of Biomedical Chemistry, Moscow, Russia

1 INTRODUCTION Recently, drug repositioning (also called drug repurposing, drug reprofiling, drug reformulating or drug redirecting) has gained popularity (Fig. 1). The attractiveness of drug repositioning leads to the organization of different thematic conferences (e.g., 7th Annual Drug Repositioning, Repositioning, and Rescue Conference; https://www. drugrepositioningconference.com/index/), occurrence of specialized scientific journals (e.g., Drug Repositioning, Rescue and Repositioning; https://www.liebertpub.com/loi/ DRRR), and development of focused portals (e.g., DRP—Drug Repositioning Portal; http://drugrepositioningportal.com/drug-repositioning-news.php), etc. According to the US National Comprehensive Cancer Network (NCCN), 50%–75% of drugs have been used off-label in the United States ( Jin & Wong, 2014). The National Center for Advancing Translational Sciences (NCATS, NIH) has invested $575 million in drug rescuing and repositioning ( Jin & Wong, 2014). The Center for World Health & Medicine (CWHM, NIH) has initiated the creation of a screening platform for the development of drugs for rare/neglected diseases (Huang et al., 2011). Ashburn and Thor defined drug repositioning as “The process of finding new uses outside the scope of the original medical indication for existing drugs” (Ashburn & Thor, 2004). In contrast to the discovery of new medicines, drug repositioning is less time-consuming, requires a significantly lower amount of financial expenses and is associated with a reduced risk of unfavorable results (Fig. 2). A significant reduction in the cost, time, and risk estimates presented in Fig. 2 may be achieved only if a drug is proposed for a repurposed indication in the same formulation, doses, and route of administration as the initial indication. Thus drug repositioning may

In Silico Drug Design. https://doi.org/10.1016/B978-0-12-816125-8.00001-8

3 # 2019 Elsevier Inc. All rights reserved.

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FIG. 1 The number of publications on drug repositioning indexed in Scopus and Web of Sciences in 2001–2017. Query: “drug repositioning” OR “drug repositioning” OR “drug reprofiling” OR “drug reformulating” OR “drug redirecting.”

FIG. 2 Drug repositioning in comparison with the discovery of new chemical entities and development of “me-toodrugs”: time, cost, and risk estimates.

differ from off-label drug use, which often suggests the utilization of medicine in different doses, particularly in pediatric practice (Wittich, Burkle, & Lanierb, 2012). Currently, there are several hundred drugs that are utilized in medical practice both for initial and repurposed indications. For example, acetylsalicylic acid (Aspirin) was launched as a nonsteroidal antiinflammatory agent in 1897, and as an antithrombotic agent in 1956; Zidovudine was launched as an anticancer agent in 1964 and as an anti-HIV agent in 1987; etc. Information about many repurposed drugs may be found at the Drug Repositioning Portal (http://drugrepositioningportal.com/drug-repositioning-news.php) as well as in published

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literature. However, to be sure that a particular drug exhibits the repositioned indication mentioned in a particular publication, it is necessary to carry out an extensive informational search and carefully analyze the results. For instance, Ceftriaxone had been suggested (Yacila & Sari, 2014) to be useful for the treatment of amyotrophic lateral sclerosis, but its efficacy was not confirmed in the subsequent clinical trials (Cudkowicz et al., 2014). Now, to check the results obtained in drug repositioning projects, it is necessary to analyze the information presented at the US FDA official website (https://www.fda.gov/Drugs/) and clinical trials database (https://www.clinicaltrials.gov/). Many new indications for old drugs were discovered by serendipity due to some additional pharmacotherapeutic effect observed in clinical or preclinical studies. Let us consider the reasons why the potentially useful pharmacotherapeutic actions of the launched drugs were not identified during their first introduction into medical practice.

2 THE FUNDAMENTALS FOR DRUG REPOSITIONING Schematically, the process of new drug discovery may be described as the progressive identification of a pathological condition associated with a particular disease, including the identification of a target, the impact on which can lead to normalization of this pathological condition, and the ligand, which can interact with this target. Such an approach corresponds to “the magic bullet” concept developed in 1900 by Paul Ehrlich, a German physician and scientist awarded the Nobel Prize (Winau, Westphal, & Winau, 2004). However, it was shown that usually “the magic bullet” interacts with several or even many targets modulating their function, which, in some cases, may be favorable for treatment of a particular disorder. Thus the concerted action of pharmaceutical agents on multiple targets was called “the magic shotguns” (Roth, Sheffler, & Kroeze, 2004). Such multitargeted ligands could be designed intentionally from information about the molecular mechanisms of binding (Morphy, Kay, & Rankovic, 2004). Molecular biological, biochemical, and cellular studies conclude that the interaction of the ligand with a particular target does not always result in the desirable pharmacotherapeutic effect. Owing to the numerous negative feedbacks, blockade of a specific node in one part of the signal transduction regulatory network may cause the activation of alternative parts of the network, which may reduce or even prevent the desirable pharmacotherapeutic effect (Hornberg, Bruggeman, Westerhoff, & Lankelma, 2006). For instance, loss of expression of ubiquitous transcription factor CREB in the human cancer cell line H295R leads to upregulation of CREMtau, which compensates the CREB deficiency to maintain CRE regulation by cAMP (Groussin, Massias, Bertagna, & Bertherat, 2000). Taking into account the complexity of modulation of the biological/pathological processes mentioned above, the impact of network pharmacology was recognized as a crucial factor of drug-target-effect relationships (Hopkins, 2007). Thus a relatively simple concept “diseasetarget-ligand” (Fig. 3) is replaced by a more complicated paradigm “disease-pathway-target-ligand” (Fig. 4). Currently, in many cases, the study of the action of drugs begins with the analysis of biological activity in biochemical or cellular tests in vitro; then their specific action must be

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1. DRUG REPOSITIONING: NEW OPPORTUNITIES FOR OLDER DRUGS

FIG. 3

Simplistic “magic bullet” concept as the basis for new drugs discovery.

FIG. 4 A network pharmacology paradigm as the basis for new drugs discovery.

confirmed in animal experiments. After confirmation of their specific activity in vivo, the safety of lead compounds is investigated in preclinical studies. If the safety and efficacy of particular compounds are substantiated, they become drug-candidates and are further investigated in clinical trials. Preclinical studies of drugs safety are carried out following the good laboratory practice (GLP) standards, and clinical studies are carried out in accordance with the good clinical practice (GCP) standards. In contrast to the latest stages of the drug research and development process conducted according to the GLP and GCP protocols, the early stages of drug discovery are often performed in exploratory studies using the nonstandardized assays. Taking into account the difference in terms and conditions of experiments, it is sometimes rather difficult to compare the results obtained. Moreover, the conclusion that the compound under study is shown to be inactive in a specific assay may be drawn due to the imperfection of this particular

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analysis for this ligand-target interaction. Also, the results obtained for one kind of experimental animals may be not transferable to other laboratory species and, finally, to the human. A good example illustrating such a situation is the CS-514 molecule, which was extracted from fungi Penicillium citrinum by Sankyo Pharma in 1970. Since its potent inhibitory activity against hydroxymethylglutaril-CoA (HMG-CoA) reductase was shown in vitro, it was suggested as a promising lead compound for the treatment of hypercholesterolemia and hyperlipidemia. However, CS-514 was found to be inactive during in vivo testing in mice and rats, which might be considered as a reason for termination of the project. Fortunately, the company decided to study CS-514 in hens, and it was found to be active. Then its activity was confirmed in rabbits, dogs and, finally, in humans (Fig. 5). In 1989 pravastatin sodium was registered as a medicine for treatment of familial hypercholesterolemia and hyperlipidemia. In 2005 Pravachol (pravastatin sodium) became a blockbuster in the United States with annual sales of 1.3 billion dollars (Yoshino et al., 1986). Thus it is necessary to be very cautious in considering a particular drug-like compound as inactive because there are different aspects that might lead to the wrong conclusions (Lipinski & Hopkins, 2004). Taking into account the concerns mentioned above, the concepts presented in Figs. 3 and 4 should be complicated (Fig. 6). It is worth reminding ourselves of the statement of Ivan Pavlov, another recipient of the Nobel Prize, made in 1894: “On the vast territory of medical knowledge pharmacology seems, one may say the border, where there is a particularly lively exchange of services between the

FIG. 5 The history of pravastatin discovery by Sankyo Pharma.

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FIG. 6 Relationships “disease-pathway-targetligand” should be considered keeping in mind the different experimental terms and conditions (TC).

natural scientific basis of medicine, physiology, and medical knowledge - therapy, and where therefore particularly felt the mutual usefulness of one knowledge to another. Pharmacology, studying animal drug action by using physiological methods, improving therapy, puts it on a rational solid ground; on the other hand, the treatment indication, subjected to laboratory analysis, often leads to the discovery of the such physiological phenomena that would remain undetected for a long time with pure physiological study” (Pavlov, 1894). Therefore investigating the biological action of drug-like substances, one should keep in mind several important issues: • Most of the ligands may exhibit multitargeted action. • Knowledge about the functioning of biological systems in normal and pathological states is incomplete. • Any in vitro or in vivo assays/experimental model provides only partial information regarding the pharmacological potential of the compound under study. Due to continual progress in the development of experimental techniques resulting in a better understanding of the pathological mechanisms and their normalization by ligandstargets-pathway interactions, new useful features of old drugs, which are currently used in medical practice or removed from the market for any reason, may be discovered.

3 DIFFERENT APPROACHES TO THE DEVELOPMENT OF NEW INDICATIONS FOR OLD DRUGS An overview of the existing approaches to drug repositioning is given in Fig. 7. They include the detection of unexpected side effects, the establishment of drug interactions with new targets, the construction of new regulatory networks specifying particular signs of disease, the identification of pharmaceutical agents that could affect individual phenotypic manifestations of a disease, and the identification of new associative linkages by the application of text-mining. In many cases, two or more of the listed approaches are applied in combination,

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3 DIFFERENT APPROACHES TO THE DEVELOPMENT OF NEW INDICATIONS FOR OLD DRUGS

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FIG. 7 Different approaches to drug repositioning.

thus allowing for the exploration of the total universe of available biomedical and clinical information. Some examples demonstrating the possibilities of the methods mentioned previously are given in the following sections.

3.1 Serendipity and Text Mining Thalidomide was initially introduced into clinical practice in 1957 as an over-the-counter sedative medicine, particularly recommended for the treatment of morning sickness in pregnant women. It was withdrawn from the market in the early 60s due to its teratogenic action, which caused severe birth defects in thousands of children (Kim & Scialli, 2011). It is necessary to mention that thalidomide safety had been tested in rodents according to the existing standards at that time; however, neither in mice nor rats was a teratogenic effect of the drug identified. Based on this observation, testing of teratogenic action in other species (guinea pig, rabbit) has been added as a requirement to the appropriate protocols for teratogenicity assessment. Despite its harmful effects, FDA approved thalidomide for the treatment of leprosy in 1998 and multiple myeloma in 2003 (https://integrity.clarivate.com/). However, the distribution of thalidomide is regulated by a particular System for Thalidomide Education and Prescribing Safety (S.T.E.P.S.) program. These additional useful applications of thalidomide have been found due to the serendipity. Text mining based on the so-called ABC Swanson’s rule allowed the identification of some new possible pharmacotherapeutic effects of thalidomide. Swanson considered association studies in medical literature as a potential source for new discoveries: “If concepts A and B

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are reported to be related to one set of publications and concepts B and C are reported to be related to another set, then A and C might be indirectly related to each other” (Swanson, 1990). Based on this rule, Marc Weeber and coauthors analyzed the available medical literature and concluded that thalidomide might be useful for the treatment of acute pancreatitis, chronic hepatitis C, Helicobacter pylori-induced gastritis, and myasthenia gravis (Weeber et al., 2003). Our informational search in PubMed at least partially supported these hypotheses, e.g., in the case of acute pancreatitis (Lv, Fan, Li, Meng, & Liu, 2015; Lv, Li, Ji, Li, & Fan, 2014), hepatitis C (Milazzo et al., 2006; Pardo-Yules et al., 2011), and myasthenia gravis (Crain, McIntosh, Gordon, Pestronk, & Drachman, 1989). Successful examples of text mining application for drug repositioning lead to systematic studies in this field (Baker, Ekins, Williams, & Tropsha, 2018; Capuzzi et al., 2018). Alex Tropsha and coauthors developed a web server for mining drug-target-disease relationships (Capuzzi et al., 2018) and applied it for the analysis of over 25 million papers in PubMed (Baker et al., 2018). As a result, they found that more than 60% of drugs or drug-candidates have been studied for two or several diseases. About 200 drugs have been tried in more than 300 diseases, which in the majority of cases were rather close to the initial therapeutic applications; however, some unexpected therapeutic areas have been identified as well (Baker et al., 2018).

3.2 Observation of Unexpected Side Effects A classic example of drug repositioning based on unexpected side effects is sildenafil (Viagra), which was initially investigated as a remedy for the treatment of a painful heart condition called angina. During the clinical trials, it was found that this medicine could be applied efficiently for the treatment of erectile dysfunction, and it is widely used now for this indication (Osterloh, 2004). Currently, information about the side effects of various drugs is available from the SIDER resource (http://sideeffects.embl.de/). SIDER 4.1 contains data on 1430 drugs, 5868 side effects, and 139,756 drug-side effect pairs. Weida Tong and coauthors proposed a phenome-guided approach to drug repositioning (Bisgin et al., 2014). The authors assumed that all known phenotypes in the human population are characterized by a combination of indications and side effects. Based on the developed latent Dirichlet allocation (LDA) model the authors were able to predict 70% of pairs of probable significance. For further validation of the LDA model, they carried out the prediction of indications. The approved indications for six drugs not listed in SIDER were predicted successfully. Also, for 908 drugs, some alternative indications were predicted; information from the scientific literature supports some of these findings. The authors came to the conclusion that the phenome can be further analyzed to discover novel associations between the launched drugs and their therapeutic uses. To provide the possibility for systematic analysis of drug-side effect associations, a particular knowledge base has been created by the review of 119,085,682 MEDLINE sentences and their parse trees (Xu & Wang, 2014). The authors identified 38,871 drug-side effect pairs, most of which have not been mentioned in FDA drug labels. The extracted drug side effects correlated positively with drug targets, metabolism, and indications.

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Recently, a novel approach has been proposed based on the analysis of information presented in social media, to identify new indications for drugs used in clinical practice (Nugent, Plachouras, & Leidner, 2016; Rastegar-Mojarad, Liu, & Nambisan, 2016). The authors (Rastegar-Mojarad et al., 2016) developed a dictionary-based system and applied a machine-learning method to analyze several thousand diseases mentioned in 64,616 patients’ comments. Ten common patterns used by patients to report any beneficial effects or uses of medication were identified. The conclusion of this preliminary study regarding the potential benefits of social media for drug repositioning was quite obvious (Rastegar-Mojarad et al., 2016).

3.3 Detection of a New Role for the Existing Targets As an example of such an approach to drug repositioning, let us consider 5 alpha-reductase inhibitor finasteride, which was approved by Merck Sharp & Dohme and Sigma-Tau for the treatment of benign prostatic hyperplasia (BPH) under the trade name Proscar in 1992 (https://integrity.clarivate.com/). In 1998 Merck & Co. launched finasteride under the trade name Propecia for stimulation of hair growth in patients with mild-to-moderate androgenic alopecia (https://integrity.clarivate.com/). 5-Alpha-reductase is a microsomal enzyme that converts testosterone into dihydrotestosterone and progesterone or corticosterone into their 5-alpha-3-oxosteroids. Overproduction of androgens (particularly DHT) is associated with benign prostatic hyperplasia; thus inhibition of this enzyme is useful in the treatment of BPH. On the other hand, overproduction of androgens leads to a hair loss problem, and drugs with an antiandrogen effect like finasteride may be useful for alopecia treatment. It is interesting to note that Propecia contains a fivefold lower dose of finasteride comparing to Proscar, and in the appropriate drug label, it is emphasized that Propecia cannot be used for the treatment of BPH (https://www.fda.gov/Drugs/). Another example of drug repositioning due to the identification of a new role for the existing targets is crizotinib, a dual inhibitor of hepatocyte growth factor receptor (c-Met/ HGFR) kinase and anaplastic lymphoma kinase (ALK) (https://integrity.clarivate.com/). In 2011 this drug was launched for the treatment of patients with ALK-positive advanced or metastatic nonsmall cell lung cancer (NSCLC). In 2016 the FDA and EMA approved crizotinib for the treatment of patients with ROS1-positive advanced NSCLC (https:// integrity.clarivate.com/). In general, to reveal a new role of known targets, which may result in drug repositioning, it is necessary to apply a systems pharmacology approach. This approach is also utilized for the discovery of new pathways associated with a particular disease. More information about systems pharmacology methods for detecting disease-pathway-target relationships may be found in recent reviews (Fotis, Antoranz, Hatziavramidis, Sakellaropoulos, & Alexopoulos, 2018; Kibble et al., 2015; Zhang, Bai, Wang, & Xiao, 2016).

3.4 Identification of New Drug-Target Interactions As we have already mentioned, most of the known pharmaceuticals exhibit pleiotropic pharmacological effects due to interaction with several or even many targets. There are many

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millions of previously synthesized drug-like compounds (http://www.chemnavigator.com/), and many more such molecules could be obtained (https://cactus.nci.nih.gov/download/savi_ download/). Testing of such a huge number of molecules against many thousands of targets (Oprea et al., 2018) is not a feasible task from both economical and practical points of view. Thus computational estimates become the “methods-of-the-choice” for launching priorities in drugtarget pairs to be tested. Both target-based and ligand-based drug design approaches are used for this purpose (Bezhentsev et al., 2017; Lagunin et al., 2014). Target-based methods are based on molecular docking, which is applied for inverse virtual screening (Lee, Lee, & Kim, 2016; Luo et al., 2016; Xu, Huang, & Zou, 2018). Some examples demonstrating the potential of this approach for drug repositioning are considered below. For instance, Vilar and coauthors (Vilar et al., 2016) proposed to calculate the so-called target interaction profile fingerprints (TIPFs), which may be used to generate a hypothesis regarding new putative drug-target interactions. Predicted interaction with monoamine oxidase B of carbonic anhydrase inhibitor ethoxzolamide, which is used for the treatment of glaucoma, was confirmed by experiment (IC50 ¼ 25 μM). Also, several drugs and drugcandidates including lapatinib, SB-202190, RO-316233, GW786460X and indirubin-30 monoxime were predicted to interact with cyclooxygenase-1. It was shown that SB-202190 and RO-316233 have IC50 values comparable with those for reference drugs diclofenac and indomethacin at the same experimental conditions (24 and 25 μM, respectively). Moderate COX-1 inhibitory activity was shown for lapatinib and indirubin-30 -monoxime as well. Another example (Hamdoun, Jung, & Efferth, 2017) demonstrated how molecular docking allowed the identification of the probable binding of anthelmintic Niclosamide to the ATPbinding site of glutathione synthetase (calculated scoring function value about 9.40 kcal/ mol). The experiment confirmed this prediction: it was found that the binding constant between niclosamide and recombinant human glutathione synthetase is about 5.64 μM. Anticancer activity of niclosamide was established in the cellular assay, and the drug exhibited potent activity against the multidrug-resistant CEM/ADR5000 leukemia cells. Despite a successful demonstration of molecular docking potential for detection of new targets of the existing pharmaceuticals, some issues have complicated its broad application for drug repositioning (Chen, 2015; Schomburg & Rarey, 2014). The most substantial limitation is that this method requires knowledge of the 3D structure of the target protein, which is not always available, particularly for membrane-bound proteins or large protein complexes. Second, there is no clear correlation between the calculated scoring function and experimentally determined binding energies. Third, time-consuming calculations are necessary to estimate the possibilities of drug binding to many target proteins. Finally, in the direct comparative study, a better performance of ligand-based methods in estimating biological activity profiles for drug-like molecules was found (Druzhilovskiy et al., 2016). Ligand-based drug-design methods require data on the structure and activity of drug-like compounds, which may be used for the creation of (Q)SAR or pharmacophore models (Bezhentsev et al., 2017; Lagunin et al., 2014). This information may be obtained from several freely available databases (e.g., PubChem (https://pubchem.ncbi.nlm.nih.gov/), ChEMBL (https://www.ebi.ac.uk/chembl/), DrugBank (https://www.drugbank.ca/), etc.) and

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commercially available web resources (e.g., Integrity; https://integrity.clarivate.com/). For instance, ChEMBL 23th version contains 2,101,843 records on compounds (1,735,442 unique structures), 11,538 records on targets, and 14,675,320 records on biological activity. This substantial information is to be used as a training set for developing multiple (Q)SAR models, which may be applied for the prediction of the biological activity of drug profiles. These computational tools have been recently described in the literature (Cheng, Zhou, Li, Liu, & Tang, 2012; Hu, Lounkine, & Bajorath, 2014; Huang et al., 2017; Pogodin, Lagunin, Filimonov, & Poroikov, 2015; Shaikh, Sharma, & Garg, 2016) and some of them are freely available via the Internet (e.g., http://prediction.charite.de; http://www.cbligand.org/ TargetHunter/; http://potentia.cbs.dtu.dk/ChemProt/; http://www.swisstargetprediction. ch/; http://sea.bkslab.org/). The first freely available web service that predicted many kinds of biological activity based on a structural formula of the drug-like compound is PASS Online (Lagunin, Stepanchikova, Filimonov, & Poroikov, 2000; http://www.way2drug.com/passonline/). Currently, PASS (prediction of activity spectra for substances) predicts over 4000 kinds of biological activity with an average accuracy of about 95% and is widely used by more than 19,000 researchers from more than 100 countries to optimize the synthesis and biological testing of the studied organic molecules (Filimonov et al., 2014, 2018). In 2001 PASS predictions regarding new pharmacotherapeutic effects of eight from the Top200 drug list were published (Poroikov, Akimov, Shabelnikova, & Filimonov, 2001). It was suggested that sertraline might be used for cocaine dependency treatment amlodipine may be used as an antineoplastic enhancer, ramipril could be used for the treatment of arthritis, and oxaprozin may be applied as an interleukin 1 antagonist. A recent analysis of the published literature confirmed those predictions (Murtazalieva, Druzhilovskiy, Goel, Sastry, & Poroikov, 2017). Special computational experiments were performed to compare the accuracy of the predicted initial and repurposed indications of 50 well-known repositioned drugs and 12 recently patented medicines (Murtazalieva et al., 2017). It was shown that PASS Online performance exceeds those from several other freely available web services predicting biological activity profiles (http:// prediction.charite.de; http://www.cbligand.org/TargetHunter/; http://potentia.cbs.dtu. dk/ChemProt/; http://www.swisstargetprediction.ch/; http://sea.bkslab.org/).

3.5 Drug Repositioning for Specific Disease Phenotypes Recent progress in genomics, transcriptomics, proteomics, metabolomics, and other OMIC sciences resulted in the proposition of the concept of P4 medicine (Galas & Hood, 2009; Hood, Rowen, Galas, & Aitchison, 2008). It is expected that the development of P4 (personalized, predictive, preventive, and participatory) medicine will result in the large-scale integration of complementary skills, technologies, computational tools, patient records, and samples and analysis of societal issues, which will be beneficial for patients, and society in general (Xu, Li, & Wang, 2013). Since a particular phenotype makes an essential contribution to a disease, “the systematic study of phenotypic relationships among human diseases and integration of disease phenotypic data with the existing genetic and ‘omics’ data will allow for elucidation of the disease genetic mechanisms and development of effective drug therapies without requiring detailed

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knowledge of the exact relationships among genes, which often are not clearly understood” ( Jadamba & Shin, 2016). Therefore several approaches have been proposed for the utilization of phenotypic information aimed at drug repositioning (Chen, Gao, Wang, & Xu, 2016; Jadamba & Shin, 2016; Oh et al., 2017; Xu et al., 2013). For instance, Xu and coauthors (Xu et al., 2013) built a diseasephenotype knowledge base by extracting information about disease-manifestation relationships from the literature. Chen and coauthors performed a combined analysis of information about human glioblastoma genomics and mouse glioblastoma phenotypes (Chen et al., 2016). Investigation of FDA-approved drugs for candidates that share similar mouse phenotype profiles with glioblastoma allowed for the prioritization of the existing drugs for the treatment of disease with a particular phenotype. However, taking into account the considerable variability of disease and patient genotypes and phenotypes, it seems that the identification of compounds modulating specific disease phenotypes is still in its infancy, and many efforts are needed to achieve some clinically useful results.

4 CONCLUSIONS Currently drug repositioning is a very hot topic that is studied by many researchers worldwide. Since drug repositioning requires significantly less time and financial expenses, it provides some possibilities for drug discovery in academia (Oprea et al., 2011; Shamas-Din & Schimmer, 2015; Strovel et al., 2012). In particular, the integration of data on the existing drugs with the predictive computational services on the freely available Drug Repositioning Platform (http://www.way2drug.com/dr) opens up new directions for further research in this field. In general, investigations on drug repositioning are based on the integration of the existing information on diseases, pathways, targets, and ligands. Considering variability of terms and conditions under which experimental and clinical data are obtained, this task seems to be highly challenging. Therefore significant and concerted community efforts are necessary for efficient usage of the existing biomedical and clinical information and extraction of knowledge from this information, which may help to provide better repositioning of the current drugs. Many studies directed at the annotation, curation, and integration of information about chemical-biological interactions are currently underway (https://pubchem.ncbi.nlm. nih.gov/; https://www.ebi.ac.uk/chembl/; https://www.drugbank.ca/; http://www. way2drug.com/dr; Arvidsson, Sandberg, & Forsberg-Nilsson, 2016; Nguyen et al., 2017; Ostaszewski et al., 2018; Williams et al., 2012). They open new opportunities in the field of drug repositioning, which will, eventually, result in the development of more safe and effective medicines in the future.

Acknowledgment The work is supported by the RSF-DST Grant No. 16-45-02012-INT/RUS/RSF/12.

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Pavlov, I. P. (1894). On the incompleteness of the modern physiological analysis of drugs action. Saint Petersburg: Typography of the Ministry of Railways. Pogodin, P. V., Lagunin, A. A., Filimonov, D. A., & Poroikov, V. V. (2015). PASS targets: ligand-based multi-target computational system based on public data and naı¨ve bayes approach. SAR and QSAR in Environmental Research, 26, 783–793. Poroikov, V., Akimov, D., Shabelnikova, E., & Filimonov, D. (2001). Top 200 medicines: can new actions be discovered through computer-aided prediction? SAR and QSAR in Environmental Research, 12, 327–344. Rastegar-Mojarad, M., Liu, H., & Nambisan, P. (2016). Using social media data to identify potential candidates for drug repositioning: a feasibility study. JMIR Research Protocols, 5, e121. Roth, B. L., Sheffler, D. J., & Kroeze, W. K. (2004). Magic shotguns versus magic bullets: selectively non-selective drugs for mood disorders and schizophrenia. Nature Reviews Drug Discovery, 3, 353–359. Schomburg, K. T., & Rarey, M. (2014). What is the potential of structure-based target prediction methods? Future Medicinal Chemistry, 6, 1987–1989. Shaikh, N., Sharma, M., & Garg, P. (2016). An improved approach for predicting drug-target interaction: proteochemometrics to molecular docking. Molecular BioSystems, 12, 1006–1014. Shamas-Din, A., & Schimmer, A. D. (2015). Drug discovery in academia. Experimental Hematology, 43, 713–717. Strovel, J., Sittampalamm, S., Coussens, N. P., Hughes, M., Inglese, J., Kurtz, A., … Weir, S. (2012). Early drug discovery and development guidelines: For academic researchers, collaborators, and start-up companies. Retrieved July 7, 2018, from: http://www.ncbi.nlm.nih.gov/books/NBK92015/. Swanson, D. R. (1990). Medical literature as a potential source of new knowledge. Bulletin of the Medical Library Association, 78, 29–37. Vilar, S., Quezada, E., Uriarte, E., Costanzi, S., Borges, F., Vin˜a, D., & Hripcsak, G. (2016). Computational drug target screening through protein interaction profiles. Scientific Reports, 6, 36969. Weeber, M., Vos, R., Klein, H., De Jong-Van Den Berg, L. T., Aronson, A. R., & Molema, G. (2003). Generating hypotheses by discovering implicit associations in the literature: a case report of a search for new potential therapeutic uses for thalidomide. Journal of the American Medical Informatics Association, 10, 252–259. Williams, A. J., Harland, L., Groth, P., Pettifer, S., Chichester, C., Willighagen, E. L., … Mons, B. (2012). Open PHACTS: semantic interoperability for drug discovery. Drug Discovery Today, 17, 1188–1198. Winau, F., Westphal, O., & Winau, R. (2004). Paul Ehrlich—in search of the magic bullet. Microbes and Infection, 6, 786–789. Wittich, C. M., Burkle, C. M., & Lanierb, W. L. (2012). Ten common questions (and their answers) about off-label drug use. Mayo Clinic Proceedings, 87, 982–990. Xu, X., Huang, M., & Zou, X. (2018). Docking-based inverse virtual screening: methods, applications, and challenges. Biophysical Reports, 4, 1–16. Xu, R., Li, L., & Wang, Q. (2013). Towards building a disease-phenotype knowledge base: extracting diseasemanifestation relationship from literature. Bioinformatics, 29, 2186–2194. Xu, R., & Wang, Q. (2014). Automatic construction of a large-scale and accurate drug-side-effect association knowledge base from biomedical literature. Journal of Biomedical Informatics, 51, 191–199. Yacila, G., & Sari, Y. (2014). Potential therapeutic drugs and methods for the treatment of amyotrophic lateral sclerosis. Current Medicinal Chemistry, 21, 3583–3593. Yoshino, G., Kazumi, T., Kasama, T., Iwatani, I., Iwai, M., Inui, A., … Baba, S. (1986). Effect of CS-514, an inhibitor of 3-hydroxy-3-methylglutaryl coenzyme A reductase, on lipoprotein and apolipoprotein in plasma of hypercholesterolemic diabetics. Diabetes Research and Clinical Practice, 2, 179–181. Zhang, W., Bai, Y., Wang, Y., & Xiao, W. (2016). Polypharmacology in drug discovery: a review from systems pharmacology perspective. Current Pharmaceutical Design, 22, 3171–3181.

1. INTRODUCTION

C H A P T E R

2 Computational Drug Design Methods—Current and Future Perspectives Fernando D. Prieto-Martı´nez*, Edgar Lo´pez-Lo´pez†, K. Eurı´dice Jua´rez-Mercado*, Jos
e L. Medina-Franco* *

Department of Pharmacy, School of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico †Medicinal Chemistry Laboratory, University of Veracruz, Veracruz, Mexico

1 INTRODUCTION The generation of, search for, and experimental evaluation of new molecules with increased potency and selectivity is booming. This is possible with the aid of techniques such as combinatorial chemistry and high-throughput screening (HTS). However, these techniques also generate a large number of false positives, so even with this evaluation capacity it is necessary to further filter screening data sets. Computer-aided drug design (CADD) is a discipline that collects multiple chemical-molecular and quantum strategies with the aim of discovering, designing, and developing therapeutic chemical agents. Many CADD approaches are based on structure-activity relationships (SAR). The main objectives of CADD are part of a multidisciplinary work for the improvement of bioactive molecules, the development of therapeutic alternatives, and the understanding of biological events at the molecular level. In general, the drug-discovery process includes three key stages: (1) the discovery phase, in which the goal is the identification of relevant molecular targets and active molecules or hits; (2) the development phase, where the compounds are evaluated using in vitro and in vivo models (this phase includes various stages: preclinical, clinical I, II, and III); and (3) the registry phase that will enable distribution on the market and the clinical use of drugs. Recent estimates indicate that the average cost of the preclinical phase is 3.4 million dollars,

In Silico Drug Design. https://doi.org/10.1016/B978-0-12-816125-8.00002-X

19 # 2019 Elsevier Inc. All rights reserved.

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2. COMPUTATIONAL DRUG DESIGN METHODS—CURRENT AND FUTURE PERSPECTIVES

increasing to 8.6 and 21.4 million dollars respectively in clinical phases II and III (Martin, Hutchens, Hawkins, & Radnov, 2017). The role of CADD in the drug-discovery process lies mainly in the discovery phase, where a primary goal is to filter and select compounds for experimental synthesis or testing. It is expected that this filtering will reduce the time and costs involved in drug development. In addition, CADD enables the possibility of systematically identifying novel potential uses for drugs already approved for other indications. As discussed later in this chapter and this book, this strategy is called drug repurposing. Computational calculations have played a significant role in the investigation of molecules that are currently in clinical use. For example, CADD has made notable contributions to the treatment of acquired immunodeficiency syndrome, influenza virus infections, glaucoma, and patients with nonsmall-cell lung cancer (Medina-Franco, Martı´nez-Mayorga, Jua´rez-Gordiano, & Castillo, 2007; Talele, Khedkar, & Rigby, 2010). With new technological advances and the application of CADD techniques it is possible to solve complex problems in the pharmaceutical area. Recent related reviews have been published (Prieto-Martı´nez & Medina-Franco, 2018a, 2018b; Talevi, 2018). For instance, Saldı´var-Gonza´lez, Prieto-Martı´nez, and Medina-Franco (2017) commented on the need to further increase these technologies and augment the multidisciplinary investigation of new drugs. Other reviews highlight that CADD represents a systematic manner to merge basic and applied science (Das, 2017). For authors such as Usha, Shanmugarajan, Goyal, Kumar, and Middha (2018), CADD includes a collection of pharmacological, pharmacodynamic, and in silico toxicity predictions, which are useful to identify or filter out active or toxic molecules, respectively. Currently, the development of new computational techniques is allowing a more comprehensive and detailed study of compounds of clinical interest, such as the application of artificial intelligence, big data, chemical space, chemoinformatics, deep learning, molecular modeling, polypharmacology, structure multiple-activity relationships (SmART), target fishing, and virtual screening. These concepts are further commented on the sections later in this chapter. The goal of this chapter is to provide a general introduction of CADD, covering their principal methods, recent successful applications in the development of compounds that currently on the market, and major challenges. The chapter is organized in six major sections. After this introduction, an overview of current methods used in CADD and their major applications is presented. Theoretical frameworks in CADD are covered briefly. Section 3 mentions recent successful examples of CADD. Section 4 discusses trending concepts and topics in the area. Section 5 covers the major challenges involved in the development and application of computational approaches. Section 6 presents summary conclusions.

2 OVERVIEW OF CURRENT APPROACHES USED IN COMPUTER-AIDED DRUG DESIGN During the past 30 years the increase in computational power and the availability of chemogenomic data have allowed computational chemistry methods to become an

1. INTRODUCTION

2 OVERVIEW OF CURRENT APPROACHES USED IN COMPUTER-AIDED DRUG DESIGN

21

FIG. 1 Schematic overview of a representative computer-aided drug design process.

indispensable part in drug discovery. To date, several marketed drugs, for example, imatinib, zanamivir, and nelfinavir, and several clinical candidates, have been discovered or optimized with the aid of molecular modeling techniques. Fig. 1 outlines a summary of the CADD process with concepts and methods discussed in this chapter. The concept of “big data” impacts our everyday life, and the area of CADD is not an exception. Through current computational processors it is possible to collect, evaluate, and analyze molecular characteristics in a massive, systematic, and logical manner. One can make use of the data of each compound to analyze them from different perspectives. In this sense one of the key questions to ask would be: What do I want to analyze? Theoretical chemistry, chemoinformatics, and machine learning (Varnek & Baskin, 2011) provide methods to guide the answers to this question. Using the principles of each major discipline, we can evaluate nearly any kind of “similarity” between molecules. It is through these disciplines that chemistry, biology, and pharmaceutical sciences converge. For drug-discovery applications, “homology modeling” makes use of structural information available to generate 3D models of biological targets that have not been crystallized

1. INTRODUCTION

22

2. COMPUTATIONAL DRUG DESIGN METHODS—CURRENT AND FUTURE PERSPECTIVES

(Sliwoski, Kothiwale, Meiler, & Lowe, 2014). Thus homology modeling is a useful tool to explore and guide, for instance, the structure-based design of novel therapeutic targets or difficult to crystallize, as is the case of calcium channels and some epigenetic and protein complexes. “Molecular docking” is one of the most used techniques to study 3D ligand-target interactions. Comprehensive reviews of docking have been published recently. One of the main purposes of docking is the generation of models that reveal the possible conformations and, thereafter, evaluate which of them are energetically more viable. However, its application is not limited to characterizing ligand-interactions to increase the potency of active molecules. Docking is also valuable to evaluate specificity and drug resistance, based on 3D structure-property relationships (Pagadala, Syed, & Tuszynski, 2017). “De novo” design is another group of CADD techniques. De novo design can be roughly compared to a puzzle, where atoms or small fragments are “fitted” into the 3D structure of a binding site. After which these small fragments need to be connected through linkers. One of the challenges may be to find the suitable linkers that can allow molecules to be synthesized in the laboratory. Therefore key questions are: How can we assemble the candidate compounds? How do we evaluate their potential quality? How do we sample the search space effectively? A comprehensive review of de novo design has been covered in the literature (Schneider & Fechner, 2005). With the advent of technological alternatives for the massive evaluation of compounds or fragments, it has become possible to emphasize the identification of structurally simple hits that can be optimized and generate more powerful ligands. “Fragment-based” screening is based on the fact that a relatively small number of fragments can represent a large fraction of the chemical space. However, for various technical reasons, including the low affinity of the hits fragments and the biophysical methods used for their discovery (e.g., nuclear magnetic resonance, surface plasmon resonance, isothermal calorimetry), fragment-based screening has been limited mainly to in vitro tests with purified proteins (Parker et al., 2017). It is noteworthy that several CADD techniques make large simplifications of the systems and assume, for the sake of speed, that macromolecules are rigid. A classic example is rigid docking. However, sometimes it is compulsory to consider the dynamics in a model, for example, the binding of small compounds to highly flexibly targets or the simulation of the binding of two macromolecules. To this end molecular dynamic techniques are employed that focus on the use of statistical mechanics, quantum chemistry, and the properties of the electric potential (force field) (Ganesan, Coote, & Barakat, 2017). The recovery and analysis of chemical information for any type of application in the physical or biological sciences enter the spectrum of chemoinformatics, as well as the relevant computational approaches for the maximized exploration of pharmaceutically relevant compounds. Some of its most used chemoinformatic approaches are quantitative structure-activity relationship (QSAR) and molecular similarity methods (Leach & Gillet, 2007). Overall, QSAR approaches allow the improvement of the pharmacological characteristics of a certain scaffold (basic structure), mediating the determination of the key interactions for a given target, that is, it refines the conformational, spatial, and electronic characteristics of a series of compounds.

1. INTRODUCTION

2 OVERVIEW OF CURRENT APPROACHES USED IN COMPUTER-AIDED DRUG DESIGN

23

“Molecular similarity” is, in principle, a method simpler than QSAR. It is founded in the premise that similar compounds will have a similar activity. One of the main applications of similarity searching is filtering compounds from existing databases, such as a database of compounds approved by the Food and Drug Administration (FDA). Similarity searching of approved drugs is a technique in drug repurposing (Bajorath, 2017).

2.1 Classification of Computer-Aided Drug Design Methods As described in this chapter, CADD includes multiple approaches to answer questions of a biological-pharmaceutical nature. In general, CADD methods can be classified in three major groups: structure-based, ligand-based, and hybrid methods. 2.1.1 Structure-Based Methods Structure-based methods depend on the 3D information of the molecular target. Prominent examples of these methods are docking and molecular dynamics (MD) (see also Section 4.5). Applications of structure-based methods include characterization of binding sites, elucidation of the mechanism of action of active molecules at the molecular level, and assessment of the kinetics and thermodynamics involved in ligand-target recognition (S´ledz´ & Caflisch, 2018). 2.1.2 Ligand-Based Methods Ligand-based methods are based on the information of the chemical structures of a set of ligands with known biological activity. One of the main goals of these methods is identifying bioactive compounds or improving the activity of active molecules. Typical examples of ligand-based methods are similarity searching and QSAR modeling (Siju et al., 2017). 2.1.3 Hybrid Methods and Methods Based on End-Points When the structure of the target is known as well as the structure of active molecules, it is possible to use hybrid or combined methods, i.e., a combination of structure-based and ligand-based methods. An example is certain methods of pharmacophore modeling. Other examples are in silico approaches to predict bioactivity based on the biological profile of compounds tested vs. one or multiple targets (Yongye & Medina-Franco, 2012).

2.2 Main Applications of Computer-Aided Drug Design CADD has two major applications: identify novel potential active compounds, for example, hit identification, and optimize the bioactivity or ADMETox profile of active compounds, for example, assist in hit-to-lead process. 2.2.1 Hit Finding A common general CADD approach to identify hit compounds is virtual screening. This technique can be compared to a filtering process: starting from a usually large number of

1. INTRODUCTION

24

2. COMPUTATIONAL DRUG DESIGN METHODS—CURRENT AND FUTURE PERSPECTIVES

compounds, structure, ligand, or hybrid approaches are used to select a reduced number of molecules (Siju et al., 2017). The working hypothesis is that the reduced number of compounds have increased probabilities to be active. Of course, experimental validation of selected compounds is mandatory. After the experimental evaluation has been conducted a second round, or more, of filtering steps is performed. In the second, or more, iteration the experimental information of the previous iteration should be considered in the selection of the molecules. Virtual screening is also applicable to filter potential biological targets for a given small molecule. The later process is called inverse virtual screening or target fishing (Yuriev, Holien, & Ramsland, 2015). 2.2.2 Lead Optimization A number of structure-, ligand-, or hybrid-based methods can be used to improve the potency or reduce side effects of active molecules. Notably, issues with absorption, distribution, metabolism, and excretion (ADME) properties may hamper the development of compounds in the clinic. QSAR and machine learning approaches (see Section 4.4 in this chapter) have been employed to address not only ADME but also toxicity and potency (Caldwell & Yan, 2014).

3 CASE STUDIES: SUCCESSFUL APPLICATIONS OF COMPUTER-AIDED DRUG DESIGN Nowadays, chemoinformatics and molecular modeling methods are useful in several scientific areas. These approaches are becoming key components in the development of new drugs. Despite the fact the computational results applied to pharmaceutical and medicinal chemistry problems are not 100% accurate, CADD represents an efficient way to help save time and costs as compared to using only experimental approaches. Often two or more methods are used in research projects. This is because CADD complement each other, helping to predict more efficiently active compounds. Table 1 summarizes representative CADD based on 3D structures. The table includes a brief description of their common use with actual approaches in the chemical-biological area. Table 2 summarizes two of the more common methods of CADD based on 2D structures with an example of a recent application. Several chemoinformatics and CADD methods in general, as exemplified in Tables 1 and 2 (and other sections of this chapter), are being employed to develop drugs that are currently on the market. Some examples are oxymorphone, saqunavir, zanamivir, dorzolamide, and norfloxacin. Table 3 summarizes the information on these drugs and their chemical structures are shown in Fig. 2. Of note, molecular similarity is not stated in Table 3 because this method was employed during the first stages of the design, therefore it is not considered as the principle method for drug discovery. However, molecular similarity principles are commonly used in CADD.

1. INTRODUCTION

4 TRENDING CONCEPTS AND TECHNOLOGIES

TABLE 1

25

Representative Computer-Aided Drug Design Approaches Based on 3D Structures

Method

Aim

Example of Recent Approach

Molecular docking

CADD is a promising strategy

Docking suggested that angiotensin II receptor blockers could bind to an active site of kynurenine aminotransferase II. This mechanism action may be advantageous in the treatment of schizophrenia (Zakrocka et al., 2017)

Pharmacophore Approach to identify in 3D the elements Construction of pharmacophore models of the required for the receptor-ligand recognition Mycobacterium structural proteome (Lone, process. If the ligand is an agonist, the Kumar, Athar, & Jha, 2018) recognition process can lead to the activation of the receptor upon binding. Pharmacophore-based design can be used to guide the chemical modifications to molecules to improve recognition with the receptor and enhance the biological activity. This can be useful to give some indication of the nature of the functional groups in the receptor responsible for binding to the set drugs De novo-design Generation of new molecules with specific and Designed and tested mini-proteins of 37–43 desired properties. In this method it is necessary residues that target influenza hemagglutinin and to use models of the molecular world to produce a botulinum neurotoxin B (Chevalier et al., 2017) trustworthy model that correctly reflects the real world, so it can be used for predicting new molecules that possess the target property reflected in the model FragmentThis approach enables to rapidly scan many Recreation of liver tumors that could been based screening molecular fragments that could have very specific avatars for high-throughput drug screening interactions with cavities in a binding pocket. The (Fong et al., 2018) fragments are later connected with linkers with the final goal of generating small molecules that should be synthetically feasible

4 TRENDING CONCEPTS AND TECHNOLOGIES The landscape of drug discovery and development is changing constantly. In this section, we aim to present a concise yet substantial picture of the current trends and emerging concepts on CADD. Table 4 summarizes the main concepts discussed hereafter.

4.1 Big Data The term “big data” has been mystified. Nowadays in the era of information and the Internet, the quantity of data generated has increased exponentially. Recently it was estimated that the volume of total stored data stacks to nearly two zettabytes, with projections of this

1. INTRODUCTION

26

2. COMPUTATIONAL DRUG DESIGN METHODS—CURRENT AND FUTURE PERSPECTIVES

TABLE 2 Representative Computer-Aided Drug Design Approaches Based on 2D Structures Method

Aim

Example of Recent Approach

Quantitative 2D QSAR methods are based on molecular Molecular docking, QSAR and ADMET structure-activity topological descriptors that are represented as studies of withanolide analogs against breast relationship (QSAR) graphs; the key elements of the method are the cancer (Yadav et al., 2017) numerical descriptors used to translate a chemical structure into mathematical variables. These methods are focused mainly on the main structural changes that are responsible to modifications in the biological activity In general, the statistical methods for QSAR are used to identify the correlation between the molecular descriptions and biological activity Molecular similarity This method is based on the relationship between the chemical structures of two molecules. A common application is similarity searching (similarity-based virtual screening) where active compounds are compared to screening molecules. Selected molecules are tested for biological activity

Application of high-throughput RNA interference screens to problems in cell and developmental biology (Groth, Fish, Nusse, & Calos, 2004)

TABLE 3 Examples of Drugs Recently Discovered With Computer-Aided Drug Design Computer-Aided Drug Design Method

Drug

Indication

Target

Status

Oxymorphone

Peripheral opioid receptor antagonists

3D molecular docking

Gene name: OPRD1 Gene name: OPRM1

Clinical trials 2015

Saquinavir

Inhibitor of HIV-1 and HIV-2 proteases

Pharmacophore

Uniprot id:Q72874 Gene name: pol

Approved 1995

Zanamivir

Antiviral (influenza A and influenza B)

Modeling de novo-design

Neuraminidase Uniprot id: P27907 Neuraminidase Uniprot id: 06818 Sialidase-2 Uniprot id: P27907

Approved 2000

Dorzolamide

Glaucoma and ocular hypertension

Fragment-based screening

Gene name: CA2 Gene name: CA4 Gene name: CA1 Gene name: CA3

Approved 2012

Norfloxacin

Inhibitor of bacterial DNA gyrase

QSAR

Gene name: gyrA Gene name: parC Gene name: TOP2A

Approved 1998

1. INTRODUCTION

27

4 TRENDING CONCEPTS AND TECHNOLOGIES

FIG. 2 Chemical structures of approved drugs developed with computer-aided drug design.

TABLE 4

Selected Trending Concepts and Technologies in Computer-Aided Drug Design

Concept

Description

References

Big data

Refers to the complex or large amount of data either in situ or on the public domain

Brown et al. (2018)

Web servers

Web based applications or databases that may be used during a virtual screening campaign

Gonza´lez-Medina et al. (2017)

Workflows

An organized series of algorithms and steps in a virtual screening campaign

Cereto-Massagu
e et al. (2015)

Machine learning

Algorithms and models that may be selffed, towards optimization and improvement of data

Khamis, Gomaa, and Ahmed (2015)

Deep learning

Implementation of machine learning based on artificial neural networks with nonlinear processing units

Zhang et al. (2017)

Artificial intelligence

Refers to an algorithm or machine, capable of mimicking cognitive functions without supervision or user input

Duch et al. (2007)

Molecular dynamics

Simulation and modeling of molecules (usually proteins) by molecular mechanics and force fields

De Vivo, Masetti, Bottegoni, and Cavalli (2016)

28

2. COMPUTATIONAL DRUG DESIGN METHODS—CURRENT AND FUTURE PERSPECTIVES

figure doubling every 2 years (Akoka, Comyn-Wattiau, & Laoufi, 2017). Therefore mining this information offers a myriad of possibilities to enhance competitivity and productivity. But to effectively use big data, one must dive directly in and not just dip one’s toes in the shores of this vast ocean. The problem with big data is not just its volume, but its complexity, leading to debate as to the true role and efficiency of statistical thinking in such an arena (Secchi, 2018). Additionally, it often happens that such broad data contains errors, duplicates, and missing values due to overcollection. Hence, preprocessing and curation of data are mandatory to correctly assess the quality of information and avoid any potential bias (Cox, Kartsonaki, & Keogh, 2018). However, data curation may be done differently by different research groups based on experience or previous reports. The need for a unified or canonical protocol for data curation arises to justify the quality of any given study. Several studies have questioned the role of quality in research due to a major focus on impact (Bornmann, 2012), with others suggesting guidelines towards an objective assessment of the true impact and quality of a given work (Ma˚rtensson, Fors, Wallin, Zander, & Nilsson, 2016). Thus the current paradigm on research stays with a “less-is-more” approach. Big data has always played a significant role in medicinal chemistry, sometimes indirectly. Methods like combinatorial chemistry and HTS produce large amounts of data over brief periods of time. Previously this was seen like a new dawn for medicinal chemistry, i.e., the ability to process large amounts of new data could reduce the time invested in the drug-development cycle. Consider the following example: human immunodeficiency virus (HIV), a global pandemic for almost 40 years with an estimated of 37 million people infected, and only 57% of patients receiving antiviral therapies (WHO, 2018). Over the years several studies have focused on the inhibition of the viral reverse transcriptase and/or integrase (Cabrera, Herna´ndez, Cha´vez, & Medina-Franco, 2018; Ghosh, Osswald, & Prato, 2016). While this approach has proven effective enough, it still has several drawbacks, such as viral resistance and poor bioavailability. During the 1990s, studies on the HIV’s entry mechanisms showed the role of CD4+ cells and CCR5 chemokine. Chemokines activity is related to their G-coupled receptors (GPCRs), in CCR5’s case it is a “C-C” receptor with 75% homology to CCR2 (Barmania & Pepper, 2013). Once CCR5 had been stablished as an interesting and druggable novel target to tackle HIV, several pharmaceutical companies turned to their GPCRs inhibitor libraries on the hunt for a putative ligand for this protein. Scientists in Pfizer identified an imidazopyridine (UK107,543) as a potent lead based on HTS assays (MacArthur & Novak, 2008). Following molecular optimization, maraviroc (Selzentry) was developed and by 2007 it acquired an approved status for the treatment of HIV-1 from the FDA (Kuritzkes, Kar, & Kirkpatrick, 2008). This helps to illustrate how medicinal chemistry can be significantly boosted using big data resources. It has been suggested that this has begun to shift the background required for medicinal chemists, towards more data-driven and informatic savvy skills (Lusher, McGuire, Van Schaik, Nicholson, & De Vlieg, 2014). In this regard, scientific research started to develop tools and systems to cope with big data storage and usage; nonetheless one of the main concerns of such platforms is security and privacy for its users (Ebejer, Fulle, Morris, & Finn, 2013). Despite this, public resources offer broad collections that may be exploited beyond the pharmaceutical scope (Kissin, 2018).

1. INTRODUCTION

4 TRENDING CONCEPTS AND TECHNOLOGIES

29

In the next section we introduce some of these web-based platforms, especially those used for data mining and virtual screening campaigns.

4.2 Web Servers As discussed in previous publications, chemoinformatics was developed in direct response to the need for informatic management of chemical data (Chen, 2006). Its role in the drug-discovery process is a stepping stone or gateway to virtual screening. Thus it is very important to know the available resources to aid such ventures. Table 5 presents examples of web-based platforms and their potential role in virtual screening. As illustrated in Table 5, there are several resources available for CADD, databases being major players in the chemoinformatic landscape. In pharmaceutical research, the nature of this big data is on the complex side, as most drug and/or small molecules have multiple records, for example, biological activity, drug-drug interactions, protein-ligand interactions, etc. Therefore a given analysis can get quite complex as some relations may not be apparent at first glance and naı¨ve assumptions can lead to wrong results or hypotheses (Hu & Bajorath, 2014). For that reason, a virtual screening protocol can be broken down into several steps. The number of cycles of a given campaign depends on its goal, as discussed in the following section.

4.3 Workflows Let us consider a common case on virtual screening for lead identification and/or optimization. A protocol usually begins by acquiring pertinent data from repositories or in-house datasets. The next step is data curation (Fourches, Muratov, & Tropsha, 2015). Next, a query or reference set is selected for comparison and filtering of the datasets (Prieto-Martinez, Fernandez-de Gortari, Mendez-Lucio, & Medina-Franco, 2016). Filtering may be done based on chemical space or similarity metrics, for example, Tanimoto index. Finally, in silico testing would involve molecular docking as a means to select lead candidates (Dhananjayan, 2015; Prieto-Martı´nez & Medina-Franco, 2018c). The example workflow could be accomplished using several tools, for example, commercial suites of software like Schr€ odinger or Molecular Operating Environment, as these aim to be complete toolboxes for drug discovery. Academic suites or programs also exist, although their performance is sometimes limited to the available methods or descriptors. However, one of the advantages of open source tools is their flexibility. As these often come as a collection of scripts from a given programming language (R, python, Perl, etc.) advanced users may modify them to cover specific needs. A third option offers the best of both worlds, found in platforms like the Konstanz information miner (KNIME). Self-described as an open source platform, KNIME is designed to aid data analysis and develop workflows. It offers a wide array of features and integration to many applications by “nodes” that can be connected and rearranged to comply with user needs. Examples of KNIME development include workflows for data integration and cataloguing (Tiwari & Sekhar, 2007) and chemoinformatics (Beisken et al., 2013; Saubern,

1. INTRODUCTION

30 TABLE 5

2. COMPUTATIONAL DRUG DESIGN METHODS—CURRENT AND FUTURE PERSPECTIVES

Examples of Web Servers Useful for Chemoinformatics, Drug Discovery and/or Lead Optimization

Name and Website

Description

Type

Binding Database https://www.bindingdb.org/

Public repository containing
1.1 million reports of binding affinity of protein-ligand complexes

Database

BRENDA https://www.brenda-enzymes.org/

Collection of enzymatic data manually curated from 82,568 proteins

Database

ChEMBL https://www.ebi.ac.uk/chembl/

Public database with more than 14 million activity values from
11,000 targets

Database

Chemspider http://www.chemspider.com/

Repository of small molecules, with >63 million structures

Database

DrugBank https://www.drugbank.ca/

Contains information on more than 10,000 drugs and almost 5000 unique targets

Database

PubChem https://pubchem.ncbi.nlm.nih.gov/

Public database comprising an extensive number of records on compounds, bioactivity, assays and targets

Database

HEMD http://mdl.shsmu.edu.cn/HEMD/

Repository on epigenetic targets data and their chemical modulators

Database

GLIDA http://pharminfo.pharm. kyoto-u.ac.jp/services/glida/

Database providing data on G-coupled receptors and their ligands

Database

PUMA Server implementing several analyses on chemical https://www.difacquim.com/d-tools/ space and diversity of small molecules

Chemoinformatic tool

USR-VS http://usr.marseille.inserm.fr/

Server implementing ultrafast shape recognition algorithms for virtual screening. Queries are compared to almost 94,000 conformers from ZINC

Virtual screening tool

UFSRAT http://opus.bch.ed.ac.uk/ufsrat/

Server for virtual screening, based on the similarity principle. Queries can be compared to more than 10 million conformers from several sources

Virtual screening tool

T-COFFEE http://tcoffee.crg.cat/

Server for homology modeling

Homology modeling tool

SwissModel https://swissmodel.expasy.org/

Server for homology modeling

Homology modeling tool

SwissDock http://www.swissdock.ch/

Web implementation of EADock DSS software, allowing the docking of small molecules based on a manually curated database of protein-ligand interactions

Molecular docking server

Molecular Docking Server Web platform for protein and ligand preparation with https://www.dockingserver.com/web several methods, while also offering docking and postprocessing capabilities

Molecular docking server

Hex Server http://hexserver.loria.fr/

Server for protein-protein docking based on shape and electrostatics of targets

Protein-protein docking

ZDOCK http://zdock.umassmed.edu/

Server for protein-protein docking based on Fourier transform, to evaluate energy on protein poses

Protein-protein docking

4 TRENDING CONCEPTS AND TECHNOLOGIES

31

Guha, & Baell, 2011). Additionally, due to the positive reception of the platform, some cloudbased services allow their users to execute KNIME nodes and workflows (Ebejer et al., 2013). Related tools include the Chemistry Development Kit (CDK, Kuhn, Willighagen, Zielesny, & Steinbeck, 2010), Chembench (Walker, Grulke, Pozefsky, & Tropsha, 2010), Scaffold Hunter (Sch€ afer et al., 2017), Pipeline pilot (Warr, 2012), and Indigo (Pavlov et al., 2011). Furthermore, these implementations can be enhanced by the introduction of machine learning, enabling batch execution, and self-optimization algorithms.

4.4 Machine Learning What is learning? Physiologically speaking, learning involves complex cognitive processes all leading to remembering certain conducts or pieces of information for a given purpose. This process can be replicated, to a certain extent, in machines and algorithms (Grosan & Abraham, 2011). For instance, consider junk mail ending in the spam tray. To accomplish this, the server makes a query based on the sender and/or message content, based on a previous registry of user activity it decides the fate of a given message. However, as the reader may know, this process is faulty, as desired mail often ends in spam tray. This helps to illustrate two fundamentals of learning: it begins with the recollection of previous experience/data to make a given choice. But to ensure said choice is the most adequate, superstition and/or false data must be filtered out and discarded (Shalev-Schwartz & Ben-David, 2014). Based on this, algorithms emulating learning can be developed, but due to this rudimentary form of learning these algorithms will depend on large amounts of data to yield significant choices (Alpaydin, 2004). Fortunately, as seen in Sections 4.2 and 4.3, many sources of information exist.

4.4.1 Applications of Machine Learning in Drug Discovery With the rise of data repositories like PubChem or ChEMBL, it is now easier to access large amounts of data. This has led to the so-called data-driven discovery: making use of said datasets it is possible to extract and identify patterns yielding predictions. Table 6 presents literature examples of machine learning techniques applied to drug discovery. As it has been shown, machine learning can be applied to a broad range of problems in drug discovery. Nonetheless, this may be the first step to more complex models. After all, most machine learning methods require user input at some level. 4.4.2 Deep Learning Deep learning involves scaling machine learning using multilayered neural networks to attempt model abstraction of big data (Cao et al., 2018). Like machine learning, deep learning evolved using a cognitive process as inspiration. In this case, deep learning mimics the perception process using neural connections to extract features based on different observations (Riesenhuber & Poggio, 1999). In other words, deep learning uses neural networks of several topologies trained to identify features corresponding to a complexity scale to recognize patterns in data (Zhang, Tan, Han, & Zhu, 2017).

1. INTRODUCTION

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2. COMPUTATIONAL DRUG DESIGN METHODS—CURRENT AND FUTURE PERSPECTIVES

TABLE 6 Examples of Applications of Machine Learning to Drug Discovery Application

Result

References

Virtual screening

Lead structures for a novel target in tuberculosis were identified. Also, hit compounds are candidates for drug repurposing

Ekins et al. (2017)

High throughput screening

Fingerprints from imaging data were developed. These allowed the correlation of biological tests and provided activity prediction

Simm et al. (2018)

Protein-ligand interactions

Machine learning methods are good for interaction recognition, although the molecular diversity is not wide enough to allow complete assessment of binding and nonbinding molecules

Colwell (2018)

Side effects prediction

A novel clustering algorithm (K-Seeds) was developed. It showed higher enrichment than other methods

Dimitri and Lio´ (2017)

Classification of carcinogenic and mutagenic properties

Models showed agreement with experimental knowledge. Multicell descriptors were developed with 70% accuracy

Moorthy, Kumar, and Poongavanam (2017)

Molecular docking accuracy

Effective representations of protein-ligand complexes were developed using the DUD set

Pereira, Caffarena, and Dos Santos (2016)

Binding affinity prediction

Machine learning models can largely benefit from molecular dynamics data. In turn, this can benefit molecular dynamics by means of forcefield improvement

P
erez, Martı´nez-Rosell, and De Fabritiis (2018)

Artificial neural networks (ANN) are trained to iteratively modify their weight values to be self-optimized. These algorithms were developed in the 1980s and evolved to supporting vector machines and random forests (Chen, Engkvist, Wang, Olivecrona, & Blaschke, 2018). An important limitation to the efficiency of deep learning methods is their focus on organizing data. ANNs cannot apply their “logic” to decision making beyond pattern recognition, which is a fundamental difference between knowledge and true insight (Groumpos, 2016). Nonetheless, deep learning has been applied successfully to organizing, filtering, and mining diverse data sources. Examples include: protein-ligand interaction prediction (Gonza´lezMedina, Naveja, Sa´nchez-Cruz, & Medina-Franco, 2017; Tian, Shao, Wang, Guan, & Zhou, 2016), plant classification by leaf morphology (Lee, Chan, Mayo, & Remagnino, 2017), nucleotide-protein interactions (Yi et al., 2018), and HTS data for activity prediction (Simm et al., 2018). 4.4.3 Artificial Intelligence Artificial intelligence (AI) is a branch of information and computer science concerned with the embedding of intelligence in machines and computers (Tzafestas, 2016). Nowadays, we encounter examples of AI in technology such as Apple’s Siri, Amazon’s Alexa, or Microsoft’s Cortana. All of which are digital assistants that serve as data sources and schedule optimizers. How about medical science? AI implementations have made incursion into hospital environments for ICU care and surgical scheduling (Bini, 2018).

1. INTRODUCTION

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33

Because of the success of said methods and due to the rise of data-driven paradigms, AI has been applied to diagnosis and medical care (Patel et al., 2009). Notable results have been found in imaging analysis and recognition, where it has been suggested that, although in its infancy, AI may replace physicians in a near future (Krittanawong, 2018). However, it is debatable if there is a real difference (at least for now) between deep learning and AI methods (Miller & Brown, 2018). Hence current developments in AI have been inspired by neuroscience developments to increase model complexity and its capabilities (Hassabis, Kumaran, Summerfield, & Botvinick, 2017). On the other hand, informatic studies on artificial learning methods have shown that the predictive power of models follows Occam’s razor: less assumptions yield better results (Pieters & Wiering, 2018). In medicinal chemistry, AI was used for QSAR and SAR studies (Klopman, 1984) to enhance their performance and predictive power. This has extended to drug design, molecular docking, and predictive toxicology (Duch, Swaminathan, & Meller, 2007). Also, most AI implementations for drug discovery are based on so-called metaheuristics (Ivanciuc, 2009). Metaheuristics involve high-level algorithmic frameworks, which in turn are capable of heuristic optimization, and include: tabu search, simulated annealing, genetic algorithms, ant colony optimization, particle swarm optimization, and neighborhood search (Glover & S€ orensen, 2015). In summary, there is no denying that machine learning and its derivatives can improve drug discovery and development. After all, as of 2017 the pharmaceutical industry as a whole has invested more than 400 million USD in AI development. Nevertheless, it is necessary to remain mildly skeptical on the true merits of AI, as with any other method.

4.5 Molecular Dynamics Molecular dynamics (MD) have evolved slowly but steadily since the 1970s. A quick search on academic repositories shows the exponential growth of interest on this matter (see Fig. 3). But why now? Considering the background on the subject, this may seem more a late blooming than a breakthrough. One of the inherent limitations of MD is its scalability. This is due to the reliance on intensive calculations for many particles, thus requiring a high CPU capability to handle the number of calculations needed to model the system. This has led to the development of a dedicated super clusters like Anton (DESRES group, Shaw et al., 2008) that achieves simulations on a microsecond scale. To accomplish this MD often approaches parallelization by domain decomposition. Briefly, this takes the simulation box and splits it into smaller systems assigned to different processors. In practice often several CPUs take a heavy load while most remain idle due to an imbalance in the atom density of the subdomains (Rissland & Deng, 2005). Another issue is latency, as processors must be in constant communication, an inefficient infrastructure results in slow computing (Younge et al., 2015) as CPUs pass more time waiting for responses than running calculations. An unexpected solution came with the development of graphics processing units (GPUs). Most of the GPU optimization is based on the compute unified device architecture (CUDA) language as developed by NVIDIA. One of the benefits of CUDA is its flexibility, allowing the

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2. COMPUTATIONAL DRUG DESIGN METHODS—CURRENT AND FUTURE PERSPECTIVES

Pubmed Web of science 20,000

Frequency

15,000

10,000

5000

17

16

20

15

20

14

20

13

20

12

20

11

20

10

20

09

20

08

20

07

20

06

20

05

20

04

20

03

20

02

20

01

20

00

20

99

20

19

19

98

0

Year

FIG. 3 Frequency of the keywords “molecular dynamics” on two main academic repositories.

use of parallelization outside graphic performance, essentially converting the GPU into a cluster. Hence, most MD software has adopted GPU support to increase their productivity and scalability. A notable example being an all atom simulation of the complete capsid of HIV-1, using nearly 4000 Tesla GPUs (Perilla & Schulten, 2017). The number of CPUs used for such a task is roughly 20,000, a figure hardly accessible to most academic or public clusters. Thus a desktop computer equipped with a GPU can produce around 500 ns/day on systems of 20,000 to 30,000 atoms (Fig. 4). Now we take a general look into the theory behind MD and their general methods. 4.5.1 General Aspects of Molecular Dynamics MD involves the application of the laws of motion to molecules. To accomplish this several simplifications are due. First, a molecule is considered as a set of spheres while bonds are represented as springs. From here it is possible to assign certain values based on experimental and theoretical information. This process is known as parametrization. The parameter set includes charges, bonded interactions, nonbonding interactions, polarization, and torsions (Vanommeslaeghe, Guvench, & MacKerell, 2014). Take, for example, the general amber forcefield (GAFF), charges are obtained from restrained electrostatic potential (RESP) at HF/6-31G* basis set or semiempirical AM1-BCC method (Wang, Wolf, Caldwell, Kollman, & Case, 2004). Thus forcefields are born: mathematical expressions used to describe energy dependence with particle location (see Eq. 1). Ideally these expressions must be simple to ensure a quick computation, but substantial enough to reproduce the properties of a given system (Gonza´lez, 2011):

1. INTRODUCTION

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4 TRENDING CONCEPTS AND TECHNOLOGIES

Production (ns/day)

500

400

300

200

100

0 GeForce GTX 680

GeForce GTX 780

GeForce GTX 780 Ti

GeForce GTX 980 Ti

GPU

FIG. 4

Molecular dynamics performance with central processing units and graphics processing units, values come from the 2016 Benchmark of Desmond.

EXPRESSION FOR POTENTIAL ENERGY, AS IMPLEMENTED IN GENERAL FORCEFIELDS V ðr Þ ¼

X bonds

2

κ b ðb  b0 Þ +

X angles

2

κ θ ðθ  θ 0 Þ +

X

κ ϕ ½ cos ðnϕ + δÞ + 1 +

torsions

X nonbond

"

qi qj Aij Cij +  6 rij r12 rij ij

# (1)

Therefore it follows that any forcefield requires extensive evaluation and updates to improve their performance. Suffice to say, for the case of proteins, the most prominent examples: AMBER, CHARMM, and OPLS, were initially developed during the 1980s and continue their development today to include lipids or even small molecules (Ponder & Case, 2003). 4.5.2 Applications of Molecular Dynamics in Drug Discovery In drug discovery, MD has been adopted as the successor of molecular docking. It has been used mainly in the modeling of putative allosteric sites (Papaleo et al., 2016; Spiliotopoulos & Caflisch, 2014) and binding mode studies (Arcon et al., 2017; Clark et al., 2016; Leone, Marinelli, Carloni, & Parrinello, 2010). Recently, MD scalability allows for newer techniques that can solve problems beyond protein flexibility. For example, consider free energy perturbations, which allow calculation of the energy involved in the binding of a ligand and several derivatives (Mortier et al., 2015). Other methods include pH-REMD, an enhanced sampling method used to assess the environment of active sites and correct pKa on sidechains (Sabri Dashti, Meng, & Roitberg, 2012), and QM/MM, a hybrid approach to model electronic

1. INTRODUCTION

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2. COMPUTATIONAL DRUG DESIGN METHODS—CURRENT AND FUTURE PERSPECTIVES

changes based on ab initio and physical changes caused by molecular mechanics (Aldeghi, Heifetz, Bodkin, Knapp, & Biggin, 2016). This method allows the study of enzymatic mechanisms such as covalent inhibition. In summary, MD has come a long way since its inception more than 40 years ago. Gaining a prominent position in drug discovery and molecular modeling to complement other techniques and methods (Alonso, Bliznyuk, & Gready, 2006). This has led to mainstream excitement about its capabilities; however, MD simulations need to improve (i.e., the performance of forcefields) to correctly model protein secondary structures (Beauchamp, Lin, Das, & Pande, 2012; Best, Buchete, & Hummer, 2008). Nonetheless, we can be cautiously optimistic and acknowledge that MD can help us to reach new highs in drug discovery (Ganesan et al., 2017).

5 CHALLENGES AND EMERGING PROBLEMS IN COMPUTER-AIDED DRUG DESIGN CADD still faces many challenges, which include, but are not limited to: (a) increasing the efficiency of virtual screening; (b) augmenting the number and quality of online computational resources; (c) further developing the field of computational chemogenomics; (d) strengthening the design of drugs aimed at multiple molecular targets; (e) improving the predictive capacity of toxicity models and side effects, and (f ) strengthening the interaction with other disciplines to optimize the search for bioactive molecules for the treatment and/or prevention of diseases. The authors’ opinions on some of the areas that need improvement in CADD are given in the following section.

5.1 Integration With Other Techniques CADD should be considered as an integral part of a multidisciplinary effort and not a single approach that will bring drugs to the market in its own (Saldı´var-Gonza´lez, PrietoMartı´nez, et al., 2017). In most practical applications this is the case and CADD is employed in combination with other technologies, such as HTS and/or combinatorial chemistry. However, there is still room for improvement because there is still a reluctance in some research groups to guide their experiments with the aid of computational methods. As discussed in Section 5, we anticipate that the education of young scientists and established researchers regarding the capabilities and limitations of computational methods will increase productivity and synergy. A brief list of examples of the integration of computational methods with other technologies include: • CADD and HTS: the computer-guided selection of screening libraries (including the informatic filtering of frequent hitters), informatic analysis of screening data, development of structure-activity relationships, data visualization, organization, storage, and management of output data. • Natural product-based drug discovery (Medina-Franco, 2015): building, organization, and mining of natural product databases, target fishing, structure elucidation, etc. (Gonza´lezMedina et al., 2016). 1. INTRODUCTION

5 CHALLENGES AND EMERGING PROBLEMS IN COMPUTER-AIDED DRUG DESIGN

37

• Combinatorial chemistry-HTS-CADD: computer-guided design of combinatorial libraries for HTS, characterization of the chemical space of combinatorial collections (Lopez-Vallejo et al., 2011), etc. • Design and analysis (including deconvolution) of phenotypic screening data (Moffat, Vincent, Lee, Eder, & Prunotto, 2017), etc.

5.2 Absorption, Distribution, Metabolism, and Excretion, and Toxicity Prediction ADME is one of the bottle necks in drug development. Computational methods are playing a key role in anticipating potential ADME and toxicity problems and reducing the number of experiments that involve animal testing. However, predicting toxicity accurately is not a simple task due to the complexity of several toxicity mechanism. A recent review on in silico prediction of chemical toxicity has been published (Yang, Sun, Li, Liu, & Tang, 2018). As such further development of the field of informatics toxicology is anticipated (Mangiatordi et al., 2016).

5.3 Difficult and Emerging Targets CADD is a promising strategy to tackle the targets that are currently considered difficult, such as protein-protein interactions (PPIs) (Dı´az-Eufracio, Naveja, & Medina-Franco, 2018). The rational design and optimization of inhibitors of PPIs represent major challenges due to the nonclassical drug-like properties of these compounds. Similarly, CADD methods offer potential new avenues to address orphan, neglected, or emerging targets with potential applications in the clinic, for example, epigenetic targets (Oprea et al., 2018; Santos et al., 2016).

5.4 Neglected Diseases There is active research towards the treatment of neglected diseases. The overall lower cost of computational methods compared to traditional experimental approaches can be of significant importance in identifying novel hits and/or optimizing their activity (Andrade et al., 2018; Melo-Filho et al., 2016; Varela, Cobo, Pawar, & Yadav, 2017; Volkamer, Kuhn, Grombacher, Rippmann, & Rarey, 2012). Here, a wide range of CADD methods are playing a crucial role, including virtual screening and database management.

5.5 Chemical Space Delineating and exploring efficiently the medicinally relevant chemical space (Lo´pezVallejo, Giulianotti, Houghten, & Medina-Franco, 2012) continues to be a challenge. This applies to exploring the chemical space of molecular targets (e.g., epigenetic targets) or exploring the chemical space from the point of view of the compounds that are commercially available (Lucas, Gr€ uning, Bleher, & G€ unther, 2015; Opassi, Gesu`, & Massarotti, 2018) and/or commonly used in HTS campaigns, for example, the dark chemical matter (Macarron, 2015; Siramshetty & Preissner, 2018; Wassermann et al., 2015). 1. INTRODUCTION

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2. COMPUTATIONAL DRUG DESIGN METHODS—CURRENT AND FUTURE PERSPECTIVES

5.6 Advance Multitarget Drug Discovery and Polypharmacology A current trend in drug discovery is shifting toward multitarget drug discovery (MedinaFranco, Giulianotti, Welmaker, & Houghten, 2013). In this regard, CADD can further address polypharmacology and associated topics: • Chemogenomics (including virtual screening) (Bajorath, 2013; Rognan, 2013) • Deconvolution of phenotypic screening: find associations molecules-targets-cell/based data-toxicity, etc. (Prieto et al., 2013) • Dual- and multitarget drug design (Atkinson et al., 2014; Cabrera et al., 2018) • Structure multiple-activity relationships (SmARTs) (Saldı´var-Gonza´lez, Naveja, Palomino-Herna´ndez, & Medina-Franco, 2017) • Drug repurposing (Brindha et al., 2017; Liu et al., 2013).

5.7 Training, Teaching, and Divulgation In addition to continuing to advance the generation of knowledge and techniques to improve CADD, it is necessary to improve education at undergraduate and graduate levels (Dı´az-Eufracio et al., 2018). Current learning is on the fly (driven by specific needs) but training can be greatly aided with the development of text books focused on CADD. Formal training can be complemented with the current availability of conferences focused on CADD.

6 CONCLUSIONS CADD is part of multidisciplinary efforts that have proved to be useful in the development of drugs that are currently in clinical use or in advanced clinical trials. As such CADD is a combination of several theoretical and computational disciplines, such as molecular modeling, chemoinformatics, theoretical chemistry, among others. There are a broad range of computational approaches used in CADD that have been used for several years, such as molecular docking, dynamics, QSAR, similarity searching, to name a few. However, these approaches continue to be improved and refined. Also, there are several novel concepts and approaches that are driving CADD into the future. Clear instances of the latter are “big data,” artificial intelligence, machine and deep learning, among others. All new and classical approaches are being used in combination with novel trends in drug discovery, such as polypharmacology and drug repurposing. Like other multidisciplinary approaches, CADD faces several challenges that include not only the refinement of the theoretical basis, but also its rational application (with the knowledge of the limitations), and training and education of the users of these technologies.

Acknowledgments This work was supported by the Programa de Apoyo a Proyectos para la Innovacio´n y Mejoramiento de la Ensen˜anza (PAPIME) grant PE200118, Programa de Apoyo a Proyectos de Investigacio´n e Innovacio´n Tecnolo´gica (PAPIIT) grant IA203718 and National Council of Science and Technology (CONACyT), Mexico grant number 282785. FD P-M acknowledges the PhD scholarship from CONACyT no. 660465/576637. KE J-M thanks CONACyT for the support (No. 16231).

1. INTRODUCTION

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Further Reading Gonczarek, A., Tomczak, J. M., Zaręba, S., Kaczmar, J., Da˛browski, P., & Walczak, M. J. (2018). Interaction prediction in structure-based virtual screening using deep learning. Computers in Biology and Medicine, 100, 253–258.

1. INTRODUCTION

C H A P T E R

3 In Silico Drug Design Methods for Drug Repurposing Bashir Akhlaq Akhoon, Harshita Tiwari, Amit Nargotra Discovery Informatics Division, CSIR—Indian Institute of Integrative Medicine, Jammu, India

1 DRUG REPURPOSING The process of finding new therapeutic indications for existing drugs to improve drug productivity and to utilize them to their full potential is known as drug repositioning (also called drug reprofiling, drug retasking or therapeutic switching). Despite advances in biological/ informational technologies, de novo experimental drug discovery is still a time-consuming and costly process. Pharmaceutical industries are making huge investments into the drug discovery and development process, but because of high-failure rates the number of new drugs that clear the clinic trials stage has not grown significantly (Eder, Sedrani, & Wiesmann, 2014; Paul et al., 2010). In contrast, drug repositioning has gained momentum in recent years due to several success stories. Since the existing US Food and Drug Administration (FDA)-approved drugs have well-known dose regimens along with already proven pharmacokinetics (PK) and pharmacodynamics (PD) properties in humans, the drug reposition could significantly shorten the time and cost from drug discovery to market availability. The identification of new targets for old drugs are less likely to fail in future clinical trials due to their proven clinical safety and/or associated toxicology, which are the areas that account for the majority of drug failures. Drug repositioning can be a promising approach for drug discovery because it is less expensive and time consuming to obtain new drug-target interaction profiles for already FDA-approved drugs with established formulations and manufacturing methods. It has been reported that the estimated cost for a discovery is 60% to 70% lower than new drug development (Chong & Sullivan, 2007; Nosengo, 2016). The process of drug repurposing is even beneficial to pharmaceutical companies, as the compounds that have failed in clinical trials for a particular defined target and have not been further investigated are kept on the shelves. These compounds can be launched in the market for new indications that reduce their attrition rates and ultimately save time and expenditure, thus there are manifold benefits

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FIG. 1

Comparison between drug-development and drug-repositioning processes. Adapted from Nosengo, N. (2016). Can you teach old drugs new tricks?. Nature News, 534(7607), 314.

for both patients and the pharmaceutical industry. Further, there are certain rare diseases for which not even a single drug is available in the market and in consideration of this, the US FDA has taken the initiative and launched a database of approved drugs (https://www. accessdata.fda.gov/scripts/opdlisting/oopd/index.cfm) that can be investigated to reposition for other diseases and in particular for orphan disorders where there is no drug treatment available so far (Tambuyzer, 2010). In fact,
30% of the newly US FDA-approved drugs and vaccines are only on the market because of drug repurposing ( Jin & Wong, 2014). Fig. 1 highlights the time period and expenditure difference between normal drug discovery and drug repositioning.

2 COMPUTATIONAL APPROACHES FOR DRUG REPOSITIONING In silico drug repositioning is gaining a global attention these days because of the availability of a large amount of information on protein structures, pharmacophores, disease data, clinical investigations, or gene expression profiles of drugs. Moreover, the increase in public social networking technologies and computational access to genetic information has greatly aided the computational approaches in predicting new indications. Thus most of the pharmaceutical companies are using modern bioinformatics or computational resources for drug repositioning from diverse chemical spaces. The increased speed and reduced cost benefits provided by the powerful in silico technology is the ultimate desire of every pharmaceutical company. With the drug-related data growth, new computational methods with increased levels of recall and precision for targeted profiling of small molecules have been developed. These methods improve the repositioning process by including chemoinformatics, bioinformatics, network biology, systems biology or genomic information to reveal unknown targets/ mechanisms of approved drugs with accelerated timelines. Accordingly, computational drug-repositioning methods can be broadly categorized into target-based, knowledge-based, signature-based, network-based, and targeted-mechanism-based methods. Based on the computational insights, a subset of compounds can be further taken for experimental testing for its validation. Therefore the hybrid approach involving both computational and experimental assays is needed to repurpose drugs for new ailments and most of the pharmaceutical industries have already adopted this strategy to evaluate the therapeutic efficacy of new indications.

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2.1 Target-Based Methods Drugs not only interact with their therapeutic target proteins but also have affinities for additional proteins, called off-targets. The side effects of drugs are mostly because of these off-target interactions. However, these extra-interactions are not necessarily always harmful and sometimes they may be beneficial to an organism for new therapeutic indications. For example, sildenafil (Viagra) was developed for the treatment of angina, but later it was redeveloped to treat erectile dysfunction. This redevelopment was performed after observing the interaction of sildenafil with a phosphodiesterase (PDE5), which was accountable for the erectile response. The conceptual diagram of target-based drug repositioning is shown in Fig. 2. The target-based drug-repositioning methods rely either on ligand or receptor structure. These drug-repositioning methods comprise high-throughput in silico screening (virtual screening [VS]) of drugs from chemical libraries using docking and/or pharmacophore models. There are several examples in the literature showing the successful utilization of computational models for VS campaigns (Akhoon et al., 2015; Matter & Sotriffer, 2011; Mehra, Rajput, et al., 2016; Mehra, Rani, et al., 2016; Xu, Huang, & Zou, 2018). These programs mostly rely on the target’s binding site and small molecules libraries, such as Pubchem, ZINC, etc., that contain chemical structure information on drugs. The main advantage of in silico, targetbased, drug-repositioning methods is that they can screen nearly all drugs for a particular target within a few days, which is why they are preferred by pharmaceutical companies. The identification of inhibitors for the transforming growth factor-b 1 (TGFβ-1) receptor kinase is one of the many examples that illustrate the potential of in silico target-based methods. A fully computational work at Biogen Idec reproduced the results of Eli Lilly, reported by using conventional wet-lab assays, and found an identical, promising lead compound for the TGFβ-1 receptor after computational screening of around 200,000 compounds

Disease X

Original indication

Protein A

Disease Y

New indication

Protein B

Disease Z

Ontarget

Drug New indication

Offtarget

FIG. 2

Conceptual diagram of target-based drug repositioning. Drug molecule usually interacts with its primary target Protein A to cure disease X. However, Protein A may also be involved in disease Y. Furthermore, it is also possible that the selected drug may have good binding affinity for off-target Protein B. Therefore a drug reported for disease X can be reinvestigated for new indications and may treat diseases Y and Z.

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(Shekhar, 2008). Such innovations highlight the power of computational approaches to screen compounds without the need for synthesizing them prior to screening, as is required for wetlab assays, thereby reducing efforts, cost, and time. The preliminary step of a VS campaign is the target selection. Among the various targetable biomolecules, such as proteins, polysaccharides, lipids, and nucleic acids, proteins are generally given prime importance due to the high specificity provided by their binding pocket properties, potency, and low toxicity. Once a protein target of interest is selected, we need its 3D structure. The 3D structures, obtained from the X-ray crystallography, NMR spectroscopy, or cryo-electron microscopy, are deposited in a crystallographic database known as the Protein Data Bank (PDB). The PDB is freely accessible at https://www.rcsb.org/ and contains 142,807 total depositions (as of 27 Feb. 2018). The structural data of a target protein can be easily retrieved using this database. In the absence of an experimental 3D structure of the target protein, homology modeling, protein threading/fold recognition, Ab initio methods, or integrated approaches are used to model the protein 3D structures. Homology modeling is also known as comparative modeling and is the easiest way of predicting 3D structures. This method is based on the assumption that similar sequences adopt practically identical structures. Therefore the proteins that share 50% sequence similarity with the target sequence are used as templates for protein modeling. The methodology has been widely used to model unknown or nonresolved protein structures (Akhoon, Gupta, Dhaliwal, Srivastava, & Gupta, 2011; Cavasotto & Phatak, 2009; Koldsø, Grouleff, & Schiøtt, 2015). Protein threading or fold recognition is used for those protein sequences that do not have homologous protein structures. The method is based on the fact that protein folds are fairly small in nature and proteins with different sequence compositions can even share similar folds. These methods find the best-fit templates for the target protein and use fold recognition tools to build protein structure. Sometimes, it is possible that the protein of interest has neither homologous structures nor suitable templates. In this case, thermodynamic and molecular energy parameters are taken into consideration to generate a protein 3D conformation with minimum entropy and maximum stability (Lee, Tiong, et al., 2017; Lee, Freddolino, Zhang, 2017). Some in silico protein modeling tools, such as I-TASSER use composite modeling by combining several methods such as threading, ab initio modeling, and atomic-level structure-refinement approaches, to predict protein structure and this program has been widely used by several authors in their studies (Akhoon et al., 2014; Rutledge et al., 2017). In fact, the recent CASP (Critical Assessment of protein Structure Prediction) 12 conducted in 2016 ranked I-TASSER as the best server for protein structure prediction. The CASP is a community-wide blind experiment and has been conducted every 2 years since 1994 to test the effectiveness and advances in the protein structure-prediction tools. Some of the most prominently used CASPcertified protein structure-prediction servers are listed in Table 1. Most of the protein structures are crystallized in the presence of ligands or drug molecules and therefore the co-crystallized ligands have well-defined protein-binding sites. However, the difficulty arises when ligands are not crystallized. In these cases, computational methods are used for protein-drug binding site predictions. These methods differ from each other with respect to the information they use for predictions. For example, some methods use only protein sequences for predictions (Capra & Singh, 2007; Zhang et al., 2008), others use structural properties (Roy & Zhang, 2012; Yu, Zhou, Tanaka, & Yao, 2009), or consider both sequence and structure features (Capra, Laskowski, Thornton, Singh, & Funkhouser, 2009;

2. THEORETICAL BACKGROUND AND METHODOLOGIES

2 COMPUTATIONAL APPROACHES FOR DRUG REPOSITIONING

TABLE 1

51

Prominently Used Protein Structure Prediction Tools

S. No. Program

Brief Description

URL

1.

I-TASSER

Models are built on the basis of multiple-threading alignments generated by LOMETS and iterative TASSER simulations

https://zhanglab.ccmb.med.umich. edu/I-TASSER/

2.

ROBETTA

The program uses Rosetta software package for http://robetta.bakerlab.org/ secondary structure prediction and comparative models are built with HHSEARCH/HHpred, RaptorX, and Sparks-X taking template from the PDB

3.

HHpred

HHpred uses profile HMM-HMM comparison by using databases like Pfam or SMART

4.

MetaTASSER MetaTASSER is a freely available tool which utilizes http://cssb.biology.gatech.edu/ the 3D-Jury approach to select threading templates skolnick/webservice/ from SPARKS, SP3 and PROSPECTOR_3 for protein MetaTASSER/index.html tertiary prediction

5.

MULTICOM

It is a 3D structure and feature prediction pipeline and http://sysbio.rnet.missouri.edu/ utilizes a wide array of information gathered from multicom_cluster/ Protein Data Bank to predict protein structure

6.

Pcons

http://pcons.net/ Pcons is a neural network-based tool to identify a consensus model. It ranks models by assessing their quality

7.

RaptorX

This program focuses on the alignment of distantly http://raptorx.uchicago.edu/ related proteins with sparse sequence profile and that StructurePrediction/predict/ of a single target to multiple templates. Presently, it consists of four major modules: single-template threading, alignment-quality assessment, multipletemplate threading, and fragment-free approach to free modeling

8.

SAM-T08

SAM-T08 is a Hidden Markov Model-based protein structure prediction webserver. It provides many intermediate results such as multiple sequence alignment, residue-residue contact prediction, etc.

https://compbio.soe.ucsc.edu/ SAM_T08/T08-query.html

9.

THREADER

This tool utilizes threading methodology for fold recognition and is freely available

http://bioinf.cs.ucl.ac.uk/?id¼747

10.

BhageerathH- It is an ab initio hybrid server, which integrates seven http://www.scfbio-iitd.res.in/ Plus computational modules to accurately predict bhageerathH+/index.php probable native structure of the amino acid sequence

https://toolkit.tuebingen.mpg.de/ #/tools/hhpred

The methods used by each program and their availability are also shown.

Wang et al., 2013; Wang, Horst, Cheng, Nickle, & Samudrala, 2008) and some even collect information from homologue proteins (Wass & Sternberg, 2009; Wass, Kelley, & Sternberg, 2010). The abovementioned methods rely mostly on geometric characteristics, but some methods are energy based and calculate the probe energy to locate energetically favorable binding sites (Hernandez, Ghersi, & Sanchez, 2009; Huang & Schroeder, 2006;

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3. IN SILICO DRUG REPURPOSING METHODS

Laurie & Jackson, 2005). Some more recent methods use ligand-binding data from similar structures to make binding-site predictions. For example, Firestar (Lopez, Maietta, Rodriguez, Valencia, & Tress, 2011; Lo´pez, Valencia, & Tress, 2007) first build the sequence alignments of a query with ligand-bound proteins available in the PDB and then focus on residue conservation for making predictions. The accuracy of the computational methods in predicting the protein amino acid residues that participate in binding with ligands has also been assessed in CASP10, where Firestar achieved the best score and was ranked highest by the Matthews Correlation Coefficient. Table 2 lists some of the best computational tools available for the prediction of protein-binding sites. For a detailed in silico understanding of the interactions between drugs and proteins, we require the chemical structures of the compounds or drugs of interest. There are several chemical databases that offer a diverse set of chemical information for efficient VS. For example, Pubchem (http://PubChem.ncbi.nlm.nih.gov), a public repository for chemical information that was initially launched in 2004 by US National Institutes of Health (NIH), presently holds >90 million compounds. Interestingly, it also stores biological assay information on chemical substances, thereby providing access to the available experimental results of the selected drugs. Similarly, DrugBank (https://www.drugbank.ca/) is another freely accessible database that contains information on FDA-approved drugs along with comprehensive drugtarget information. The latest version of DrugBank (version 5.0.11, released on 2017-12-20)

TABLE 2 List of Some of the In Silico Tools Available for Prediction of Protein-Binding Sites S. No. Programme

Description

URL

1.

FireStar

Predict functionally important residues using the functionally important residues in the FireDB database

http://firedb.bioinfo.cnio.es/ Php/FireStar.php

2.

SP-Align

The program optimizes SP-score for structure alignment. http://sparks-lab.org/yueyang/ SPalign was applied to recognize proteins within the server/SPalign/ same structure fold and having the same function of DNA or RNA binding

3.

Seok-server

It gathers information about ligand binding site by molecular docking approach

http://galaxy.seoklab.org/cgibin/submit.cgi?type¼SITE

4.

HHPredA

The program uses profile HMM-HMM comparison by using databases like Pfam or SMART

https://toolkit.tuebingen.mpg. de/#/tools/hhpred

5.

IntFold3

A multiple-template modeling approach guided by global http://www.reading.ac.uk/ and local quality estimates is used for predictions bioinf/IntFOLD/IntFOLD2_ form.html

6.

COFACTOR

BioLiP protein-function database is used to identify functional sites and homologies with threading

7.

3DLigandSite 3DLigandSite provides ligand-binding sites by http://www.sbg.bio.ic.ac.uk/ superimposing ligands bound to structures like the query 3dligandsite/

8.

Atom2

https://zhanglab.ccmb.med. umich.edu/COFACTOR/

It performs comparative docking of ligands after protein- http://atome.cbs.cnrs.fr/AT23/ protein superposition, and evaluates the pose using index.html Medusa and AutoDock scores

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holds 11,000 drug entries. Likewise, ZINC database (http://zinc.docking.org/) contains over 35 million ready-to-dock commercially available compounds for VS. These databases provide adequate information on chemicals and thereby facilitate drug repositioning. Table 3 provides a list of some of the important databases that are commonly used for VS. TABLE 3

List of Some of the Important Databases That Are Commonly Used for Virtual Screening

S. No. Database

About

URL

1.

ZINC

It is a free database of purchasable compounds, http://zinc15.docking.org/ which contains 230 million ready to dock and over 750 million compounds that can be utilized for analog search

2.

ChEMBL

A database of bioactive drug-like compounds and https://www.ebi.ac.uk/ their calculated physicochemical properties and chembl/ bioassays. The current version of ChEMBL contains 2,101,843 chemical compounds, 11,538 targets and 14,675,320 activities from 67,722 publications

3.

Chemspider

ChemSpider is a free chemical structure database that http://www.chemspider. contains data for 63 million molecules from over 280 com/ data sources

4.

DrugBank

Current version of DrugBank (version 5.0.11, https://www.drugbank.ca/ released 2017-12-20) contains 11,002 drug entries including 2504 approved small-molecule drugs, 943 approved biotech (protein/peptide) drugs, 109 nutraceuticals and over 5110 experimental drugs

5.

PubChem

PubChem contains the largest open chemistry https://pubchem.ncbi.nlm. database maintained by National Institute of Health nih.gov/ (NIH). It contains 94,702,974 compounds. 242,303,091 substances, 1,252,878 BioAssays, 2,570,177 tested compounds, 4,157,565 tested substances, 170 RNAi BioAssays, 234,421,530 BioActivities, 10,857 protein targets and 22,106 gene targets records

6.

BindingDB

BindingDB is a public, web-accessible database that https://www.bindingdb.org/ contains 1,439,799 binding data, for 7042 protein bind/index.jsp targets and 644,978 small molecules

7.

ChemBridge Drug library

A commercial database that contains more than 1.1 million drug-like and lead-like compounds

8.

ChemDiv’s The resource holds a library of 1.5 M purchasable Compound Libraries diverse compounds for virtual screening

9.

MolPort

http://www.chembridge. com/screening_libraries/ http://www.chemdiv.com/ services-menu/screeninglibraries/

MolProt contains dataset of purchasable chemical https://www.molport.com/ compounds. It can be access through MolPort Online shop/screeening-compoundportal, FTP download, MolPort API and Public database chemical databases (ChemSpider, PubChem and ZINC) Continued

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TABLE 3 List of Some of the Important Databases That Are Commonly Used for Virtual Screening—cont’d S. No. Database

About

10.

ChEBI

Chemical Entities of Biological Interest (ChEBI) is a http://www.ebi.ac.uk/ freely available database of molecular entities (small chebi/ molecular compounds) and ontologies derived from a nonproprietary source

11.

SuperDrug2 Database

SuperDRUG2 database contains 4600 active pharmaceutical ingredients. It also provides information about physicochemical properties and potential drug-drug interactions

12.

SuperNatural II Database

Super Natural II, a database contains 325,508 natural http://bioinf-applied.charite. compounds (NCs) de/supernatural_new/index. php

13.

SuperHapten

This database contains currently 7257 haptens, 453 commercially available related antibodies, and 24 carriers

http://bioinformatics.charite. de/superhapten/

14.

Ligand Expo

Ligand Expo is the collection of small molecules co-crystalized with proteins entries in Protein Data Bank

http://ligand-expo.rcsb.org/

15.

Mcule

It is commercial database of purchasable small molecules. In addition it also provides molecular modeling tools

https://mcule.com/ database/

16.

ChemBank

Public, web-based informatics environment created http://chembank. by the Broad Institute’s Chemical Biology Program. broadinstitute.org/ Includes freely available data derived from small molecules and small-molecule screens, and resources for studying the data

17.

Commercial It is a freely available database of single and Compound multiconformational molecules that can be utilized Collection (CoCoCo) for HTVS in Phase, Catalyst, and SDF, etc. formats

http://cococo.isof.cnr.it/

18.

Virtual library Repository

http://rocce-vm0.ucsd.edu/ data/sw/hosted/virtuallib/

FDA Fragment database containing around 4544 small-molecule fragments of FDA-approved compounds

URL

http://cheminfo.charite.de/ superdrug2/

In target-centered drug repositioning, in silico affinity prediction plays a vital role in the shift from a hit compound to a lead. The computational placement of a small molecule in the binding pocket of a receptor to estimate its binding affinity is known as molecular docking. There are two main components of docking: (1) sampling, and (2) scoring. The generation of different drug poses within the target active site is known as sampling whereas the evaluation of the binding strength of target-ligand complex is referred to as scoring. There are different scoring functions available to predict the Gibbs free energy of binding or, more colloquially, “binding affinity” of ligands and to rank them according to their binding energies. Multiple studies have discussed the importance of the different types of scoring functions (Li et al., 2018; Liu & Wang, 2015); however, a realistic scoring function is still a dream.

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Usually three types of scoring functions are commonly used to rank the ligands: (1) force field, (2) empirical, and (3) knowledge-based scoring function. Physical atomic interactions such as van der Waals (VDW) or electrostatic interactions and bond stretching, bending or torsional forces form the basis of force-field scoring functions. The parameters for these scoring functions are derived either from the experimental or from ab initio quantum mechanical calculations. The major limitation of this scoring function is that it does not consider the role of the solvent in ligand binding. Empirical scoring functions predict the binding affinity of a complex on the basis of weighted energy terms like VDW energy, electrostatic energy, hydrophobicity, hydrogen bonding energy, desolvation, entropy, etc. The empirical scoring functions are comparatively much faster in predictions than the force field scoring functions due to their simple energy terms. The major drawback of this scoring function lies in its dependency on the training data set. Also, interactions such as p-p stacking, cation-p interaction and the impact of water molecules on calculations are not taken into consideration in this type of scoring function. Knowledge-based scoring functions are derived from the statistical observations of the intermolecular close contacts embedded in the experimentally resolved atomic structures available in large 3D structural databases, such as PDB. This method is based on the occurrence frequency of pairwise potentials that are directly calculated from the 3D structures using the inverse Boltzmann’s method (Sippl, 1990; Thomas & Dill, 1996). This scoring function has been greatly improved by several researchers through the inclusion of the solvation and entropy effect (Huang & Zou, 2010). However, it is still better for a docking method to use a hybrid scoring function for the prediction of ligand affinity. For example, the AutoDock program uses adjustable weighting factors for its force field energy function along with other empirical terms, such as ligand entropic penalties or desolvation effects (Morris et al., 2009). Some of the commonly used scoring functions are shown in Table 4. There are several docking programs available to represent the potentiality of ligand binding based on the scoring functions previously discussed and some of the important ones are summarized in the Table 5. Moreover, it was recently found that Glide (XP) and GOLD consistently predict the docking poses with around 90.0% accuracy in most cases (Wang et al., 2017). TABLE 4

List of Some Commonly Used Scoring Functions in Docking Calculations

S. No. Scoring Function Type

Brief Description

1.

LigScore

Empirical scoring function

It is comprised of three distinct terms, i.e., the van der Waals (VDW) interaction, polar attraction, and the desolvation penalty

2.

LUDI

Empirical scoring function

This scoring function is the combination of five scores generated by five individual contributors, i.e., contributions from ideal hydrogen bonds, from perturbed ionic interactions, from lipophilic interactions, from freezing of internal degrees of freedom and contributions due to the loss of translational and rotational entropy of the ligand

3.

Chem-Score

Empirical scoring function

It is derived from a set of 82 protein-ligand complexes whose binding affinities were available. The score is obtained by summing up various types of physical contributions to binding and total free energy on ligand binding Continued

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TABLE 4 List of Some Commonly Used Scoring Functions in Docking Calculations—cont’d S. No. Scoring Function Type

Brief Description

4.

GOLD-Score

Force field-based scoring function

Gold scoring function takes four energy terms, i.e., external hydrogen bond, external VDW, internal VDW and internal torsion strain energy into account. Fifth component, internal H-bond energy can be added optionally. GOLD-Score is more suited for the prediction of ligand-binding positions rather than the prediction of binding affinity

5.

MedusaScore

Force field-based scoring function

It consist of six energy terms, attractive and repulsive part of the VDW interaction, the solvation energy, and hydrogen-bond energies formed between backbone atoms, between side chains, and between backbone and side chains, respectively

6.

DOCK3.5 score

Force field-based scoring function

This is based on a grid-based scoring algorithm. It calculates ligand desolvation, steric and electrostatic interactions between the ligand and receptor

7.

PMF (potential of Knowledge-based mean force) scoring function

PMF scoring function is obtained by converting the structural data of known protein-ligand complexes collected from the Protein Data Bank into distance-dependent Helmholtz free interaction energies of protein-ligand atom pairs

8.

ASP (Astex Statistical Potential)

Knowledge-based scoring function

ASP is an atom-atom distance potential derived from a database of protein-ligand complexes

9.

ITScore

Knowledge-based scoring function

ITscore is obtained by summing up all intermolecular interactions, i.e., protein-ligand atom-pair interactions

TABLE 5 List of Software tools Commonly Used for Molecular Docking S. No. Program

Description

URL

1.

AutoDock

It is freely available automated docking tool, it consists of an auto-grid that computes a grid prior to docking, and an auto-dock, which docks ligands on the grid computed by the auto-grid

http://autodock.scripps.edu/

2.

AutoDock Vina

AutoDock vina unlike AutoDock does not require a precomputed grid and is faster and more accurate than AutoDock

http://vina.scripps.edu/

3.

GOLD

It utilizes a genetic algorithm for the pose of prediction and is highly configurable

https://www.ccdc.cam.ac.uk/ solutions/csd-discovery/ components/gold/

4.

FlexAID

It can be utilized to dock small molecules or peptides to protein/nucleic acid targets. It utilizes a surface complementarity-based soft scoring algorithm for pose prediction. It can be used programmatically or through the NRGsuite graphical user interface

http://biophys.umontreal.ca/nrg/ NRG/FlexAID.html

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TABLE 5

List of Software tools Commonly Used for Molecular Docking—cont’d

S. No. Program

Description

URL

5.

FlexX

It is a fully automated tool for flexible ligand docking. It uses the Incremental Construction algorithm for protein-ligand docking. It is provided by BioSolveIT

https://www.biosolveit.de/FlexX/

6.

FRED

FRED utilizes an exhaustive search algorithm to find the best binding pose for a ligand in a protein receptor site. It is provided with OEDocking suite

https://docs.eyesopen.com/ oedocking/fred.html

7.

CDocker

CDocker utilizes CHARM for molecular docking. Conformational sampling and refining are done with high-temperature molecular dynamics (MD) and simulated annealing MD respectively. It can be accessed from Discovery Studio and Pipeline Pilot provided by BIOVIA

http://accelrys.com/products/ collaborative-science/bioviapipeline-pilot/ http://accelrys.com/products/ collaborative-science/bioviadiscovery-studio/

8.

HADDOCK HADDOCK (High Ambiguity Driven protein-protein DOCKing) server for flexible biomolecular docking. It can be employed for protein-protein, protein-RNA and protein-ligand docking

http://www.bonvinlab.org/ software/haddock2.2/

9.

GLIDE

This is a molecular docking software provided by Schr€ odinger. Glide provides standard precision (SP), extra precision (XP), virtual screening and peptide docking modes for docking studies

https://www.schrodinger.com/ glide

10.

SwissDock

It is a freely available web server provided by Swiss Institute of Bioinformatics (SBI). It utilizes CHARM and FACTS for binding energy calculation and evaluation respectively

http://www.swissdock.ch/

11.

ZDOCK Server

This is automated protein-protein docking server that applies to rigid-body docking programs ZDOCK and M-ZDOCK for pose prediction. ZDOCK is freely available for academic and nonprofit users

http://zdock.umassmed.edu

12.

PatchDock

This is a freely available molecular docking server based on Shape Complementarity Principles.

https://bioinfo3d.cs.tau.ac.il/ PatchDock/

13.

DOCK Blaster

It is a freely available virtual screening and molecular docking server that utilizes UCSF DOCK 3 as a docking engine against Zinc database

http://blaster.docking.org/

14.

MEDOCK

MEDOCK is a webserver that utilizes MaximumEntropy based Docking. Its search algorithm utilizes maximum entropy property of the Gaussian distribution

http://medock.ee.ncku.edu.tw/

15.

SurflexDock

This applies a combination of docking, 2D, and 3D molecular similarity to predict the probable pose of ligand docked on a protein surface. It does predocking minimization and post-docking optimization of all atoms in the pocket

http://www.jainlab.org/ downloads.html

Continued

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TABLE 5 List of Software tools Commonly Used for Molecular Docking—cont’d S. No. Program

Description

URL

16.

LigandFit

It is a rapid docking software for protein-ligand docking that combines shape- directed docking and monte Carlo conformational search for generating poses. This is provided by BIOVIA

http://accelrys.com/products/ collaborative-science/bioviadiscovery-studio/

17.

iScreen

This is the first and the only web-based server for virtual screening and docking against Traditional Chinese Database (TCM), which utilizes LEA3D genetic algorithm

http://iscreen.cmu.edu.tw/intro. php

18.

MOLS 2.0

This is freely available software for peptide modeling and protein-ligand docking based on mutually orthogonal Latin squares (MOLS) algorithm

https://sourceforge.net/projects/ mols2-0/files/

Inverse VS (IVS), a complementary approach to traditional VS, docks one or few ligands to many proteins that have specific pharmacological activity, such as antibacterial, antitumoral, etc. Therefore this type of VS is very helpful in re-examining the targets of known drugs, i.e., drug reposition. IVS includes both structure-based and ligand-based approaches for target fishing. The inverse receptor-based pharmacophore mapping and docking methods are commonly used as a structure-based approach, whereas the ligand-based approach involves reverse pharmacophore mapping, 2D fingerprints, and 3D similarity searches. For structurebased methods, a ligand-binding structure or, more precisely, a receptor is required to build a pharmacophore model from the interaction site. The pharmacophore is an ensemble of steric and electronic features that are sufficient for a particular biological or pharmacological interaction and this term was originally coined by Paul Ehrlich (Ehrlich, 1909). Structure-based pharmacophore methods analyze the target binding site or search for the common protein-ligand interaction pattern that corresponds to a particular or desired pharmacological effect. Usually, a pharmacophore is composed of functional groups like hydrogen bond donors or acceptors, aliphatics, aromatics, cations, anions, basic groups, acidic groups, and hydrophobic area. ZINCPharmer is a wonderful example of structure-based methods for target fishing (Koes & Camacho, 2012). It is a pharmacophore search software that identifies the desired pharmacophore directly from the structures deposited in the PDB. Additionally, it also has the potential to screen the ZINC ligand database for potential leads using the Pharmer pharmacophore search technology (Koes & Camacho, 2011). Another open-source platform called PharmMapper uses reverse pharmacophore mapping approach to identify potential target hits for the given compounds (Liu et al., 2010). For target fishing, PharmMapper uses a pharmacophore database, namely PharmTargetDB, which contains annotated target information collected from BindingDB, TargetBank, DrugBank, and Potential Drug Target Database (PDTD). Recently, the program was updated and its pharmacophore database is now six times larger than the earlier one. Presently, it holds 16,159 druggable pharmacophore models and 51,431 ligandable pharmacophore models. Similarly, another database known as Therapeutic Target Database (TTD) provides information about the therapeutic protein and nucleic acid targets, and the associated drugs, pathways, and diseases. The updated TTD contains intensive therapeutic information, such as drug-resistance mutations, target/drug-regulatory genes, disease-relevant differential expression profiles, clinical trial data, etc., and is freely 2. THEORETICAL BACKGROUND AND METHODOLOGIES

2 COMPUTATIONAL APPROACHES FOR DRUG REPOSITIONING

59

accessible at http://bidd.nus.edu.sg/group/ttd/ttd.asp. The pharmacophore modeling approach is computationally less demanding and some studies have even reported its efficacy as being higher than the molecular docking (Chen et al., 2009; Kr€ uger & Evers, 2010). The inverse docking is a complementary approach of the conventional docking paradigm. In other words, we can say that instead of docking multiple ligands with a particular target, this approach involves docking of a ligand against a variety of biological targets and the selected targets are then scored based on their affinities for that particular ligand. Fig. 3 highlights the differences between traditional docking and inverse docking. INVDOCK was the first reverse docking program developed by Chen and Zhi (2001). Presently, we have several reverse docking programs available, such as TarFisDock (Li et al., 2006) and idTarget (Wang, Chu, Chen, & Lin, 2012) iRAISE (Schomburg et al., 2014), to cater to the needs of polypharmacology. In fact, some researchers have successfully used conventional

FIG. 3 Flowchart of traditional and inverse docking approaches used for drug repositioning.

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docking programs like Autodock-Vina as an inverse docking tool for finding new indications (Scrima et al., 2014). Several researchers have successfully implemented inverse docking protocol for target fishing (Akhoon, Pandey, Tiwari, & Pandey, 2016; Xu et al., 2018). Ligand profiling is also helpful to predict the off-target interactions and has a key role in drug repurposing. Ligand profiling can be performed by finding chemical similarities with known compounds, as well as with ligand-based pharmacophores. Chemical similarity is an important concept in chemoinformatics and is often used to predict the structural and functional properties of compounds. It can be hypothesized that similar compounds exert similar biological properties. Molecular similarity can be represented by molecular fingerprints, which are abstract representations of the molecular properties of compounds. 2D molecular fingerprints use a binary representation of each property, where 1 represents the presence and 0 represents the absence of a molecular property. The Tanimoto coefficient is the most widely used measure to calculate the similarity between two compounds. The value ranges between 0 and 1, where 1 represents maximum similarity. 3D similarity search methods are applicable when no significant similarity exists between ligands or in case of diverse ligands. A 3D similarity search provides the relative position of ligands with respect to their targets. As conformational flexibility of shape is a major contribution to the biological properties of molecules, a 3D similarity search provides a more accurate prediction than 2D fingerprintbased searches. In a recent article Gurung et al. (2016) integrated the inverse docking and reverse pharmacophore mapping approaches and identified Telomere: G-quadruple, VEGFR-2, CDK-6, CDK-2, Topoisomerase II, and Topoisomerase I as potential targets for glycopentalone, with varying affinities. Similarly, Meshram, Baladhye, Gacche, Karale, and Gaikar (2017) applied a pharmacophore-probing approach and identified a hepatocyte growth factor receptor (c-Met) as a biological target for newly synthesized thiadiazole compounds. Moreover, a pharmacophore-based approach along with quantitative structureactivity relationship (QSAR) is widely used by computational chemists for ligand-based VS (Nargotra et al., 2009; Mahajan et al., 2017; Mehra, Rajput, et al., 2016; Mehra, Rani, et al., 2016). Some of commonly used pharmacophore mapping or docking programs are listed in the Table 6. Instead of using a target-based approach that identifies the target hits of an interested chemical, it is better to examine the genome-wide perturbation stimulated by that chemical. For example, we can expose some cell lines to a certain dose of chemical and then measure the changes at the transcriptome level in comparison to the untreated control. Thus genomicsbased drug repurposing methods can help us to understand how drugs are changing the entire biological system, as the intact response is captured as a pattern or signature. Plenge, Scolnick, and Altshuler (2013) used genomic approaches and suggested the new therapeutic indications of IL-6-based receptor therapies for the treatment of coronary artery disease. Therefore in subsequent sections we will focus on the system-level approaches for drug repositioning.

2.2 Knowledge-Based Methods Knowledge-based drug-repositioning methods compile known information on drugs, drug-target networks, signaling or metabolic pathways, clinical trial information, and other relevant drug phenotypic information using bioinformatics or chemoinformatics approaches. Using the knowledge-based methods, the prediction accuracy for drug-repositioning process 2. THEORETICAL BACKGROUND AND METHODOLOGIES

2 COMPUTATIONAL APPROACHES FOR DRUG REPOSITIONING

TABLE 6

61

The Pharmacophore Mapping and Inverse Docking Programs Used for Drug Repositioning

S. No. Software

Description

URL

1.

TarFisDock

TarFishDock utilizes potential drug target database for reverse ligand-protein docking. This is based on DOCK 4.0 scoring function

http://www.dddc.ac.cn/ tarfisdock/

2.

MDock

MDock is an automated molecular docking software based on ensemble docking algorithm for ligand docking and sphere-ligand matching algorithm for possible ligand conformation development. It utilizes knowledge-based scoring function

http://zoulab.dalton. missouri.edu/resources_ mdock.html

3.

idTarget

It is a freely available webserver for biomolecular target identification which utilizes a divide-and-conquer docking approach for target identification. It utilizes MEDock scoring function

http://idtarget.rcas.sinica. edu.tw/

4.

INVDOCK

INVDOCK has been developed for the identification of protein or nucleic acid (DNA or RNA) targets for small molecules. It utilizes simplified DOCK without pose optimization of ligands scoring function

http://bidd.nus.edu.sg/ group/softwares/invdock. htm

5.

PharmMap

PharmMap is a machine learning technique that utilizes the Monte Carlo random walk structure generator and QSAR model

http://www.meilerlab.org/ index.php/research/show? w_text_id¼32

6.

PharmMapper

It is widely used for reverse pharmacophore mapping. It utilizes a number of databases, such as DrugBank, BindingDB, PDBBind, and PDTD, for describing the binding mode of known ligands at the binding sites of protein targets. After that it utilizes LigandScout for pharmacophore mapping from collected structural data

http://lilab.ecust.edu.cn/ pharmmapper/index.php

7.

ZINCPharmer

It is a pharmacophore hypothesis-based screening tool that can be utilized to screen purchasable libraries from ZINC database. It can import pharmacophore hypothesis generated by Pharmer, LigandScout and MOE. It can also derive a pharmacophore hypothesis from Protein Data Bank structures

http://zincpharmer.csb.pitt. edu/

8.

TargetHunter

It helps in target identification by extracting knowledge from the bioactive compound-target pairs reported in literature

http://www.cbligand.org/ TargetHunter/

9.

Similarity ensemble approach (SEA

It establishes a correlation between proteins based on set-wise chemical similarity among their ligands and generates cross-target similarity maps

http://sea.bkslab.org/

10.

SuperPred

It predicts targets for compounds based on a pipeline consisting of 2D, fragment, and 3D similarity searches. It also classifies molecules according to the Anatomical Therapeutic Chemical (ATC) classification

http://prediction.charite.de/

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is greatly enhanced, as they incorporate a large amount of existing information. For example, Yildirim and coworkers analyzed the US FDA-approved drugs in connection with their linked target proteins and associated diseases (Yıldırım, Goh, Cusick, Baraba´si, & Vidal, 2007 Surprisingly, the topological analyses of bipartite graph/network revealed an overabundance of “follow-on” drugs. These kinds of drug-protein networks can help us to design new drugs that target diverse proteins instead of the existing drug ones, thereby improving polypharmacology. While considering the polypharmacology of drugs, Cheng and coauthors (Cheng et al., 2012) built three models for drug-target interactions (DTI): (1) drug-based similarity inference (DBSI), (2) target-based similarity inference (TBSI), and (3) network-based inference (NBI). Among these, NBI was found to perform better on four benchmark data sets. The network inference validation assays showed that five drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, have polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV. Although the conventional NBI methods provide significant new indications in many cases, like other methods they have some limitations. These methods use naive topology-based inference without taking account significant features within the drug-target (DT) domain. Alaimo and coworkers (Alaimo, Pulvirenti, Giugno, & Ferro, 2013) presented a new NBI method, called domain tuned-hybrid (DT-Hybrid). This method incorporates important features within the DT domain to predict more reliable DTIs. While incorporating the domain-based knowledge including drug and target similarity, there was an increase in the average areas under the ROC curves (AUC) of the data set in the DT-Hybrid algorithm. These results indicate that the addition of domain information (similarity among drugs and targets) increases the prediction accuracy and precision of the DT-Hybrid algorithm, and this approach is more reliable than the naive topology-based inference methods, such as NBI, that take account of only network topology to score computation. Elucidation of the structural similarity of protein-binding sites can help the discovery of new targets for known drugs. Studies have shown that many proteins share similar binding sites. For example, the binding of celecoxib to carbonic anhydrases and staurosporine binding to synapsin have similar binding pockets (De Franchi et al., 2010; Weber et al., 2004). Therefore proteins with similar binding sites are most likely to bind with the same ligands. In one of the earlier studies conducted by Li and coworkers (Li, Cheng, Wang, & Bryant, 2010), it was found that among 189,807 pubchem bioactive compounds that were assayed for binding with proteins, 62% of them bound to multiple targets. With the availability of numerous bioinformatics tools for binding-site comparisons (Ehrt, Brinkjost, & Koch, 2016; Konc & Janezˇicˇ, 2014; Perot, Sperandio, Miteva, Camproux, & Villoutreix, 2010) and the wealth of ever-growing PDB structural data, we can easily compare the protein binding sites on a proteome-wide scale to identify drug off-targets. Though numerous methods are available to find similarities between protein-binding sites based on the structural-fold similarities or common sequence motifs with similar functions (Ehrt et al., 2016; Haupt & Schroeder, 2011), specialized methods are required for drug repositioning, as similar ligands can bind to several proteins that share little or have no overall structural or sequence pattern similarity. Ligand-based approaches can be used to look for chemically similar ligands that bind to different proteins (Keiser et al., 2009). However, most of the ligand’s binding sites are unknown therefore the binding site prediction methods (described in the target-based approach) are required to fill this gap. The computational methods used to compare binding sites consider the interaction profiles, proteins surfaces, backbone structure, and physicochemical properties of protein-binding 2. THEORETICAL BACKGROUND AND METHODOLOGIES

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sites. Therefore these methods are categorized according to the model they use to represent the ligand-binding active site features. Most of the methods use graph models where atoms, functional groups, or surface points are shown as nodes, and the distances between the two nodes are represented as edges. Other comparison methods use geometric features, such as points, volumes, or clouds, to describe the binding sites. Some methods even use pharmacophore-based features like fingerprints to represent binding sites and these types of approaches are computationally very fast. The quality of the binding-site alignment is determined by scoring functions. The basic score used is the Tanimoto-index, which is calculated by detecting the number of aligned features divided by the total number of features, and the matched points are weighted by RMSD of geometry or using other physicochemical properties. The resultant scores are further adjusted by prioritizing the cases that involve hydrogen bonds, π-stacking, etc. Table 7 lists some of the majorly used computational tools for calculating the binding site similarity of proteins. Minai, Matsuo, Onuki, and Hirota (2008) successfully implemented the binding site comparison methods to compare 48,347 binding sites and reported on the new binding of ibuprofen to porcine pancreatic elastase, which was earlier reported to bind phospholipase A2 (original target). In another study, a chemical systems-biology approach was used to reposition the drugs used for Parkinson’s disease (Entacapone and tolcapone) for the treatment of

TABLE 7

List of Some Protein-Binding Site Comparison Tools

S. No.

Software

Description

URL

1.

SiteHopper

It applies a Shape and Color approach to compare binding site of proteins. The active site of the proteins is colored by the chemical properties of the protein residues It can integrate OEChem, Shape, and Spicoli toolkits for advance analysis

https://www.eyesopen.com/ sitehopper

2.

SiteComp

It performs three important analyses based on molecular interaction fields (MIFs) descriptors, which are bindingsite comparisons, binding-site decompositions, and multiprobe characterization. MIFs describes the difference between the interaction energy between a target molecule and probe

http://scbx.mssm.edu/ sitecomp/sitecomp-web/ Input.html

3.

bsitefinder

It works on the hypothesis that protein chains have ˚ co-crystalized ligands, all residues within a distance of 8 A of ligands are the components of the binding site. It defines a binding site by superimposing and locating the ˚ of a co-crystalized ligand residue within 10 A

http://binfo.shmtu.edu.cn/ bsitefinder/

4.

AutoSite

It predicts binding sites by using energetic aspects to select high-affinity points in space around the receptor, and gathering these points into clusters called fills and corresponding them to potential binding pockets

http://adfr.scripps.edu/ AutoDockFR/autosite.html

5.

EDTSurf

It is utilized to calculate construct triangulated surfaces for macromolecules. It generates van der Waals surface, solvent-accessible surface, and molecular surface and identifies cavities inside macromolecules

https://zhanglab.ccmb.med. umich.edu/EDTSurf/

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drug-resistant tuberculosis (Kinnings et al., 2009). It was found that these drugs directly bind to the substrate binding site of enzyme InhA for its inhibition. In their investigation, the SOIPPA algorithm showed that NAD and SAM binding sites of Rossmann fold and SAM methyltransferases share significant similarities. Yang et al. (2015) used several computational approaches, such as 2D VS, ligand screening for 3D shape, and binding site similarity, and identified tyrosine kinase inhibitor pazopanib as a potential inhibitor of acetylcholinesterase (AChE). It was reported that pazopanib has the similar potential as that of donepezil to restore memory loss and cognitive dysfunction. Some knowledge-based computational approaches use disease-specific pathways constructed from the gene expression profiles for drug repositioning ( Jadamba & Shin, 2016; Li & Lu, 2013). These methods work on the hypothesis that the key components (proteins) of disease pathways may serve as potential drug targets (Li & Agarwal, 2009; Strittmatter, 2012). Based on the manual pathway analysis, Li and Lu (2013) developed a computational method by linking drugs to diseases through target- and gene-involved pathways. The method was successfully applied to the discovery of new uses for existing drugs and provided significant information for drug repositioning, including the potential re-use of several drugs used for Crohn’s disease. Similarly, based on the transcriptional changes in cells before and after drug treatment, another drug repositioning method was developed for cancer treatment by Jin et al. (2012). Using the literature on signaling pathways and cancer mechanisms, signaling proteins were connected to oncogenic proteins and the network component was named cancer-signaling bridges (CSBs). The statistical regression model and Bayesian factor regression model (BFRM) were integrated with CSB to develop a new hybrid method called CSB-BFRM. The CSB-BFRM method was tested in breast and prostate cancer cells using the US FDA approved drugs for the same, and the results showed the potential of this method to accurately predict clinical responses in more than 90% of these drugs. Interestingly, the method also suggested several drug repositioning opportunities for cancer therapy. Motivated by the CSB concept, Zhao et al. (2013) developed a CSB computational model based on the available patient gene expression data. The method was applied to obtain specific downstream signaling pathways for several breast cancer subtypes to unravel the unknown target-disease connections and mechanisms. While applying this method for the repositioning of breast-cancer drugs associated with brain, lung, and bone metastases, it was found that the FDA-approved drugs, sunitinib and dasatinib, prohibited brain metastases derived from the breast cancer. These examples show the impact of knowledge-based computational methods for accelerating the drug-repositioning process. More recently, computational drug-repositioning methods are using phenotypic data for their predictions. The clinical phenotypic information mimics a phenotypic screen of the drug effects on humans as it is generated from the actual patient data. Systematic analyses revealed that phenotypic screening surpasses the target-based approaches for the discovery of new indications (Swinney & Anthony, 2011). In fact, the clinical side effects can now suggest several new drug indications (Campillos, Kuhn, Gavin, Jensen, & Bork, 2008). To address this kind of polypharmacology, researchers have even developed databases that contain data drugs, targets, side effects, and, most importantly, the ways in which these drugs cause adverse reactions. SIDER (side effect resource, http://sideeffects.embl.de) database is one such effort that presently holds data on 1430 drugs, adverse drug reactions (ADRs) and 140,064 drug-ADR pairs. Yang and Agarwal (2011) retrieved 3175 side effect-disease relationships by compiling

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the data from the drug labels and the drug-disease relationship information from the PharmGKB database. Based on the Side-Effectome drug-repositioning method, they suggested several new indications, including the indication of hypoglycemia drugs for diabetes. Similarly, Ye, Liu, and Wei (2014) reported novel drug indications based on the assumption that drugs with similar side-effect profiles may have similar therapeutic properties. Using the phenome information of SIDER, Bisgin et al. (2014) developed a latent Dirichlet allocation model to explore new indications for existing drugs. Some commonly used web servers for drug repositioning, such as DINIES, also use side-effect profiles for drugs to predict DTIs (Yamanishi et al., 2014). Although this approach provides a new opportunity for novel drug indications, there are certain limitations to this approach. For example, it is not necessary that the drugs sharing similar side effects have target proteins in common. Also, the drug’s phenotypical outcomes are greatly dependent on the subject’s genetic traits, medication history and others. Therefore for drug repositioning it is better to incorporate phenome data with other drug-relevant information.

2.3 Signature-Based Methods Gene signatures or gene expression signature is defined as single or combined group of genes with a uniquely characteristic gene expression pattern resultant of a normal or pathological condition. The Human Genome Project (HGP) has catalyzed the drug-discovery process by deciphering the functional and biological role of genes present in the human genome. Studying the cellular gene expression pattern of thousands of genes at once, we can understand living systems more precisely. The availability of high-throughput gene-expression data has enabled comprehensive monitoring of transcriptional responses associated with various disease states and drug treatments. The utilization of this data to interpret the gene signatures of various diseases and drug treatments is the grounding for signature-based drug-repositioning methods. Genome wide expression profiles have a significant role in drug repurposing, as they help us to understand the interaction of a drug with various pathways in a cell. Signature-based methods provide a better insight into drug indications by uncovering drugs’ unknown mechanisms of action. A comparative analysis of sets of genes, which are unregulated or downregulated in diseased or normal states, is helpful to create a signature of that disease. Disease signatures are also important to understand the molecular basis of a drug’s mechanism of action when expression profiling is carried out in the presence of a drug molecule. Gene signatures can be broadly categorized into the following classes: prognostic gene signatures, diagnostic gene signatures, and predictive gene signatures. Prognostic gene signature predicts overall outcome of the condition regardless of therapeutic intervention. A diagnostic gene signature serves as a biomarker and helps to identify a specific biological or pathological state. Predicted gene signature predicts the effect of a treatment in a disease phenotype and helps to find therapeutic targets. Predictive gene signatures are of the most importance in the context to drug repurposing. Advance gene sequencing methodologies like microarray and next generation sequencing (NGS) technology are used extensively to generate high-throughput gene-expression data at low cost and in a short time. DNA microarray techniques are widely applied to identify gene

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signatures. Using DNA microarray we can study the cellular expression of thousands of genes at a time. The application of microarray techniques has revolutionized the field of traditional drug discovery and development process. DNA microarray is a robust tool for target identification, validation, and lead optimization. With the help of microarray technique, one can hypothesize the complex disease mechanisms by correlating the gene expression profile of the diseased state with the normal state. This also helps in finding new therapeutic indication by identifying putative targets involved in disease pathophysiology. DNA microarrays are classified into four major classes based on probe used: cDNA microarrays: These are also known as spotted DNA arrays. These microarrays use complementary DNA (DNA synthesized by single-stranded RNA by reverse transcription) to hybridize complementary probes spotted on the array. Oligo DNA microarrays: These are microarrays that use chemically synthesized oligonucleotides (short DNA or RNA molecules) on the array as a probe. BAC microarrays: These microarrays utilize a template amplified by polymerase chain reaction as the probe. SNP microarrays: These are used to detect polymorphisms within a population. The DNA microarray technique is opening new avenues for drug repurposing. While traditional structure-based drug design does not consider off-target indications, one can easily measure any additional or unexpected effects of a lead compound with microarray techniques. Microarray expression is an unbiased technique to identify both the on- as well as off-target effects of compounds. In a recent article, Xie et al. (2016) performed in silico molecular docking and gene expression studies for repurposing of several FDA-approved drugs against seven targets involved in Alzheimer’s disease (AD). Several FDA-approved drugs (1553) were screened through VS and 74 drugs were identified based on their binding energy (less than 10 kcal/mol for all seven targets) and the cell-based microarray assays of this dataset were available in the cMap database (Lamb et al., 2006). This work led to the repurposing of four drugs (risperidone, droperidol, glimepiride, and glipizide) for AD. Another indispensable technology to generate high-throughput gene-expression data is NGS. NGS is a high-throughput DNA sequencing methodology for the rapid sequencing of DNA or RNA samples. NGS is based on three steps: library preparation, clonal amplification, and sequencing. Some of the important platforms of NGS studies are Illumina (Solexa), Roche 454, Ion torrent, Proton/PGM sequencing, SOLiD sequencing, etc. The advantages and disadvantages of these platforms are compared in Table 8. NGS has a wide range of applications in drug discovery, ranging from genome sequencing to finding deeper druggable target regions in DNA or RNA sequences, and it allows users to discriminate between single bases of the genetic code. In contrast to capillary electrophoresisbased Sanger sequencing, which takes the HGP over 10 years, NGS takes only 1 year to analyze the entire human genome and at a lower cost. The high-throughput gene-expression data generated by abovementioned techniques are stored in various gene-expression databases (Table 9). These databases serve as repositories for measurable data and manage a searchable index making it suitable for use in analyses and interpretations carried out by other applications. Gene-expression databases play an important role in signature-based drug repurposing by providing access to comprehensive gene-expression datasets. There are several tools that provide comprehensive platforms to analyze and compare drug and disease gene-expression profiles from public expression repositories:

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TABLE 8

Some Commonly Used Next Generation Sequencing Platforms for DNA Sequencing

Company

Platform

Principle

Read Length

Advantage

Roche

Roche 454

Pyrosequencing

700 bp

Long read length Error rate

Illumina (Solexa)

MiniSeq, MiSeq, Reversible terminator NextSeq, HiSeq X sequencing by synthesis

2  101 bp High throughput, lower cost

Short read assembly

Life technologies

SoLiD

75 bp

Accuracy

Short read assembly

Pacific Biosciences

PacBio RS PacBio Single molecule, Real-time RS II DNA sequencing by synthesis

1500 bp 10 kb

Long read length High error rate

Oxford Nanopore Technologies

MinION

Nanopore, semiconductorbased sequencing

5  200 kb Long read length, low cost

Thermo Fisher Scientific

IonTorrent

Semiconductor-based sequencing

Sequencing by ligation

Low cost and fast run

Disadvantage

Unknown Error rate High rate of sequencing errors

The advantages and limitations are compared.

TABLE 9

List of Some Important Gene-Expression Databases Used for Signature-Based Drug Repositioning

Database

Remarks

URL

Molecular Signatures DB (MSigDB)

This database allows users to search and download annotated gene sets for GSEA software

http://software. broadinstitute.org/gsea/ msigdb

Gene Expression Omnibus (GEO)

GEO is a public functional genomics data repository by NCBI that accepts array and sequence-based data. It allows users to search and download expression profiles

https://www.ncbi.nlm.nih. gov/geo/

DisGeNET

This is one of the largest publicly available databases containing genes and variants associated to human diseases

http://www.disgenet.org/ web/DisGeNET/menu

ImmGen database

ImmGen is a publicly accessible database containing complete microarray of gene expression and its regulation in the immune system of mouse

https://www.immgen.org/

GENEVESTIGATOR GENEVESTIGATOR contains gene expression data from microarray and RNAseq experiments. It contains expression data for almost 500 diseases. One can use this database to study gene function, metabolisms, and target identification

https://genevestigator.com/ gv/

Ensembl

It is a genome browser from EMBL-EBI that can be utilized for comparative genomics, sequence variation and evolution. It contains gene expression data for different organism like human, mouse, zebra fish, etc.

https://asia.ensembl.org/ index.html

ArrayExpress

Array express is a public repository provided by EMBL-EBI, that contains high throughput genomic expression data

https://www.ebi.ac.uk/ arrayexpress/ Continued

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TABLE 9 List of Some Important Gene-Expression Databases Used for Signature-Based Drug Repositioning— cont’d Database

Remarks

URL

TiGER

Tissue-specific Gene Expression and Regulation (TiGER) contains tissue-specific gene expression profiles and EST data cis-regulatory module and combinatorial gene regulation data

http://bioinfo.wilmer.jhu. edu/tiger/

GeneSetDB

It contains a meta database of gene expression sets that provides tools for gene set enrichment and statistical analysis

http://genesetdb.auckland. ac.nz/haeremai.html

ArrayWiki

It is a data repository for sharing expression data generated by microarray studies

http://arraywiki.bme.gatech. edu/index.php/Main_Page

DeSigN by Lee, Tiong, et al. (2017) and Lee, Freddolino, Zhang, (2017) DeSigN is a webbased tool for signature-based drug repurposing. This is basically used for predicting drug efficacy against cancer cell lines. It predicts drug-gene connections based on phenotypespecific gene signatures derived from differentially expressed genes with predefined gene-expression profiles associated with drug-response data (IC50) from 140 drugs. ksRepo Drug Repositioning Platform by Brown, Kong, Kohane, and Patel (2016): ksRepo is an open-source tool, freely available at http://github.com/adam-sam-brown/ksRepo. It is a flexible tool that takes input from any microarray platform and uses a generalized K-S enrichment test and bootstrapping for drug repurposing. NFFinder by Setoain et al. (2015): NFFinder is a computationally extensive tool, it takes input as two separate lists of upregulated and downregulated genes. It utilizes MetaMap to mine related metadata from various expression data repositories like GEO, CMap, and DrugMatrix to identify potential biological relationships between drugs and diseases. It is freely available at http://nffinder.cnb.csic.es. Drug versus Disease (DvD) by Pacini et al. (2012): DvD is based on the principle of negative correlations in the gene expression of drugs and diseases, it uses gene set-enrichment analysis to match drug and disease expression profiles. It provides a pipeline, using R or Cytoscape, to compare and visualize drug and disease gene-expression profiles from public microarray repositories. This also provides dynamic access to expression databases like Array Express and GEO, and data from the Connectivity Map. DvD can be downloaded from http://cran.ma. imperial.ac.uk/. Connectivity maps (CMap) implemented by Lamb et al. (2006) is an invaluable technique behind successful signature-based drug repositioning. The Cmap database contains information about 27,927 genetic perturbagens, which are profiled to create 476,251 expression signatures. These signatures can be used to find new therapeutic indications; drugs with highly similar signatures are hypothesized to be functionally related. By connectivity mapping one can easily predict connections between disease, genes, and drugs by creating a drug similarity network. These connections can also be used to establish relations between the chemistry, biology, and the clinic, by exploiting genome-wide similarities or differences. Genomic antisimilarity between drug-cell signatures and disease signatures forms a strong foundation upon which to find new drug indications. Drug and disease genome-wide expression metrices are vital to finding negative correlations between drug and disease signatures.

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Dudley et al. (2011) mined publicly available gene-expression data on Crohn’s disease and ulcertive colitis in human intestinal tissue from NCBI-GEO, to create a disease geneexpression signatures. These signatures were compared with the drug induced gene expression profiles in Connectivity map in order to identify their anticorrelation. With this approach, topiramate, an anticonvulsant drug, was successfully repositioned for the treatment of inflammatory bowel disease. Similarly, Sirota et al. (2011) combined the gene expression of 100 diseases and the gene-expression measurements on 164 drugs and successfully predicted novel therapeutic indications for the antiulcer drug cimetidine for lung adenocarcinoma, by studying the molecular signatures in the drug-disease pairs. They used a similarity score for every pairing of a drug and disease, ranging from +1 (perfect correlation) to 1 (uncorrelated). These studies strengthened the role of the gene-expression data in drug repurposing, even when little molecular data is present. The signature-based drug-repurposing methods are solely based upon drug behavior in the cellular environment, regardless of their chemical structure, represented in terms of the gene-expression signature. This helps to create a more robust and reliable hypothesis for drug repurposing. Regardless of these advantages, the availability of gene-expression data is a major limiting factor for signature-based drug repurposing. Another factor influencing the success of signature-based drug repurposing is the analysis of the expression data. The interpretation of the gene-expression data needs rigorous statistical analysis and computational cost.

2.4 Network-Based Methods The biological networks that are used to model the interactions of different biological entities are usually composed of two main components: (1) nodes, and (2) edges. Nodes represent various biological components, such as genes, proteins, or even multifaceted phenotypes, such as diseases; whereas edges show the relationship between the different components. Network-based drug-repositioning methods provide new drug indications by reconstructing the disease-specific pathways using the omics data, signaling or metabolic pathway information, or protein-interaction networks (Hong, Chen, Jin, & Xiong, 2013; Lotfi Shahreza, Ghadiri, Mousavi, Varshosaz, & Green, 2017; Oprea et al., 2011; Ye et al., 2014). Cheng et al. (2012) implemented three computational methods, namely DBSI, TBSI, and NBI to predict DTI. They observed that among these three methods NBI performed comparatively better. The NBI-based method predicted some new DTIs and in vitro assays showed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole have good inhibitory potential (0.2–10 μM) for estrogen receptors or dipeptidyl peptidase-IV. Furthermore, simvastatin and ketoconazole were also reported to have potent antiproliferative activities on the human MDA-MB-231 breast cancer cell line. In network-based models for DTI, a bipartite interaction network is built where drugs and targets are represented as nodes and their interactions as edges. There are several networkbased methods for predicting potential DTIs. Some DTI methods are solely based on pharmacological and clinically relevant associations, whereas some are extended by incorporating additional information in the network, such as drug-drug similarity or protein-protein similarity. The later models are more robust than the earlier ones. Similarity-based approaches,

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also known as guilt by association (GBA) methods, have been increasingly used for drug repositioning. For instance, Chiang and Butte (2009) built a disease-disease similarity network to predict novel drug-disease associations. They assumed that if two diseases shared the same medications, then other medications that are presently used for either of the two diseases may also have therapeutic potential for the other. By using the GBA method for 2022 FDA-approved and off-label drugs that were connected to 726 diseases a huge list of 57,000 novel drug use suggestions were produced and many of these were experimentally validated. Similarly, Gottlieb, Stein, Ruppin, and Sharan (2011) developed a computational method, PREDICT, to predict novel drug indications. The method uses multiple drug-drug and disease-disease similarity measures and is based on the assumption that similar drugs treat similar diseases. Instead of using either drug-drug or disease-disease similarity approaches, Wu, Gudivada, Aronow, and Jegga (2013) used a more holistic approach by incorporating drug-disease relationships. The drug-disease heterogeneous network was clustered by connecting the nodes (one drug or one disease) that share genes/targets and enriched features, such as biological process, pathway, and phenotypes. The closely associated connections were weighted by a Jaccard score to reveal new drug repositioning candidates. Likewise, Zhang, Wang, and Hu (2014) developed a unified computational framework to predict new drug indications based on multiple drug similarity, multiple disease similarity, and the known drug-disease associations. A large-scale in silico study conducted on 799 drugs against 719 diseases revealed several new drug indications and some of their predictions were justified by clinical trials databases. The developed computational DDR method achieved a tenfold cross validation AUC of 0.87 and its ranking procedure suggested that drug side effect information had a major contribution to make, followed by the chemical structure, and finally their known targets for new drug indications. There are several types of biological networks depending on the dataset they represent. For example, gene regulatory networks (DNA-protein interaction networks) can utilize the transcriptomic data (which represents the dynamic properties of a cell) to unravel the drug’s mechanism of action (Dai & Zhao, 2015). Such methods are based on the assumption that drugs sharing similar gene-expression signatures are likely to target the same proteins. Presently, there are numerous well-established technologies like NGS sequencing or gene microarrays that provide significant gene-expression profiles and such information is easily accessible, as there are multiple online resources that store this data (see signature-based methods). Such information can be used to prioritize potential drug targets (Emig et al., 2013). Iskar et al. (2010) defined a signature for each drug using the normalized geneexpression profiles from CMap to characterize drugs with similar mechanisms and identified new targets for some drugs. Interestingly, the analysis of drug-induced coregulated genes revealed that zaprinast (a clinically unsuccessful drug) interacts with a new target PPARγ and therefore can be further investigated in the context of diabetes. Similarly, in protein-protein interaction networks (PPINs), the nodes denote proteins and the edges represent the protein-protein interactions. It has been reported that proteins infrequently work in isolation and are mostly functional in the context of interaction networks (Zhang & Huan, 2010). There are proteins that interact with only a few proteins; however, most of the proteins have interactions with a large number of proteins and these proteins are knows as hubs. Hub proteins usually include enzyme families, transcription factors or

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intrinsically disordered proteins. PPI network methods predict new drug targets based on the assumption that proteins targeted by similar drugs are functionally associated and lie close to each other in the PPIN. These methods are very beneficial in shortening the complex pathways that contain a large number of proteins to small specific networks involving only a few targets (proteins). Further, metabolomics also provides a huge amount of information for drug repositioning (Robertson & Frevert, 2013). The nodes of the metabolic network denote chemical compounds and metabolites, whereas the edges represent the enzymatic reactions. The mass flow of a compound caused by certain enzymes may cause a disease and such enzymes can be manipulated by drugs to balance the chemical concentration, thereby treating that particular disease. In one drug reposition study, researchers systematically integrated proteomics, metabolomics, and genome-wide association studies (GWAS) data to reposition drugs for the treatment of diabetes (Zhang, Luo, Xi, & Rogaeva, 2015). The study revealed 992 proteins as potential antidiabetic targets in humans and nine drugs were finally repositioned for the treatment of diabetes. Among them, diflunisal, nabumetone, niflumic acid, and valdecoxib that target COX2 showed new indications for treating type-1 diabetes, whereas phenoxybenzamine and idazoxan targeting ADRA2A (Alpha-2A adrenergic receptor) were repurposed for type-2 diabetes. In another study, publically available “omics” data, including genomics, epigenomics, proteomics, and metabolomics data were mined for drug repositioning for AD (Zhang et al., 2016). The AD pathogenesis data was retrieved from the OMIM and PubMed databases, and the drug-target information was collected from the DrugBank and Therapeutic Target Database. The data analysis revealed 524 AD-related proteins and among them 18 proteins were observed to be the targets for 75 existing drugs. Zhang and coworkers prioritized anti-AD targets using the computational algorithm and reported CD33 and MIF as top targets for seven existing drugs. The study suggested that seven drugs could be repurposed to help treat the cognitive symptoms of AD, as they were the promising candidates for the anti-AD target acetylcholinesterase. The reliability of the network methods to predict new drug indications can be further improved by including other biologically relevant information. For example, studies have shown that inappropriate expression of microRNAs (miRNAs) is associated with complex diseases (Esquela-Kerscher & Slack, 2006) and drugs can modulate the expression levels of miRNAs (Rhodes et al., 2012). Chen and Zhang (2015) took advantage of miRNA-related information and evaluated the role of miRNAs in drug repositioning by constructing the miRNA-driven inference model. The model, built with the hypothesis that drugs linked with diseases should share common miRNA partners in mind, integrated several experimentally resolved drug-miRNA associations and miRNA-disease associations to predict numerous drug-disease associations. Some of the predictions were justified by the experimental data available in the comparative toxicogenomics database (CTD), which demonstrates the usefulness of this inference model. Despite the fact that network methods are comparatively more reliable than the earlier approaches, it is quite clear that they are based on the available biological information for association inference. Nevertheless, the biological information connecting drugs with diseases is not complete therefore the accuracy of the inferences remains questionable. Chen, Zhang, Zhang, Cao, and Tang (2015) improved this concept by including two inference methods, ProbS and HeatS, in the heterogeneous network. These inference methods use only the basic network topology measure to predict direct

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drug-disease associations and then prioritize the predictions using bipartite network topology. Both methods showed respectable AUC values of 0.9192 and 0.9079 and suggested many new drug indications for further studies.

2.5 Target Mechanism-Based Methods The target mechanism-based drug repurposing approach focuses on delineating disease targets and their associated mechanisms of action to find new therapeutic indications. This approach combines a computational biological system with biological pathways study to gain better insight into the mode of action and diseases associated with the therapeutic compounds. The biological system is a complex system showing emergent properties, which arise due to the collaborative functioning of the system but are not displayed as their individual parts. Systems biology is a science that provides a comprehensive environment to study how biological systems interact functionally over time and paves new ways for target mechanism-based drug repurposing. The emergence of systems biology has revolutionized the field of drug discovery and researchers can now do multiscale modeling of biological networks, i.e., networks of networks. Systems biology combines network models with quantitative mathematical network models to derive the dynamic behavior of biological systems. From the perspective of drug repurposing, these networks can help in finding new therapeutic targets and the novel mechanisms linked with them, which forms the basis of target mechanism-based drug repurposing methods. Another important technique for target mechanism-based repurposing is systems pharmacology. Systems pharmacology is the integration of systems biology and pharmacology and this approach is based on the one drug, multiple targets phenomenon. By applying this approach, one can easily predict chemical-protein, protein-protein, genetic, signaling, and physiological interactions in a biological system. While traditional network-based models suffer from the disadvantage of having prediction potential targets for known drugs only, with the newer systems pharmacology-based approaches, such as balanced substructure-drugtarget network-based inference (bSDTNBI, Wu et al., 2016), it is now feasible to predict potential targets for newer entities, old drugs, and failed drugs. By applying the bSDTNBI method, Wu and coworkers identified compounds with dual-effect (i.e., agonistic as well as antagonistic) on estrogen receptor α (ERα), which can be utilized for the treatment of osteoporosis or breast cancer. This approach can be widely applied for target mechanism-based repurposing. For successful systems biology-based target-mechanism prediction, pathway analysis is a milestone process. Pathway analysis integrates knowledge from a large amount of publicly accessible high-throughput “omics” data (Table 10), including genomics, epigenomics, proteomics, and metabolomics data, to establish new relationships between different pathways involved in normal as well as diseased states. This resultant relationship between complex biological pathways has widely facilitated drug target mechanism-based repurposing by delineating unknown mechanisms of actions of drugs. Target mechanismbased drug repurposing also helps to combat acquired resistance to therapy. For instance, in tuberculosis, where resistance to existing drugs is a major cause of mortality among patients, understanding the whole pathway during drug resistance will be a boon to establishing a novel drug therapy. Unlike the other predictive methods used in drug

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TABLE 10

Some of the Omics Databases Used for Target-Mechanism Based Drug Repurposing

Database

Description

URL

KEGG

It is a freely available database containing information about the complex biological pathways

http://www.kegg.jp/

WikiPathways

This is the open source pathway database maintained by the scientific community

https://www. wikipathways.org

Therapeutic Target Database (TTD)

TTD is a freely available database that stores information about therapeutic protein and nucleic acid pathway, etc.

http://bidd.nus.edu.sg/ group/cjttd/

BioCyc Database

It contains data on pathway/genome. It also provides the tools to analyze them

https://biocyc.org/

PANTHER

PANTHER (Protein Analysis THrough Evolutionary Relationships) is a database that classifies proteins and their gene for high throughput analysis

http://www.pantherdb. org/

MiRTarBase

A microRNA-target interactions database

http://mirtarbase.mbc. nctu.edu.tw/php/index. php

TRANSFAC

TRANSFAC is a database of eukaryotic transcription factors, their genomic binding sites, and DNA binding profiles

https://en.wikipedia.org/ wiki/TRANSFAC

BioCarta

This database comprises dynamic maps of metabolic and signaling pathways

http://www.biocarta.com

Reactome

It is a freely available and manually curated signaling pathway database, and metabolic molecules and their relations are organized into biological pathways and processes

https://reactome.org/

repurposing, the target mechanism-based approach explores the whole target space of the drug candidate based on chemical structure similarity and phenotypic effect similarity by making optimal use of millions of compound-protein interactions. Integration of phenotypic similarities during drug repurposing studies helps in the development of precision medicine. Precision medicine is a medical model that considers genetic, environmental, and lifestyle factors while proposing treatment for any ailment. Despite all of these advantages, target mechanism-based methods are computationally extensive, and require high computing power and knowledge of computational algorithms. There is a strong need to develop new algorithms that are more efficient and less computationally exhaustive so that a holistic model can be developed from disease-specific as well as normal-state omics data.

3 EXAMPLES OF SUCCESSFUL DRUG REPOSITIONING Using a combination of bioinformatics and experimental approaches, several drugs have been successfully repositioned to new indications and many of the repositioned compounds are undergoing clinical trials. As a matter of fact, most drugs fail in the late developmental

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stage because of poor efficacy in the phase III clinical studies. These, otherwise safe, latephase-failed compounds are most suitably placed to be explored for new indications and drug repositioning. Some of the successful stories of drug repositioning are listed in Table 11.

TABLE 11 Successful Drug-Repositioning Stories Drug

Original Indication

New Indication

References

Allopurinol

Tumor lysis syndrome

Gout

Lupton and Odom (1979)

Amphetamine

Stimulant

Hyperkinesis in children

Montagu and Swarbrick (1975)

Atomoxetine

Parkinson’s disease

ADHD

Bymaster et al. (2002)

Bupropion

Depression

Smoking cessation, obesity

Ferry and Johnston (2003) and Tek (2016)

Duloxetine

Depression

Diabetic peripheral neuropathy, stress urinary incontinence, fibromyalgia, chronic musculoskeletal pain

Raskin et al. (2005) and Lunn, Hughes, and Wiffen (2014)

Dapoxetine

Analgesia and depression

Premature ejaculation

McMahon (2011)

Everolimus

Immunosuppressant

Pancreatic neuroendocrine tumors

Yao et al. (2011)

Finasteride

Benign prostatic hyperplasia

Alopecia

Kaufman et al. (1998)

Fluoxetine

Depression

Premenstrual dysphoric disorder

Cohen et al. (2002)

Imatinib

Chronic myeloid leukemia

Gastrointestinal stromal tumor

Heinrich et al. (2003)

Lidocaine

Localanesthetic

Arrhythmia

Gianelly, Von der Groeben, Spivack, and Harrison (1967)

Lumigan

Glaucoma

Hypotrichosis simplex

Law (2010)

Methotrexate

Cancer

Rheumatoid arthritis

Kremer et al. (1994)

Mifepristone

Pregnancy termination

Cushing’s syndrome

Fleseriu et al. (2012)

Milnacipran

Depression

Fibromyalgia syndrome

Vitton, Gendreau, Gendreau, Kranzler, and Rao (2004)

Minoxidil

Hypertension

Hair loss

Messenger and Rundegren (2004)

Paclitaxel

Cancer

Restenosis

Tepe et al. (2008)

Phentolamine

Hypertension

Dental anesthesia reversal agent

Tavares et al. (2008)

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TABLE 11

75

Successful Drug-Repositioning Stories—cont’d

Drug

Original Indication

New Indication

References

Ropinirole

Parkinson’s disease

Restless legs

Trenkwalder et al. (2004)

Sibutramine

Depression

Obesity

Hansen et al. (2001)

Sildenafil

Angina

Erectile dysfunction

Boolell et al. (1996)

Sunitinib

Gastrointestinal stromal tumor, renal cell carcinoma

Pancreatic neuroendocrine tumors

Raymond et al. (2011)

Thalidomide

Morning sickness

Multiple myeloma

Singhal et al. (1999)

Topiramate

Epilepsy

Migraine

Diener et al. (2004)

Trastuzumab

HER2-positive breast cancer

HER2-positive metastatic gastric cancer

Croxtall and McKeage (2011)

Zidovudine

Cancer

HIV/AIDS

Volberding et al. (1990)

Naltrexone

Opioid addiction

Obesity

Tek (2016)

4 OPPORTUNITIES AND LIMITATIONS OF IN SILICO DRUG REPOSITIONING Computational drug repositioning is transforming the traditional drug-discovery process. Drug repositioning is the quickest and cheapest way to develop new therapeutic indications because a wealth of data is already available on the drugs. The methodologies followed in drug repurposing are changing continuously, initially drugs were serendipitously tested among various targets to find new indications. Thalidomide (Brynner & Stephens, 2001) is the best example for serendipitous drug repurposing. Increasing knowledge of drugs and their underlying mechanisms has accelerated the pace of computational drug repurposing. Application and advancement of the previously established drug-discovery principles to drug repurposing has motivated researchers to extract drugs for new therapeutic indications. For example, Luo et al. (2016) integrated the principle of chemical similarity (chemically similar compounds may exhibit similar biological process) with the Bi-Random walk (BiRW) algorithm and the proposed new method, MBiRW, for drug repurposing, which outperforms many previously known methods. The accumulation of omics data is providing a new platform to establish correlations between diseases and targets. Network biology utilizes this omics data to create either individualized or integrated networks for drug repurposing. For example, Vitali et al. (2016) integrated the drug-target network with the human disease network to identify targets that are involved in multiple diseases to design multitarget therapy and drug repurposing. Integrated networks can also be used to design combination drug repurposing, as networks give a comprehensive view of complex biological processes. Diseasome (a network of disease

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relationships) cluster-based repurposing (Langhauser et al., 2018) is an extended application of network biology. Langhauser et al. generated human diseasomes with common genetic origins, shared protein interactions, symptom similarity, and comorbidity, and combined in silico studies with in vivo and in vitro models for successful repositioning of soluble guanylate cyclase activators from smooth-muscle relaxation to direct neuroprotection. With high computing machines and efficient algorithms, scientists are now able to take network biology to a systems level. Researchers are placing more emphasis on integrative processes, such as the combination of gene-expression signature-based approaches to network biology, to find true positive hits, by successive screening and refining. In recent research by Goldstein et al. (2018) calcium channel blockers were repurposed for gestational diabetes by mining high-throughput gene-expression and electronic health records to establish a relationship between phenotype and genotype variants. Such studies are important for personalized medicine as direct phenotypic effects are also considered during repurposing. In other research, Baker, Ekins, Williams, and Tropsha (2018) scanned more than 25 million research papers by text mining for bibliometric drug repurposing. With this approach Baker et al. identified many drugs that have been tried on more than one disease. Such approaches are important and more comprehensive because more and more research data is accumulated each day. In addition to commonly occurring diseases, repurposing is also playing a major role in the field of rare-disease research. The treatment of rare diseases is a costly process that increases the socio-economic burden on the patient; despite this there are very few companies that are involved in rare-disease research, due to the small amount of profit. For this reason, repurposing is a ray of hope for rare-disease treatment. In addition to cutting the cost of rare-disease research, the rate of successful treatment using repurposed drugs is higher than in classical drug research. Drug repurposing can be a milestone during sudden outbreaks of infections and for the treatment of new pathogens. Due to the availability of pharmacokinetics data, approved drugs can be directly advanced to higher phases of clinical trials by passing preclinical and phase-1 studies. For example, recently in research published by Shiryaev et al. (2017), they repurposed the antimalaria drug chloroquine for Zika-virus treatment and prophylaxis. Interdisciplinary research between disciplines like bioinformatics, genomics, molecular biology, etc., is pivotal for successful drug repurposing and is increasing. Moreover, top pharmaceutical companies are collaborating with academia or hospitals to find new indications. This collaboration not only helps in finding intuitive novel indications with the help of highly qualitative quantitative data, but also paves a new path for establishing new business models for repurposed drugs. Although computational drug repositioning has gained massive interest from the academic and pharmaceutical sectors in recent years, there are numerous caveats to the in silico methods employed for drug repositioning. As a matter of fact most, if not all, computational approaches are dependent on different data sets. Overall, the methods may be either target based or disease based. Target-based methods get inference from the compound-protein interactions, whereas disease-based approaches rely on comparing the characteristics and similarities of different diseases. As already mentioned, searching for chemical similarities is one of the commonly used computational methods for drug repositioning. However, many of the available drug structures and their associated chemical properties contain errors, and some information

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is even withheld as proprietary. Moreover, the drugs undergo complex uncharacterized metabolic or pharmacokinetic transformations in the human body; therefore looking for just chemical similarities is not adequate for drug repositioning. Likewise, target-based repositioning methods also make binding-site comparisons for new indications, but to be precise there is no state-of-the-art best-performing algorithm to compare the binding sites of unrelated proteins. Some methods even predict binding sites of new proteins nevertheless as these methods are based on training data they fail to accurately predict the binding sites of new proteins. The pharmacophore methods also face these caveats and the probability of identifying an active molecule is small, as these methods rely on precomputed databases that do not contain enough low-energy conformations per molecule. Moreover, these methods totally fail in the identification of new kinase inhibitors because kinase inhibitors possessing similar structures have different activity profiles. Overall, these methods have limited applicability for drug repositioning, particularly for truly novel classes of compounds or targets. Although the inverse docking approach is widely used for target-based drug repositioning and provides clues on new drug uses, it also faces several limitations. These methods provide inadequate results because of the limitations of the docking tools as well as of the target databases. We know the docking program is a blend of search algorithms and a scoring function, and there are several scoring functions to rank best hits, but each of them has limitations, as described in the target-based drug repositioning section. Also, despite the development of improved docking programs, fully matching the ligand with the target space is still unrealistic, as the 3D structures of many proteins are unknown. Furthermore, under normal physiological conditions, proteins and ligand molecules undergo significant conformational changes and are highly flexible in solution. It is true that almost all docking tools provide enough flexibility to the ligand, but some of them consider the receptor as rigid and some provide only limited flexibility to the residues around the binding site. Although molecular dynamic (MD) methods can narrow down the limitations of docking results by providing enough conformational flexibility, incorporating water molecules, salts, etc., to mimic the natural conditions, they have their own pitfalls. The major concern is the inadequate sampling of conformations. Since MD is computationally very intense and demands supercomputing facilities, most of the researchers draw their conclusions from hundreds of nanoseconds or a few microseconds. But the fact is that an actual complete folding of a protein requires milliseconds to seconds to complete. Although network pharmacology is gaining momentum these days and is illuminating the numerous off-target effects of drugs, there are still some intrinsic limitations. The major drawback of some network approaches is their ad hoc tendency to tune parameters and thresholds that determine the presence or absence of edges, thereby causing discrepancies in the extraction of subnetworks. Gene signatures are often used to address the off-targets of drugs in network pharmacology. These methods have the potential to identify genes that are changed during drug treatment, but they cannot explicate the associations between gene-expression changes. Thus they fail to predict the drug off-target effects on these genes. Further, these methods also skip frequently changed genes that are not part of the gene signatures. In fact, the results from gene-expression signature-comparison methods are not robust because of the presence of noise in some gene-expression data, and this leads to biased extracted responsive network predictions. The relevance of the mathematical optimum of the responsive networks with the maximal biological importance is not necessarily correct as the genes used as drug

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targets and those regulated by a target, such as transcription factor, do not necessarily have significant expression changes. Likewise, PPIN maps also suffer from some limitations. These methods are based on the protein-protein interaction data derived from the experimental sources that are not yet characterized in detail. The data required for network construction is noisy and incomplete and results in biased predictions. Further, as described for the gene regulatory networks, the mapping between a responsive network and the living organism’s response is not so simple. Similarly, numerous methods have been developed to elucidate a correlation in drugs’ mechanisms of actions by analyzing the phenotypic data (indications and side effects). But given that the side effect information of drugs is noisy and scant, these methods are also not sufficient to probe the drug repositioning. Also, a major concern related to phenotypic methods is that phenotype outcomes are highly dependent on the subject’s genetic map, medication history, and traits. Therefore a direct comparison of the phenotype information between two drugs to explore their mode of action is not highly reliable and has little chances of success. We conclude that it is always a better idea to integrate information from multiple resources for robust in silico predictions as none of the available methods, if used in isolation, is promising for finding new indications for drugs. Overall, we would like to close this chapter by highlighting that the goal of computational methods is not to predict experimentally validated results, but instead to promote experimental studies to put forward new ideas for drug repositioning studies that ultimately have to be verified experimentally.

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Further Reading Cherkasov, A., Muratov, E. N., Fourches, D., Varnek, A., Baskin, I. I., Cronin, M., … Consonni, V. (2014). QSAR modeling: where have you been? Where are you going to? Journal of Medicinal Chemistry, 57(12), 4977–5010. Hansch, C., Maloney, P. P., Fujita, T., & Muir, R. M. (1962). Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature, 194(4824), 178. Jain, R. K., Duda, D. G., Clark, J. W., & Loeffler, J. S. (2006). Lessons from phase III clinical trials on anti-VEGF therapy for cancer. Nature Reviews. Clinical Oncology, 3(1), 24. Pan, Y., Cheng, T., Wang, Y., & Bryant, S. H. (2014). Pathway analysis for drug repositioning based on public database mining. Journal of Chemical Information and Modeling, 54(2), 407–418. Setoain, J., Franch, M., Martı´nez, M., Tabas-Madrid, D., Sorzano, C. O., Bakker, A., … Pascual-Montano, A. (2015). NFFinder: an online bioinformatics tool for searching similar transcriptomics experiments in the context of drug repositioning. Nucleic Acids Research, 43(W1), W193–W199. Wei, G., Twomey, D., Lamb, J., Schlis, K., Agarwal, J., Stam, R. W., … Golub, T. R. (2006). Gene expression-based chemical genomics identifies rapamycin as a modulator of MCL1 and glucocorticoid resistance. Cancer Cell, 10 (4), 331–342.

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C H A P T E R

4 Computational Drug Repurposing for Neurodegenerative Diseases Kyriaki Savva*, Margarita Zachariou*, Anastasis Oulas*, George Minadakis*, Kleitos Sokratous*, Nikolas Dietis†, George M. Spyrou* *

Bioinformatics ERA Chair, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus † Medical School, University of Cyprus, Nicosia, Cyprus

1 NEURODEGENERATIVE DISEASES Neurodegenerative diseases are a group of diseases that involve the progressive loss of function and neurons in the central nervous system (CNS) and are prevalent in a large proportion of the total population (Nieoullon, 2011). In addition, the prevalence of neurodegenerative diseases mainly caused by aging is predicted to sharply increase worldwide in the upcoming years, considering the fact that life expectancy is increasing (Wyss-Coray, 2016). A characteristic example is Alzheimer’s disease (AD) and the number of AD patients is expected to reach 34 million by 2025 (Alzheimer’s Association, 2017). Overall, the number of people living with dementia worldwide is currently estimated to be 35.6 million. This number is expected to double by 2030 and more than triple by 2050 (World Health Organization, 2012). People suffering from a neurodegenerative disease face a sharp decrease in life expectancy and quality of life, which adds a substantial financial burden to the health care system (Brown, Lockwood, & Sonawane, 2005). According to the Alzheimer’s Association, the money spent for AD patients in 2017 was about $259 billion, which is expected to grow to $1 trillion annually by 2050 (Alzheimer’s Association, 2017). Any therapeutic intervention that could delay disease onset by at least a year would dramatically decrease the number of expected cases. Therefore new ways of detecting novel and effective therapies are urgently needed in order to cure this large group of diseases (Noble & Burns, 2010).

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Although neurodegenerative diseases share various common features, there is no doubt that characteristic distinctions are also present, including symptoms, pathological hallmarks and genetic involvement in their cause of initiation (for a list of all neurodegenerative diseases see Table 1). Some neurodegenerative diseases are caused by a direct genetic change, such as TABLE 1

A Comprehensive List of All Neurodegenerative Diseases

Neurodegenerative diseases • Ataxia-telangiectasia • Autosomal dominant cerebellar ataxia • Autosomal recessive spastic ataxia of Charlevoix-Saguenay • Baggio-Yoshinari syndrome • Batten disease • Cell-cycle hypothesis of Alzheimer’s disease • Corticobasal degeneration • Corticobasal syndrome • Creutzfeldt-Jakob disease • Estrogen and neurodegenerative diseases • Fatal familial insomnia • Fragile X-associated tremor/ataxia syndrome • Frontotemporal dementia and parkinsonism linked to chromosome 17 • Hereditary motor and sensory neuropathy with proximal dominance • Infantile Refsum disease • JUNQ and IPOD • Kufor-Rakeb syndrome • Kufs disease • Locomotor ataxia • Lyme disease • Machado-Joseph disease • Mental retardation and microcephaly with pontine and cerebellar hypoplasia • Mitochondria associated membranes (MAM) • Multiple system atrophy • Neuroacanthocytosis • Niemann-Pick disease • Occupational exposure to Lyme disease • Pontocerebellar hypoplasia • Protein aggregation • Pyruvate dehydrogenase deficiency • Refsum disease • Transneuronal degeneration • Sandhoff disease • Shy-Drager syndrome • Spinocerebellar ataxia • Subacute combined degeneration of spinal cord • Subacute sclerosing panencephalitis • Tabes dorsalis • Tay-Sachs disease • Toxic encephalopathy • Toxic leukoencephalopathy • Transneuronal degeneration • Wobbly hedgehog syndrome Retrieved April 16, 2018, from https://en.wikipedia.org/wiki/Category:Neurodegenerative_disorders.

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Huntington’s disease, which is caused by mutations in the Huntingtin gene that result in abnormal CAG triplet repeats (Myers, 2004). Other neurodegenerative diseases, such as AD and Parkinson’s disease (PD), may involve either mutations in a single gene or in a number of different genes. For example, mutations in the APP gene (amyloid precursor protein) and/ or PSEN1 and PSEN2 genes (presenilin) have been shown to be linked to familial AD (Zekanowski et al., 2003; Zhang, Quan, & Tang, 2015); mutations in the LRRK2 (dardarin) and/or SNCA (a-synuclein) genes have been involved in PD (Liu, Aliaga, & Cai, 2012); whereas mutations in SOD1 (superoxide dismutase 1) and/or TDP-43 (transactive response DNA binding protein) genes have been associated with amyotrophic lateral sclerosis (ALS) (Higashi, Tsuchiya, Araki, Wada, & Kabuta, 2010). A significant feature common to various neurodegenerative diseases is the accumulation of abnormally folded proteins, which often leads to protein aggregation. Examples are the deposition of beta amyloid and neurofibrillary tangles in AD, and Lewy bodies in PD (Ross & Poirier, 2004). Aberrations in the function of mitochondria, in pathways involving protein degradation and in axonal transport, comprise another common feature of neurodegenerative diseases (Takalo, Salminen, Soininen, Hiltunen, & Haapasalo, 2013). The complexity of the CNS makes the investigation of neurodegenerative diseases an elusive goal. Neurodegenerative diseases are very complex by nature and the majority of neurodegenerative diseases are complex multifactorial diseases since they are caused from a combination of genetic and environmental factors. The genetics behind these diseases are also complex and heterogeneous due to the interaction of several gene mutations or polymorphisms and nongenetic factors (Ringman et al., 2014). Moreover, these diseases are highly prevalent in the population and, thus, constitute a major threat to human health. One of the key reasons that neurodegenerative diseases have become more and more prevalent is the increase in life expectancy in recent years (Gitler, Dhillon, & Shorter, 2017; Kawamata & Manfredi, 2011). Unfortunately, there is no cure for the majority of neurodegenerative diseases, with current therapies focusing on symptom relief and delaying the progression of the disease ( Jucker, 2010). The high failure rate of drugs in development for neurodegenerative diseases may have many reasons, such as a lack of molecular understanding of the diseases, lack of knowledge of the causality of the diseases, lack of treatment methodology for genetic mutations, etc. Despite the lack of an effective treatment, it is important to consider the reasons for the failure of clinical trials in order to improve the potential for success in drug development. Some of the reasons that should be taken into consideration are the following: (1) animal models can only show specific aspects of the disease, such as amyloidosis in AD, but do not show a full spectrum of the pathology present in humans; (2) small-molecule drugs need to be able to cross the blood-brain barrier in order to show any effect; (3) the tolerated dose must be determined in order to achieve the best possible outcome, etc. (Cummings, 2017). Over the last decade there has been a great effort to understand the pathogenesis of neurodegenerative diseases and develop novel individual treatments for a number of different neurodegenerative diseases. One such example is AD, for which to date a substantial amount of money has been invested in drug development. Regardless of this huge financial investment, most of the drugs that have been tested in clinical trials have failed with no clear success apparent. Currently there are 105 drugs/combinations undergoing clinical trial testing for AD. With the average cost to develop an approved drug estimated at $2.6 billion, this means

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that the amount of money spent on this single disease is massive, let alone for all neurodegenerative diseases (Mullard, 2014). Despite the high number of drugs entering the preclinical phase for AD and other neurodegenerative diseases, there is a higher failure rate than for other diseases, due to the aforementioned hurdles (Paul et al., 2010). Drugs, such as first generation of beta-secretase 1 (BACE1) inhibitors, have failed due to low oral bioavailability. Moreover, second- and third-generation BACE1 inhibitors, such as LY2811376 and verubecestat, have failed in clinical trials due to liver toxicity and an absence of efficacy in mild-to-moderate AD patients, respectively (Kennedy et al., 2016; Lahiri, Maloney, Long, & Greig, 2014). Other examples of failed drugs are γ-secretase inhibitors semagacestat and avagacestat, which were discontinued because of worsening of symptoms and severe adverse effects, respectively (Hung & Fu, 2017). Due to the massive investments and the lack of any effective treatments for neurodegenerative diseases in general, novel approaches that will be more time and cost effective are needed for detecting drugs. One such approach, known as drug repurposing or drug repositioning, aims at detecting novel uses for existing drugs. Drug repurposing will be described in more detail in the next section (Wu, Gudivada, Aronow, & Jegga, 2013).

2 DRUG REPURPOSING Until the mid-1990s drug research heavily relied on the “one drug, one target” concept as the basic strategy for reducing the side effects of developed drugs, focusing on drug specificity and selectivity during the discovery process (Winau, Westphal, & Winau, 2004). However, in recent years this concept has gradually given way to multitargeting pharmacology and the development of multifunctional ligands as a new strategy for reduced side effects (Dietis et al., 2009), and this is supported by the concept of polypharmacology. Polypharmacology is a strategy that employs one or multiple drugs to interact with multiple molecular targets toward a specific outcome. It is a concept based on great recent advances in areas, such as receptor dimerization (i.e., receptors coming together forming entities with distinct pharmacology; one drug, two targets), allosteric modulation (i.e., ligands causing target modulation after binding in secondary sites; two drugs, one target), and synergistic pharmacology (i.e., combination of drugs that have synergistic pharmacodynamics or pharmacokinetic efficacy; multiple drugs, multiple targets) (Medina-Franco, Giulianotti, Welmaker, & Houghten, 2013). Based on the fact that a single drug can bind to, or affect the behavior of, multiple and diverse molecular entities in a wide range of different cells and tissues resulting in a plethora of different biological effects, it is only natural to assume that one drug can be used therapeutically against different diseases. However, most of today’s clinical drugs are used for different conditions; they affect one common molecular target but the effects are expressed in different tissues. For example, opioids bind to opioid receptors both in the nervous tissue and in the gut, resulting in two distinct biological effects (pain relief and reduced gastrointestinal motility), therefore they are used both against severe pain (Rosenblum, Marsch, Joseph, & Portenoy, 2008) and diarrhea (Leppert, 2012). On the other hand, a smaller but significant number of clinical drugs are used for different conditions due to the fact that they

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bind to different molecular targets. One example is bromocriptine, which is used against acromegaly (Arihara et al., 2014), Parkinson’s (Van Hilten, Ramaker, Stowe, & Ives, 2007) and diabetes (Mahajan, 2009), since it binds to a large number of different receptors (i.e., dopamine, serotonin, alpha-adrenergic, and beta-adrenergic receptors). Drug repurposing or repositioning is the detection of novel indications for existing drugs in order to treat new diseases (Langedijk, Mantel-Teeuwisse, Slijkerman, & Schutjens, 2015). Over recent years this process has emerged as a developing concept that aims toward the detection of new therapeutic applications for existing drugs. Exceptional advances in scientific and technological fields have led to intense research in this field over the last decade (Sleigh & Barton, 2010). Even though a substantial amount of money has been invested in drug design and discovery, this process remains time consuming, expensive and with low rates of success, which explains the relatively small number of newly designed drugs that are approved by the Food and Drug Administration (FDA) every year (Pammolli, Magazzini, & Riccaboni, 2011). Therefore alternative solutions are needed in order to overcome these problems. Drug repurposing has emerged as a promising strategy for addressing the existing challenges in drug research. A recent report by Graul, Cruces, and Stringer (2014) further supports this fact, by providing evidence that in 2013, 20% of the new drugs on the market were a result of drug repurposing (Graul et al., 2014).The drug repurposing approach has several advantages compared to de novo drug discovery. A key advantage of drug repurposing is that intellectual property (IP) and patent protection for a novel indication of an existing drug can be easily obtained, with the requirement that the new detected indication was not demonstrated in the original patent (Naylor, Kauppi, & Schonfeld, 2015). Moreover, drug repurposing can support the protection of the original IP from competitor pharmaceutical companies and the rights can be out-licensed for the new indication of the specific drug. Out-licensing of a drug occurs when the pharmaceutical company that developed the drug does not have the resources to continue with clinical trials for the new indication, and hence it out-licenses it to a bigger pharmaceutical company (Mucke & Mucke, 2015). Another major advantage of the drug repurposing strategy is that it involves approved compounds that have passed the safety toxicological screening process, with a known pharmacokinetic profile, and hence repositioned drugs can enter directly into clinical phase II, which renders the clinical phase process much faster than for newly developed drugs and in a more cost-effective way. Fig. 1 illustrates the timeline and steps of the traditional drug discovery compared to drug repurposing. Hence, by using existing pharmacokinetic, toxicology and safety data, drug repurposing substantially reduces the delays related to the traditional de novo discovery of drugs (Boguski, Mandl, & Sukhatme, 2009). Moreover, in addition to approved treatments, drugs that are deemed ineffective for their original indications but are shown to be safe, could be salvaged using the drug-repurposing approach (Kim, 2015). The process of drug repurposing began several years ago through serendipity, an inherently ambiguous word that holds the notion of making unexpected beneficial discoveries by accident (Campa, 2007). These discoveries usually derive from untargeted research, exploratory or basic research, or even by accidental findings that eventually may lead to: (1) a solution to the given problem, (2) a solution to an entirely different problem, (3) a solution to a preexisting problem, or (4) a solution waiting for a problem. In the context of drugrepurposing, serendipity represents the discovery of a drug activity by accident, during the process of research for an entirely unrelated aim (Ma, Chan, & Leung, 2013).

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De novo drug discovery (11–17 years process)

Drug discovery and screening (0.5–1 years)

Target discovery (1–2 years)

Expression analysis In vitro function In vivo validation

Drug discovery Screening: in vivo, in vitro, ex vivo

Lead optimisation (1–3 years)

Traditional and rational drug design

Preclinical studies (1–2 years)

Clinical trials (5–6 years)

Bioavailability testing for absorption, distribution, metabolism, excretion

Registration (1–2 years)

Clinical trial testing starting at phase I

Drug approval

Bioinformatics

Drug repurposing (3–12 years process)

Compound identification (1–2 years)

Compound acquisition (0–2 years)

Searches are targeted Detection through serendipity

Licence and novel intellectual property Internal sources

Preclinical studies (0–1 years)

Bioavailability testing for absorption, distribution, metabolism, excretion

Clinical trials (1–6 years)

Clinical trial testing starting at preclinical, phase I or Phase II

Registration (1–2 years)

Drug approval

FIG. 1 Comparison of traditional drug discovery versus drug repurposing timelines. De novo drug discovery is a time-consuming process, requiring 11
17 years to introduce a drug to the market. On the other hand, drug repurposing is a time- and cost-effective process since it can bypass several phases. This is due to the fact that some stages are bypassed for the original indication of the drug. (Modified from Ashburn, T. T., & Thor, K. B. (2004). Drug repositioning: identifying and developing new uses for existing drugs. Nature Reviews Drug Discovery, 3(8), 673–683. doi:https://doi.org/10.1038/nrd1468).

A famous drug-repurposing example that was detected through serendipitous observations is the repurposing of sildenafil for male erectile dysfunction. The initial use of sildenafil was as an antiangina drug. However, it was observed afterwards that this drug could improve male erectile dysfunction. Further studies identified that sildenafil is an antagonist of phosphodiesterase-5 (PDE-5), an enzyme that controls blood flow to the penis and its expression is absent in heart tissue. Later on, sildenafil was shown to act as a therapeutic for pulmonary arterial hypertension. In the case of male erectile dysfunction the drug was

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marketed as Viagra, whereas in the case of pulmonary arterial hypertension as Revatio (Ghofrani, Osterloh, & Grimminger, 2006). Another success story of drug repurposing through serendipity was the antiemetic thalidomide, which was withdrawn for its primary use due to the teratogenic effects it had in pregnant women. Later on the drug was redeveloped and repurposed for leprosy and multiple myeloma, with use restrictions in pregnant women (Schulz, 2001).

3 ACTIVITY-BASED DRUG REPURPOSING Activity-based or experimental drug repurposing refers to the actual screening of drugs against a target or a disease, without the application of any computational methods. When little information exists about a disease, phenotypic screening approaches are the most suitable. However, when information about the disease target exists, target-based approaches can be exploited (Zheng, Thorne, & McKew, 2013).

3.1 Phenotypic Screening Approach A phenotypic approach, also referred to as “blinded drug repurposing,” is a type of screening that detects drugs that alter the disease phenotype to the desired state (Swinney & Anthony, 2011). A cell-based assay can be developed based on a characteristic associated with a disease, where a large number of compounds are screened in order to eliminate the disease phenotype. This approach is more advantageous for diseases where specific drug targets have not been detected or validated, making it a useful approach for several rare diseases lacking effective treatment. However, as the phenotypic approach works only with knowledge of the phenotype, it is unlikely to lead to the discovery of the disease’s underlying mechanism and the specific targets/actions of the drug ( Jin & Wong, 2014).

3.2 Target-Based Screening The target-based approach, also known as reverse pharmacology, begins with the detection of the target(s) of a specific disease. These targets can be detected using animal models as well as phenotypic observations of patients. Once these targets are detected they are used to perform high-throughput screening (HTS) of large compound libraries to detect the best hits against the targets (Vogt & Lazo, 2005). The best hits can then be confirmed, validated, and further characterized in in vitro or in vivo assays that are specific to the target of interest. When compared to phenotypic screening approaches, target-based approaches increase the likelihood of drug repurposing since a particular target was previously associated with a disease. The more information available with respect to the disease target, the higher the probability that repurposed drugs can be identified (Zheng et al., 2013). For instance, cultured mammalian neurons can be used for in vitro HTS performances. However, due to the fact that they lack the complexity required to characterize AD, researchers use an alternative model organism, Caenorhabditis elegans. Since several models of Tau and Aβ oligomer toxicity have been created, this model organism has been widely used

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to detect numerous compounds that could modulate the AD pathology (Lublin & Link, 2013). Moreover, C. elegans is an easy-to-manipulate organism, thus making it a good candidate to be exploited for AD treatment. Moreover, the zebrafish is also a promising model for drug discovery and repurposing since it is easy to handle and allows high-throughput whole-animal drug screens (Delvecchio, Tiefenbach, & Krause, 2011). A recent example of applying activitydrug repurposing was documented by Halliday and colleagues, in a study in which the phenotypic screening of a library consisting of 1040 drugs was used, in order to prevent neurodegeneration in mice (Halliday et al., 2017). This was achieved through the inhibition of PERK/eIF2a-P activity, a signaling pathway that is continually activated in neurodegenerative diseases such as AD, resulting in memory impairment and neuronal loss. The phenotypic screening assay detected two drugs, trazodone hydrochloride and dibenzoylmethane, which produced promising results in both in vitro and in vivo experiments. Both of these drugs had neuroprotective effects in two different mouse models with no apparent toxicity and were regarded as potential candidates against neurodegeneration in AD and related disorders for future clinical trials. Even though there are several advantages when applying activity-based drug repurposing of in vitro and in vivo screenings (e.g., low false-positive rates, quantification of biological activity, quality assessment of molecular interaction, etc.), there are some limitations, such as the requirement of full collections of the drugs that need to be tested as well as developing a screening assay for this purpose. Therefore activity-based drug repurposing can become a time-consuming and cost-ineffective process. These limitations together with the rapid advances in computational and mathematical sciences have led to the development of modern computational methods for drug repurposing (Shim & Liu, 2014).

4 COMPUTATIONAL DRUG REPURPOSING In drug repurposing, computational approaches and analyses aim to optimize the use of traditional activity-based methodologies, since they allow the generation, evaluation, and prioritization of different drugs. This in effect aims to reduce the economic and time effort (Hurle et al., 2013) in the pharmaceutical industry, providing also a scientific and market challenge toward identifying benchmark computational methods for this purpose. In recent years more systemic in silico computational approaches have been employed that involve new theoretical concepts ranging from advance statistical methodologies up to complex-networks and machine-deep-learning approaches; all in the context of modern drug-repurposing research. Herein it is worth mentioning that, although computational drug-repurposing methodologies can be applied to several stages of the drug-discovery process, the greatest potential can be fulfilled in cases where safety tests have already been applied on the drug. Recently there has been a significant growth in the literature regarding the field of drug repurposing, where novel as well as more systematic computational approaches are presented, including structure-, ligand-, transcriptomic-, and network-based approaches. In the following section we provide a detailed review on these novel scientific directions. A list of the popular tools that are described in this section can be found in Table 2. Examples related to neurodegenerative diseases, when available, are presented for each computational method described.

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TABLE 2

List of Tools Used for Drug Repurposing According to Each Computational Approach

Tool Name

Method Type

Reference

Website (If Available)

Lincscloud

Transcriptomicbased

Corsello et al. (2017)

https://clue.io/

Gene2Drug

Transcriptomicbased

Napolitano et al. (2017)

http://gene2drug.tigem.it/

AutoDock

Structure-based

Morris et al. (1998)

http://autodock.scripps.edu/

ICM

Structure-based

Abagyan, Totrov, and Kuznetsov (1994)

http://www.molsoft.com/icm_ browser.html

QXP

Structure-based

McMartin and Bohacek (1997)

–

GOLD

Structure-based

Verdonk, Cole, Hartshorn, Murray, and Taylor (2003)

–

DIVALI

Structure-based

Clark and Ajay (1995)

–

DARWIN

Structure-based

Taylor and Burnett (2000) –

DrugBank

Structure-based

Knox et al. (2011)

https://www.drugbank.ca/

ChemSpider

Structure-based

Pence and Williams (2010)

http://www.chemspider.com/

PubChem

Structure-based

Bolton, Wang, Thiessen, and Bryant (2008)

https://pubchem.ncbi.nlm.nih. gov/

ChemBioServer

Structure-based

Athanasiadis, Cournia, and Spyrou (2012)

http://chembioserver.vi-seem. eu/

MANTRA 2.0

Network- and gene expression-based

Carrella et al. (2014)

http://mantra.tigem.it/

Re:fine Drugs

Integration method

Moosavinasab et al. (2016)

http://drug-repurposing. nationwidechildrens.org/search

Computational analysis of novel drug opportunities (CANDO)

Ligand-based

Minie et al. (2014)

http://ram.org/compbio/ protinfo/cando/

DOCK

Reverse docking

Ewing, Makino, Skillman, http://dock.compbio.ucsf.edu/ and Kuntz (2001)

GLIDE

Reverse docking

Friesner et al. (2004)

FlexX

Reverse docking

Rarey, Kramer, Lengauer, https://www.biosolveit.de/ and Klebe (1996) FlexX/

TarFisDock

Reverse docking

Li et al. (2006)

http://www.dddc.ac.cn/ tarfisdock/

MDock

Structure-based

Yan and Zou (2016)

http://zoulab.dalton.missouri. edu/mdock.htm

–

Continued

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TABLE 2 List of Tools Used for Drug Repurposing According to Each Computational Approach—cont’d Tool Name

Method Type

Reference

Website (If Available)

DAD

Literature-based

Weeber, Klein, De JongVan Den Berg, and Vos (2001)

–

MetaMap

Literature-based

Aronson (2001)

https://metamap.nlm.nih.gov/

LitLinker

Literature-based

Pratt and Yildiz (2003)

–

Telemakus

Literature-based

Fuller, Revere, Bugni, and – Martin (2004)

Web Engine for Nonobvious Drug Information (WENDI)

Literature-based

Zhu, Lajiness, Ding, and Wild (2010)

–

DrugMap Central (DMC)

Integration

Fu et al. (2013)

http://r2d2drug.org/index.html

Examples for other diseases are also used in the case where to our knowledge, no examples on neurodegenerative diseases are available. This was done in order to describe the potential of the methods, which could be applied to neurodegenerative diseases in the future.

4.1 Structure-Based Virtual Screening (Molecular Docking) Rapid advances in computational science have led to the development of structure-based virtual screening, a key tool in both drug discovery and development, as well as in the field of drug repurposing. This approach can be used as a prioritization tool for compounds and concurrently provides the top candidate compounds to be used for HTS. Virtual screening can be classified into target- (also known as molecular docking) and ligand-based approaches (such as similarity-based approaches). Target-based approaches, also known as molecular docking, exploit both the 3D model of the target and the candidate ligand or the drug that will bind to the binding pocket of the target. The key steps of the molecular docking process include: (1) the generation of a 3D-structure of the target and the ligands, (2) a computer simulation to dock the ligand within the binding site of the target, and (3) the scoring of how favorable the interaction between target and ligand is, by measuring their binding affinity (Ma et al., 2013). The virtual screening process is based on the evaluation of the interactions between a molecular target of interest and large structural libraries of compounds (Li et al., 2016; Ma et al., 2013). During this process, commercially available compounds contained in large libraries are computationally screened against the target of interest with a known structure. The next step is to test experimentally the drug-like compounds that show high-binding predictions (Lavecchia & Giovanni, 2013). These compound libraries are comprised of existing drugs whose bioavailability and tolerability are confirmed. Once a hit compound is generated from the virtual screening process, cell cultures or animal models can be used to confirm it. Moreover, virtual screening is time- and cost-effective since there is no need to purchase several hundreds of compounds to perform drug repurposing of existing drugs against new novel molecular targets (Lyne, 2002; Ma et al., 2013).

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There are several available public virtual libraries that enhance drug repurposing. These libraries contain existing and approved drugs, drugs that reached the final stages of clinical trials yet were not marketed, as well as drugs that were abandoned or are still under investigation. This information can be found in freely accessible drug databases such as DrugBank (Knox et al., 2011), e-Drug3D (Pihan, Colliandre, Guichou, & Douguet, 2012) and KEGG DRUG (Kanehisa, Goto, Furumichi, Tanabe, & Hirakawa, 2009). The combination of virtual screening and drug repurposing has led to promising results. In a study by Kumar, Chowdhury, and Kumar (2017), a molecular docking approach was used for already known antipsychotic drugs to repurpose them for AD. They screened 150 antipsychotic drugs against five well-known target proteins of AD (AChE, BuChE, BACE 1, MAO, and NMDA). The best candidate drugs were selected based on a docking score and their binding affinity. Compared to already known inhibitors of the aforementioned target proteins, some of the detected repurposed drugs showed better binding affinity and docking score. From the list of antipsychotic drugs that were tested, benperidol was selected as the best candidate drug, since it showed interactions with multiple protein targets of AD. Even though molecular docking is a very popular and widely used approach, it faces some challenges. Molecular docking can only be used when a 3D structure of the target is present which, due to experimental limitations, is not feasible for every single protein. Moreover, it is a computationally demanding approach, making it inefficient in large-scale prediction processes (Hodos, Kidd, Khader, Readhead, & Dudley, 2016). In addition, the scoring functions used to rank the poses generated by the docking of a compound to the target of interest lack receptor reorganization as well as a full treatment of desolvation, resulting in a limited accuracy of docking calculations. In addition, scoring based on a single structure gives a poor capture of entropic factors, leading to the generation of large number of false-positive hits (Gallicchio et al., 2014). A very important issue when dealing with molecular docking is the ligand and target flexibility (the ability of a protein to change its conformation), which can be solved by implementing conformation changes to both the ligand and the target that can lead to increased binding affinity. Several approaches can be employed in order to solve the flexibility issue of ligand and target, such as (1) ligand incremental construction, (2) the generation of multiple conformers prior to docking, and (3) stochastic methodologies. Regarding the first approach (ligand incremental construction), a fragmentation of all ligands into smaller pieces is performed, which are then docked inside the binding pocket of the target. The fragments of the ligand are docked within the binding pocket in an additive way and the whole ligand is reassembled by finding the perfect fit (Kramer, Rarey, & Lengauer, 1999). For the second approach, several low-energy copy conformations of the same ligand are generated and tested against the target protein (Kearsley, Underwood, Sheridan, & Miller, 1994). This approach is known as the multiple receptor conformations, which uses ensembles of structures. These conformations can be generated by experimental approaches such as nuclear magnetic resonance (NMR) spectroscopy and X-ray crystallography (Rueda, Bottegoni, & Abagyan, 2009). Currently, the most popular strategy to deal with ligand flexibility is the third approach, which includes stochastic methodologies. In stochastic methods, single or multiple ligand conformations are modified randomly, by searching the conformational space. Examples

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of stochastic methodologies comprise of Monte Carlo (MC) and genetic algorithms (GA) (Meng, Zhang, Mezei, & Cui, 2011). MC methods generate different conformational positions of the ligand within the binding pocket of the target, by varying a single parameter each time (Hart & Read, 1992). These changes are based on bond rotation, rigid-body translation, or rotation. The result of this process is then tested using an energy-based selection criterion, and the conformation is either accepted or rejected. If it is accepted, further modifications are applied, and the next conformation is generated. This process is repeated several times, until the correct quantity of conformations is obtained (Meng et al., 2011). Examples of tools utilizing MC algorithms are an earlier version of AutoDock (Morris et al., 1998), ICM (Internal Coordinates Mechanics) (Abagyan et al., 1994), and QXP (McMartin & Bohacek, 1997). GAs are the second class of widely used stochastic methods. Compared with MC methods, this approach adopts a different idea, which is based on Darwin’s theory evolution. In GA, conformations of the ligands are represented as “chromosomes,” and these “chromosomes” are allowed to undergo crossovers as well as mutation. By performing crossovers and mutations, several conformations of the ligands are produced, and the most competent ones are used in the next generation (Ma et al., 2013; Meng et al., 2011). The way to assess whether a conformation is accepted to the next generation is by using a scoring system that predicts the binding free energies of the target and the ligand. The ones that produce the lowest binding free energies are the successful ones. This procedure is iterated several times until the local energy minimum is achieved between the target and ligand (Ferrara, Gohlke, Price, Klebe, & Brooks, 2004). A number of different tools utilize genetic algorithms, such as AutoDock (Morris et al., 1998), GOLD (Verdonk et al., 2003), DIVALI (Docking wIth eVolutionary AlgorIthms) (Clark & Ajay, 1995), and DARWIN (Taylor & Burnett, 2000).

4.2 Ligand-Based Methods As reviewed in the previous section, molecular docking is thought to be a target-based approach. This is due to the fact that a large-scale library of compounds is evaluated against the target of interest. An alternative method is the ligand-based approach. When a list of compounds is present but no or insufficient information about the target of interest is available, ligand-based methods can be employed. Examples of ligand-based methods are the pharmacophore modeling (Yang, 2010) and quantitative structure-activity relationship (QSAR) methods (Ortiz, Pisabarro, Gago, & Wade, 1995). These approaches are based on the idea that similar compounds may also exhibit analogous biological properties (Hodos et al., 2016; Ma et al., 2013). 4.2.1 Pharmacophore Model Method This approach includes the construction of a pharmacophore model, which, according to the International Union of Pure and Applied Chemistry (IUPAC), is the grouping of steric and electronic characteristics, features that are crucial for obtaining ideal supramolecular interactions with a specific target structure and to activate or deactivate its biological response (Langer & Wolber, 2004). In order to do that analysis of the binding pocket of, or the known compounds that interact with, the target of interest is performed. A list of available

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compounds is then aligned and compared to the pharmacophore model, in order to find similar compounds based on a scoring system (Hodos et al., 2016). When compared to molecular docking, this approach does not require the 3D structural information of the target of interest. Moreover, the pharmacophore model approach can be used as a preprocessor of compound libraries in order to eliminate the ones that do not comply with the binding conditions and to rank the rest of the compounds in a list of priority. Overall, pharmacophore-based drug repurposing is considered to be less computationally demanding, with higher accuracy than molecular docking. (Chauhan, Gajjar, & Basha Hussain, 2016; Hodos et al., 2016; Kr€ uger & Evers, 2010; Palakurti & Vadrevu, 2017). However, as with any other approach, pharmacophore-based virtual screening has its challenges. Out of all the virtual hits that are produced, only a very low percentage represents actual bioactive compounds, meaning that results exhibit a high false-positive rate. Some of the reasons for such high false-positive rates are the quality and conformation of the pharmacophore model, as well as the degree to which the target of interest is involved in the construction of the model. Optimization and validation of the pharmacophore model could potentially address this challenge (Chen et al., 2009; Hodos et al., 2016; Yang, 2010). For instance, Crisan, Avram, and Pacureanu (2017) developed a novel pharmacophore model based on glycogen synthase kinase-3 (GSK-3) inhibitors. Through virtual screening and drug repurposing a class of purine nucleoside antileukemic drugs was detected, which could act as a potential inhibitor of GSK-3 and hence GSK-3-driven diseases. The role of GSK-3 in AD is well-characterized since it is dysregulated during its pathogenesis. GSK-3 was investigated as a therapeutic target by using the specific GSK3 inhibitor SB216763, which showed effective inhibition of enzyme activity. In AD, GSK-3 activity is usually increased, accompanied by increased levels of tau, caspase-3, gliosis, etc. (Hu et al., 2009). Moreover, GSK-3 is shown to interact with tau, β-amyloid (Aβ), and α-synuclein, leading to the conclusion that it might also be involved in PD. Specifically, lithium, which is also a GSK-3 inhibitor, was able to affect tau, Aβ, and α-synuclein in cell culture as well as animal models. These findings strongly support the importance of GSK-3 in neurodegenerative diseases and understanding its mechanism of action could lead to the detection of novel therapies for these diseases (Lei, Ayton, Bush, & Adlard, 2011). 4.2.2 Quantitative Structure-Activity Relationship Methods QSAR computational models use the relationship between ligand features and the target protein as well as the interaction between the two, and have a key role in the analysis of the properties of drugs. QSAR models rely on the fact that molecules with similar structures also show similar biological activity. Moreover, they employ statistical methods in order to associate activities of drug-target interactions with different molecular features. Hence, in order to employ this approach, there is a need for enough training data to be present to allow features to be extracted. Linear regression methods can be used to detect important molecular features. However, linear regression can only be used if the relationship between biological activity and the descriptors is linear. In cases where this is not true, machine learning approaches, such as support vector machine and neural networks, can be exploited in order to create QSAR models (Grinter & Zou, 2014). The key difference between the pharmacophore model and QSAR methods is that the former is generated based only on the key features of a ligand, whereas

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the latter approach considers both the key features of the ligand and the features associated with the activity between the ligand and the target (Sliwoski, Kothiwale, Meiler, & Lowe, 2014). In a study by Islam, Zaman, Jahan, Chakravorty, and Chakraborty (2013), a comparison of quercetin, a natural compound found in several foods (such as cauliflower, nuts, tea, etc.), with conventional acetylcholine esterase inhibitors was performed in order to detect a more potent drug for AD. Initially, molecular docking of the conventional compounds and quercetin against the active site of acetylcholine esterase was performed using AutoDock (Morris et al., 1998). The binding affinity score of quercetin was shown to be higher at the pocket site when compared to the rest of the compounds. Detection of functional groups was achieved through QSAR analysis of quercetin in order to detect a more effective acetylcholine esterase inhibitor. A number of different tools can be exploited in order to detect structural similarities between compound structures. The structural information of a compound can be downloaded using tools such as DrugBank (Knox et al., 2011), PubChem (Bolton et al., 2008), and ChemSpider (Pence & Williams, 2010). From these databases .sdf or .mol files of drugs can be downloaded for further use (.sdf and .mol files are text files containing structural information about a compound, together with some additional information). The computer software Open Babel (O’Boyle et al., 2011) is another tool that can be used for the process of structural similarity detection. Open Babel is a chemoinformatics tool used for the conversion of different chemical file formats. Moreover, ChemBioServer (Athanasiadis et al., 2012), a publicly available web application, can be used for filtering chemical compounds, to cluster compounds based on physicochemical properties, and, hence, to cluster structurally similar compounds. This tool can be used to enhance the compound selection efficacy of compounds for experimental validation. 4.2.3 Reverse Docking Methods Compared to molecular docking, which supports the one target-many ligands hypothesis, reverse or inverse docking supports the one ligand-many targets hypothesis and, hence, can be viewed as the opposite concept to molecular docking. In reverse docking a library of clinically relevant targets is screened against a single ligand to expand the indications for the compounds by drug repositioning. The result of this process is a ranked list of the targets based on a specific scoring system. The top scoring targets are the ones that could be used for repurposing, since their potential for binding to the specific ligand is higher (Kharkar, Warrier, & Gaud, 2014). The requirements for reverse docking for drug repurposing is a structural library of the target proteins as well as information about the potential ligand binding regions (Lee, Lee, & Kim, 2016). The ligand should be either an approved or experimental/investigational drug and this information can be obtained from databases such as DrugBank (Knox et al., 2011), NIH Chemical Genomics Center Pharmaceutical Collection (Huang et al., 2011), and ChEMBL (Davies et al., 2015). Several target databases are available for this purpose, such as the Protein Data Bank (PDB) (Berman, 2000) and the Potential Drug Target Database (PDTD) (Lee et al., 2016). In reverse docking, it is crucial to define the correct binding sites for each protein in order to obtain more accurate results as well as to increase the method’s efficiency and

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decrease its computation time. For this purpose, sc-PDB (Desaphy, Bret, Rognan, & Kellenberger, 2015) can be used, which is a 3D ligand binding-site library consisting of binding site entries of target-ligand complexes in PDB. A number of different molecular docking programs with a few modifications can also be used for reverse docking, since reverse docking is a more demanding process due to the presence of a large number of target proteins. Such examples are GOLD (Verdonk et al., 2003), DOCK (Yang, Chen, & He, 2009), FlexX (Rarey et al., 1996), and Glide (Friesner et al., 2004). Moreover, Target Fishing Dock (TarFisDock) (Li et al., 2006), a web-based tool for reverse docking, produces a ranked list of the candidate targets by calculating the binding energy of the ligand-target complex. Generally, the tools and programs available for reverse docking are few compared with conventional ligand-target docking. The key reason for this is the dependency of this method on the 3D structure information of the target proteins, which is available for only a small number of proteins. Moreover, MDock (Yan & Zou, 2016), a software package used for reverse docking, exploits a knowledge-based scoring function, the ITScore, which underwent several evaluations and validations for this purpose (Huang & Zou, 2007).

4.3 Transcriptomic-Based Methods Gene-expression data, also known as transcriptomic data, are crucial in understanding the underlying mechanisms of a particular disease since it provides a connection between the mRNA and the phenotype. This type of data is represented as lists of over- and underexpressed genes in two or more different experimental conditions such as patient cohorts that have undergone drug treatment vs. controls or untreated cohorts. These gene lists can then be used as an input to a query, which makes a comparison to a reference set of gene expression profiles that were generated from specific cell lines that have undergone drug treatment or not (Hurle et al., 2013). An essential source that exploits transcriptomic data and can be used to enhance the concept of drug repurposing is the Connectivity Map (CMap) project (Lamb et al., 2006) and the Library of Integrated Network-Based Cellular Signatures (LINCS) (Keenan et al., 2018), which is its extended project. The basic concept behind these projects was that by modulating the gene expression of a system by either treating it with perturbagens (a small molecule/genetic reagent, causing gene expression changes in cell lines) or not, it is possible to obtain a better understanding of the cellular function of that particular system. Cmap is a gene expression-profiling database of different human cancer cell lines, which were either treated or not with perturbagens. This database was created in order to build a map of functional connections among diseases using genetic and chemical perturbations. This was initiated by treating cells in three different cancer cell lines with 164 perturbagens. The next step was to measure the mRNA expression of these cells using Affymetrix microarrays. Although Cmap gave a great insight into the discovery of unknown off-targets or unknown disease mechanisms of action, its use was limited due to the small-scale testing of perturbations (missing both chemical and genetic perturbations) and cancer cell lines (Subramanian et al., 2017).

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LINCS is the second effort made to produce large-scale gene expression-profiling data based on a reduced representation of the transcriptome, using a substantially larger number of drugs when compared to Cmap. This project exploited the L1000 platform to produce geneexpression profiles, which is based on a reduced representation of the transcriptome, meaning that only a thousand transcripts were monitored using an mRNA expression profiling technique, whereas the rest of the transcriptome was computationally inferred (thereby the name L1000) (Subramanian et al., 2017). These gene expression datasets treated with different perturbations can be found on CLUE, a cloud-based software platform used to query and analyze perturbational datasets. It also provides a wide array of software tools that the researchers utilize to analyze their data (https://clue.io/). The results of a query would be presented as a list of drugs that can reverse a disease condition (drugs with a strong negative value) and drugs that can mimic a disease condition (drugs with a strong positive value). Currently, there are several recently developed databases that correlate phenotypes with changes in gene expression induced by drugs. Such databases are of a great value when performing drug repurposing (Li et al., 2016). This approach of using publicly available transcriptomic data for drug repurposing has been used successfully in several cases with promising results. For instance, in a recent study (Ren et al., 2016) a Cmap query gene signature was acquired from publicly available data in GEO in order to detect novel radioprotectors. This method detected baclofen, a compound with no previous known actions as a radioprotector. The compound was validated via cell-based methods as well as a rodent model, showing that baclofen causes reduction in radiation-induced cytotoxicity with P < 0.01 in vitro and increases survival in vivo with P < 0.05. In another recently published study (Gao et al., 2014), an integration of differentially expressed genes from GEO, the Parkinson’s disease gene expression database (ParkDB) (Taccioli et al., 2011), the Online Mendelian Inheritance in Man (OMIM) (Amberger & Hamosh, 2017) database, and the Comparative Toxicogenomics Database (CTD) (Mattingly, Colby, Forrest, & Boyer, 2003) were used as inputs for a Cmap query. This process was carried out to detect compounds that could potentially be used as anti-Parkinson agents. It identified a ranked list of compounds, including alvespimycin (17-DMAG), which was further evaluated in a rotenone-induced neurotoxicity model in human SH-SY5Y neuroblastoma cells, as well as in isolated rat brain mitochondria. This compound, which was originally evaluated in solid tumors (clinical trials phase I), was found to increase cell viability and decrease the mitochondrial respiratory dysfunction. These results further support the applicability of this approach and suggest that alvespimycin could be potentially used as a neuroprotective compound. A similar approach was used by Kunkel et al. (2011) to detect potential disease-reversions in skeletal muscle atrophy. Using signatures of skeletal muscle atrophy in Cmap, ursolic acid was detected as a potential therapy. Ursolic acid administration, a natural compound found in apples, resulted in a reduction in muscle atrophy, stimulated muscle hypertrophy, and reduced adiposity in mouse models and in a clinical study in humans. Compared to Cmap, LINCS is a high-throughput database, which contains larger numbers of gene-expression profiles. Specifically, LINCS includes gene-expression profiles of small molecules, a feature also supported by Cmap, but it also contains gene knocked-down profiles using short hairpin RNA or overexpressed genes using complementary DNA.

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Moreover, the number of small molecule perturbagens was exponentially increased, as well as the number of human cancer cell lines (nine cell lines instead of three). Overall, there was a greater then 1000-fold increase in the CMap pilot dataset, leading to the conclusion that LINCS is able to provide more reliable predictions (Subramanian et al., 2017). Lately, gene-expression data has been strongly supported by and used for drug repurposing in the scientific community. However, this approach still faces some challenges, such as the selection of the up- and downregulated genes, the number of different cell lines treated with drugs, as well as the limited testing concentration and duration (Qu & Rajpal, 2012). In addition, the responses of cell lines after treatment do not always resemble how the drug acts in biological systems. Hence, this difference between the phenotype and the molecular responses is another limitation of this approach (Vilar & Hripcsak, 2017). Additional steps are necessary to validate these gene-expression changes.

4.4 Genome-Wide Association Studies-Based Methods Genome-wide association studies (GWAS) is another source of data that over the past few years has been used to associate particular genomic variations known as single nucleotide polymorphisms (SNPs), with complex diseases, such as AD, multiple sclerosis, etc. These SNPs occur at a higher frequency in people with a specific disease trait when compared with healthy individuals. GWAS detects thousands of SNPs simultaneously and researchers use these data to detect genes that are associated with a specific disease trait and to explore how these variations affect responses to drugs. Another use of GWAS is the rapid and systematic identification of alternative indications for existing drugs (Hurle et al., 2013). Sanseau et al. (2012) proposed a strategy based on how to utilize GWAS data to detect novel indications for existing drugs. The process undertaken for drug repurposing using GWAS involved analysis being performed on the list of SNPs associated with the disease to identify a subgroup of genes that are thought to be drug targets according to the druggability of the gene’s product. The next step of the process is to select which of these gene products, if any, are targets for the drugs that are in the pharmaceutical pipelines at that point. One such example detected by this approach is Biib-033 (Biogen Idec, Cambridge, MA, USA), which is an antibody targeting the leucine-rich repeat and immunoglobulin domain-containing 1 (LINGO-1), which was developed for multiple sclerosis. Two GWAS detected LINGO-1 as a target for essential tremor, which is a neurological disorder, suggesting that it could be repurposed for essential tremor disorders (Clark et al., 2010; Stefansson et al., 2009). Moreover, experimental studies, using in vitro and in vivo models, proposed that therapy against LINGO-1 could be useful in neurodegenerative diseases (e.g., Parkinson’s disease) (Agu´ndez, Jimenez-Jimenez, Alonso-Navarro, & Garcı´aMartı´n, 2015). Even though a lot of investment has been made in GWAS, the process of translating these genetic findings to the clinic is still at its infancy. GWAS are able to detect gene-disease associations, which are valuable for drug repurposing, yet challenges such as lack of information about whether an activator or inhibitor is needed to observe an effect, makes it difficult to use GWAS information alone (Hurle et al., 2013).

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4.5 Network-Based Methods Recently, great progress has been made in the field of system biology, which has led to advancements in applications such as drug repurposing. Network models are used to show pairwise relations between various objects. With respect to drug repurposing, these objects can be drugs, diseases, and target proteins. Schematically, networks are represented by nodes that are connected by edges, where the former can be an entity such as a biomolecule in a cell (e.g., a gene, RNA, protein, metabolite), a drug or a phenotypic state (e.g., a disease), and the latter depicts the relationship between two nodes (e.g., the relationship between drugs and known gene targets). By depicting biological systems as networks and utilizing several network measures and analyses, useful information about how these systems are organized can be extracted, which is not accessible via other, nonnetwork-related approaches (Wu, Wang, & Chen, 2013). With the emergence of high-throughput technologies, a large amount of data is being produced every day. Network-based approaches provide the means of connecting this huge amount of data and dealing with its complexity. As a result, the integration of large-scale data can render drug repurposing by a complete view of drug actions. The discovery of the mechanism of action of drugs alone is a time-consuming and labor-intensive process. For this reason, computational tools can be exploited to fill the gap in drug-protein interactions created by experimental approaches (Bellera et al., 2015). This has led to the construction of different types of networks, depending on the type of data. For instance, when genomic data are available, gene regulatory networks can be constructed that illustrate gene-expression regulation by showing the molecular relationships. When proteomic data are available, physical protein-protein interaction (PPI) networks can be constructed in order to illustrate the interplay among proteins. Moreover, by integrating networks at different levels, a more comprehensive and meaningful network can be produced. The integration of multiple datasets (Siavelis, Bourdakou, Athanasiadis, Spyrou, & Nikita, 2016) and of different types of data (Zachariou, Minadakis, Oulas, Afxenti, & Spyrou, 2018; Zhang et al., 2016) is one of the leading approaches in computational drug repurposing and can be facilitated by network approaches. This is due to the fact that a disease does not originate from a single factor, but from multiple interrelated factors. When integrating different types of data at different molecular levels, more efficient analyses can be performed, leading to more reliable results (see Fig. 2A). For instance, a recent line of work (Rakshit, Chatterjee, & Roy, 2015) suggested a bidirectional drug repurposing approach that included a tripartite indication-drug-target network, by also taking into consideration the topological significance of drugs. Topological significance means that the most potent drugs were chosen based on seven topological parameters, such as degree, betweenness, centroid, closeness, eccentricity, radiality, and stress, which are basic network measures used to analyze a network. By using these parameters, the most significant nodes were detected. This approach detected nine candidate repurposed drugs for PD. Another study (Liu, Song, Guan, Luo, & Zhuang, 2016) proposed a different approach based on a random walk on drug-disease heterogeneous networks. A two-pass random walk was applied on three different types of networks: (1) integrated drug-drug similarity,

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FIG. 2 Integration of data and methods for more efficient drug repurposing. (A) Integration of data. Different types of information (e.g., drug, protein, and diseases) are organized at the level of separate networks. These networks can be integrated into larger networks to combine information and serve as input to drug repurposing methods. (B) Integration of methods. Different types of computational drug repurposing methods can be combined in order to achieve higher efficiency. Methods that are more frequently combined together are linked with an edge. Network-based methods are often combined with other methods.

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(2) disease-disease similarity, and (3) drug-disease networks. This method was evaluated and further validated on a case study of AD. This study showed the strength of this approach since nine of ten drugs detected were already approved or investigated in neurodegenerative diseases, such as PD. These drugs are muscarinic antagonists or antimuscarinic-like agents. It is widely known that even though there are great differences in the pathogenesis of the two diseases, they are both age-related and also associated with protein aggregation in the brain.

4.6 Integration of Methods Several recent studies have relied on the integration of different computational methods, with network-based methods being the key players, as well as integration of different data types (see Fig. 2B). The main aim of integration methods and data is to potentially achieve more accurate and robust results. In this section we present a few example case studies that used integration of different computational drug repurposing methods. For instance, in 2012, Li and Lu proposed a systematic approach to detect novel indications for existing drugs based on drug similarity. This was done by constructing a bipartite drugtarget network method, using common drug targets and drug structural information. Validating this method by comparing to control drugs showed a favorable outcome compared to drug repurposing based on single information. These results suggest that integration of chemical structure and protein target information can lead to better performance compared to the individual methods (Li & Lu, 2012). In the following year, the same group established a causal chain network, which they named CauseNet (Li & Lu, 2013), by exploiting publicly available, curated biological information, in order to detect novel indications of existing drugs. CauseNet is a multilayered pathway of gene, disease, and protein target. The authors used a statistical approach to calculate the transition likelihood of the causal chains based on the known interactions and using this computational model they were able to identify potential drugs for Crohn’s disease. In another study by Chen and Butte (2016) a gene-drug-disease heterogeneous network was constructed, in which two different clustering approaches were applied to detect drug-disease pairs for drug repurposing. Nodes represented either drugs or diseases whereas shared biological processes, pathways, and genes were represented by the edges. Evaluation of the method was followed by performing literature and clinical trial investigations to detect any overlaps. As an example, they used AD and γ-secretase inhibitor data, and by performing this method they found that hidradenitis suppurativa and AD clustered together with γ-secretase inhibitors. Moreover, in a study by Xie et al. (2016), an integration of computational reverse docking and gene-expression data was performed in order to detect candidate treatment for AD. They performed virtual screening of 1553 approved drugs on seven known AD targets. Several drugs showed extremely high binding free energies for the seven targets. They also used data generated from Cmap and analyzed 74 drugs that were also detected by the reverse docking process. Analysis of the results identified other potential candidate drugs for AD, acting on multiple targets, including droperidol, glimepiride, and risperidone. These drugs were validated in SH-SY5Y cells, showing elimination of the toxicity generated by Aβ oligopeptides. In a study by Issa et al. (2016), a platform that implemented integration of different computational approaches, known as DrugGenEx-Net (DGE-NET), was presented. This platform

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exploits gene expression-analysis data in order to detect desired disease targets and prioritize drugs for repurposing. DGE-NET can predict drug-target interactions in the context of protein-protein interaction level, biological pathways, and molecular functions. This platform was applied to gene-expression data for several diseases such as rheumatoid arthritis, inflammatory bowel disease, AD, and PD. By using this platform, they were able to detect mebendazole as a drug repurposing candidate for rheumatoid arthritis. Several drugs were also detected as therapeutic drug repurposing candidates for AD and PD. A study by Iorio et al. (2010) presented the development of a method, known as MANTRA (Mode of Action by NeTwoRk Analysis), which integrated gene expression profiling information following drug treatment as well as network theory. In order to do that, they exploited Cmap to obtain transcriptional responses across different cell lines and dosages. A network of drugs separated into groups, known as communities, was constructed. These communities contained drugs that have a similar mode of action or act on the same pathway. Through this approach, they were able to detect Fasudil as a potential repurposed drug for several neurodegenerative diseases, since it causes cellular autophagy enhancement (Iorio et al., 2010). This novel indication for Fasudil as a cellular autophagy enhancer was evaluated in wild-type human fibroblasts and a robust activation of cellular autophagy was shown upon treatment. These effects were further confirmed in HeLa cells.

4.7 Machine Learning-Based Approaches Machine learning is a discipline of computer science that gives a computer the ability to learn from data by using statistical techniques, in order to detect associations among data (Deo, 2015). Machine learning-based approaches utilize a substantial amount of data sources to study the underlying mechanisms either of a disease or of the action of drugs, and to detect novel indications for existing drugs. In the recent past, these methods have received a lot of attention and several methods have been proposed (Li et al., 2016). For instance, Napolitano et al. (2013) used a drug-centred computational approach, which predicts novel indications for existing drugs based on machine-learning algorithms. This method enabled the integration of multiple-type information, such as the chemical structure similarity-based distance of the drugs, the closeness of targets in a protein-protein interaction network, and the correlation of gene-expression profiles upon treatment. All data information was combined to train a multiclass support vector machine classifier. Receiver operating characteristic (ROC) curves demonstrated that the combination of multiple data allows a much better performance of the particular classifier. This classifier showed an accuracy of 78%, which is consistent with information found in the literature. Another method by Gottlieb, Stein, Ruppin, and Sharan (2011), known as the PREDICT algorithm, exploits the fact that drugs that are “similar” can also work for “similar” diseases. This method uses known drug-disease association measures and genetic features, and by using a logistic regression classifier it weighs the different features and ranks additional associations in order to predict novel indications of drugs. This method obtained an AUC of 0.9 when it was cross-validated, showing a high sensitivity and specificity. Overall, machine learning-based approaches give the possibility of combining several features into a model, aiming for higher accuracy in drug repurposing. However, machine learning-based methods experience some limitations, for example, the requirement for

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sufficient training data. Introduction of novel computational approaches has been of great priority in the past few years (Lecun, Bengio, & Hinton, 2015). Such an example is the introduction of deep learning methods, which are able to automatically discover features in order to perform classification. Hence, deep learning methods promise to enhance the efficiency of drug repurposing methods. The approach that deep learning uses, known as the deep neural networks (DNNs), includes artificial neural networks with numerous hidden layers between the input and output layers. Even though DNNs were initially applicable to image, voice, and language recognition, they have started to be exploited by life sciences (Ma, Sheridan, Liaw, Dahl, & Svetnik, 2015). An example of employing deep learning was given by Aliper et al. (2016), in which they classified several drugs to therapeutic groups by only using transcriptional profiles. What they did was to use transcriptomic data before and after treatment obtained from the LINCS project to train the DNN and link them to therapeutic classes. They managed to show that DNN outperformed support vector machines (machine learning algorithm) on every classification problem. In both gene and pathway level classification, DNN convincingly outperformed the support vector machine (SVM) model on every multiclass classification problem; however, models based on a pathway level classification perform better. Moreover, Ma et al. (2015) used a DNN on a set of large diverse QSAR data sets and showed that this approach works better than traditional machine-learning algorithms such as SVM and random forest. Even though DNN training is a more computationally intensive approach, this can be managed using graphical processing units (GPUs). Furthermore, DNNs have already been applied to finding drug-target interactions using chemical structures and known interactions and promising results have been obtained (Wen et al., 2017). Nevertheless, deep learning methods are in the initial stages of being used in drug repurposing approaches and are still underestimated in this type of application. Even though DNN seem to be promising, they also face some limitations. For instance, they require large amounts of data to be trained. If the data is not enough then it is hard to outperform traditional machine-learning approaches. Moreover, they are more computational intensive, requiring more time to perform training (Vanhaelen et al., 2017).

4.8 Literature-Based Discovery Methods Literature-based discovery (LBD) is the strategy in which publicly accessible scientific literature can be processed to detect indirect connections and relationships in seemingly unrelated literature. With all the available literature in both biological and pharmacological aspects of drug discovery, it is possible to detect new drug indications through computational literature-based approaches (Frijters et al., 2010). LBD handles large amounts of scientific literature data and analyzes the connections between the relevant findings to detect and describe the underlying molecular mechanisms of a disease. This is the same method that systems biology uses to study the interconnections among all components of a cell or an organism. This process can be performed to propose new drug indications through (1) identifying drug effects and evaluating the benefits of their action, and (2) discovering novel interconnections between drugs and diseases (Andronis, Sharma, Virvilis, Deftereos, & Persidis, 2011).

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FIG. 3 B

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The Swanson’s ABC model. A-B and B-C information is extracted through literature and hence relationships are known (black arrows). A-C relationship is a novel discovery through connecting information from the existing sources (Modified from Weeber, M., Vos, R., Klein, H., De Jong-Van Den Berg, L. T. W., Aronson, A. R., & Molema, G. (2003). Generating hypotheses by discovering implicit associations in the literature: A case report of a search for new potential therapeutic uses for thalidomide. Journal of the American Medical Informatics Association, 10(3), 252–259. doi:https://doi.org/10.1197/ jamia.M1158).

Novel discovery

A key methodology used for literature-based drug repurposing is the Swanson’s ABC model (Fig. 3), which connects two different theories that appear not to be connected, by identifying a common aspect between them. For instance, if there is a scientific evidence showing that characteristic B is reported in disease C, and another piece of evidence shows that drug A affects characteristic B, then a connection between A and C can be made through characteristic B. Drug A that affects characteristic B could potentially be repurposed for disease C (Ganiz, Pottenger, & Janneck, 2005; Tari & Patel, 2014). An essential step of drug repurposing by LBD is the selection of the appropriate sources of information. Such an example is Medline, a key resource providing information on biological interactions. Medline is a knowledge database of life sciences and biomedical information. For the purpose of drug repurposing, the abstracts that are available in Medline are often used to extract information on gene and disease interactions and protein-protein interactions. A number of different tools have been employed to enhance drug repurposing through LBD. For instance, Weeber et al. (2001) exploited the Unified Medical Language System (UMLS), the largest biomedical thesaurus to date, to detect scientific conceptions in Medline abstracts. They developed a tool, known as DAD, which was based on the MetaMap program. MetaMap was developed to map biomedical text to the UMLS Metathesaurus. LitLinker (Yetisgen-Yildiz & Pratt, 2006) is another method that uses statistical methods to perform text-mining in biomedical literature. To do this LitLinker uses Medical Subject Headings (MeSH) terms. MeSH is a controlled vocabulary, which is also used as a thesaurus for term searches. LitLinker can be used to detect new connections between drugs, diseases, and proteins. For instance, Yetisgen-Yildiz and Pratt (2006) used LitLinker to identify disease protein correlations such as AD with endocannabinoids. Another method, known as Telemakus (Baker & Hemminger, 2010; Fuller et al., 2004) not only enhances text-mining in biomedical literature, but also creates visual representations of the connections that are created through networks. Wren, Bekeredjian, Stewart, Shohet, and Garner (2004) used another method to identify connections between seemingly unrelated literature, using Medline records. These connections were then presented in a network model, and by using this model they found that chlorpromazine, originally used as an as antipsychotic and antiemetic drug, could decrease cardiac hypertrophy progression and it was tested in a rodent model.

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In a paper by Zhu et al. (2010), a web-based tool was proposed named WENDI (Web Engine for Nonobvious Drug Information), which was created in order to collect biological information associated with a specific drug that is not obvious and to detect novel relationships between the drug, diseases, and genes.

5 DRUG REPURPOSING CHALLENGES 5.1 Validation of Methods Despite the great advances in computational drug repurposing, validation of these approaches is still needed. The key goal of drug repurposing is to detect the best drug candidate that has the highest potential to treat patients, whereas computational methods usually predict numerous interesting candidates. Therefore validation approaches are necessary to advance this process toward an actual therapy in the clinic. Experimental validation of the detected hits through in vitro and in vivo experiments is one of the key validation strategies. Moreover, electronic health records can be also used to cross-validate results detected by computational methods as shown previously by Khatri et al. (2013) and Xu et al. (2014). In Khatri et al. (2013) electronic medical records of renal transplant patients were used to validated the benefits of atorvastatin, which indeed showed improvement of graft survival. In Xu et al. (2014), the analysis of electronic medical records of cancer patients suggested that the use of metformin was related to reduced mortality upon cancer diagnosis, a robust and cost-effective way to validate candidates for drug repurposing. Equally important are the validation strategies of the computational method. Three main categories of validation exist: (1) case study, (2) sensitivity-based, and (3) sensitivity- and specificity-based validation. From the three, the latter is the most popular and rigorous approach used for validation. Sensitivity- and specificity-based validation methods include the area under the receiver operating characteristic curve (AUC) as well as methods that directly detect sensitivity and specificity. These methods require information regarding the false-positive indications, something that sensitivity-based methods lack. Introducing a “gold standard” for comparison of new computational methods could improve the accuracy of drug repurposing and such an approach could increase the number of successful cases and the quality of the validation strategies (Brown & Patel, 2018).

5.2 Drug Combination Recently, a lot of interest has been given to drug combinations as a therapeutic regime instead of single-agent therapies. A drug combination can provide synergy, meaning that when two drugs are combined the effect is greater than the sum of the individual effects but without an accumulated side effect risk (Bansal et al., 2014). Synergy can be achieved through (1) pharmacodynamics, meaning that two drugs affect the same target and through some specific molecular interaction there is a synergistic effect in efficacy; or (2) pharmacokinetics, meaning that two drugs do not affect the same target, but one helps the other in terms of their, for example, concentration in the blood, distribution in the body, metabolism, or excretion, in such a way that an increased efficacy is seen as a result (Cascorbi, 2012). 2. THEORETICAL BACKGROUND AND METHODOLOGIES

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Very few computational approaches are available for the use in compound synergy prediction. Computational methods for predicting compound synergy can potentially complement synergy HTS, but the few that have been published lack rigorous experimental validation or are appropriate only for compounds that modulate well-studied molecular pathways (Fitzgerald, Schoeberl, Nielsen, & Sorger, 2006). Hence, new approaches and methods that predict compound synergy are essential. In order to address the significance of this challenge, the DREAM Challenges Initiative (competitions run by researchers in order to address questions in systems biology) tested a number of different methods in order to predict compound synergy. Such initiatives show the significance of drug combinations that should be taken into consideration in drug repurposing (Bansal et al., 2014). In a study by Anastasio (2015), computational approaches to detect drug combinations for decreasing microglial inflammation in AD were used. As it is widely known, neurodegenerative diseases are characterized by brain inflammation, mediated by microglia, and therefore a reduction in this inflammation could potentially halt the neurodegenerative process. Hence, in this study a computational approach was used to identify and prove that combinations of approved drugs could potentially be more effective than monotherapies in AD. They used a computational model of microglia, together with two computer programming modalities, an imperative and a declarative. The purpose of the former is efficient computation while that of the latter is computational analysis. They used the imperative programming modality to detect the most efficient drug combinations among ten drugs, investigating the mechanisms of action of those combinations. They identified seven combinations, which managed to move simulated microglia 50% or more of the transition from a neurotoxic to a neuroprotective phenotype.

6 DISCUSSION AND CONCLUSION Due to an increase in life expectancy, it is assumed that the prevalence of already widespread neurodegenerative diseases, specifically AD, is expected to sharply increase in the near future. Hence, new and effective treatments are urgently needed to address this global health crisis. Traditional drug-discovery techniques are very costly and timely processes and as a result drug repurposing has recently gained popularity due to its potential to reduce time and costs by prioritizing a list of candidate drugs for further experimental testing. Drug repurposing provides the opportunity to speed up the process of introducing a new drug to the market, since it bypasses the pharmacokinetic and toxicological tests of traditional drug-discovery methods (Ma et al., 2013). Moreover, existing drugs have usually passed clinical trial safety tests and hence have a greater chance of being approved when tested again in clinical trials, and a smaller chance of having side effects (Kim, 2015). In addition, some drugs have passed the time-consuming and expensive phase IV safety data. Due to these advantages pharmaceutical companies are refocusing their efforts toward drug repurposing. Even though several drugs have been repurposed through serendipity, this is not a methodology that can be utilized and hence, novel targeted approaches needed to be developed (Vanhaelen et al., 2017). Nowadays, a number of different approaches, both experimental and computational are used to support this process. Advances in computational and mathematical sciences have led to the progress of computational drug repurposing. Due to the lower cost, drug repurposing is an attractive approach 2. THEORETICAL BACKGROUND AND METHODOLOGIES

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when compared to traditional drug discovery. Yet, it is faced with certain major challenges concerning financial and intellectual property issues (Talevi, 2018). Moreover, despite the great promise of drug repurposing approaches, it also requires a substantial amount of money for investment, with a major challenge being persuading companies to invest in drugs that were unsuccessful for their initial aim (Novac, 2013). A lack of funding and interest from the pharmaceutical industry limits the clinical development trajectory (Kato et al., 2015). Moreover, the intellectual property rights concerning the initial indication of the drug can pose a major obstacle to repurposing the drug. Implementation of novel drug repurposing methods is a way of overcoming these limitations in order to achieve successful drug repurposing (Tartaglia, 2006). Computational drug repurposing is also limited due to a lack of biological data, such as the absence of 3D structures for many proteins, which are essential to perform molecular docking. All of the abovementioned computational drug repurposing approaches have their advantages and disadvantages (see Table 3). Despite these limitations and challenges, the introduction of computational drug repurposing methods in order to detect novel indications for existing drugs has dramatically decreased the cost and time required, compared with in vivo and in vitro drug repurposing approaches (Shim & Liu, 2014). Hence, future work should be focused on both optimizing each method and importantly investigating the optimal combination of these methods in order to achieve better outcomes. Moreover, interesting hits detected by computational approaches need to be further validated and confirmed by experimental approaches such as in vivo and in vitro models. A major contribution to drug repurposing would be a large-scale experimentation of the validity of a large number of repurposed drugs and the collection of the repurposed drugs that have finally failed. Another key issue that the scientific community has to face is the robustness of the repurposed drug lists in the presence of small perturbations in the input level either as structural or as transcriptomic variations. Currently, most neurodegenerative diseases lack efficient therapeutic drugs. Hence, drug repurposing is an alternative approach that can be used to detect therapeutic compounds. In the future, more effort should be put into the integration of different computational repurposing approaches, which could potentially address the absence of treatment of these diseases. TABLE 3 Advantages and Disadvantages of Computational Drug Repurposing and Activity-Based Drug Repurposing (as Discussed in Oprea et al., 2012; Shim & Liu, 2014)

Advantages

Disadvantages

Activity-Based Drug Repurposing

Computational Drug Repurposing

Low false-positive rate of detected hits

Time efficient

Validation of hits is easier

Does not require a screening assay

No limitations regarding information about the target and drugs

Virtual screening of libraries of drugs

Time inefficient

High false-positive rate of detected hits

Large collections of drugs are needed

Usually requires structural information of drug target

Requirement of screening assay

Requires disease phenotype information

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C H A P T E R

5 Repurposed Molecules: A New Hope in Tackling Neglected Infectious Diseases Christopher Ferna´ndez-Prada*, Noelie Douanne*, Aida Minguez-Menendez*, Joan Pena*, Luiza G. Tunes†, Douglas E.V. Pires†, Rubens L. Monte-Neto† *

Pathology and Microbiology Department, University of Montreal, Saint-Hyacinthe, QC, Canada † Rene Rachou Institute, Belo Horizonte, Brazil

1 INTRODUCTION Infectious diseases have shaped the world as we know it. They have silently driven the evolution of many species and greatly influenced human civilization for centuries. According to the World Health Organization (WHO), infectious diseases are responsible for at least 15 million deaths worldwide each year. However, mortality is not the only factor to consider when measuring the burden of infectious diseases. Most researchers and medical organizations prefer to rely on disability-adjusted life years (DALYs) as a tool to draw a more realistic picture of the impact of infectious diseases. Neglected infectious diseases (NIDs) are a group of chronic, debilitating, and poverty-promoting diseases, which include parasitic, bacterial, viral, and fungal infections. NIDs thrive mainly among the most poverty-stricken populations and The Global Burden of Disease Study 2013 estimated that NIDs are among the world’s most common conditions, with more than 2 billion NIDs prevalent globally in 2013 (Herricks et al., 2017). While it was estimated that 141,800 deaths could be attributable to NIDs in 2013, their collective disease burden, estimated to reach 25 million DALYs, may be greater than the DALYs attributable to better known conditions such as tuberculosis (Hotez, Bottazzi, Franco-Paredes, Ault, & Periago, 2008) or liver cancer (Herricks et al., 2017).

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Despite these alarming figures, NIDs have been traditionally abandoned in terms of funding and policy, leading to an almost nonexistent development of therapeutic agents targeting these “forgotten pathogens.” Most of the few existing treatments for NIDs rely on “old fashioned” molecules that often present major obstacles and constraints. These include rapid emergence and spread of drug resistance, modest safety profiles, high costs, and the need for complex and supervised drug administration (Wyatt, Gilbert, Read, & Fairlamb, 2011). Furthermore, Big Pharma has shown a very limited interest in improving current therapeutics against NIDs because of the expected low return of investment when dealing with populations possessing little to no purchasing power (Berenstein, Magarinos, Chernomoretz, & Aguero, 2016; Robertson & Renslo, 2011). As a consequence of this failure to interest drug developers, less than 1% of the 1400 new drugs that reached the market between 1975 and 2000 were for the treatment of NIDs (Trouiller et al., 2002; Wyatt et al., 2011). This figure has improved slightly from 2000 to 2011. During the latter period, new drugs for NIDs reached 4% of the total drugs approved (Pedrique et al., 2013). However, all is not lost. The Drugs for Neglected Diseases initiative (DNDi), a nonprofit research and development organization founded by Medecins sans Frontie`res (MSF) and other public and private partners, has been advocating for change since 2004 in order to raise awareness of the NIDs crisis among key policy- and decision-makers (Pecoul, 2004). In addition, DNDi performs high-throughput untargeted screenings of novel-drugs libraries for NIDs as well as identifying new drug candidates using targeted compounds from repurposing libraries. In fact, this latter approach, repurposing of approved drugs, has become a very appealing strategy to tackle NIDs and is gaining supporters worldwide. Finding new indications for existing drugs has many benefits, mainly lower cost and a shorter time before marketing the drug (Chong & Sullivan Jr., 2007; DiMasi, Hansen, & Grabowski, 2003; Novac, 2013). Repurposing approved drugs helps avoid complications during clinical trials, such as drug toxicity or unfavorable pharmacokinetics (Zheng, Sun, & Simeonov, 2018). In this chapter we aim to explore the current situation of NIDs (Part I), discuss existing treatments and emerging challenges, and depict the different tools, approaches, and strategies that can be implemented to maximize the chances of effectively repurposing a drug for use against these neglected diseases (Part II). We close with a discussion of the potential impacts of drug repurposing on developing countries and some comments on future trends in the field.

2 PART I: CURRENT KNOWLEDGE AND CHALLENGES IN NEGLECTED INFECTIOUS DISEASES NIDs are a diverse group of communicable infectious diseases that threaten the life of more than one billion people in the world. Most affected people are from impoverished, underdeveloped, tropical, and subtropical countries. Although living and hygiene conditions are improving over time in these countries, the global impact of neglected tropical diseases remains substantial. The definition and classification of NIDs is not always clear and varies depending

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FIG. 1

Current classification and impact of neglected infectious diseases (NIDs) according to the WHO. NIDs are a heterogeneous group of infections caused by parasites, viruses, and bacteria that affect over a billion of the world’s poorest people and pose a significant social and economic burden to developing economies.

on the source consulted. In this chapter we will rely on the 17-disease list provided by WHO composed of two groups of parasite-driven diseases, including protozoan and helminth parasites, bacteria and viruses (see Fig. 1 for a detailed list).

2.1 Eukaryotic Neglected Infectious Diseases 2.1.1 Leishmaniases Leishmaniases are a group of vector-borne diseases caused by protozoan parasites belonging to the genus Leishmania. Among NIDs, leishmaniases rank second in mortality and fourth in DALYs. Leishmania parasites cycle between the motile promastigote form in the gut of the sand-fly vector and the intracellular amastigote stage within the macrophages of the host. Leishmaniases are endemic in at least four continents (88 countries), spanning tropical and subtropical regions of Central and South America, Africa, Asia, and the Mediterranean basin (Louzir et al., 2013; Tables 1 and 2).

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TABLE 1 Current and Prospective Drugs for Treatment of Parasitic Neglected Infectious Diseases Disease

Current Treatment

Repurposing Initiatives

Chagas disease

Benznidazole Nifurtimox

Metronidazole

Dracunculiasis

–

–

Echinococcosis

Albendazole Mebendazole Praziquantel

Tamoxifen Ursolic acid Amphotericin B

Foodborne trematodiases

Praziquantel Triclabendazole

Artemisinin derivatives Mefloquine OZ78 ozonide

Human African trypanosomiasis

Pentamidine Nifurtimoxeflornithine Suramin Melarsoprol

Diamidine derivatives Fexinidazole Oxaborole SCYX-7158 Quinolone amide GHQ 168 Tafenoquine

Leishmaniasis

Pentavalent antimonials Amphotericin Ba Miltefosinea Paromomycina

Camptothecin derivatives Indenoisoquinolinic compounds Auranofin

Lymphatic filariasis

Ivermectin Albendazole Diethylcarbamazine

Rifampicin combined with albendazole

Onchocerciasis

Ivermectin

Suramin Moxidectin

Schistosomiasis

Praziquantel Oxamnique

Artemisinin derivatives Mefloquine Piperaquine phosphate Trioxaquine PA1647 Nucleoside transport inhibitors combined with tubercidin or nebularine Acridine derivative Ro 15-5458 Nonsteroidal antiinflammatories Edelfosine

Soil-transmitted helminthiases

Albendazole Mebendazole Levamisole Pyrantel pamoate

Trichlorfon Bitoscanate Sertraline Paroxetine Chlorpromazine

Human taeniasis

Niclosamide Praziquantel Tribendimidine Albendazole

Paromomycin Quinacrine (Mepacrine) Tamoxifen

a

Drugs borne of past repurposing initiatives currently in use as treatments.

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TABLE 2

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Current and Prospective Drugs for Treatment of Bacterial and Viral Neglected Infectious Diseases

Disease

Current Treatment

Repurposing Initiatives

Buruli ulcer

Streptomycin and rifampicin

Avermectins (ivermectin, moxidectin, selamectin)

Chikungunya virus

–

Suramin

Dengue virus

–

Suramin Prochlorperazine

Leprosy

Rifampicin, clofazimine, and dapsone

Thalidomide

Rabies virus

–

–

Trachoma

Azithromycin

Mycophenolate mofetil

Yaws

Penicillin Azithromycin

–

In the absence of an effective vaccine, the control of leishmaniasis has traditionally relied on chemotherapy (Singh & Sundar, 2012), with a very limited number of registered molecules available. Moreover, significant drawbacks such as the complex route of administration, high toxicity, emergence of drug resistance, and astronomical costs limit their use in endemic areas (Sundar & Singh, 2018). The primitive and toxic pentavalent antimonials are the first choice of treatment and, in addition to their toxicity and long treatment schedules, they are frequently associated with drug resistance (Mohapatra, 2014; WHO, 2010). Strikingly, leishmaniasis is a great example in terms of drug repurposing. Amphotericin B (AmB) was first identified because of its antifungal activity and later repurposed against visceral leishmaniasis in the 1960s in Brazil (Furtado, Cisalpino, & Santos, 1960). More recently, AmB liposomal formulations were introduced for the treatment of visceral leishmaniasis in antimonial-nonresponsive regions of Bihar (India) (Ouellette, Drummelsmith, & Papadopoulou, 2004). While clinical resistance to AmB is rare (Lachaud et al., 2009), a recent study in India has reported an L. donovani field strain resistant to AmB (Srivastava, Prajapati, Rai, & Sundar, 2011). Another leishmanicidal drug introduced in the early 21st century is the alkyl-phospholipid analogue miltefosine (MF). MF was originally developed as a local treatment for cutaneous metastases of breast cancer and its oral formulation was evaluated in several phase II studies involving different types of solid tumors (Croft & Engel, 2006; Planting, Stoter, & Verweij, 1993; Verweij, Gandia, Planting, Stoter, & Armand, 1993). However, it was discontinued due to its side effects in cancer patients. In 1998 MF underwent phase II trials against leishmaniasis and showed promisingly high cure rates in the treatment of several forms of this NID (Sundar et al., 1998). Unfortunately, since its approval in 2002, it has had increasing relapse rates and there has been the emergence of drug-resistant strains (Bhattacharya et al., 2007; Sundar & Chakravarty, 2015). Sequential treatment of liposomal AmB followed by a short 7-day administration of MF has been introduced to fight antimony-resistant Leishmania populations in India (Olliaro, 2005, 2010). Unfortunately, a recent paper demonstrated the risk of emergence of cross-resistance between AmB- and MF-treated parasites in vitro (Fernandez-Prada et al., 2016).

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A third drug that was successfully repurposed in 2013 against Leishmania is the aminoglycoside-aminocyclitol paromomycin (Ben Salah et al., 2013), which was first isolated as a broad-spectrum antibiotic against Gram-positive and Gram-negative bacteria (Davidson, den Boer, & Ritmeijer, 2009). More recently, two different families of drugs have been proposed for repurposing against Leishmania. Both Food and Drug Administration (FDA)approved antitumor camptothecin derivatives and experimental indenoisoquinolinic compounds targeting the leishmanial DNA topoisomerase have shown great antileishmanial potential in both in vitro and in vivo models (Balana-Fouce et al., 2012; Prada, Alvarez-Velilla, Balana-Fouce, et al., 2013). Additionally, auranofin (2,3,4,6-tetra-O-acetyl-1-thio-β-Dglucopyranosato-S-[triethyl-phosphine]gold), an FDA-approved drug for use against rheumatoid arthritis and human dysentery, has been recommended as a candidate to be repositioned against Leishmania and has laid the foundation for possible exploitation of gold(I)-based complexes as chemical tools or the basis of therapeutics for leishmaniasis (Sharlow et al., 2014). 2.1.2 Chagas Disease Caused by Trypanosoma cruzi, Chagas disease (a.k.a. American trypanosomiasis) is responsible for major public health concerns affecting 21 South American countries as well as a large portion of the southern United States (Bern, Kjos, Yabsley, & Montgomery, 2011). Recent reports show approximately 10 million new cases of infection and 14,000 deaths per year (Coura, 2015). This vector-born NID is normally transmitted by various species of three genera of blood-sucking triatomine insects, also known as kissing bugs. At the moment of infection, if the parasitic load is very high, the patient develops a highly dangerous acute phase that can lead to death. This acute phase generally lasts from 4 to 8 weeks. Parasitemia then decreases, resulting in a chronic condition that can lead to irreversible damage to the cardiac tissue, esophagus, and colon as well as major nerve damage to the aforementioned organs (Ferraz et al., 2018). Anti-Chagas treatment is always suggested for acute Chagas disease, reactivated infections, and chronic disease in children under 18 years. With regards to chronic infections, systematic review and metaanalysis showed that treatment had little to no benefit when compared with placebo (Perez-Molina et al., 2009). Strikingly, the annual cost of follow-up and medical care for those affected by Chagas is greater than 267 million (USD). Currently, only two compounds, benznidazole (since 1972) and nifurtimox (since 1967), are approved for the treatment of Chagas disease (Reyes & Vallejo, 2005). While both drugs are far from ideal in terms of toxicity and side effects, benznidazole is generally preferred over nifurtimox. In addition to toxicity, drug resistance has been raised as a major concern in terms of treatment failure and relapse (Campos, Leon, Taylor, & Kelly, 2014). Recently, metronidazole, a wellknown and safe nitroimidazolic derivative used as an antibiotic and antiprotozoal against anaerobic bacteria and parasites, such as Giardia or Trichomonas, has been included in repurposing initiatives against Chagas disease due to its high suitability for combinatorial treatments (Simoes-Silva et al., 2017). 2.1.3 Human African Trypanosomiasis Human African trypanosomiasis (HAT), or sleeping sickness, is a vector-borne parasitic disease endemic in sub-Saharan Africa. It is caused by protozoan parasites of the Trypanosoma

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brucei species, which are transmitted to humans via infected tsetse flies. HAT presents two stages of progression, from a hemolymphatic acute stage to a meningo-encephalitic chronic stage. Without treatment, HAT almost invariably progresses to death. Two different subspecies are responsible for human disease: T. brucei rhodesiense causes East African HAT (mainly acute infection), while T. brucei gambiense causes the West African form of the infection (mainly chronic HAT) (Kennedy, 2008). HAT prevalence has been profoundly influenced by the socio-economic uncertainties of endemic countries (Simarro, Jannin, & Cattand, 2008). Fortunately, thanks to sustained control efforts over the last two decades, the number of reported cases has fallen drastically. While approximately 300,000 cases were estimated in 1998, less than 3000 cases were reported in 2015 (Buscher, Cecchi, Jamonneau, & Priotto, 2017). The four main drugs available against HAT are very toxic; melarsoprol in particular kills 5% of treated patients (Kennedy, 2008). The choice of treatment varies according to both the stage of the disease and the infecting parasite. Pentamidine is used for S1 T. b. gambiense, nifurtimox-eflornithine for S2 T. b. gambiense, suramin for S1 T. b. rhodesiense, and melarsoprol for S2 T. b. rhodesiense (Carvalho et al., 2015; Chappuis, 2018; Tiberti & Sanchez, 2018). With the exception of suramin, alarming levels of drug resistance for all antiHATs have been detected in the field, especially for melarsoprol (Barrett, Vincent, Burchmore, Kazibwe, & Matovu, 2011). For this reason, various de novo drug discovery and drug repurposing initiatives are currently in development. These include several new leads involving diamidine derivatives, fexinidazole, oxaborole SCYX-7158, or quinolone amide GHQ168 (Berninger et al., 2018); as well as repurposed molecules like oral antimalarial tafenoquine, which has demonstrated impressive in vitro activity against T. brucei (Carvalho et al., 2015). 2.1.4 Human Taeniasis Human taeniasis is a complex zoonotic infection caused by the adult stages of Taenia saginata (beef tapeworm), Taenia solium (pork tapeworm), or Taenia asiatica (Asian tapeworm) (Hobbs et al., 2018; Okello & Thomas, 2017). Infection by accidental consumption of T. solium eggs from the environment can lead to aberrant encystment of the larval form (cysticercosis) in various locations within the human body, leading to cysts in subcutaneous and muscular tissue as well as in the eyes and central nervous system. The latter case, known as neurocysticercosis, has the greatest morbidity. Human taeniasis causes major health and socioeconomic problems in different regions of sub-Saharan Africa, South America, and South Asia. The 2010 Global Burden of Disease survey estimated that human cysticercosis caused by T. solium was responsible for 503,000 DALYs lost annually (Murray et al., 2012); 53 million people are currently infected, leading to about 28,000 deaths each year (Torgerson et al., 2015). Infections with the adult stage of Taenia spp. are responsive to the common anthelmintic drugs niclosamide, praziquantel, tribendimidine, and albendazole (reviewed by Okello & Thomas, 2017). In addition, mass drug administration (MDA) has been proposed as a suitable control strategy for human taeniasis either alone or in combination with other strategies, such as vaccination and anthelmintic treatment of the porcine host (Allan et al., 1997; Del Brutto et al., 1996; Sarti et al., 2000). Regarding drug development and drug repurposing against Taenia spp., several molecules have been investigated in recent decades. In this way, Salem and el-Allaf (1969) proved in the 1960s the taenicidal power of paromomycin, an antibiotic recently approved against Leishmania spp. (see above). Similarly, quinacrine (mepacrine),

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an ancient antimalarial drug used during World War II, has become a very interesting option to treat niclosamide-tolerant patients infected with T. saginata (Koul, Wahid, Bhat, Wani, & Sofi, 2000). Finally, tamoxifen, a well-known antitumor drug, has shown strong cysticidal and antitaeniasic effects on T. solium, which has encouraged the conducting of more studies on the subject (Escobedo, Palacios-Arreola, Olivos, Lopez-Griego, & Morales-Montor, 2013). 2.1.5 Echinococcoses Echinococcoses are parasitic diseases of major public health importance caused by larval stages of taeniid cestodes belonging to the genus Echinococcus (Kern et al., 2017). Within this genus, six species cause infections in humans (McManus, 2013). Among them, E. multilocularis and E. granulosus, the etiological agents of alveolar echinococcosis (AE) and cystic echinococcosis (CE), respectively, are the species of major importance in terms of public health concerns (Cadavid Restrepo et al., 2016). Larval development varies with species, leading to different degrees of disease severity (Nakao, Lavikainen, Yanagida, & Ito, 2013). Currently, around 2–3 million people are infected by AE or CE. This figure increases by a worrisome 200,000 new cases every year (reviewed by Cadavid Restrepo et al., 2016). In terms of DALYs, the burden of CE and AE reaches extreme annual values of approximately 285,407 and 666,434, respectively (Budke, Deplazes, & Torgerson, 2006; Torgerson, Keller, Magnotta, & Ragland, 2010). The treatment of echinococcosis varies according to the Echinococcus spp. involved, the size and location of the cyst(s) and the complications following an eventual rupture of said cyst(s). Normally, patients must undergo surgical removal of the cyst(s) accompanied by a course of chemotherapy lasting at least 2 years post surgery. For inoperable patients, chemotherapy remains the only option (reviewed by Hemphill & Muller, 2009). Current treatments rely on two benzimidazoles, albendazole and mebendazole, and the pyrazinoisoquinoline derivative, praziquantel. Considerable efforts have been undertaken to improve treatment options for CE and AE. These have involved ivermectin (Reuter, Manfras, Merkle, Harter, & Kern, 2006), different molecules from the Malaria Box (Stadelmann et al., 2016), and several kinase-inhibitors ( Joekel, Lundstrom-Stadelmann, Mullhaupt, Hemphill, & Deplazes, 2018). Unfortunately, most of these repurposing initiatives have fallen by the wayside because of their poor in vivo results. On a more positive note, three FDA-approved drugs have been recently proposed as highly repurposable candidates: (1) antitumor drug tamoxifen, which is effective against adult forms and cysts of E. granulosus (Nicolao, Elissondo, Denegri, Goya, & Cumino, 2014); (2) ursolic acid, which is highly active against different stages of E. granulosus, both in vitro and in vivo (Yin, Liu, Shen, Zhang, & Cao, 2018) and; (3) antileishmanial amphotericin B, which could be extremely useful in the case of drug resistance or intolerance to benzimidazoles in AE infections (Reuter et al., 2003). 2.1.6 Schistosomiasis In terms of morbidity and mortality, schistosomiasis is considered the most important helminth-borne disease affecting human beings (Disease and Injury Incidence and Prevalence Collaborators, 2016). This NID is primarily caused by three species of trematode worms of the genus Schistosoma: S. mansoni and S. japonicum, which cause hepato-intestinal schistosomiasis; and S. haematobium, which causes urogenital schistosomiasis (Ross et al., 2002). Infection occurs when people are exposed, during routine domestic, agricultural, occupational, and recreational activities, to the larval forms of the parasite (released by freshwater snails) that

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contaminate freshwater sources. Schistosomiasis currently affects 240 million people in 74 developing countries, causing a loss of 10.4 million DALYs (King, 2010). Given the absence of an effective vaccine, praziquantel is the mainstay of treatment and a critical part of community-based schistosomiasis control programs (Caffrey, 2007), in addition to environmental and behavioral modification (Ross et al., 2002). Praziquantel is currently administered to more than 100 million people each year (Kuntz et al., 2007), which has raised the alarm for the emergence of drug resistance (Doenhoff, Cioli, & Utzinger, 2008; Gryseels, Polman, Clerinx, & Kestens, 2006). Until recently, oxamnique, a semisynthetic tetrahydroquinoline, was the drug of choice against S. mansoni in Brazil (Fenwick, Savioli, Engels, Robert Bergquist, & Todd, 2003) and has been proposed for combinatorial treatment with praziquantel (Gouveia, Brindley, Gartner, Costa, & Vale, 2018). However, the results derived from various clinical trials are inconclusive and further studies on the subject are necessary. Several drugs have been tested against schistosomiasis, either alone or combined (reviewed by Gouveia et al., 2018). While artemisinin derivatives (artemether, artesunate, and dihydroartemisinin) demonstrate significant activity against juvenile infections (Keiser & Utzinger, 2012), mefloquine has proven to target both juvenile and adult parasites (Panic, Duthaler, Speich, & Keiser, 2014). Two other antimalaria drugs that are currently in the spotlight are piperaquine phosphate (Synriam) and trioxaquine PA1647. Administration of the former led to a less severe liver pathology in S. mansoni infections (Mossallam, Amer, & El-Faham, 2015), whereas the latter showed an additive or synergistic effect against juvenile forms when combined with praziquantel (Portela et al., 2012). Gouveia and co-workers reviewed several interesting combinations of drugs with praziquantel to determine their activity against different forms of schistosomiasis. The drugs included nucleoside transport inhibitors, especially NBMPR-P or dialazep, in combination with tubercidin or nebularine (el Kouni, Messier, & Cha, 1987); acridine derivative Ro 15-5458 (Kamel, Metwally, Guirguis, Nessim, & Noseir, 2000); nonsteroidal antiinflammatory agents, such as ibuprofen and naproxen (Mahmoud, Zoheiry, & Nosseir, 2002); and synthetic lipid edelfosine (Yepes et al., 2014), among others (Gouveia et al., 2018). 2.1.7 Food-Borne Trematode Infections Humans are afflicted by numerous parasitic food-borne zoonoses, most of which are caused by trematodes. Trematodes have indirect and complicated life cycles involving different larval stages, two nonhuman intermediate hosts, and a definitive mammalian host. Agents of food-borne trematode infections include liver flukes (clonorchiasis, opisthorchiasis, and fascioliasis), lung flukes (paragonimiasis), and intestinal flukes (diplostomiasis, echinostomiasis, fasciolopsiasis, gymnophalloidiasis, and heterophyiasis) (Furst, Keiser, & Utzinger, 2012; Toledo, Esteban, & Fried, 2012). In terms of public health, food-borne trematode infections are responsible for more than 200,000 new infections and more than 7000 deaths each year. Recent estimates indicate that more than 40 million people are currently infected, leading to a global burden of 665,352 DALYs (Keiser & Utzinger, 2005, 2009; Sripa, Kaewkes, Intapan, Maleewong, & Brindley, 2010; Toledo, Bernal, & Marcilla, 2011). Praziquantel is the molecule of choice for treatment of all food-borne trematodiases (Keiser & Utzinger, 2004), except for fascioliasis, which is effectively treated with triclabendazole (reviewed by Toledo et al., 2012). Due to the low cure rate found in a study with praziquantel

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in Vietnam (Tinga et al., 1999), as well as established triclabendazole drug resistance in veterinary medicine (Keiser & Utzinger, 2004), there are concerns drug resistance may become problematic. This spurs the search for new alternatives. Along those lines, two artemisinin drugs, artemether and artesunate, have been successfully tested against liver flukes in rodent models (Panic et al., 2014). Another antimalaria drug, mefloquine, was effective against different stages of liver fluke in hamster models (Keiser, Duthaler, & Utzinger, 2010). However, these promising results were not translated to humans in clinical trials (Soukhathammavong et al., 2011). Finally, OZ78 ozonide, an analog of synthetic peroxide, has proved effective against triclabendazole-resistant F. hepatica in rat models (Keiser et al., 2007). 2.1.8 Lymphatic Filariasis Lymphatic filariasis is a mosquito-borne NID that represents a major public health problem in 74 countries due to its association with substantial morbidity and disability (Ramaiah & Ottesen, 2014). Lymphatic filariasis, caused by Wuchereria bancrofti, causes severe damage to the lymphatic system, frequently leading to elephantiasis (lymphedema) and hydrocele. WHO estimates 25 million men are currently affected with filarial-borne hydrocele and over 15 million people with lymphedema, and ranks lymphatic filariasis as one of the world’s leading causes of permanent and long-term disability (WHO, 2017a). In addition, Ton, Mackenzie, and Molyneux (2015) calculated in the ranks of 5.09 million DALYs the burden of depression attributable to filariasis, which doubles previous calculations by the Global Burden of Disease Study of 2010. In order to tackle this disease, The Global Program to Eliminate Lymphatic Filariasis (GPELF) was launched in 2000. GPELF relies on MDA of three anthelmintics: ivermectin, albendazole, and diethylcarbamazine. Albendazole is used in combination with ivermectin in Africa, whereas combination with diethylcarbamazine is approved for use in areas where filariasis is not endemic. Unfortunately, these drugs are incapable of eliminating adult worms, and the strong selective pressure on parasite populations due to MDA could lead to drug-resistant strains (Cobo, 2016). Recently, targeting the essential filarial endosymbiont Wolbachia was proposed as a suitable strategy to tackle this NID. Turner and co-workers demonstrated that the combination of albendazole and rifampicin depleted endosymbionts after a 7-day course of treatment, leading to accelerated macrofilaricidal activity (Turner et al., 2017). Importantly, these results represent a solid validation of previous in vitro screens aimed at the repositioning of different FDA-approved drugs against Wolbachia endosymbionts in order to implement novel regimes for macrofilaricidal therapy ( Johnston et al., 2014). 2.1.9 Onchocerciasis “River blindness,” or human onchocerciasis, is a vector-borne NID caused by the filarial nematode Onchocerca volvulus, transmitted by blackflies from the genus Simulium (Udall, 2007). During the infection, O. volvulus male adults move through subcutaneous tissue and skin, causing intense pruritus, skin lesions, and disfigurement. Meanwhile, female adults produce microfilariae with significant preference for the eyes, resulting in visual impairment and blindness (Babalola, 2011). Recently, the burden of this NID has been related not only to a lower quality of life, but also to an increase in mortality (Walker et al., 2012). About 37 million people are currently infected. Of those infected, 300,000 have been blinded by this NID and a

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further 500,000 are visually impaired. The estimated cost of the disease in 2015 was 1.1 million DALYs (DALYs & HALE Collaborators, 2016). Current treatment of onchocerciasis relies on ivermectin, an anthelminthic widely used in veterinary and human medicine. Unfortunately, ivermectin can merely control progression of this disease; the drug’s very limited effects on microfilariae prevent it from completely blocking infection (Omura & Crump, 2004). Additionally, drug resistance is ever-present, especially in the case of ivermectin. Regarding this issue, suramin has been recently repurposed and approved to fight O. volvulus, but its toxicity and availability interfere with its success. Another promising option is moxidectin, which has reached phase II trials after its activity and safety was demonstrated (Udall, 2007). Similar to the mechanism proposed for lymphatic filariasis (see above), several repurposing strategies targeting Wolbachia endosymbionts are currently in development ( Johnston et al., 2014). 2.1.10 Soil-Transmitted Helminthiasis Soil-transmitted helminthiasis (STH) is an NID caused by several genera of intestinal worms transmitted to humans through contaminated soil, either by accidental consumption of infective eggs/larvae (roundworms and whipworms), or by active penetration through the skin (hookworms). The four main nematode species that infect humans are Ascaris lumbricoides (roundworm), Trichuris trichiura (whipworm), and Necator americanus and Ancylostoma duodenale (hookworms). Helminthiases cause blood loss, anemia, malnutrition, intellectual impairment, and cognitive development problems (Pabalan et al., 2018). STH is the most frequent NID, currently affecting about 1.45 billion people worldwide. The global burden of STH was estimated at 3.4 million DALYs in 2015 (DALYs & HALE Collaborators, 2016). Nowadays, STH treatment relies on several anthelmintic medicines, including albendazole and mebendazole as first-line drugs, and levamisole and pyrantel pamoate as second-line. In fact, albendazole is the sole drug effective against all four parasites, being the only treatment active against hookworms. Moreover, all these drugs present important performance issues, especially against T. trichiura (Moser, Schindler, & Keiser, 2017). In order to alleviate this problem, drug repurposing possibilities must be urgently explored to find new effective compounds. For instance, antitumor drugs—protein kinase inhibitors in particular—have shown interesting anthelmintic potential in vitro. However, in vitro results did not necessarily translate into successful in vivo tests, thus hindering further repurposing of these drugs for STH (Cowan, Raimondo, & Keiser, 2016). Additionally, libraries of FDA-approved compounds are being currently tested in clinical and preclinical trials against STH infections. Results showed interesting anthelminthic properties for trichlorfon and bitoscanate (Keiser et al., 2016), as well as sertraline, paroxetine, and chlorpromazine, which could substitute or complement existing drugs in drug-resistance scenarios (Weeks et al., 2018). 2.1.11 Dracunculiasis Dracunculus medinensis, or the so-called “Guinea worm,” is the etiological agent of dracunculiasis, a crippling parasitic NID. D. medinensis worms affect communities in rural, deprived, and isolated areas that depend on open-surface water sources for drinking water and irrigation (Tayeh, Cairncross, & Cox, 2017). Despite this disease being present in many tropical countries, with a prevalence of approximately 3.5 million cases in 1985, eradication programs

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had reduced this figure to 30 confirmed cases in 2017. According to provisional reports issued by WHO, in the first quarter of 2018, only three cases of human dracunculiasis were reported, all of them in Chad. In terms of mortality, Guinea-worm disease is rarely fatal. Frequently, however, the patient remains sick for several months due to the pathology (and severe morbidity) caused by these parasites, mainly triggered by the migration (sometimes in aberrant locations), emergence, and accidental rupture of the worm (Greenaway, 2004; Lupi et al., 2015). Currently, the only effective treatment for dracunculiasis is the painful and laborious technique of winding the emerging worm around a piece of gauze or small stick to manually remove the parasite. In the 1970s, before the massive eradication campaign was begun, several anthelminthic compounds, such as metronidazole, were studied as a treatment against dracunculiasis (Antani, Srinivas, Krishnamurthy, & Borgaonkar, 1972; Pardanani & Kothari, 1970). However, results were unsatisfactory and, consequently, the repurposing initiative was rapidly abandoned. Likewise, a more recent study by Eberhard, Brandt, Ruiz-Tiben, and Hightower (1990) evaluated the potential use of diethylcarbamazine, albendazole, and ivermectin against dracunculiasis with similar outcomes as those observed for metronidazole.

2.2 Bacterial NIDs 2.2.1 Buruli Ulcer Buruli ulcer (BU) is a chronic skin infection caused by the bacteria Mycobacterium ulcerans. This disease is present in more than 33 countries of tropical and subtropical climate. The mode of transmission of M. ulcerans is unknown. The disease starts as a painless nodule or plaque that, without treatment, can cause necrotizing ulceration leading to permanent deformities and disability. In 2004 WHO established a treatment protocol using streptomycin and rifampicin in addition to surgery. This protocol is effective in cases of early, limited disease (Nienhuis et al., 2010). Recently, two avermectins, ivermectin and moxidectin, were tested in repurposing trials against BU in order to reduce reliance on streptomycin as well as its side effects (Omansen et al., 2015). In addition to a synergistic killing effect with rifampicin, Omansen et al. (2015) in vitro findings highly suggest that avermectins should be further evaluated for the treatment of M. ulcerans, particularly in combination with different families of antibiotics, such as fluoroquinolones. Similarly, Scherr, Pluschke, Thompson, and Ramon-Garcia (2015) tested the family of commercially available macrocyclic lactones against M. ulcerans and demonstrated that selamectin is a very promising avermectin candidate for anti-BU treatment. Interestingly, selamectin is already approved for veterinary treatment, which may accelerate its progression into clinical trials. Very recently, a library of compounds from a drugdevelopment program against tuberculosis was screened against M. ulcerans. Promisingly, Scherr, Pluschke, and Panda (2016) found five leads with high anti-BU activity, demonstrating that screening of libraries targeting related diseases is a good starting point for lead generation/repurposing. Finally, Malhotra, Mugumbate, Blundell, and Higueruelo (2017) released TIBLE (www-cryst.bioc.cam.ac.uk/tible/), a web-based, freely accessible resource

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for small-molecule binding data for mycobacterial species, which represents a major source of information to assist in drug development and repurposing against Mycobacterium species. 2.2.2 Leprosy (Hansen’s Disease) Leprosy is an ancient disease, caused by infection with the bacillus Mycobacterium leprae. It is present in 136 countries on all six continents and affects around 200,000 people each year. Three countries are responsible for 80% of cases: Brazil, India, and Indonesia (WHO, 2017c). Controlling this disease is a difficult task, mainly because its transmission is still poorly understood, diagnosis is difficult, and patients delay or refuse to seek health care for fear of the stigma (Steinmann, Reed, Mirza, Hollingsworth, & Richardus, 2017). Leprosy, or erythema nodosum leprosum, affects mainly the skin and peripheral nerves, causing nerve damage and nerve function impairment that can lead to deformity and disability. People affected by leprosy can be limited in the use of their limbs, restricted with regards to social activities, and suffer from substantial social stigma and discrimination, all of which contributes to economic loss (van Brakel et al., 2012). The first drug systematically used to treat leprosy in the 1940s was dapsone, also known as diaminodiphenyl sulfone (DDS) (Noordeen, 2016). However, DDS had weak bactericidal activity and treatment took years, which reduced patient compliance. For that reason, resistance to DDS rapidly increased and by the 1960s DDS monotherapy was no longer possible. In 1981, WHO established a multidrug therapy protocol using rifampicin, clofazimine, and dapsone. This multidrug therapy is still in use, but has several limitations including high cost, questionable efficacy, potential adverse effects, and length of treatment (2-year regimen) (Smith et al., 2017). The importance of finding new drugs to treat leprosy is indisputable. Although M. leprae is susceptible to a wide range of antibiotics, the inability to grow this bacillus axenically is a major drawback to drug discovery. An interesting example of drug repurposing against leprosy is thalidomide, which become one of the first drugs approved for this indication (Baek, Jung, Kang, Lee, & Bae, 2015). This drug was first marketed as a sedative and as a treatment for morning sickness in pregnant women (Costa et al., 2018). However, thalidomide presents major caveats, such as the risk of teratogenicity and neurotoxicity, which forced its withdrawal from the market (Anon., 1962). 2.2.3 Trachoma Trachoma is an ocular infection caused by the intracellular bacteria Chlamydia trachomatis. It is transmitted by direct contact with infected ocular secretions, especially among children. Around 192 million people are at risk of contracting trachoma and 450,000 are irreversibly blind (Satpathy, Behera, & Ahmed, 2017). The control strategy endorsed by WHO since 1993, called SAFE, consists of surgery for preventing blindness in patients with trichiasis, treatment with antibiotics to reduce the reservoir of C. trachomatis, facial cleanliness, and environmental safety measures to reduce transmission (WHO, 2017c). Azithromycin is the current treatment for trachoma. WHO recommends between three and five annual MDA to entire communities, aiming to treat individual cases of infection, reduce the reservoir, and interrupt transmission. Given that the logistics of treating entire communities in remote areas are complicated and that the number of rounds and doses of treatment has not been systematically established, MDA is still unable to eliminate transmission of this disease

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(Last et al., 2017). Despite the lack of concrete evidence for C. trachomatis resistance to azithromycin, there is a concern for the development of antimicrobial resistance since some communities still present persistent infection even when using multiple rounds of MDA (West et al., 2014). There are several reports of in vitro C. trachomatis resistance to rifamycins, macrolides, and quinolones, increasing the concern for development of clinical resistance (Sandoz & Rockey, 2010). An interesting alternative to traditional drug treatment is the development of host-directed therapies against C. trachomatis. In this way, Rother et al. (2018) integrated human genome-wide RNAi and metabolomics analyses to explore the growth requirements and signaling pathways essential for C. trachomatis. The authors found an extremely interesting target, the inosine-50 -monophosphate dehydrogenase 2, which is specifically inhibited by the clinically approved and well-studied drug mycophenolate mofetil. The aforementioned would make an excellent candidate for repurposing against trachoma. 2.2.4 Yaws Yaws, a chronic skin, bone, and cartilage infection, is caused by the bacteria Treponema pertenue. This microorganism is a subspecies of Treponema pallidum that is responsible for venereal syphilis as well as chronic pinta in South America (Stamm, 2015a). Treponematoses are endemic in warm, humid, and tropical forest areas of Africa, Asia, Latin America, and the Pacific. The affected population is usually isolated, with limited access to health care (Marks, Mitja, Solomon, Asiedu, & Mabey, 2015). In 2012, WHO’s strategy for the eradication of yaws recommended MDA with single doses of azithromycin followed by ongoing active surveillance in affected communities. Although this strategy greatly reduced the burden of this disease in different yaws-endemic island populations, many cases are reported in communities spread over a contiguous land mass, where MDA approaches are more difficult (Abdulai et al., 2018). If left untreated, yaws may progress to multiple skin lesions, bone and cartilage inflammation, tissue necrosis, and disfigurement (Marks et al., 2015). Treatment has, for over 50 years, consisted of a single dose of long-acting, injectable penicillin. Even with such extensive use, there are no reports of resistance. In 2012, a study evidenced that a single dose of oral azithromycin (30 mg/kg) is as effective as penicillin (Mitja et al., 2015). Given the widespread resistance to azithromycin in sexually transmitted strains of T. pallidum, the risk of T. pertenue also developing resistance must be considered (Stamm, 2015b). Indeed, five cases of clinical failure of azithromycin were reported in a village in Papua New Guinea. The patients were infected with a T. pertenue strain carrying mutations associated with macrolide resistance in the 23s rRNA gene, suggesting the emergence of resistance to azithromycin (Mitja et al., 2018).

2.3 Viral Diseases 2.3.1 Dengue Dengue is a fast-emerging viral disease that is transmitted to humans through bites of Aedes aegypti mosquitoes. Dengue is widespread in more than 100 countries of tropical and subtropical regions. The exact burden of this disease is uncertain because many cases go unreported, yet, in 2015 alone, there were a known 3.2 million cases (WHO, 2017c).

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Infection with dengue virus has a wide clinical spectrum that can vary from asymptomatic to severe hemorrhagic fever progressing to death (McFee, 2018). There is no specific treatment for dengue, only management of symptoms. Maintaining the patient’s circulating fluid volume is the most important strategy of care in severe cases. Promising drug candidates are being developed, but the four different serotypes of dengue virus and the fact that treatment is expected to inhibit all of them poses a challenge to drugdiscovery research (Tian, Zhou, Takagi, Kameoka, & Kawashita, 2018). In terms of drug repurposing, suramin (a drug marketed to fight river blindness) showed important inhibitory properties against dengue virus’ helicase in a high-throughput study of 1600 compounds (Basavannacharya & Vasudevan, 2014). Another example is prochlorperazine, a drug formulated to treat nausea, vomiting, and headache [very similar to the major symptoms of dengue virus ( Jones et al., 2011)], which recently demonstrated a promising ability to block dengue infection and improve symptoms at nontoxic doses (Simanjuntak, Liang, Lee, & Lin, 2015). 2.3.2 Chikungunya Chikungunya is a viral disease that has emerged as an epidemic threat over the past 15 years (Tharmarajah, Mahalingam, & Zaid, 2017). It is transmitted by mosquitoes of the Aedes genus and affects over one million people yearly. Chikungunya occurs in tropical regions and major outbreaks reported globally in the 2000s caused great strain on affected areas (Ganesan, Duan, & Reid, 2017). The symptoms of chikungunya infection are fever, rash, and debilitating joint pain that can progress to a chronic state and last from months to years. Consequently, chikungunya virus is associated with low mortality and high morbidity. Like dengue, there is no specific treatment for this disease and health care is focused on management of symptoms, usually using nonsteroidal antiinflammatory drugs. There are studies describing inhibitors of chikungunya virus using known antivirals or new molecules; however, those drug candidates still need to be validated in in vivo models of the disease (Tharmarajah et al., 2017). These include the abovementioned suramin, which is currently approved to treat onchocerciasis and is under investigation for use against dengue virus. Interestingly, suramin has shown considerable activity against chikungunya virus (Albulescu et al., 2015; Henss et al., 2016; Ho et al., 2015). Because treatment only requires administering this (otherwise toxic) drug for a short period of time, suramin appears to be a promising candidate for continued studies. 2.3.3 Rabies Rabies is a fatal viral infection that kills around 60,000 people each year according to WHO. The transmission of this disease occurs mainly through bites from infected dogs. Rabies is present in 150 countries, affecting especially Asia and Africa. Because it is an underreported disease, the true incidence and burden of rabies is unknown (Fisher, Streicker, & Schnell, 2018; WHO, 2017b). Human rabies is 100% preventable by vaccination and can be treated using vaccine therapy and immunoglobulin. However, the treatment is only effective if administered early and quickly to bite victims, which is difficult for patients in remote areas ( Jackson et al., 2003). In such circumstances, it would be beneficial to have promptly available drugs. There are reports of antiviral compounds that were effective in vitro, but none have as yet been proven to have enough efficacy to be used clinically (Smreczak et al., 2018).

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3 PART II: TOOLS AND STRATEGIES 3.1 Useful Databases for Drug Repurposing in Neglected Infectious Diseases Drug-repurposing pipelines benefit from different methods in computational pharmacology that integrate multiple databases, each playing a critical role in the efficient translation of basic science to therapeutics. These transforming tools enable new and exciting links among different fields, such as medicinal chemistry, bioinformatics, drug discovery, systems biology, and genomics. Current databases contain a vast variety of information related to, among others, drug structures, drug-target interactions (DTI), drug-disease associations, phenotypic drug screens, and functional genomics. This section will describe some of these databases, not with the intention of providing a comprehensive list, but rather to highlight new and recently updated high-quality resources for drug repurposing (these are summarized in Table 3). TABLE 3 Useful Databases for Drug Repurposing in Neglected Infectious Diseases Resource Type

Resource

General PubChem databases for drug information

Drug-target interactions

Genomic resources

Description

URL

References

Database of chemical structures, https://pubchem. identifiers, chemical and physical ncbi.nlm.nih.gov properties, biological activity, etc. (95M compounds)

Kim et al. (2016)

ChEMBL

Database of structures, calculated https://www.ebi.ac. properties and abstracted bioactivities uk/chembl/ (1M compounds)

Bento et al. (2014)

DrugBank

Database of drug data and target information. Drug clinical and repurposing trials

Wishart (2007), Wishart et al. (2006, 2018), and Wishart and Wu (2016)

CDD

Collaborative web-based database that http://www. contains thousands of drug structures collaborativedrug. and bioactivity data com/

www.drugbank.ca

Hohman et al. (2009)

BindingDB Database of binding affinities, interactions of drug-target proteins. Integrates information from multiple databases

www.bindingdb.org

Chen et al. (2001)

STITCH

Information about metabolic pathways, crystal structures, binding experiments, and drug-target relationships

http://stitch.embl. de/

Kuhn et al. (2008)

TDR Targets Database

Tool for the identification and http://tdrtargets.org prioritization of drugs and drug targets in neglected infectious diseases

Aguero et al. (2008)

GeneDB

Genome sequences and annotated data www.genedb.org for prokaryotic and eukaryotic pathogens

Logan-Klumpler et al. (2012)

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TABLE 3 Resource Type

Useful Databases for Drug Repurposing in Neglected Infectious Diseases—cont’d Resource

Description

EuPathDB

Collection of databases of eukaryotic http://eupathdb.org pathogens and selected pathogen hosts

Aurrecoechea et al. (2017)

Standard set of true positives (approved drugs) and true negatives (failed drugs)

http://apps. chiragjpgroup.org/ repoDB/

Brown and Patel (2017)

Clinical Trials

A registry and results database of 274,049 clinical studies with human participants in 204 countries

www.ClinicalTrials. gov

McCray and Ide (2000)

SIDER

Database of drugs and adverse druf reactions (ADRs). 1430 drugs, 5880 ADRs and 140,064 drug-ADR pairs

http://sideeffects. embl.de

Kuhn et al. (2016)

Offsides database

Complement resource to SIDER. 438,801 off-label side effects for 1332 drugs, and 10,097 ADRs

http://tatonettilab. org/resources/ tatonetti-stm.html

Tatonetti et al. (2012)

Drug-disease RepoDB associations

Drug-side effect associations

URL

References

If we take the compound as the starting point for any repurposing pipeline, PubChem (Kim et al., 2016) and ChEMBL (Bento et al., 2014) represent two major databases encompassing biomolecules and their activities. PubChem contains the largest collection of publicly available chemical information (95 M compounds, comprising both small and large molecules), including chemical structures, identifiers, chemical and physical properties, biological activity, toxicity data, etc. (Kim et al., 2016). ChEMBL, a manually curated database, is focused on bioactive drug-like small molecules and contains structures (1 M compounds), calculated properties, and abstracted bioactivities. The latter are frequently normalized into a uniform set of end-points and units. ChEMBL often tags the links between a molecular target and a published assay with a set of varying confidence levels (Bento et al., 2014), a useful feature. In addition, ChEMBL is currently integrating additional data on the clinical progress of compounds, which will provide important information for selecting and characterizing repurposed drug candidates with a particular focus on guaranteeing human safety (Corsello et al., 2017). Moving forward in the repurposing pipeline requires a precise identification of DTIs and a complete elucidation of the functional response of the target molecule. Depending on the chosen database, one can retrieve different levels of DTI information. An example is DrugBank (Wishart, 2007; Wishart et al., 2006, 2018; Wishart & Wu, 2016), an exceptional bio/ cheminformatics database that brings together detailed drug information with compressive drug target data. Interestingly, its recent 5.0 version integrates novel “pharmaco-omic” data describing the influence of drugs on metabolite levels and levels of gene and protein expression. Moreover, this up-to-date tool now includes the results of hundreds of investigational drug clinical trials and various repurposing trials (Wishart et al., 2018). Another major public, web-accessible resource in terms of DTI is BindingDB (Chen, Liu, & Gilson, 2001). This database compiles and structures binding affinities, focusing mainly on the interactions of

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proteins considered to be drug targets (more than 7225) with small, drug-like molecules (more than 621,060). In addition, BindingDB integrates information from other databases, such as confirmatory BioAssays from PubChem or ChEMBL, entries for which a well-defined protein target is provided, as well as links to experimentally solved protein structures and protein-ligand complexes, when available, at the Protein Data Bank (Chen et al., 2001; Gilson et al., 2016; Liu, Lin, Wen, Jorissen, & Gilson, 2007; Wassermann & Bajorath, 2011). Collaborative Drug Discovery (CDD), a spin-out of Eli Lilly, created a key tool for drug repurposing against NIDs. CDD is a collaborative web-based database that contains thousands of drug structures and bioactivity data (Ekins, Williams, Krasowski, & Freundlich, 2011). However, the major strength of this tool is to allow the user to perform sophisticated structure-activity relationship (SAR) analysis, including chemical pattern recognition, physical-chemical property calculations, and Boolean search, and to save capabilities for potency, selectivity, toxicity, and other experimentally derived properties (reviewed by Hohman et al., 2009). While efforts to integrate data are increasing, information on interactions between proteins and drug-like small molecules spans a massive number of databases that differ in structure, information content, and prediction methods, making it very difficult to understand the current level of evidence. In order to address this issue, Kuhn, von Mering, Campillos, Jensen, and Bork (2008) developed STITCH, a database that integrates information about interactions from metabolic pathways, crystal structures, binding experiments, and drug-target relationships of known and predicted interactions between chemicals and proteins. Interestingly, in the latest version of STITCH, a very useful binding-affinity network view has been implemented that allows the user to rapidly identify the different potential effects of the molecule in its interaction network (Szklarczyk et al., 2016). This feature is especially relevant in drug repurposing as the pathways or protein complexes affected may differ when targeting a different pathogen. Genetic studies represent a major source of information for the identification of drug targets and may increase the likelihood of drug repurposing success (Pritchard, O’Mara, & Glubb, 2017). In this way, researchers devoted to tackling neglected tropical diseases can take advantage of the Tropical Disease Research (TDR) Targets Database, a powerful tool that exploits the availability of diverse data sets to facilitate the identification and prioritization of drugs and drug targets in neglected disease pathogens (Aguero et al., 2008). In fact, TDR Targets serves as an excellent tool for prioritization of targets in whole genomes as it allows users to assess the role of a defined gene in the biology of the pathogen, as well as to predict whether pharmacological targeting of this role is likely to kill the pathogen (Magarinos et al., 2012). In addition to its team’s curatorial efforts, TDR Targets integrates other primary data sources such as the abovementioned ChEMBL, DrugBank, and PubChem, in addition to data collected from several high-throughput screening initiatives (Magarinos et al., 2012). Based on TDR Targets’ predictions, Fernandez-Prada and co-workers pinpointed the DNA topoisomerase IB of Leishmania infantum, the etiological agent of NID visceral leishmaniasis, as a promising target for drug intervention due to its unique nature and structural differences with regard to its mammalian counterpart (Prada et al., 2012; Prada, Alvarez-Velilla, DiazGozalez, et al., 2013). These authors assessed in vitro and ex vivo the leishmanicidal activity of three camptothecin analogues used in cancer therapy. Strikingly, one of the compounds, gimatecan (ST1481), an orphan drug (EU/3/03/174) used in the treatment of glioma (Teicher, 2008), demonstrated an extraordinary leishmanicidal power and a therapeutic selectivity

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index threefold higher than that observed for the antileishmanial drug miltefosine (Prada, Alvarez-Velilla, Balana-Fouce, et al., 2013). More recently, Berenstein et al. (2016) used the TDR Targets Database to construct a multilayer network of protein targets, chemical compounds, and their relations to guide drug-discovery/positioning efforts against neglected diseases. Networks represent a natural framework to integrate an extensive diversity of data sources, identifying and quantifying relationships between entities (e.g., gene expression correlation, or the existence of a defined interaction) (Martinez, Navarro, Cano, Fajardo, & Blanco, 2015). Interestingly, some of the candidates predicted by this network model were supported by independent experimental validations; such as in the case of the orphan compound (TDR Targets ID 599594), active against the malarial agent Plasmodium falciparum, which was connected by the network model to several hydroxamic acid derivatives that are known to inhibit bacterial peptide deformylases (Berenstein et al., 2016). Using this method, actinonin (Wiesner, Sanderbrand, Altincicek, Beck, & Jomaa, 2001), a widely used peptide-deformylase inhibitor, as well as other hydroxamates, have shown promising antimalarial power (Hynes, 1970). This highly valuable network-based tool provides a cohesive view of repurposing strategies by enabling a better prioritization of drug targets as well as a superior identification of potential targets for orphan bioactive drugs (Berenstein et al., 2016). GeneDB (Logan-Klumpler et al., 2012) and EuPathDB (Aurrecoechea et al., 2017) are two major databases that can enhance drug repurposing opportunities through a better understanding of pathogens’ genetics. While not restricted to NIDs, many neglected pathogens are covered by these two highly interconnected genomic tools. GeneDB is one database that provides a portal to genome sequence and annotation data for prokaryotic and eukaryotic pathogens as well as closely related organisms. This data is mainly produced by the Pathogen Genomics group at the Wellcome Trust Sanger Institute (Logan-Klumpler et al., 2012). Another example is EuPathDB, a major collection of databases covering more than 170 eukaryotic pathogens and selected pathogen hosts. The latest update has dramatically expanded the range of EuPathDB’s content, which now includes protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics (Aurrecoechea et al., 2017). Despite all these extremely useful databases, finding a promising novel candidate drug for a defined neglected disease remains a major challenge. When choosing a computational repurposing tool (or combining several), it should be possible to subsequently confirm predictions in the field through different analytic validation methods. Many of these validation methods rely on databases of true drug-indications pairs as their unique source of information, which tend to underestimate the potential of novel repurposing candidates as leads (Brown & Patel, 2017). In order to address this limitation, Brown and Patel recently developed the repoDB, a database that contains a standard set of true positives (approved drugs) and true negatives (failed drugs), which can be used to properly and reproducibly benchmark computational repurposing methods (Brown & Patel, 2017). This promising tool brings together approved indications from DrugCentral (Ursu et al., 2017) and failed ones from the Aggregate Analysis of Clinical Trials Database (McCray & Ide, 2000), which opens the door to explore exciting new paths in the field of drug repurposing that would likely be overlooked by traditional approved-drug-based approaches. Last but not least, drug repurposing approaches must try to precisely predict and explain potential side effects and adverse drug reactions (ADR). Pharmacovigilance of new side

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effects’ associations with approved drugs can be improved with computational tools, allowing analysis and deconvolution of complex data surrounding the drug’s use and effects (White, Tatonetti, Shah, Altman, & Horvitz, 2013; Yom-Tov & Gabrilovich, 2013). Drug-side effect associations are very dispersed through public information databases. In order to address this issue, Kuhn, Letunic, Jensen, and Bork (2016) created the SIDER (Side Effect Resource) database of drugs and ADRs. The current version contains data on 1430 drugs, 5880 ADRs, and 140,064 drug-ADR pairs, which provides a more detailed picture of the ways in which drugs cause adverse reactions. The Offsides database is an extremely interesting complementary resource to SIDER. Offsides contains 438,801 off-label side effects for 1332 drugs and 10,097 ADRs. Interestingly, this database covers approximately 39% of SIDER associations from adverse event reports, leading to the discovery of different associations from those reported during clinical trials before drug approval (Tatonetti, Ye, Daneshjou, & Altman, 2012).

3.2 Computational Tools For Drug Repurposing in Neglected Infectious Diseases: A Virtual Translational Reality From Computers to Clinics Computational methods have an increasing importance in the rational design of novel, economical, efficient, and personalized drugs. These processes not only guide experimental approaches, but also assist in the interpretation of their outcomes. It is worth mentioning that modern drug discovery/development is one of the areas positively impacted by the fourth industrial revolution (industry 4.0), where cyber and physical spaces are constantly connected. These integrative approaches are helping in the challenging task of drug repurposing to tackle neglected diseases. Some aspects of this reality include: personalized medicine, big data analytics and advanced algorithms, cloud computing, and the need for appropriately trained individuals. Virtual findings are supported by experimental validation whose outcome can also be predicted computationally, thus integrating solutions to improve the chemotherapy arsenal for tackling NIDs. This includes methods that can help resurrect orphan drugs (Aronson, 2006), supporting R&D focused on life-saving programs to develop products for treatment of NIDs (Villa, Compagni, & Reich, 2009). In general, computer-aided drug design (CADD) can be divided into target-based and ligand-based approaches (Issa, Kruger, Byers, & Dakshanamurthy, 2013). Structure- or target-based methods use available information from the 3D structure of the biological target (usually proteins), either experimentally solved or obtained via comparative modeling, to develop novel ligands for specific pockets or binding sites, taking into consideration the complementary features (both geometrical and physicochemical). Docking, virtual screening, and molecular dynamics simulations are examples of methods that have been applied to target-based drug design. Ligand-based drug design, on the other hand, relies on the information of the structure of a compound as a way to identify a minimum set of characteristics required for a molecule to be active/bind to the target of interest. Quantitative structure-activity relationships models (QSAR) are an example of this class of methods. In the following sections, we introduce and discuss the available computational methods that could prove useful for drug repositioning against NIDs, presenting examples of potential drug candidates discovered through ligand-based or target-based computer-assisted strategies.

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3.2.1 Ligand-Based Approaches Traditional QSAR-based methods are very common in early drug design and discovery, whether to establish new lead compounds or for use in drug repurposing. The main task is to predict biological activity—in this case, related to the expected antiinfectious disease potential—ascertained from structural features of known compounds. In this context, a library set of known drugs can have their biological activity predicted, associated with their chemical structure and based on physicochemical properties or theoretical molecular descriptors of chemicals. Once a hit (repurposed drug) is experimentally validated, the results of these assays can be used to perform more accurate QSAR analysis, repeating the workflow. A lead compound is reached when criteria such as low toxicity and potential for further modifications are met (Williams et al., 2015). This method can also be adapted to predict target affinity and identify new potential targets. QSAR-based approaches have been widely used to discover new hits against infectious diseases for drug repurposing (Gomes et al., 2017; Melo-Filho et al., 2016; Neves et al., 2016) and have also been applied to lead optimization and virtual screening (Verma, Khedkar, & Coutinho, 2010). Some examples of studies applying QSAR to identify repurposable drugs against NIDs are presented in Table 4. TABLE 4 Examples of Drug Candidates for Repurposing Against Neglected Infectious Diseases Determined by Computer-Aided Methods Drug Candidate

Originally for Treatment of

Repurposed Against

NMT inhibitors Cancer Filarial DDD85646 and Fungal infection nematodes DDD100870 Viral infection Protozoal infection

Target

Computational Method Used References

N-Myristoyltransferase Comparative (NMT, EC 2.3.1.97) homology modeling

Galvin et al. (2014)

Human PDE4 Inflammation inhibitor GSK- (inhibitors of 256066, NPD- human PDE4) 008, NPD-039

Phosphodiesterase B1 Trypanosomes (PDEs, EC 3.1.4) (African and American trypanosomiasis)a

Structureactivity relationship

Blaazer et al. (2018) and Ochiana, Bland, Settimo, Campbell, and Pollastri (2015)

Human kinase Cancer inhibitors

Trypanosomatids Cyclin-dependent (Leishmania, kinase (CDK) Trypanosomes) Casein kinase Alpha kinase

–

Reviewed by Dichiara et al. (2017)

Paroxetine

Depression

Schistosomes

Inhibition of Schistosoma Homology mansoni serotonin modeling transporters Molecular docking

Weeks et al. (2018)

Atovaquone Carvedilol

Pneumocystis Malaria Toxoplasma Leishmania (atovaquone) Congestive heart failure (carvedilol)

Helminths

Inhibition of Ascaris suum mitochondrial rhodoquinol-fumarate reductase

Uzochukwu, Olubiyi, and Akpojotor (2014)

Molecular docking Molecular dynamics simulations

Continued

2. THEORETICAL BACKGROUND AND METHODOLOGIES

TABLE 4 Examples of Drug Candidates for Repurposing Against Neglected Infectious Diseases Determined by Computer-Aided Methods—cont’d Drug Candidate

Originally for Treatment of

Ritonavir Lopinavir Nelfinavir Ivermectin

Viral infection Virus (dengue) Parasite infestation

NS3 helicase

Cinnarizine Griseofulvin Tetrabenazine Clotrimazole Aprindine

Allergies Fungal infection Psychosis Bacterial infection Arrhythmia

Voltage-dependent Target-based calcium channel chemogenic (Smp_159990.1) screening Tubulin β chain (Smp_192110.1) Vesicular amine transporter (Smp_121920.1) Ca2+-activated K+ channel (Smp_161450.1) HSP73 (Smp_106930.1) Calmodulin (Smp_134500.1)

Neves et al. (2015)

Amiodarone Bromocriptine

Cancer Typanosomes Arrhythmia Parkinson’s disease Hyperprolactinemia Type-2 diabetes

Cruzainb (E.C. 3.4.22.51)

Bellera et al. (2013) and Martinez et al. (2015)

Odanacatib (human cathepsin K inhibitor)

Postmenopause Osteoporosis

Trypanosomes

Cruzain (E.C. 3.4.22.51) Molecular modeling

Ndao et al. (2014) and Sajid, Robertson, Brinen, & McKerrow (2011)

Trypanosomes

Cruzain (E.C. 3.4.22.51) Virtual screening

Bellera et al. (2014)

Trypanosomes Triclabendazole Fungal infection Sertaconazole Parasite infestation Leishmania Paroxetine Depression

Trypanothione synthetase

Modeling Virtual screening

Alberca et al. (2016)

Budlein A

Inflammation

Trypanosomes

Unknown

QSARc

Schmidt, Da Costa, Lopes, Kaiser, and Brun (2014)

Helenalin

Inflammation Cancer

Trypanosomatids Unknown

HQSARd

Schmidt, Nour, Khalid, Kaiser, and Brun (2009)

Lysine deacetylase inhibitors

Cancer Psychosis

Trypanosomatids Lysine deacetylase Helminths (KDAC)

Homology Wang et al. modeling (2015) Ligand docking

Levothyroxine Hypothyroidism

a

Repurposed Against

Schistosomes

Target

Computational Method Used References Molecular docking Molecular dynamics simulations

Modeling Virtual screening

Reviewed by Botta, Rivara, Zuliani, & Radi (2018)

African trypanosomiasis: Sleeping sickness caused by Trypanosoma brucei (Human African trypanosomiasis—HAT) and nagana (the animal form of sleeping sickness); American trypanosomiasis: Chagas’ disease, caused by T. cruzi. b Cruzain is the recombinant version of Cruzipain (truncated at C-terminal end) (Eakin, Mills, Harth, McKerrow, & Craik, 1992). c QSAR, quantitative structure-activity relationship. d Hologram QSAR.

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Several software tools are available to perform QSAR modeling, including: QSAR and Modelling Society (http://www.qsar.org/); Cheminformatics (http://www.cheminformatics.org/) (https://chembench.mml.unc.edu/); Multiple Linear Regression-QSAR: QSARINS (www. qsar.it); Partial Least Squares-QSAR: QSAR modeling (http://lqta.iqm.unicamp.br); 3D-QSAR: Open 3D-QSAR (http://open3dqsar.sourceforge.net); 4D-QSAR-LQTA-QSAR (http://lqta.iqm.unicamp.br); and VCCLab (www.vcclab.org). Some of the aforementioned software tools do not include molecular descriptor computing modules, but many are available: Dragon (www.vcclab.org/lab/edragon); Molconn-Z (www.edusoft-lc.com/molconn); Model (http://jing.cz3.nus.edu.sg/cgi-bin/model/model.cgi); PaDEL-descritor (http://padel.nus. edu.sg/software/padeldescriptor); Marvin (www.chemaxon.com); KNIME (www.knime. org); 3D-QSAR (https://www.3d-qsar.com/); DTC Lab tools (http://teqip.jduv.ac.in/ QSAR_Tools); other tools and support: (http://bigchem.eu/sites/default/files/Online2_ Tetko.pdf); (http://bidd.nus.edu.sg/BIDD-Databases/TTD/Reference1.pdf). A set of library compounds can be used to guide ligand-based virtual screening as structures for the training and generation of pharmacophore models (to be validated in the literature). Next, results from pharmacophore modeling are applied in virtual screening and database searching. Molecular similarities methods and computational pharmacophore modeling can use existing data to model and filter chemical entities as inputs for an in vitro assay. Computational 2D similarity has been applied to predict cross-reactivity in immunoassays (Krasowski, Siam, Iyer, & Ekins, 2009), and this same strategy can be applied to search for compounds having pharmacophore-feature similarities with known drugs, supporting the idea of cross-reactive compounds; when similar structures also share biological activity. Simple similarity analysis can be applied to large, publicly available compound libraries (see Part II Section 3.1) for comparison with existing drugs. Prediction of activity spectrum for substances (PASS) is an example of a method that can reveal the repurposing potential of drugs (Poroikov, Filimonov, Borodina, Lagunin, & Kos, 2000). Using the same similarity logic, molecular docking can be applied to ligands, searching for complementary shape and electrostatic interactions with known targets (Li, An, & Jones, 2006), followed by experimental validation. Another important feature of the 2D ligand-based approach is the use of biological information (e.g., proteome) to establish relationships between biological activity spectra and structure (Fliri, Loging, Thadeio, & Volkmann, 2005). Integration of biospectra, networks, and various databases, among others, can be useful not only to identify potential repurposing drugs, but to make inferences with respect to toxicity, off-target effects, and polypharmacology. A recent approach developed at Massachussetts Institute of Technology (MIT) was the development of a synthetic library of “xenoproteins” that consist of nonnatural amino acids (the stereoisomer levo-amino acid instead of the natural dextroamino acid) that are being screened to find chemotherapy alternatives against Ebola virus (Gates et al., 2018). Another class of methods for drug repurposing involves the use of a systematic biological/ pharmacological approach to assess transcriptional data for the identification of complementary gene-expression signatures. DrugSig (Wu, Huang, Zhong, & Huang, 2017), gene2drug (Napolitano et al., 2018), and DrugDiseaseNet (Peyvandipour, Saberian, Shafi, Donato, & Draghici, 2018) are examples of such tools. Despite considerable efforts to optimize pharmacokinetic and toxicological properties (Absorption, Distribution, Metabolism, Excretion, Toxicity; ADMET) of compounds in the early stages of drug development, safety and toxicity remain the principle causes of failure during clinical trials (Waring et al., 2015). 2. THEORETICAL BACKGROUND AND METHODOLOGIES

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Computational methods capable of predicting and optimizing these properties play an important role when repurposing lead compounds. Pires and co-workers have recently developed pkCSM (Pires, Blundell, & Ascher, 2015), a comprehensive platform for assessing the pharmacokinetics and toxicological properties of small molecules. This platform uses the concept of graph-based signatures (Pires et al., 2011) to train and test predictive models via supervised learning. It is available as a user-friendly web-server capable of predicting 30 different ADMET properties. Other interesting alternatives include admetSAR (Cheng et al., 2012) and SwissADME (Daina, Michielin, & Zoete, 2017). 3.2.2 Target-Based Approaches Target-based repurposing relies on the knowledge that a plethora of given drug-like chemicals could bind a specific protein target, driving the search for new applications for known drugs. Evolutionarily conserved targets support the fact that shared features lead to common active inhibitors amongst different organisms, guiding repositioning from a drug’s original purpose to another disease (Pollastri & Campbell, 2011). The main advantage of adopting target-based methods is that the target is known, allowing virtual structure screenings by either homology modeling or X-ray crystallography, and facilitating further mechanistic experimental validations (Klug, Gelb, & Pollastri, 2016). In this regard, the known target from a given pathogen can be selectively screened, avoiding side effects on human target homologues. For this reason, unique/exclusive pathogen targets are preferable when repurposing drug candidates against NIDs. High levels of amino acid-sequence identity between pathogen and host target reduces the chances of virtually identifying a specific inhibitor. However, the structure-based drug design (SBDD) approach overcomes this concern by considering physicochemical aspects, such as kinetics and structural features. This combined knowledge helps isolate pathogen enzyme-specific inhibitors (Larson et al., 2008). The democratization of massive sequencing technologies allows one to obtain fully sequenced genomes that, combined with systematic screening of drug-like molecules, can be used in a high-throughput manner to identify lead candidates; a strategy known as chemogenomics (Andrade et al., 2018; Pollastri & Campbell, 2011). A team headed by Dr. Carolina H. Andrade (Universidade Federal de Goias, Goiania, Brazil) was able to identify six potential already-marketed drug candidates with antifungal, antiallergic, antipsychotic, antibacterial, and antiarrhythmic properties with predictive antischistosomal activity (Table 4; Neves, Braga, Bezerra, Cravo, & Andrade, 2015). This predictive result was validated when previously reported repurposed antischistosomal drugs were found using the target-based chemogenomic strategy (Neves et al., 2015). Another target-based approach considers not only the specific target, but also the target class as well. In this case, the repurposed drug can act on a plethora of homologous target classes within the infectious agent. Even when a specific target is absent, the pathogen can maintain cellular function associated with a given target class. As such, phenotypic characterization is essential to this method. To illustrate: lapatinib is an anticancer drug that acts through human EGFR (epidermal growth factor receptor) tyrosine kinase inhibition, and is repurposed as an antrypanosomal agent (Patel et al., 2013). However, trypanosomatids do not possess any tyrosine kinase receptors (Naula, Parsons, & Mottram, 2005), although there is evidence of tyrosine phosphorylation. Imatinib, an anticancer drug used to treat chronic myelogenous and acute lymphocytic leukemia, acts by inhibiting Bcr-Abl tyrosine kinase,

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but can also stop Leishmania sp. proliferation in culture and stimulate CRK3 (cyclin dependent serine/threonine kinase, essential for normal cell cycle progression) expression (Rubens do Monte-Neto, personal communication). Thus, even though the mammalian target for lapatinib and imatinib is absent in Trypanosoma sp. and Leishmania sp., respectively, they are active through a target class yet to be described. A given therapeutic target can also be studied based on computational simulations, taking into consideration the physicochemical properties of atoms/molecules on molecular dynamics. One must take into account the idea of a system in constant dynamic evolution with interacting particles, bearing in mind their forces and potential energies. As such, structural insight can contribute to guide drug repurposing in a live, dynamic system aided by computational simulations. For example, molecular dynamics simulations have been applied to pinpoint an RNA-editing ligase of Trypanosoma brucei (TbREL1), an essential protein for parasite survival—and a promising target for structure-based drug design (Amaro, Swift, & McCammon, 2007). It is also worth noting that one must consider the in vivo system as an aqueous solvation and include free energies from ions and water in simulations. For example, Hoelz et al. (2016) brought insight on Nequimed176 (NEQ176)’s inhibition of cruzain, which, in aqueous solvent at pH 5.5, adopts a closed conformation in the presence of an inhibitor and when influenced by hydrogen-bonding interactions. Cruzain, the recomabinant version of cruzipain, is one of the main therapeutic targets from Trypanosoma cruzi considered for drug repurposing (Table 4). The wealth of structural information accumulated over the years, as well as advances in comparative homology modeling, have enabled the elaboration of computational methods to assist the drug development process. These methods have helped elucidate mechanisms of binding and action of drugs, as is demonstrated in the work of Dror et al. (2011) on GPCR drugs via molecular dynamics. These methods have also helped in the further understanding of the molecular effects of mutations leading to resistance (Phelan et al., 2016). When a potential target of interest lacks an experimentally solved structure, comparative modeling can be used if the structure of a homologue is known. The two most widely used tools are Modeller (Sali & Blundell, 1993), developed and maintained by Andrej Sali, and SWISS-MODEL (Arnold, Bordoli, Kopp, & Schwede, 2006), a web-based alternative that not only permits the modeling process (including the search for homologues with known structures), but also encompasses a proteomic scale repository of homology models (over 1.4 million) for different organisms. Once a structure is available (either experimentally, or modeled via comparative modeling), identifying druggable (Volkamer, Kuhn, Rippmann, & Rarey, 2012) sites or pockets is an important step prior to performing structure-based virtual screening for hit identification. A widely used open-source tool for pocket detection is Fpocket (Le Guilloux, Schmidtke, & Tuffery, 2009). Fpocket’s method is based on clustering vertices from Voronoi tessellations and can be run on a large scale. It also provides feature characterization and ranking of pockets. An interesting alternative is Ghecom (Kawabata, 2010), a web-based, grid-based method that identifies pockets by placing probe spheres of different radii on protein surfaces and defining the pocket as a “space into which a small spherical probe can enter, but a large probe cannot” (Kawabata, 2010). In this way, the tool not only defines the pocket region, but also characterizes it by depth, a feature that can be used for prioritization.

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Docking is one of the most common computational techniques for structure-based virtual screening of compounds. Given a protein pocket (usually considered a rigid body) and a compound (with its rotatable bonds), docking aims to identify the preferred compound pose (position and orientation) that optimizes interactions to form a stable complex. The two main components of docking algorithms are (1) a search algorithm that tests possible conformations, and (2) a scoring function that evaluates and ranks the optimal poses. Recent efforts have been focused on proposing new and better scoring functions (Pires & Ascher, 2016; Wojcikowski, Ballester, & Siedlecki, 2017). Several docking methods are freely available and allow for automated screening of compounds. Web-based services for protein-ligand docking include SwissDock (Grosdidier, Zoete, & Michielin, 2011), DockingServer (Bikadi & Hazai, 2009), and DOCK Blaster (Irwin et al., 2009), while more expert systems that allow for advanced parametrization include, amongst others, rDock (Ruiz-Carmona et al., 2014), EADock (Grosdidier, Zoete, & Michielin, 2007), and, the most widely used, Autodock Vina (Trott & Olson, 2010). Machine learning-based methods are a scalable alternative to docking for identification of lead compounds. They are usually based on extracting features from protein pockets to predict which ligands are most likely to bind. These could range from small molecules (Pires, de Melo-Minardi, da Silveira, Campos, & Meira Jr., 2013) to fragments (Tang & Altman, 2014). Another important aspect during drug development is predicting effects of mutations on protein-small molecule affinity as a means of understanding and anticipating drug resistance. Aside from computationally intensive methods, such as molecular dynamics, a web-based approach, mCSM-lig (Pires, Blundell, & Ascher, 2016), is capable of quantitatively predicting effects of missense mutations in protein-ligand affinity complexes, aiding in the identification of potential resistance hot-spots in protein pockets, which could then be preemptively avoided during hit optimization.

3.3 Integration of Databases and Computational Tools for Drug Repurposing in Neglected Infectious Diseases Lara-Ramirez et al. (2017) in a recent work have applied a computational drug repositioning method based on molecular docking to identify new inhibitors for the Trypanosoma cruzi trans-sialidase. A structure-based virtual screening using Autodock Vina was performed for 3180 FDA-approved drugs that were collected from the ZINC database. The compounds with best poses were analyzed regarding their physicochemical properties and most of them complied with Lipinkski’s rule of five. From this pool, seven compounds were selected for experimental validation in vitro and in vivo and four drugs showed trypanocidal effects in the range of 75%–100%, showing how integration of computational resources (databases and tools) can effectively assist drug repositioning in NIDs.

4 PERSPECTIVES AND CONCLUDING REMARKS Massive efforts are still based on direct in vitro, FDA-approved drug screenings (Ekins et al., 2011; Zheng et al., 2018); however, rational computer-aided methods must be included

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in the drug-discovery pipeline for NIDs. There is no reason to invest in trial-and-error methods without prior computational insight. We understand that specialized human resources represent a bottleneck in this field, and computer scientists should be endorsed. Although computer-assisted methods followed by experimental validation sounds like a logical pathway in drug discovery and development (DDD), this workflow is less frequent than direct in vitro drug screening without previous computational analysis—evidence that computer scientists and a multidisciplinary team are necessary to accelerate the drug repositioning pipeline. Predictive computational methods are essential to increase success rates at the experimental validation point. Collaborative initiatives must be supported to bring together efforts from academia, research institutes, and the pharmaceutical industry, and to stimulate private and governmental translational programs aimed at funding solutions for more effective treatments. For obvious reasons many CADD studies focused on drug repurposing are dedicated to Chagas disease, sleeping sickness, schistosomiasis, and leishmaniasis, but little or no attention is dedicated to diseases such as dracunculiasis, fascioliasis, yaws, echinococcosis or river blindness, thus neglecting the neglected. Efforts are still needed to cover, at least computationally, a wide spectrum of NIDs and unearth the lead compounds to be experimentally validated. Among the CADD methods, virtual screening, QSAR (ligand-based), and molecular dynamics simulations (target-based) are broadly adopted as the main strategies to guide drug repurposing against NIDs. Predictive ADMET or the combination of different approaches, including in-house strategies, are not common for already-marketed drugs. This differs when testing new small molecule compound libraries. Many studies in medicinal chemistry have resulted in innovative computational solutions to identify novel uses for old drugs. These types of solutions should be encouraged, and a computer-aided repurposing pipeline must be supported not only by academics, but also by policy-makers and public health programs. Closing this chapter, it is worth highlighting that, for all infectious diseases, drug development is an intricate process rather than the linear pipeline often pictured (Baxter et al., 2013). Drug repurposing provides “shortcuts” in the process, because: (1) we can work on previously known “druggable” targets; and (2) we can rely on massive amounts of already available high-quality data that can be used and presented to the regulatory authorities (Ekins et al., 2011). Consequently, the overall R&D process of bringing a new drug to market becomes more time- and cost-effective when compared to traditional de novo drug discovery pipelines (Fig. 2). Several examples for NIDs have been described in this chapter, including compounds with leishmanicidal, antitrypanosomal, or anti-Chagas disease activity. However, despite that, drug repurposing potentially provides faster and cheaper drug-discovery pipelines (especially for NIDs where time, resources, and information are scarce), the whole process is limited by the fact that the drug must be capable of repositioning against a precise NID, which is not always possible. Consequently, drug repurposing and the de novo development of novel drugs must be considered two independent but complementary approaches to achieve novel and effective therapeutic agents against NIDs. This is especially important for pharmaceutical companies beginning to realize that repositioning can effectively extend the market for a compound (additional commercial value) and bring novel applications for orphan and shelved compounds at a very interesting level of financial risk. In fact, as the concept of market is

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FIG. 2

A comparison of traditional de novo drug discovery and development versus drug repurposing. The de novo drug discovery and development process is likely to last at least 10–17 years from the initial idea to a marketed product (thick-line path). The probability of success is lower than 10%. Drug repositioning has many benefits, mainly lower cost and a shorter time before marketing the drug (thin-line path). Repurposing approved drugs helps avoid problems during clinical trials, such as drug toxicity or unfavorable pharmacokinetics.

essential for the companies producing drugs, different governmental agencies have tried to find and implement various vehicles to increase the interest of pharmaceutical companies in working on drug repurposing for NIDs. Tax credits and voucher schemes have been attempted as incentives in recent years (Croft, 2016). However, a voucher-triggered review aiming to detect possible pitfalls found little evidence that vouchers for NIDs were achieving their public health aims (Mullard, 2015). Additionally, in a recently published paper, Prof. Simon Croft, first R&D Director of DNDi (from 2008 to 2014), pointed to two major aspects that should not be forgotten when addressing the problem of drug development/ repurposing in NIDs: the need to both generate high-quality data in many endemic countries and effectively increase the engagement of endemic countries in the R&D process (Croft, 2016). MDA programs have been proposed as a solution to tackle NIDs [e.g., MDA for schistosomiasis (Wang & Liang, 2015)]. However, the MDA approach is controversial as it risks exacerbating the already-prevalent problem of drug/multidrug resistance observed for many pathogens. Particularly in the NIDs domain there is a worrying increase in the number of drug/multidrug-resistant strains, which urges the rapid identification and marketing of novel therapeutic agents, thus hindering the implementation of effective MDA programs. A difficult but refined option to slow the potential spread of drug resistance through

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MDA would be the implementation of mass screening and treatment (MSAT) approaches, whereby drugs are administered only to NID-diagnosed individuals. Nonetheless, to achieve durable MSAT we require effective, affordable drugs against both wild-type and drugresistant strains, in addition to better advocacy coupled with enhanced scientific and public health programs. Consequently, the issue of drug resistance presents new opportunities and challenges in terms of drug repurposing.

Acknowledgments Authors would like to thank Victoria Wagner for her assistance in editing the manuscript. C. Ferna´ndez-Prada is supported by a Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grant (RGPIN-2017-04480).

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Further Reading Martinez-Mayorga, K., Byler, K. G., Ramirez-Hernandez, A. I., & Terrazas-Alvares, D. E. (2015). Cruzain inhibitors: efforts made, current leads and a structural outlook of new hits. Drug Discovery Today, 20(7), 890–898. https://doi. org/10.1016/j.drudis.2015.02.004. WHO. WHO/The top 10 causes of death. World Health Organization, May 2018. Retrieved from http://www.who.int/ mediacentre/factsheets/fs310/en/index2.html.

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C H A P T E R

6 Molecular Docking: A Structure-Based Approach for Drug Repurposing Shivani Kumar, Suresh Kumar University School of Biotechnology, GGS Indraprastha University, Dwarka, New Delhi, India

1 INTRODUCTION Many novel therapeutic targets (enzymes, receptors, transport proteins, and nucleic acids) have been identified in recently due to advancements in biomolecular methods like crystallographic and nuclear magnetic resonance (NMR) techniques, which contribute to the details of the atomic structure of the protein as well as the protein-ligands complexes (Ferreira, dos Santos, Oliva, & Andricopulo, 2015). To study the structures of these complexes, in silico tools have become increasingly significant to correspond with the in vitro and in vivo experimental results. Today, all the aspects of drug discovery, i.e., virtual screening (VS) for hit identification and lead optimization, are mainly due to advancements in computational methods. Virtual screening, specifically molecular docking, is comparatively more rational as well as direct drug discovery approach compared to the experimental high-throughput screening method (Meng, Zhang, Mezei, & Cui, 2011). For VS, molecular docking methods are the most commonly used approach followed by pharmacophore development. Molecular docking is a modern bioinformatics tool used to help in understanding the molecular interactions between the ligand and a target, as it predicts the probable experimental orientation as well as the binding affinity needed to form a stable complex structure (Guedes, de Magalha˜es, & Dardenne, 2014). This method is widely used for the identification of novel inhibitors and already known drugs (drug repurposing) for the target of interest. Drug repositioning, repurposing, redirecting, or reprofiling is a process of discovering and developing new uses for existing drugs other than their original medical indication (Ashburn & Thor, 2004). This process is believed to be advantageous over traditional de-novo drug discovery in that it reduces the developmental risks and saves time and money (Kim, 2015). Structure-based VS is one of the approaches used for drug repurposing. The most commonly used tool in

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thestructure-based approach to drug discovery is molecular docking, which uses the target and ligand structure to predict the lead compound or repurpose the drugs for therapeutic use.

2 MOLECULAR DOCKING In pharmaceutical and biomedical research, the mechanism of the drug-target complex at molecular level becomes crucially important (de Ruyck, Brysbaert, Blossey, & Lensink, 2016). Molecular docking studies are essentially used to predict the binding affinity, preferred binding pose, and interaction of the ligand-receptor complex with minimum free energy. Proteinligand, protein-nucleotide, and protein-protein interactions (PPIs) are all possible in docking studies. There can be various types of noncovalent interactions, such as ionic bond, hydrogen bond, and van der Waals (Tripathi & Misra, 2017).

2.1 Basic Requirements for Molecular Docking The basic requirements for docking studies include the structural information of the target as well as of the ligand and computational support. In the case of a protein, if the structure is known, this information is provided by X-ray crystallographic or NMR techniques, and if the structure is unknown, homology modeling plays an important role. The structure of ligands can be designed or library of compounds can be used (Fig. 1A). Protein is generally considered to be rigid in most docking algorithms and the ligand to be flexible. Binding pose in the receptor’s binding pocket is also considered alongside the conformational degree of freedom. 2.1.1 Structural Data 2.1.1.1 LIGAND REPRESENTATION

First of all, a neutral pH is generated, which is considered dominant for the structure. Specific chirality is retained and low energy-ring conformers are generated. Further, hydrogens are added or removed to approximate the pKa values. The selection of an accurate atom typing is one of the major steps, as the wrong acceptor or donor type may lead to error in docking methodology. Then the various tautomeric forms of the specific atoms are generated. For the ligand to be selected, the main criteria is Lipinski’s rule, i.e., its molecular weight should not be above 500 Da, it should not contain more than five hydrogen bonds and ten hydrogen bond acceptors, and its log P should not be above five (Lipinski, Lombardo, Dominy, & Feeney, 2001). 2.1.1.2 RECEPTOR REPRESENTATION

The main determinant of docking calculations is the quality of protein structure. Generally, the crystal structure is considered better if it has a higher resolution. For this, various steps are done to prepare the protein receptor, i.e., bond orders are assigned, hydrogens are added, extra water molecules are added (which were not considered important for protein’s function), heteroatoms (if any) are deleted, and disulfide bonds are created, as necessary. It is then

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2 MOLECULAR DOCKING Identification of target Threading/ homology modelling is used to predict the structure

Three dimensional structure available

De - novoligand design

Virtual compound library

Optimization of structure Optimization of structure Binding site of target Compound structure library Target structure

Dock and score compounds

Optimization of docked complex

Rank the compound and hits were taken

Experimental trials

Results were analysed

(A)

F H N

O

+

N N

F

Ligand structure (B)

Protein structure

Ligand-protein interaction

FIG. 1 (A) Use of molecular docking methodology in the drug discovery program and (B) structure of protein along with ligand structure used to study ligand-protein interaction (Kumar, Chowdhury, & Kumar, 2017).

optimized and minimized to be used for further docking studies. After the receptor is minimized, the active site within the protein is identified through either software or the co-crystallized structure. The receptor can have one or more site of interest; one site should be used for docking (Fig. 1B). 2.1.1.3 ROLE OF WATER IN DOCKING STUDIES

Generally, a protein molecule is surrounded by water molecules forming a hydration shell. They make important contributions to catalyst, substrate, and molecular recognition and are

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responsible for electrostatic screening (de Ruyck et al., 2016). All solvent molecules, other small molecular ligands and ions are removed as a general adopted practice. This is a simpler approach but it leads to disagreement between the physiological conditions and the groove circumstances for docking. Strong hydrogen bonding is observed with some of the water molecules that bridge the ligand-receptor binding, which are difficult to replace, otherwise it will give false-positive results. On the other hand, the binding conformation and affinity will be affected as the entire space between the protein and the ligand may be occupied by water molecules. In this reference, to evaluate its energetics and simulate water molecules many attempts have been made. However, there are two main challenges to solving this problem: first predicting how the ligand affects the water molecule as there is no reliable theoretical approach; and second, the lack of coordinate information for the hydrogen atoms or oxygen atoms of the water molecules in the X-ray crystal structure due to limited resolution. So, it is impossible to estimate the number of water molecules in the groove that would be replaced by potential ligands, and the ligand binding would disturb the entire hydrogen bonding network. A similar challenge arises for endogenous ligands and/or counterpart ions in the binding pocket of the protein (Michel & Essex, 2010; Wang & Zhu, 2016).

2.2 Types of Docking Methodology The docking methodologies can be categorized in the following ways: 1. Rigid ligand and rigid receptor docking: Both the receptor molecule and ligand have internal geometry that is rigid/fixed and shows tight binding with each other. 3D complementarity is defined by this method. An example of this type involves the early version of DOCK, FLOG, and some of protein-protein docking software FTDOCK. 2. Flexible ligand and rigid receptor docking: As the name suggests, here the ligand is flexible and receptor molecule is kept rigid for docking. Here there is a trade off between computation time and accuracy. Common examples of this type include Autodock, FlexX. 3. Flexible ligand and flexible receptor docking: On the basis of induced fit docking, both the receptor and ligand are conformationally flexible to maximize the bonding forces between them. In every rotation, the energy and the surface cell occupancy are calculated; then the most favorable pose is selected. This also shows the intrinsic behavior of protein with ligand binding through molecular dynamics (MD) but it involves high computation cost, hence it does not allow screening of large chemical databases. As the flexibility increases, the system becomes more complex and slower. 4. Ensemble docking: This approach focuses on complexity and flexibility of conformation of proteins that correlate with the theory of conformer selection. It uses multiple rigid protein structures that are separately docked to the ligand, and the results are combined based on the method of choice. DOCK provides ensemble docking, which generates an average potential energy grid and used in different ways. 5. Hybrid method: This is a strategy that also considers receptor flexibility. It uses various methods together for docking, hence the name hybrid method. Examples are Glide, IFREDA, QXP, and Affinity (Chaudhary & Mishra, 2016).

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2.3 Types of Interactions Interactions between a ligand and a receptor molecule can be explained as an outcome of forces between the particles, which are mainly of four types: 1. Electrostatic forces: Forces due to charged entities residing in matter. The most common examples are dipole-dipole, charge-dipole, and charge-charge. 2. Electrodynamics forces: These are mainly van der Waals interaction. 3. Steric forces: These interactions are generated when atoms of different particles come into close proximity to each other and affect the reactivity of one another. The free energy system and chemical reactions are affected by the resultant forces. This is generally caused by entropy. a. Solvent-related forces: The forces generated between the receptor protein or ligand and solvent due to chemical reactions are solvent-related forces. Hydrophobic interactions and hydrophilic interactions (hydrogen bonds) are examples of this category. b. Other physical factors: Other changes that lead to further conformational changes in the ligand as well as the protein are often considered significant for docking studies (Agarwal & Mehrotra, 2016). Recent studies have shown that the irreversible inhibitor of the protein target forms a covalent bond. It provides a high level of selectivity and potency as it forms strong bonds between a nucleophile (protein) and an electrophile (ligand) (Kumalo, Bhakat, & Soliman, 2015). As covalent inhibitors have high affinity towards their molecular targets, longlasting pharmacological responses are observed (Li, Zhang, & Cao, 2013). Examples of Food and Drug Administration (FDA) approved covalent drugs are warfarin, isoniazid, aspirin, azacytidin, rivastigmine, etc. This covalent bonding concept can be used for lead optimization, VS, pharmacophore studies, and MD simulations ( Johnson, Weerapana, & Cravatt, 2010). Covalent drugs have well-known drawbacks like a lack of specificity, high reactivity, and toxicity, which have led most pharmaceutical companies to avoid such compounds (Bianco, Forli, Goodsell, & Olson, 2016).

2.4 Approaches of Molecular Docking There are two approaches in particular that are used for molecular docking, i.e., the shape complementarity approach and the simulation approach. The former uses the ligand and protein surface complementarity while the latter uses a simulation process where protein-ligand pair-binding energies are calculated (Li et al., 2013). Both approaches are defined below. 2.4.1 Shape Complementarity Approach The key concept of molecular recognition is complementarity. Shape complementarity/ geometric matching methods employ the ligand’s and protein’s molecular surface/complementary surface features that are used for docking (Kuroda & Gray, 2016). The ligand surface is shown to the matching surface with respect to the receptor molecular surface is shown in terms of solvent-accessible surface area illustration (Morreale, Gil-Redondo, & Ortiz, 2007). Shape-matching descriptors help in understanding the complementarity

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between two surfaces, which helps in identifying the complementary groove for the ligand on the receptor’s surface (Axenopoulos, Daras, Papadopoulos, & Houstis, 2011). Another area of interest is estimating the hydrophobicity of the receptor molecule using the number of turns in the main chain atoms. The shape complementarity approach is comparatively quicker and, hence, several thousands of ligands are scanned in a few seconds to discover the binding properties of the ligands on the molecular target surface. As they use geometric descriptors to find the optimum binding energy of ligands, this approach can also be helpful in pharmacophore development (Axenopoulos et al., 2011; Morreale et al., 2007). 2.4.2 Simulation Approach This approach is a comparatively complicated process. In the simulation approach, the ligand and the receptor are segregated by a physical distance and the ligand is then allowed to bind to its receptor’s active site after a definite number of moves in its conformational space. These moves incorporate various changes in the structure of ligands through external (rotations and translations) or internal (torsional angle rotations) ways. With each of the moves of the ligand, the total energetic cost of the system is induced; hence, the total energy of the system is calculated after every move. In this approach, the ligand’s flexibility is also taken into account, therefore this method has advantages over shape complementarity. In addition, it is more accurate to judge the molecular recognition between receptor and target. Since, they have to transverse a larger energy landscape, it takes a longer time to assess the optimal orientation. Nevertheless, recent techniques, like fast optimization method and grid-based techniques, have significantly helped in overcoming this problem (Alonso, Bliznyuk, & Gready, 2006).

2.5 Mechanism of Docking Docking is performed via two interrelated steps, i.e., sampling conformation of the ligand in the receptor groove and then ranking these conformations through scoring function. The experimental binding mode is reproduced by the sampling algorithms and is ranked by a scoring function (Novicˇ, Tibaut, Anderluh, Borisˇek, & Tomasˇicˇ, 2016). The accomplishment of a docking program depends upon these two perspectives. 2.5.1 Search Algorithms A search algorithm consists of all the possible conformations and orientations of a ligand binding with a receptor protein (Halperin, Ma, Wolfson, & Nussinov, 2002). Search algorithms are used to explore the free energy involved in determining the best ligand pose. Experimental ligand-receptor conformation, i.e., the native binding mode, will correspond to the global minima of energy, if the thermodynamic system, i.e., entropy and enthalpy, is correctly modeled and an alternative binding orientation is given by the local minima. Current docking procedures involve approximations, as entropy does not have straightforward

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effects. Hence, there is no guarantee that the native binding orientation will correspond to the global minima. Docking methods employ three main strategies: ➢ When the receptor structure is considered rigid, then the rotational and translational degree of freedom of ligands are considered, due to which the ligand is also considered rigid and, hence, there is no internal degree of freedom. ➢ When the receptor structure is rigid and all degrees of freedom of ligand are explored, i.e., rotational, conformation, and translational. ➢ Lastly, the receptor structure is partially or totally flexible and all the degrees of freedom are also explored. Most of the docking programs uses flexible ligands, and several are attempting to model flexible receptor proteins (Brooijmans & Kuntz, 2003). The stochastic algorithm is one where the search is carried out by modifying the ligand conformation or population of ligands (Meng et al., 2011), whereas the systematic method uses all the available combinations of structural information (Ferreira et al., 2015). Docking analysis uses different types of algorithms, which are listed below: ➢ ➢ ➢ ➢ ➢ ➢ ➢

Monte Carlo methods Molecular dynamics Genetic algorithms Point complementary methods Fragment-based methods Systematic searches Distance geometry methods. The first three are stochastic types of algorithm (Meng et al., 2011; Table 1). TABLE 1 Examples of Conformational Search Algorithms Systematic Search

Stochastic/Random Search

GLIDE

AutoDock

FRED

Gold

DOCK

Molegro Virtual Docker

eHiTS

PRO_LEADS

SLIDE

MOE_Dock

Surflex-Dock

ICM

FlexX

MolDock

Flog

LigandFit

EUDOC

EADock

Hammerhead

PLANTS

ADAM

GlamDock CDocker

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2.5.2 Scoring Functions Scoring function is a mathematical method used to prognosticate the binding affinity and strength of the noncovalent interactions of receptor-ligand complex (Ma, Chan, & Leung, 2013). By various assumptions and simplifications, instead of calculating, the scoring function approximates the binding affinity between the docked complexes. The method has also been developed to estimate the strength of other different types of intermolecular interactions, such as protein-protein, protein-drug, or protein-DNA complexes. Scoring functions can be divided into three categories: force field-based, empirical, and knowledge-based scoring functions (Pham & Jain, 2008). 2.5.2.1 FORCE FIELD-BASED SCORING FUNCTION

It is derived from the classical force field, and it evaluates the binding energy as a sum of nonbonded (van der Waals and electrostatic) interactions. Columbic formulations are used to calculate the electrostatic terms, whereas the Lennard-Jones potential function is used for van der Waals terms (Huang, Grinter, & Zou, 2010). At short distances the strong repulsion gives rise to atomic clashes, which is regarded as one of the main problems of force field-based scoring functions. Therefore to handle nonbonded interactions, a cut-off distance is used. Slow computational speed is another problem associated with this type of scoring function, hence decreasing the accuracy of long-range effect. Examples of docking software that uses forcefield scoring function are GOLD, AutoDock, and DockThor (Guedes et al., 2014). 2.5.2.2 EMPIRICAL-BASED SCORING FUNCTION

The empirical-based scoring function has been developed to replicate the experimental binding affinity with higher accuracy (Liu & Wang, 2015). This scoring function is a weighted sum of various types of interactions between receptor and ligand. In this function, binding energy can be split into various energy terms like binding entropy, hydrogen bond, and van der Waals interactions (Meng et al., 2011) and it can be written as: ΔGbinding ¼ c1 ΔGvdw + c2 ΔGHbond + c3 ΔGentropy where c1 terms are weighted coefficient obtained from respective terms (Guedes et al., 2014). The coefficients of various types of protein-ligand interactions can generally include apolar interactions like lipophilic and aromatic interactions and polar interactions like electrostatic and hydrogen bonding, which are optimized by fitting the binding energy and structure of the training set into the regression model (Ma et al., 2013). Examples of this type of scoring function include GlideScore, ChemScore, PLANTSCHEMPLP, and ID-Score (Wang, Lu, & Wang, 2003). 2.5.2.3 KNOWLEDGE-BASED SCORING FUNCTION

In this function, the structural information of known ligand-receptor complexes is collected and statistically analyzed to obtain the distance between the receptor protein and ligand and/or interatomic contact frequencies (Meng et al., 2011; Wang et al., 2003). The mean-force potential is the pseudopotential that defines the preferred geometry of the pairwise atom data. The specific chemical-physical complexity of the ligand-protein complex

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is captured using this strategy. Therefore this perspective is highly dependent on the diversity and number of experimental structures used (Guedes et al., 2014). This scoring function is calculated by penalizing the repulsive interaction and favoring preferred contact between each of the atoms in the protein and ligand within a given cut off (Guedes et al., 2014). Examples of this scoring function include PMF and DrugScore (Wang et al., 2003). The important point for all molecular docking studies is that no universal scoring function exists. To enhance the reliability and efficiency of docking studies, the strategy is to identify and use the functions best adapted to the problem. It is necessary to know whether all atom types of both protein and ligand are set in the scoring function applied. In this case, to overcome the deficiencies of the single-scoring function, consensus scoring is used. To increase the performance of both binding energy estimation and pose prediction, the main goal of this approach is to use more than one assessment through a consensus scheme. This approach makes use of a distinct scoring function to assess the ligands using results obtained through different docking studies from distinct softwares (Kitchen, Decornez, Furr, & Bajorath, 2004). 2.5.2.4 TARGET-SPECIFIC SCORING FUNCTION

This function has emerged as another approach for the improved prediction of experimental binding energy. A specific target family is the main focus for the development and parameterization of this type of scoring function. Hence, as far as a target class of interest is concerned, it is expected that this scoring function will be more effective (Kroemer, 2007). 2.5.3 Estimating Binding Affinity With Scoring Functions To identify near-native and native binding modes, scoring functions were developed. During the analytical process, affinity data is not considered for this type of scoring function and it plays a role in bearing the feature in mind prior to docking experiment. Both GoldScore and DockThor are examples of such a scoring function being used with the aim of pose prediction when no experimental binding affinity data is considered (Meng et al., 2011). 2.5.4 Exploring the Energy Landscape in Docking The affinity of binding of ligands with receptors are separated by different energy barriers. The lowest energy pose represents the native binding mode, whereas the local minimum energy pose represents the nonnative conformations. Generally, to obtain the native binding mode (single lowest energy conformer), docking studies are conducted. 2.5.5 Protein Flexibility and Binding Affinity Prediction VS and pose prediction give satisfactory results; however, affinity prediction still remains a challenge. As mentioned earlier, most docking software contemplates protein as a rigid body. Pose evaluation is a significant strategy for better affinity prediction when both proteins and ligands are flexible (Tuffery & Derreumaux, 2012).

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2.6 Postdocking Analysis In identification of potential drug hits through VS, scoring function plays a significant role. But, a major limitation of current scoring functions is the inability to predict accurate binding energy, which compromises the quality of docking score. For the time being, visual examination continues to be useful in post-docking analysis; however, as the sampling size increases the technique becomes proportionately less efficient. In recent years, to eliminate false positives, a number of methods have been designed to ameliorate the hit rate in following in vitro assay. Molecular interaction fingerprints (IFPs) are small bits that convert 3D information on ligand-protein interactions into 1D vector representations. These can further be compared through conventional methods to prioritize the most pertinent orientation of molecular scaffolds or fragments in order to improve the precision of docking (Marcou & Rognan, 2007). Another strategy is to rank the docked poses by inspecting the interatomic interactions between the binding site of biomolecular receptor and the ligand. The study showed that this approach differentiates active compounds from decoys using contact footprint clustering techniques (Bouvier, Evrard-Todeschi, Girault, & Bertho, 2009). Another study reported that a binding energy landscape analysis can be used to differentiate true positives from decoys. This approach mainly focuses on two aspects: the number of local binding wells in the landscape and the energy gap, this information reflects the thermodynamic stability and energy penetrability of the binding energy landscape (Terp, Johansen, Christensen, & Jørgensen, 2001).

2.7 Applications of Molecular Docking Molecular docking studies can reveal the practicability of any enzymatic reaction; hence, it plays an important role in various applications related to in silico drug design. The inhibition or activation of enzymes is the result of the binding interactions of small molecules. The applications of molecular docking are as follows: ➢ Hit identification Large databases are evaluated using docking with a scoring function for identifying an in silico potential drug candidate, against the target of interest (Ferreira et al., 2015). ➢ Lead optimization An optimized orientation of a ligand may be predicted by docking studies. As it predicts the various binding modes of a ligand in the binding pocket of the protein, more selective, efficient, and potent drug candidates are developed (Shoichet, McGovern, Wei, & Irwin, 2002). ➢ Bioremediation Ligand-protein complex can be used to predict enzymes degrading pollutants. ➢ ➢ ➢ ➢ ➢ ➢

DNA-drug interactions Binding site prediction De-orphaning of protein Protein-protein/nucleic acid interactions Studies of structure-function Searching for lead structures for protein targets 2. THEORETICAL BACKGROUND AND METHODOLOGIES

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➢ Mechanisms of enzymatic reactions ➢ Protein engineering.

2.8 Limitations of Molecular Docking Despite the significant achievements it has obtained, the full potential of molecular docking is yet to be exploited. The current challenges of molecular docking studies are (David, Nielsen, Hedstrom, & Norden, 2005): ➢ A more effective scoring function is needed ➢ There is a trade-off between accuracy and efficiency ➢ A better model of flexibility is needed.

2.9 Available Software for Docking Some of the docking scores presented in a chronological order are shown in Table 2. A comparison of advantages and disadvantages of a few of the previously mentioned software is listed in Table 3 (Kellenberger, Rodrigo, Muller, & Rognan, 2004). TABLE 2

Available Docking Software

Docking S. No. Software

Published Year

1.

AADS

2.

AutoDock

3.

Description

Licence/Web Service

References

2011

Automated active site detection, docking, and score (AADS) used for protein having known structure based on Monte Carlo method

Free to use online

Singh, Biswas, and Jayaram (2011)

1990

Automated docking of ligand to protein structure by Lamarckian Genetic algorithm and empirical free energy scoring function

Freeware, no web server available

Goodsell, Morris, and Olson (1996)

AutoDockVina 2010

New version of AutoDock

Open source, no web server available

Morris et al. (2009)

4.

Blaster

2009

Combines DOCK with ZINC Freeware, no web databases to find out ligand to server available target of interest

Irwin et al. (2009)

5.

DOCK

1988

AMBER-type potential function Academic licence is and genetic algorithm free, no web server available

Ewing et al. (2001)

6.

DockingServer 2009

As the name suggest, it integrates a number of computational chemistry software

7.

DockVision

Genetic algorithm, Monte Carlo Commercial software, Hart and Read based and for database no web server available (1992) screening

1992

Commercial software, Bikadi and Hazai no web server available (2009)

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TABLE 2 Available Docking Software—cont’d Docking S. No. Software

Published Year

Description

Licence/Web Service

8.

eHITS

2006

Exhausted search algorithm

Commercial software, Zsoldos, Reid, no web server available Simon, Sadjad, and Johnson (2007)

9.

FlexX

2001

Based on incremental build

Commercial software, Rarey et al. (1996) no web server available

10.

FLIPDock

2007

Docking program based on genetic algorithm represents ligand-protein complex using FlexTree data

Free for academic use, Zhao and Sanner no web server available (2007)

11.

FLOG

1994

Rigid body docking using pregenerated conformation database

Academic licence, no web server available

12.

FRED

2003

Exhaustive, nonstochastic, Free for academic use, McGann (2012) systematic examination of all no web server available possible orientation with protein binding pocket combined with scoring function

13.

GEMDOCK

2004

Molecular docking uses generic Freeware, no web evolutionary method server available

14.

Glide

2004

Docking based on exhaustive search

Commercial licence, no Friesner et al. (2004) web server available

15.

GOLD

1995

Partial flexibility for protein, flexible ligand, genetic algorithm based

Commercial licence, no Jones, Willett, Glen, web server available Leach, and Taylor (1997)

16.

HADDOCK

2003

Mainly developed for protein- Freeware, web server protein docking but can also be available used for ligand-protein ligand

Dominguez, Boelens, and Bonvin (2003)

17.

Hammerhead

1996

Fully automated docking of protein binding site to the flexible ligand

Academic licence, no web server available

Welch, Ruppert, and Jain (1996)

18.

ICM

1994

Pseudo-Brownian sampling base docking program

Commercial licence, no Abagyan, Totrov, web server available and Kuznetsov (1994)

19.

LigandFit

2003

Docking program based on CHARMm

Commercial licence, no Venkatachalam, web server available Jiang, Oldfield, and Waldman (2003)

20.

LigDockCSA

2011

Ligand-protein docking Academic licence, no program using conformational web server available space annealing

21.

LIGIN

1996

Surface complementarity based Commercial licence, no Sobolev, Wade, docking software web server available Vriend, and Edelman (1996)

2. THEORETICAL BACKGROUND AND METHODOLOGIES

References

Miller, Kearsley, Underwood, and Sheridan (1994)

Yang and Chen (2004)

Shin et al. (2011)

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TABLE 2

Available Docking Software—cont’d

Docking S. No. Software

Published Year

22.

MCDOCK

23.

Description

Licence/Web Service

References

1999

Nonconventional Monte Carlo simulation technique-based docking program

Freeware, no web server available

Liu and Wang (1999)

MEDock

2005

Web server based on Freeware maximum-entropy docking at web server available providing an efficient utility for prediction of binding site

24.

Molecular operating environment (MOE)

2008

Docking application within MOE

Commercial licence, no Vilar, Cozza, and web server available Moro (2008)

25.

MolDock

2006

Heuristic based search algorithm that combines differential evolution with pocket prediction algorithm

Academic licence, no web server available

Thomsen and Christensen (2006)

26.

MOLS 2.0

2016

Rigid small molecule-protein docking, flexible proteinpeptide interaction

Open source, no web server available

Paul and Gautham (2016)

27.

MS-DOCK

2008

Multistage scoring/docking protocol

Academic licence, no web server available

Sauton, Lagorce, Villoutreix, and Miteva (2008)

28.

ParDock

2007

Monte Carlo based all-atom energy, rigid protein docking

Freeware, web server available

Gupta, Gandhimathi, Sharma, and Jayaram (2007)

29.

PatchDock

2002

The algorithm carries out rigid Freeware, web server docking, with surface available flexibility/variability implicitly addressed through liberal intermolecular penetration

30.

PLANTS

2006

Stochastic optimization algorithm based

Free for academic use, Korb, Stutzle, and no web server available Exner (2009)

31.

PRODOCK

1999

Monte Carlo-method based plus energy minimization

Academic licence, no web server available

Trosset and Scheraga (1999)

32.

PSI-DOCK

2006

Pose-sensitive inclined (PSI)DOCK

Academic licence, no web server available

Pei et al. (2006)

33.

PythDock

2011

Program is based on Heuristic Academic licence, no docking program that utilizes web server available Python programming language with a simple scoring function

Chung, Cho, and Hah (2011)

34.

QXP

1997

Based on Monte Carlo perturbation with energy minimization

McMartin and Bohacek (1997)

Academic licence, no web server available

Chang, Oyang, and Lin (2005)

SchneidmanDuhovny, Inbar, Nussinov, and Wolfson (2005)

Continued 2. THEORETICAL BACKGROUND AND METHODOLOGIES

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TABLE 2 Available Docking Software—cont’d Docking S. No. Software

Published Year

Description

Licence/Web Service

References

35.

SANDOCK

1998

Guided matching algorithm

Academic licence, no web server available

Burkhard, Hommel, Sanner, and Walkinshaw (1999)

36.

Score

1998

It calculated different docking scores of receptor-ligand complexes

Freeware, Wang, Liu, Lai, and web server is available Tang (1998)

37.

SOFTDocking

1991

Molecular surface cubes are matched

Academic licence, no web server available

38.

Surflex-Dock

2003

Idealized active site ligand based

Commercial licence, no Jain (2003) web server available

39.

SwissDock

2011

Interactions between a small molecule and receptor are predicted

Free web server for academic use

Grosdidier, Zoete, and Michielin (2011)

40.

YUCCA

2005

Rigid small molecule-receptor ligand interaction

Academic licence, no web server available

Choi (2005)

Jiang and Kim (1991)

TABLE 3 Advantages and Disadvantages of Docking Software S. No.

Software

Advantages

Disadvantages

1.

DOCK

Binding sites are small Cavities are opened Hydrophobic ligand are small

Ligands are flexible Ligands are highly polar

2.

FLEXx

Binding sites are small Hydrophobic ligand are small

Ligands are very flexible

3.

FRED

Binding sites are large Ligands are flexible Hydrophobic ligands are small Speed is high

Polar, small, buries ligands

4.

Glide

Ligand are flexible Hydrophobic ligands are small

Very polar ligands are ranked Low speed

5.

Gold

Binding sites are large Hydrophobic ligands are small

Very polar ligands are ranked Ranking ligands in large cavities

6.

Slide

Side-chain are flexible

Sensitivity to ligand input coordinates

7.

Surflex

Cavities are opened and large Binding sites are small Ligands are very flexible

Low speed for large ligands

8

QXP

Optimizing known binding mode

Sensitivity to ligand input coordinates

2. THEORETICAL BACKGROUND AND METHODOLOGIES

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175

As the numbers of new molecules discovered every year continue to decrease due to the astronomical costs involved in research and development, this could lead to an increase in drug development costs by millions or even billions of dollars over the next 15–20 years. This could result in drugs becoming unaffordable for patients in developing countries in the near future. To overcome the problem of cost and time faced by pharmaceutical companies, they are finding innovative ways to reduce the cost of drug development. Drug repurposing has therefore emerged as an alternative to the conventional de novo drug discovery method. Since 2007 approximately 40% of the biologics or drugs that are available on the US markets are drugs that are repurposed for reformulation or indications, or a new mixture of existing drugs. Drug repurposing using the structure-based approach, in which molecular docking is being widely used, will further reduce cost and time.

3 DRUG REPURPOSING Drug repurposing or repositioning is the process of discovering new applications outside of the native medical indications for the existing compounds or drugs (Ma et al., 2013). A repurposed drug has a known safety profile and proven bioavailability and therefore has a lot of advantages, like reduced development cost, an accelerated research and development procedure, and a reduced failure rate due to safety. The application of in silico tools for drug repurposing appears very useful due to the growth in computation software as well as hardware (Li et al., 2016). Drug development requires almost 10–15 years and the approximate cost lies between $500 million and $2 billion. Simultaneously, the number of drugs approved by the FDA continues to decrease, whereas life-threatening side effects have increased (Deftereos, Andronis, Friedla, Persidis, & Persidis, 2011). Almost 63% of the total cost of discovering and developing new drugs for the market goes on clinical trials. In view of this, drug repurposing finds new therapeutic proof for existing drugs, as toxicology information and an extensive clinical history are already available (Li, An, & Jones, 2011). It is performed either by a specific computation approach called “in silico drug repurposing” or by utilizing an experimental approach called “activity-based drug repurposing.” The former approach is one of the recent implementations of computational pharmacology. Technically, there are two technological trends that made the development of efficient algorithms for computational method possible. First, the collection of high-throughput data obtained from various research areas like genomics, proteomics, phenomics, and chemoproteomics. Second, mathematical and computation sciences have progressed to a great extent and they can combine software, repurpose algorithms, and help maintaining the databases that are required for classifying and gathering experimental data. In silico approaches are faster and are associated with reduced costs when compared with activity-based repurposing approaches. In addition, highresolution structural information for proteins, gene-expression profiles, or phenotypic information is required by the activity-based methods. Despite these challenges, various researchers rely on protein-based or ligand-based approaches to enrich the traditional methods (Fig. 2). Hence, in silico repurposing methods are followed by pharmaceutical companies to prioritize existing drug candidates to perform a targeted search and discover new targets for repurposing (Vanhaelen et al., 2017). Drug repurposing is performed on a regular

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6. MOLECULAR DOCKING: A STRUCTURE-BASED APPROACH FOR DRUG REPURPOSING Phenotypic or target based screening

Lead compound was identified

(A)

Candidate compound

Preclinical development

Clinical development

Registration

Market

Serendipitous discovery

Drug repurposing

Activity based approach

(B)

Approved/ clinical drugs

Selection & validation

In silico based approach

Clinical development

Registration

FIG. 2 (A) Showing conventional drug discovery methodology and (B) drug repurposing method (Kim, 2015).

basis by some of the pharmaceutical companies like Novartis, Biovista, Eli Lily, Pfizer, SOM Biotech, Ore Pharmaceuticals, Numedicus, and Melior Discovery (Agrawal, 2015). One example of drug repurposing is sildenafil citrate (brand name: Viagra), which was generally used for common hypertension and is now a therapy for erectile dysfunction (Novac, 2013). Similarly, National Comprehensive Cancer Network (NCCN) approximates 50%–75% of the biologics or drugs used for cancer in the USA are used off-label (Pfister, 2012). However, companies rely on conventional drug discovery techniques to look for repurposing opportunities. From 1998 to 2008, of the 75 approved agents (25 biologics and 50 small molecules), 28 of the small molecules were identified by phenotypic screening and 17 were discovered using target-based methods, representing more than 50% of the FDA-approved biologics and small molecules ( Jin & Wong, 2014). Phenotypic screening identifies drug candidates from libraries, whereas the target-based approach includes the known target information, hence improving the repurposing method. The complicated mechanism and complex combinations are reduced and simplified by computational methods. These methods take advantage of the tools available like bioinformatics, cheminformatics, systems biology, and network biology to make full utilization of the known disease biomarkers or pathways, targets, and drugs, thus leading to the development of evidence and design of clinical studies within less time. Currently, drugrepurposing methods are dramatically developing, therefore understanding these methods and prioritizing them becomes essential.

3.1 Types of Drug Repurposing Various methods of drug repurposing are discussed in the following section (Fig. 3). 3.1.1 Blind Search or Screening Method This method does not include the biological, structural, or pharmaceutical information about the mechanism of action of drugs. It mainly depends on serendipitous identification

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3 DRUG REPURPOSING

Drug repositioning

Drug oriented

FDA off-label use

Disease oriented

Clinical adverse effects Chemical information

Phenotypic screening

Target based

Serendipitously tested and screened

Proteomics Metabolics

DNA RNA protein Pathway

Target

Blinded

Genetics Genomics

Knowledge based

Disease omics data

Signature based

Cheminformatics and bioinformatics

Treatment oriented

Genetics Genomics DNA RNA Protein

Proteomics Metabolics

Targeted pathway

Drug omics data

Pathway or Targeted mechanism network based based Network biology and systems biology

Mechanism or knowledge

FIG. 3 Categories of drug repurposing methods (Jin & Wong, 2014).

from experiments aimed at particular drugs and diseases. This approach includes phenotypic screening and FDA off-label use, as it has high flexibility, thus it can be used for a large spectrum of diseases (Ma et al., 2013). 3.1.2 Target-Based Method This approach includes in vitro and in vivo high-content and/or high-throughput screening (HCS/HTS) for the biomarker or protein of interest and docking or ligand-based screening of compounds or drugs from libraries. As compared to a blinded method, most of the targets link directly to the mechanism of disease, which remarkably improves the probability of drug discovery in target-based methods. Compared with the traditional blinded method, there is a higher probability of discovering useful drugs in the case of the target-based method as it integrates target information into the drug repurposing process. Researchers are able to screen all compounds or drugs using the targeted-based method, i.e., through docking that take only a few days. Many pharmaceutical companies like Melior and Genentech are using this method to discover new indications (Reaume, 2011). 3.1.3 Knowledge-Based Methods This method is used by those who are applying a chemoinformatics or bioinformatics approach to incorporate into the drug repurposing studies the already available information about the target-drug network; FDA-approved labels; structures of drug and target; metabolic or signaling pathway; clinical trial information, mainly the side effect; and many more. The target-based and blinded method cannot be used to recognize new mechanisms beyond the already-known targets. On the other hand, knowledge-based methods include the type of information that could predict an undiscovered mechanism, like a new biomarker for the disease, unknown drug-drug similarities, and an unknown target for drugs. The advantage of

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this method is that a large quantity of known information is included to improve the precision of drug repurposing (Deftereos et al., 2011). 3.1.4 Signature-Based Methods This method uses the gene signature obtained from the disease omics data with or without treatments, to find an unknown disease mechanism or unknown off-targets. With the development of next-generation sequencing and microarray techniques, the generation of large amounts of data accelerates, which is relevant to drug repurposing studies as unknown mechanisms are discovered by gene signature. Publicly available genomics data are Sequence Read Archive (SRA), Cancer Cell Line Encyclopedia (CCLE), National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO), and Connectivity Map (CMAP). The advantage of this method is that it reveals unknown mechanisms of action for drugs and small molecules. In contrast with the knowledge-based approach, this involves more molecular-level mechanisms, like the importantly changed gene by in silico approach (Law, Tisoncik-Go, Korth, & Katze, 2013). 3.1.5 Network- or Pathway-Based Method This method uses the available metabolic or signaling pathways, protein-interaction network, and disease omics data to reconstruct specific disease pathways that serve as the key target for repurposing drugs. From the general-signaling network of a large number of proteins it narrows the focus to few target proteins with a specific network. A signaling mechanism of metastatic subtypes in case of breast cancer has been recently studied in one drug repurposing report (Wu, Wang, & Chen, 2013). 3.1.6 Targeted Mechanism-Based Method This method combines the available signaling pathway information, protein interaction, and omics data to describe the unknown mechanism of drug action. For example, certain patients on cancer therapy acquire resistance to the drug therapy within few months of treatment. This fact cautions that even after a successful drug treatment, supplementary information is required regarding the mechanism of action of a drug to provide better treatment. The advantage of this method is that not only is the mechanism associated with the drug or disease discovered, but also the targets that are directly related to the treatment of that specific disease (Reaume, 2011). Available drug repurposing methods are given in Table 4 (Jin & Wong, 2014). The available databases are given in Table 5.

4 CASE STUDIES 4.1 Repurposing of Antipsychotic Drugs for Alzheimer’s Disease Alzheimer’s disease (AD) is one of the most common forms of dementia. Several factors, such as reduced levels of acetylcholine (ACh), neurofibrillary tangles, β-amyloid (Aβ) accumulation, oxidative stress, dyshomeostasis of biometals, free radical formation, mitochondrial dysfunction, and inflammation of neurons, play significant roles in the pathogenesis

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4 CASE STUDIES

TABLE 4

Comparison of Different Drug Repurposing Methods Methods Applied

Field Used

Category

Approach Summary

Examples

Drug-oriented approach Phenotypic screening

Blinded approach

Screening

In vitro and vitro highcontent/high-throughput screening

High drug efflux cancer cell inhibitors

FDA off-label use

Blinded approach

Serendipity

Clinical trial

Rituximab, sildenafil citrate, etoposide, and fluorouracil

Phenotypic screening

Target-based approach

Screening

In vitro and high-content/ Used by Genentech high-throughput screening

Structural information of ligand and drugs; 3D target structure

Target-based approach

Cheminformatics

Computation MLR-1023 screening, i.e., both docking and ligand-based screening

Adverse effects through clinical trial information

Knowledgebased approach

Bioinformatics

Disease is correlated to the adverse effects and to the drug used

FDA approval labels and adverse effects

Knowledgebased approach

Bioinformatics

Drug similarity measurement is defined using principle component analysis

Protein-drug information, structural information of drug and protein

Knowledgebased approach

Chemoinformatics, bioinformatics

Protein-drug predictions

Ketoconazole and simvastatin

Available pathway information

Knowledgebased approach

Bioinformatics

Disease mechanism

Vismodegib

Genetic data

Signaturebased approach

Bioinformatics

To identify key target, genome-wide analysis is done

Disease omics data

Signaturebased approach

Bioinformatics

To identify key target from gene signature in disease of interest

Available pathway information, protein interaction network and disease omics data

Network or pathway based

Network biology

Disease-specific pathways Dasatinib and and networks are sunitinib reconstructed

Disease-oriented

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TABLE 5 Various Database Available for Drug Repurposing Database Protein 3D structure

RCSB Protein Data Bank (PDB) OPM (membrane proteins) TOPSAN OCM Proteopedia

Ligands structure

Pubchem Drugbank ChemSpider Collaborative Drug Discovery Vault iScienceSearch ChemDB DistilBio ChemFrog Therapeutic Target Database (TTD) Chemicalize (ChemAxon) DrugMap Central (DMC) Pharmacogenetics Knowledge Base (PharmaGKB)

Drug-target information

Therapeutic Target Database (TTD) SuperTarget BindingDB Pharmacognetics knowledge base (PharmaGKB) Drugbank DrugMap Central (DMC) METADOR STITCH (Protein-Chemical Interactions) Psychoactive Drug Screening Program Ki (PDSP Ki) GPCR-LigandDatabase(GLIDA)

FDA label information

FDALABEL (US FDA) DailyMed (US FDA) DrugMap Central (DMC) Structured Product Labelling (SPL)

2. THEORETICAL BACKGROUND AND METHODOLOGIES

TABLE 5 Various Database Available for Drug Repurposing—cont’d Database Clinical trial information and adverse effects

FAERS (US FDA) Iowa Drug Information Service (IDIS) DrugMap Central (DMC) SIDER Adverse Reaction Database (Canada) Clinicaltraial.gov

Protein interaction information

Biological General Repository for Interaction Datasets (BioGRID) STRINGS MIPS (mammalian protein-protein interaction database) Human Protein Reference Database (HPRD) IntAct PathwayCommons Database of Interacting Proteins (DIP)

Pathway information

BioCarta NCI Pathway Interaction Database (NCI-PID) PathwayCommons KyotoEncyclopediaof Genes and Genomes (KEGG) DrugMap Central (DMC) Reactome

Molecular omics data

Sequence Read Archive (SRA) ArrayExpress Oncomine National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) Stanford Microarray Database Princeton University MicroArray database (PUMAdb) CellMiner (for NCI-60) Cancer Cell Line Encyclopedia (CCLE)

Drug omics data

National Center for Biotechnology Information-Gene Expression Omnibus (NCBI-GEO) Connectivity Map (CMAP) Cancer Cell Line Encyclopedia (CCLE) Sequence Read Archive (SRA)

Genetic data or information

Online Mendelian Inheritance in Man (OMIM) Sequence Read Archive (SRA) dbSNP

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of AD. Various enzymes, such as acetylcholinesterase (AChE), butyrylcholinesterase (BuChE), monoamine oxidease (MAO), N-methyl-D-aspartate (NMDA), and beta secretase cleavage enzyme (BACE1), are responsible for the pathogenesis of AD. As this disease has a complex nature, the traditional “one molecule, one target” approach does not work in this case. In 2017 the author(s) screened a library of already available antipsychotic drugs using molecular docking. Nine drugs were selected and visually examined. Benperidol was suggested to be the best multitarget drug for AD and melperone to be the second best; in addition, they share structural similarity. Based on this study, Benperidol can be a promising drug for AD in future (Kumar et al., 2017).

4.2 Repurposed Drug of G-Protein Coupled Receptor Inhibitor G-protein coupled receptor inhibitor (GPCRs) is a huge family of transmembrane receptors that shares a common feature, i.e., a seven α-helix structural motif. It controls a range of signal transduction pathways, including neurotransmitter and hormone releasing, as well as olfactory and visual sensory function, therefore this class is considered important as a drug target. All of the 5-hydroxytryptamine (5-HT) receptors (except 5-HT3) are GPCRs that control the signaling cascade by either inhibition or production of secondary messengers. In 2012 a study was conducted aimed at discovering the 5-HT2A inhibitor by structure-based VS, in which 1430 drugs ZINC FDA drug database and DrugBank were screened by in-house docking software against two receptors. The best scoring orientation of the compound was then rescored and by visual assessment and further analysis and 200 of the highest scoring compounds were selected. They were further analyzed using the cyproheptadine model for superior pose prediction and increased enrichment of this model. Some of the compounds were then discarded based on hydrogen bonding with conserved Asp residue, which resulted in 99 compounds. Based on their reported activity with GPCRs, 73 compounds out of these 99 compounds were then removed. Ultimately six compounds were selected out of 26 for in vitro examination, depending upon their commercial availability. So, finally a cancer drug, i.e., sorefenib was discovered to inhibit 5-HT2A in a concentration-dependent manner (Guimara˜es & Cardozo, 2008).

4.3 Repurposed Drugs as Modulators of Protein-Protein Interactions Recently, attempts have been made to target PPIs, which plays a significant role in managing cell proliferation, differentiation, and apoptosis. However, the PPIs are considered to ˚ ) compared to ligand-protein bindbe a demanding task due to their large size (up to 3000 A ˚ ing site (up to 1000 A). Besides this, there is another challenge associated with proteinprotein docking, which is their intrinsic flexibility. One of the major problems for the discovery of small molecules in the case of a PPI modulator is that under the influence of small ligands or under dynamic equilibrium, the perturbations of loops and the movement of the side chain influences the conformation of the surface of a protein and hence forms a “transient” binding pocket that may not be present in the protein-protein complex or the static structure of free target protein. Current studies have shown that specific “hot spots”

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are determinant of the stability of these protein complexes rather than the size of the complex. A small group of amino acids form these hot spots, which interact with the proteinprotein complexes, hence, it is the main contributor to the binding affinity. With the help of structure-based VS, PPI modulators have been discovered. The study reported that to discover the inhibitor of tumor necrosis factor-a (TNF-a), a database of commercially available drugs was screened. The highest scorer was visually analyzed and seven of these are experimentally examined. Two of them, namely ezetimibe and darifenacin, disrupted the in vitro TNF-a-TNF-a receptor interaction and TNF-a gene expression was down regulated (Tsou, Cheng, & Cheng, 2012).

4.4 Repurposed Drug as a Nuclear Receptor Antagonist Ligand-inducible transcription factors are nuclear receptors that arbitrate gene expression. “Orphan receptors” are the nuclear receptors whose ligand does not exist or has not been recognized. They share common three structural domains: C-terminal ligand-binding domain (LBD), the DNA-binding domain (DBD), and the N-terminal transactivation domain (TAD). The LBDs share a high structure and sequence similarity, which makes the discovery of the antagonists difficult. In one of the studies, structure-based high throughout docking studies have identified androgen receptor (AR) antagonists from a library of existing drugs. One of the superfamily of nuclear receptors is human AR, its dysregulation causes the growth and development of prostate cancer. Ligands from the library of commercial oral drugs were prepared by energy minimization and the compounds were docked using ICM method against receptor pocket. The top hits were visually examined and eleven compounds were selected for further in vitro examination. The reports suggested that modification of the functional groups of the drug lowers the affinity to the target and improves the secondary pharmacological effect (Bisson et al., 2007).

4.5 Repurposed Drug as a Stabilizer of G-Quadruplex DNA To target double helical DNA, anticancer DNA is designed that forms covalent cross-linking of DNA strands and hence prevents the replication and transcription process. Currently, as an alternative drug target, noncanonical forms of DNA have emerged. In 2011, structure-based VS was performed to identify a potential G-quadruplex from existing drugs. Methylene blue (MB) was recognized by preliminary docking results as a potential scaffold for further optimization. MB is known for its pharmacological properties as it intercalates DNA and has been analyzed for the treatment of cancer, nitrate poisoning, and malaria. Ma et al. designed 50 MB derivatives keeping the phenothiazinium template as a base using ICM-chemist-pro software. This library was docked with G-quadruplex and three topscoring compounds were synthesized and investigated for in vitro activity. It was discovered that compound four was able to stabilize the G-quadruples in a concentration-dependent manner in a polymerase chain reaction (PCR)-stop assay and inhibit the cancer cell growth with micromolar potency (Chan et al., 2011).

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5 SUCCESS OF PRIVATE-PUBLIC PARTNERSHIP FOR DRUG REPURPOSING Drugs selected by pharmaceutical companies are accessible for drug repurposing projects through industry-academia partnership. AstraZeneca and Medical Research Council (MRC) have initiated an open innovation drug repurposing project. Additionally, the National Centre for Advancing Translational Sciences (NCATS) has collaboratively initiated drug repurposing projects with eight pharmaceutical companies that will work on 58 compounds. Of these, nine were selected to be conducted by academic researchers. The initiative of MRC and NCATS has helped pharmaceutical companies in the hope of ultimately discovering new manifests and recouping some of the money spent on failed drug candidates (Kim, 2015). AZD0530 (Saracatinib), a drug used in cancer, was among the seven compounds considered in the drug repurposing project (NCATS), with the aim of potential repurposing it for therapeutic use in AD. The concept of repurposing of this drug was based on scientific findings that revealed that phosphorylation of tyrosine kinase is associated with tau- and Aβrelated dysfunction in AD. However, the study was discontinued due to inadequate efficacy (Roberson et al., 2011).

6 LIMITATIONS OF DRUG REPURPOSING The limitations of drug repurposing are discussed in the following section.

6.1 Intellectual Property Considerations In spite of all the advantages of drug repurposing, there are various hurdles in this method. Noteworthy challenges include continued demand for clinical trials and the commercialization process associate with patent issues. The patent document should not be ignored, even if the format differs (Mucke, 2017). “Use” patent is of immense importance in cases of drug repurposing, as it protects the intellectual property once the drug is available for commercial propose. An example is BG-12 (dimethyl fumarate), a multiple sclerosis drug, which was repurposed by Biogen, who commercialized it by Use patent. According to Orphan Drug Act of 1983, if the repurposed drug is under orphan drug status, 7 years of exclusivity is granted (Burness & Deeks, 2014).

7 CONCLUSION Molecular docking is a structure-based approach that has resulted in the identification of a number of drugs originally indicated for particular diseases that were repurposed for stress incontinence, erectile dysfunction, obesity, psychosis, attention deficit disorder, cancer, irritable bowel syndrome, smoking cessation, cardiovascular disease, Parkinson’s disease, and AD. This strategy of drug repurposing has proved to be effective with respect to both time and economics. The successful examples of drug repurposing in various pharmacological

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targets, like drug-ligand, protein-protein, drug-DNA, and drug-receptor, have been discussed and highlighted in this chapter. Drug repurposing has emerged as a cost-effective substitute to conventional de novo drug development as it surpasses the initial stage of pharmacokinetic and toxicological parameters. Hence, in silico methodologies, specifically the structure-based approach, prove to be significant in the initial stages of drug discovery.

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Li, Y., Guo, B., Xu, Z., Li, B., Cai, T., Zhang, X., … Zhu, W. (2016). Repositioning organohalogen drugs: a case study for identification of potent B-Raf V600E inhibitors via docking and bioassay. Scientific Reports, 6, 31074. Li, Y., Zhang, X., & Cao, D. (2013). The role of shape complementarity in the protein-protein interactions. Scientific Reports, 3, 3271. Li, Y. Y., An, J., & Jones, S. J. (2011). A computational approach to finding novel targets for existing drugs. PLoS Computational Biology, 7(9), e1002139. Lipinski, C. A., Lombardo, F., Dominy, B. W., & Feeney, P. J. (2001). Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings1. Advanced Drug Delivery Reviews, 46(1–3), 3–26. Liu, J., & Wang, R. (2015). Classification of current scoring functions. Journal of Chemical Information and Modeling, 55 (3), 475–482. Liu, M., & Wang, S. (1999). MCDOCK: a Monte Carlo simulation approach to the molecular docking problem. Journal of Computer-Aided Molecular Design, 13(5), 435–451. Ma, D. L., Chan, D. S. H., & Leung, C. H. (2013). Drug repositioning by structure-based virtual screening. Chemical Society Reviews, 42(5), 2130–2141. Marcou, G., & Rognan, D. (2007). Optimizing fragment and scaffold docking by use of molecular interaction fingerprints. Journal of Chemical Information and Modeling, 47(1), 195–207. McGann, M. (2012). FRED and HYBRID docking performance on standardized datasets. Journal of Computer-Aided Molecular Design, 26(8), 897–906. McMartin, C., & Bohacek, R. S. (1997). QXP: powerful, rapid computer algorithms for structure-based drug design. Journal of Computer-Aided Molecular Design, 11(4), 333–344. Meng, X. Y., Zhang, H. X., Mezei, M., & Cui, M. (2011). Molecular docking: a powerful approach for structure-based drug discovery. Current Computer-Aided Drug Design, 7(2), 146–157. Michel, J., & Essex, J. W. (2010). Prediction of protein–ligand binding affinity by free energy simulations: assumptions, pitfalls and expectations. Journal of Computer-Aided Molecular Design, 24(8), 639–658. Miller, M. D., Kearsley, S. K., Underwood, D. J., & Sheridan, R. P. (1994). FLOG: a system to select ‘quasi-flexible’ ligands complementary to a receptor of known three-dimensional structure. Journal of Computer-Aided Molecular Design, 8(2), 153–174. Morreale, A., Gil-Redondo, R., & Ortiz, A. R. (2007). A new implicit solvent model for protein–ligand docking. Proteins: Structure, Function, and Bioinformatics, 67(3), 606–616. Morris, G. M., Huey, R., Lindstrom, W., Sanner, M. F., Belew, R. K., Goodsell, D. S., & Olson, A. J. (2009). AutoDock4 and AutoDockTools4: automated docking with selective receptor flexibility. Journal of Computational Chemistry, 30 (16), 2785–2791. Mucke, H. A. (2017). Drug repositioning in the mirror of patenting: surveying and mining uncharted territory. Frontiers in Pharmacology, 8, 927. Novac, N. (2013). Challenges and opportunities of drug repositioning. Trends in Pharmacological Sciences, 34(5), 267–272. Novicˇ, M., Tibaut, T., Anderluh, M., Borisˇek, J., & Tomasˇicˇ, T. (2016). The comparison of docking search algorithms and scoring functions: an overview and case studies. In Methods and algorithms for molecular docking-based drug design and discovery (pp. 99–127). Hershey, PA: IGI Global. Paul, D. S., & Gautham, N. (2016). MOLS 2.0: software package for peptide modeling and protein–ligand docking. Journal of Molecular Modeling, 22(10), 239. Pei, J., Wang, Q., Liu, Z., Li, Q., Yang, K., & Lai, L. (2006). PSI-DOCK: towards highly efficient and accurate flexible ligand docking. Proteins: Structure, Function, and Bioinformatics, 62(4), 934–946. Pfister, D. G. (2012). Off-label use of oncology drugs: the need for more data and then some. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 30(6), 584–586. Pham, T. A., & Jain, A. N. (2008). Customizing scoring functions for docking. Journal of Computer-Aided Molecular Design, 22(5), 269–286. Rarey, M., et al. (1996). A fast flexible docking method using an incremental construction algorithm. Journal of Molecular Biology, 261(470–489), 29. Reaume, A. G. (2011). Drug repurposing through nonhypothesis driven phenotypic screening. Drug Discovery Today: Therapeutic Strategies, 8(3–4), 85–88.

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Roberson, E. D., Halabisky, B., Yoo, J. W., Yao, J., Chin, J., Yan, F., … Palop, J. J. (2011). Amyloid-β/Fyn–induced synaptic, network, and cognitive impairments depend on tau levels in multiple mouse models of Alzheimer’s disease. Journal of Neuroscience, 31(2), 700–711. Sauton, N., Lagorce, D., Villoutreix, B. O., & Miteva, M. A. (2008). MS-DOCK: accurate multiple conformation generator and rigid docking protocol for multi-step virtual ligand screening. BMC Bioinformatics, 9(1), 184. Schneidman-Duhovny, D., Inbar, Y., Nussinov, R., & Wolfson, H. J. (2005). PatchDock and SymmDock: servers for rigid and symmetric docking. Nucleic Acids Research, 33(Suppl_2), W363–W367. Shin, W. H., Heo, L., Lee, J., Ko, J., Seok, C., & Lee, J. (2011). LigDockCSA: protein–ligand docking using conformational space annealing. Journal of Computational Chemistry, 32(15), 3226–3232. Shoichet, B. K., McGovern, S. L., Wei, B., & Irwin, J. J. (2002). Lead discovery using molecular docking. Current Opinion in Chemical Biology, 6(4), 439–446. Singh, T., Biswas, D., & Jayaram, B. (2011). AADS—an automated active site identification, docking, and scoring protocol for protein targets based on physicochemical descriptors. Journal of Chemical Information and Modeling, 51(10), 2515–2527. Sobolev, V., Wade, R. C., Vriend, G., & Edelman, M. (1996). Molecular docking using surface complementarity. Proteins: Structure, Function, and Bioinformatics, 25(1), 120–129. Terp, G. E., Johansen, B. N., Christensen, I. T., & Jørgensen, F. S. (2001). A new concept for multidimensional selection of ligand conformations (multiselect) and multidimensional scoring (multiscore) of protein-ligand binding affinities. Journal of Medicinal Chemistry, 44(14), 2333–2343. Thomsen, R., & Christensen, M. H. (2006). MolDock: a new technique for high-accuracy molecular docking. Journal of Medicinal Chemistry, 49(11), 3315–3321. Tripathi, A., & Misra, K. (2017). Molecular docking: A structure-based drug designing approach. JSM Chemistry, 5(2), 1–5. Trosset, J. Y., & Scheraga, H. A. (1999). PRODOCK: software package for protein modeling and docking. Journal of Computational Chemistry, 20(4), 412–427. Tsou, L. K., Cheng, Y., & Cheng, Y. C. (2012). Therapeutic development in targeting protein–protein interactions with synthetic topological mimetics. Current Opinion in Pharmacology, 12(4), 403–407. Tuffery, P., & Derreumaux, P. (2012). Flexibility and binding affinity in protein–ligand, protein–protein and multi-component protein interactions: limitations of current computational approaches. Journal of the Royal Society Interface, 9(66), 20–33. Vanhaelen, Q., Mamoshina, P., Aliper, A. M., Artemov, A., Lezhnina, K., Ozerov, I., & Zhavoronkov, A. (2017). Design of efficient computational workflows for in silico drug repurposing. Drug Discovery Today, 22(2), 210–222. Venkatachalam, C. M., Jiang, X., Oldfield, T., & Waldman, M. (2003). LigandFit: a novel method for the shape-directed rapid docking of ligands to protein active sites. Journal of Molecular Graphics and Modelling, 21(4), 289–307. Vilar, S., Cozza, G., & Moro, S. (2008). Medicinal chemistry and the molecular operating environment (MOE): application of QSAR and molecular docking to drug discovery. Current Topics in Medicinal Chemistry, 8(18), 1555–1572. Wang, G., & Zhu, W. (2016). Molecular docking for drug discovery and development: A widely used approach but far from perfect. Future Medicine Chemistry, 8(14), 1707–1710. Wang, R., Liu, L., Lai, L., & Tang, Y. (1998). SCORE: a new empirical method for estimating the binding affinity of a protein-ligand complex. Molecular modeling annual, 4(12), 379–394. Wang, R., Lu, Y., & Wang, S. (2003). Comparative evaluation of 11 scoring functions for molecular docking. Journal of Medicinal Chemistry, 46(12), 2287–2303. Welch, W., Ruppert, J., & Jain, A. N. (1996). Hammerhead: fast, fully automated docking of flexible ligands to protein binding sites. Chemistry & Biology, 3(6), 449–462. Wu, Z., Wang, Y., & Chen, L. (2013). Network-based drug repositioning. Molecular BioSystems, 9(6), 1268–1281. Yang, J. M., & Chen, C. C. (2004). GEMDOCK: a generic evolutionary method for molecular docking. Proteins: Structure, Function, and Bioinformatics, 55(2), 288–304. Zhao, Y., & Sanner, M. F. (2007). FLIPDock: docking flexible ligands into flexible receptors. Proteins: Structure, Function, and Bioinformatics, 68(3), 726–737. Zsoldos, Z., Reid, D., Simon, A., Sadjad, S. B., & Johnson, A. P. (2007). eHiTS: a new fast, exhaustive flexible ligand docking system. Journal of Molecular Graphics and Modelling, 26(1), 198–212.

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Further Reading Liu, Q., Sabnis, Y., Zhao, Z., Zhang, T., Buhrlage, S. J., Jones, L. H., & Gray, N. S. (2013a). Developing irreversible inhibitors of the protein kinase cysteinome. Chemistry & Biology, 20(2), 146–159. Liu, Z., Fang, H., Reagan, K., Xu, X., Mendrick, D. L., Slikker, W., Jr., & Tong, W. (2013b). In silico drug repositioning– what we need to know. Drug Discovery Today, 18(3–4), 110–115. Warren, G. L., Andrews, C. W., Capelli, A. M., Clarke, B., LaLonde, J., Lambert, M. H., & Tedesco, G. (2006). A critical assessment of docking programs and scoring functions. Journal of Medicinal Chemistry, 49(20), 5912–5931.

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C H A P T E R

7 Data Science Driven Drug Repurposing for Metabolic Disorders Selvaraman Nagamani, Rosaleen Sahoo, Gurusamy Muneeswaran, G. Narahari Sastry Centre for Molecular Modeling, CSIR-Indian Institute of Chemical Technology, Hyderabad, India

1 INTRODUCTION Data science, artificial intelligence, and machine learning are quickly making inroads not only into all aspects of our life and businesses, but also into all branches of science, engineering, and medicine (Craven & Page, 2015; Hand, 2015; Issa, Byers, & Dakshanamurthy, 2014). Health care is an extremely important aspect of human survival and sustainability, and we believe that data-driven approaches have great potential in tackling a number of the challenges that health care may face in the coming decades. Collection and analysis of huge amounts of data provide a detailed understanding of the pathophysiology and manifestation of diseases. In the postgenomic era, data-driven/knowledge-driven approaches and different big-data applications have changed the face of the health-care system (Aronson & Rehm, 2015). Advancements in big-data analytics in the health-care system at both individual and population levels are leading towards personalized medicine. Health-care systems generate a large amount of biomedical data including electronic health records, medical imaging, multiomics data, and scientific articles (Craven & Page, 2015; Hand, 2015; Issa et al., 2014). The results of high-throughput cellular and protein-binding assays can be analyzed using big-data analytics, which broadens our understanding of the drug-discovery process and can be used in the development of chemoinformatics-based databases (Manzoni et al., 2018). Various databases act as the central hub for information, which is collated from different biological, clinical, and physicochemical data. Data mining plays an important role in finding new therapeutic targets, drug-target associations and drug-repurposing hypotheses. It is very interesting to note that new processes and methodologies have been developed to automate the drug-discovery process with a high priority placed on drug repurposing (Schneider, 2018).

In Silico Drug Design. https://doi.org/10.1016/B978-0-12-816125-8.00007-9

191 # 2019 Elsevier Inc. All rights reserved.

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Alterations in metabolic processes lead to metabolic disorders and are large amount of metabolic disorders data are available in the public domain. High-calorie diets are responsible for the dysregulation of a large number of metabolic pathways, which contributes to the development of metabolic diseases. Metabolic syndrome and metabolic disorders are the most common type of metabolic diseases. Metabolic diseases have largely been attributed to genetic background, diet, physical activity, environmental factors, and ageing processes. This chapter provides an overall description of metabolic disorders and drug repurposing. Further, it discusses various areas ranging from the pathophysiology of metabolic disorders, big-data applications in metabolic disorders, genomics, proteomic and epigenetic aspects of metabolic disorders, metabolic health and disease, the metabolic and immune systems, repurposed drugs for metabolic diseases, to the role of data science in drug discovery.

2 OVERVIEW OF METABOLIC DISORDERS In this section we give a brief description of metabolism, metabolic disorders, metabolic syndrome, and the different factors responsible for metabolic disorders.

2.1 Metabolism The human body gets energy from nutrients through all the biochemical reactions in each cell and this process is known as “metabolism.” The metabolic processes are categorized as anabolism and catabolism (Fig. 1) (Lehninger, Nelson, & Cox, 2000).

FIG. 1 Types of cell metabolism: catabolism and anabolism.

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Anabolism is the build-up process where complex molecules such as carbohydrates, nucleic acids, and proteins are formed from small molecules. Catabolism is the destructive process where complex molecules are broken down into smaller units to produce energy. Catabolic processes require energy (ATP, NADH, NADPH, and FADH2) for the breakdown of the large complex molecules (e.g., splitting of carbohydrates, proteins, lipids, etc., into their smaller units). The energy obtained from these processes is utilized for construction, maintenance, and several other processes of the body and the remainder is excreted from the body (Lehninger et al., 2000). Metabolic pathways are generally referred to as a series of enzyme-mediated chemical reactions in the cell. Catabolic pathways are convergent in nature, whereas anabolic pathways are divergent in nature. The structure of metabolic pathways is linear, branched, and cyclic. In metabolic pathways, a single precursor may give rise to multiple end products or several precursors may be converted into a single product (Lehninger et al., 2000; Pi-Sunyer, 1993). Metabolites are the substances essential to the metabolic processes or the products of the metabolic processes. Metabolites are classified into primary metabolites and secondary metabolites. Primary metabolites are produced by glycolysis and tricarboxylic acid (TCA) cycle and secondary metabolites are produced by other pathways (e.g., fatty acid derivative biosynthesis, antibiotic biosynthesis, etc.). Diseases, disorders, and syndromes are three different terms associated with the alterations or abnormalities in normal chemical reactions. Diseases generally occur due to the pathophysiological response of external or internal factors. Disorders are the disruption of the normal or regular function of the body as a consequence of the disease. A syndrome is a collection or set of signs and symptoms that characterize or suggest a disease. Any type of disease or disorder that disrupts the normal metabolic processes is known as a metabolic disease, which can either be genetic or inherited.

2.2 Metabolic Disorders and Metabolic Syndrome Metabolic disorders occur mainly due to deficiencies in the enzyme that are necessary to convert one metabolite to another metabolite. The abnormalities or manifestations of metabolic disorders are either due to the accumulation of large amounts of one metabolite or a deficiency of one or more metabolites. Metabolic disorders can be broadly classified into inherited metabolic disorders and acquired metabolic disorders. Inherited metabolic disorders are due to the inborn errors of metabolism, which result from genetic defects, and this leads to deficiencies in the production of enzymes or abnormalities in their function. Abnormal metabolic function in humans causes various types of inherited metabolic disorders, such as lysosomal storage disorders (Hurler syndrome, Tay-Sachs disease, Gaucher’s disease, and Fabry disease), peroxisomal disorders (Zellweger syndrome and adrenoleukodystrophy), and metal metabolism disorders (Wilson’s disease and hemochromatosis), etc. (Sliwinska, Kasinska, & Drzewoski, 2017). Acquired metabolic disorders are associated with external factors, such as an unhealthy lifestyle along with little physical activity and excessive caloric intake. Evidence has shown that human lifestyle is associated with an inherited epigenetic pattern, which affects gene expression, and the activity of proteins that leads to the development of metabolic diseases or disorders (Eckel, Alberti, Grundy, & Zimmet, 2010). Metabolic syndrome is the most common metabolic disorder associated with the global epidemic of obesity and diabetes (Boelens &

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Wynn, 2017). The increased risk for Type-2 diabetes and cardiovascular disease provides a strong indication of the increased risk for metabolic disorders as a whole, so strategies are required to prevent the emerging global threat. Weight reduction and a reasonable level of physical activity is the fundamental approach and drug treatment can be appropriate for risk reduction in patients with diabetes and cardiovascular disease (Heal, Gosden, & Smith, 2009).

2.3 Different Components Underlying Metabolic Syndrome Unhealthy food and lifestyle has led to an epidemic of obesity in adults and in children, mostly at a young age. An imbalance in the caloric intake and energy expenditure is the leading cause of the accumulation of adipose tissue. Obese patients are at high risk of developing comorbid metabolic conditions, including type-2 diabetes, hypertension, hyperlipidemia, and cardiovascular diseases (Wisse, 2004). There are different criteria for the diagnosis of metabolic syndrome given by the NCEPATP III (National Cholesterol Education Program Adult Treatment Panel III), WHO (World Health Organization), and IDF (International Diabetes Federation) (Table 1) (Eckel TABLE 1 Risk Factors or Criteria for the Diagnosis of Metabolic Syndrome According to WHO (World Health Organization), NCEP ATP III (National Cholesterol Education Program Adult Treatment Panel III), and IDF (International Diabetes Federation)

S. No.

Criteria

WHO T2D, IGT, Glucose Intolerance and/or IR + ≥2 Others

NCEP ATP III ≥3 Risk Factors

IDF Central Obesity + ≥2 Other Risk Factors

1

IGT (impaired glucose tolerance)


5.6 mmol/L (100 mg/dL)


6.1 mmol/L (110 mg/dL)


5.6 mmol/L (100 mg/dL)

2

Insulin resistance

Hyperinsulinemic/euglycemic clamp Glucose intake below lowest quartile

Not included

Not included

3

Obesity

BMI >30 Waist-hip >0.9 M; >0.85 F

Waist >102 cm (40 in.) M; >88 cm (35 in.) F

Waist >94 cm (37.4 in.) M; >80 cm (31.8 in.) F

4

Hypertension


140/90 mmHg


130/85 mmHg


130 mmHg or
85 mmHg

5

Serum triglycerides


1.7 mmol/L (150 mg/dL)


1.7 mmol/L (150 mg/dL)


1.7 mmol/L (150 mg/dL)

6

HDL-cholesterol

22 M (ZINC12) >750 M (ZINC15)

>1 M (ZINC15)

Commercially available compounds prepared for docking-based virtual screening

ChEMBL

>1.7 M

>14.7 M

Binding, functional assay, and ADMET https://www.ebi.ac.uk/ information for bioactive compounds chembl/ extracted from scientific literature Bento et al. (2014); Kanehisa (2002); Gaulton et al. (2017)

PubChem BioAssay

>2.2 M

>230 M

Data from high-throughput screening experiments including chemogenomic, medicinal chemistry, and functional genomics research

MACE

>48.6 K

>75.5 K

Chemical response and gene expression http://mace.sookmyung.ac. data on cancer cell lines kr/ Jeong et al. (2015)

KEGG

>18.3 K

>100 K

Data on genome sequencing and other high-throughput experimental technologies

http://www.genome.jp/ kegg/ Kanehisa (2002)

CMAP

>19.9 K

>200 K

Transcriptional expression data

https://clue.io/ Subramanian et al. (2017)

https://pubchem.ncbi.nlm. nih.gov/assay/assay.cgi Wang et al. (2017)

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2 AVAILABLE CHEMICAL DATA

TABLE 1

Examples of Databases Used for Drug Repurposing—cont’d

Database

No. of No. of Data Compounds Points

DrugBank


11 K


17 K

Drug Repurposing Hub

>5.6 K

>660 drug Drugs and drug candidates carefully indications annotated by targets, diseases, and suppliers. Compounds are available in plated format for HTS assays, checked for purity

https://clue.io/repurposing Corsello et al. (2017)

>1000 Repurposed drugs with thorough drug annotations indications

http://repurposedb. dudleylab.org/ Shameer et al. (2018)

RepurposeDB >300

Description

References

Comprehensive molecular information https://www.drugbank.ca/ about drugs and drug candidates, their Wishart et al. (2018) targets, mechanisms, and interactions

BindingDB

>652 K

>1.4 M

Database of protein-small molecule binding data including US patent data and data from a set of journals

https://www.bindingdb.org/ bind/index.jsp Chen, Liu, and Gilson (2001); Gilson et al. (2016)

STITCH

>0.5M

1.6BN

Database of known and predicted interactions between proteins and chemical substances including direct physical and functional associations

http://stitch.embl.de/ Kuhn, von Mering, Campillos, Jensen, and Bork (2008); Szklarczyk et al. (2016)

SuperTarget

>196 K

>330 K

Database of drug, proteins, and side effects containing information on drugprotein, protein-protein, and drugside effect relations

http://insilico.charite.de/ supertarget/ G€ unther et al. (2008); Hecker et al. (2012)

heterogeneous data, and several attempts to integrate different resources were made (Digles et al., 2016; Fu et al., 2013; Grondin et al., 2016). The general approaches to the representation of chemical structures and to the analysis of their similarity as a measure of closeness in chemical space were recently reviewed (Radchenko, Makhaeva, Palyulin, & Zefirov, 2017). Depending on specific problems, the chemical structures may be represented in many ways (Fig. 2A). At a certain level, the similarity and other relationships between structures can be analyzed in terms of their common (matching) and different (mismatching) parts or fragments. Chemical structures may be also encoded as vectors consisting of molecular descriptors. A molecular descriptor is a numerical parameter representing certain features or facets of structures (Radchenko et al., 2017). More than 5000 types of descriptors have been proposed, including physico-chemical descriptors, topological indices, 2D fingerprints, etc. (Todeschini & Consonni, 2009). In addition, the compounds can be characterized by diverse data representing their effects on various biological systems, including target-based or phenotype-based bioactivity assays (Gaulton et al., 2012, 2017; Bento et al., 2014; Wang et al., 2012, 2017), gene expression profiles (Lamb et al., 2006; Subramanian et al., 2017) as well as metabolomic data (Wishart et al., 2017), protein-protein interactions data (Szklarczyk et al.,

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FIG. 2 (A). Different representations of chemical structure for Apixaban drug and methods of chemical space analysis based on these representations. (B) Structural and biological fingerprints.

2017), etc. For the calculation of chemical similarity, chemical structures are usually represented as 2D fingerprints, which are binary fragmental descriptors reflecting the presence (1) or absence (0) of a particular fragment or feature (Leach & Gillet, 2007) (Fig. 2B). However, other fingerprint types were also developed. For example, highthroughput screening fingerprints (HTSFP) (Helal, Maciejewski, Gregori-Puigjane, Glick, & Wassermann, 2016; Swamidass, 2011; Wassermann et al., 2014) (Fig. 2B) were successfully used for the mining of HTS results. In the network-based approaches to chemical space analysis, the structures are abstracted as nodes while some kinds of similarity relationships between them are represented by edges. In the coordinate-based approaches, the descriptor vectors for a set of molecules define a set of points in space. Since these vectors are usually high-dimensional, the dimensionality reduction algorithms may be used to facilitate the visualization and analysis of such spaces.

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365

Alternatively, the recently developed “latent” chemical space approaches can be used to derive continuous focused representations. Finally, the scaffold-based representation of chemical space provides a very convenient and easily interpretable method for chemical data analysis.

3 NETWORK-BASED REPRESENTATIONS Network-based approaches belong to the most commonly used tools for the analysis of complex biological systems and are extensively applied to drug repurposing (Lotfi Shahreza, Ghadiri, Mousavi, Varshosaz, & Green, 2018; Talevi, 2018). The vertices in a network (representing drugs, targets or diseases) are linked with edges if their similarity or association metric is above a certain threshold. The constructed network can be analyzed in terms of topological features such as node degrees, clustering coefficient, shortest path length, community structures, network density, and degree assortativity. The network-based methods may be classified according to the information used for the creation of nodes and edges (drug structures, biological targets, or phenotypic manifestations, such as gene expression or side effects) or according to the types of machine learning algorithms used to infer new drug-target associations (supervised or unsupervised). The rapid accumulation of heterogeneous data on the effects caused by small organic molecules in various biological systems (see Section 2) opens the way to more systematic understanding of complex biological systems and new methods of computer-aided drug design. For instance, several network-based approaches ignoring the drug-drug chemical similarity were used to predict the drug mode of action from transcriptional responses (Iorio et al., 2010) and gene expression profiles (Dudley et al., 2011; Hizukuri, Sawada, & Yamanishi, 2015; Sirota et al., 2011). Such methods utilize the similarity only between drug properties but not their chemical structures, providing somewhat unusual but still valid means of navigation in chemical space. In this section we classify all the network-based methods depending on the volume of the underlying chemical information. Two broad categories were identified: drug chemical space and bioactive chemical space. The first group comprises all the methods using a rather small portion of chemical space, i.e., drugs or drug candidates, while the methods of the second group are based on large chemical interactome data (starting from 10 K compounds). We do not distinguish between the methods focused on the topological characteristics of networks complemented with some inference algorithms and the various machine learning methods that use the representation of a chemical structure by a node connected by links to some other nodes. The network-based analysis of drug space validated in vitro or in vivo will be considered. An overview of the approaches discussed in this section is presented in Table 2. Most of them seem quite promising; however, more broad experience of practical applications is required for thorough evaluation of their relative advantages and disadvantages. For more details on network-based approaches and methods for the prediction of drug-target associations, the reader is referred to recent comprehensive reviews (Cichonska, Rousu, & Aittokallio, 2015; Chen et al., 2016; Zhang & Yan, 2017; Vanhaelen et al., 2017; Lotfi Shahreza et al., 2018; Ezzat, Wu, Li, & Kwoh, 2018).

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TABLE 2 Examples of Network-Based Approaches to Drug Repurposing Chemical Space Coverage

Name and References

Network Topology

Validation of Predictions

Campillos, Kuhn, Gavin, Jensen, and Bork (2008)

Drugs

Simple network in chemical space representing side effect and chemical similarity of compounds

Experimental

Yildirim, Goh, Cusick, Baraba´si, and Vidal (2007)

Drugs

Bipartite drug-target association network

Not applicable

Yamanishi, Araki, Gutteridge, Honda, Drugs and Kanehisa (2008)

Bipartite drug-target association network

Retrospective

Bipartite local models (BLM) Bleakley and Yamanishi (2009)

Drugs

Bipartite drug-target association network

Retrospective

BLM with neighbor-based interaction- Drugs profile inferring (BLM-NII) Mei, Kwoh, Yang, Li, and Zheng (2013)

Bipartite drug-target association network

Retrospective

Network-based inference (NBI) Cheng, Liu, et al. (2012)

Drugs

Bipartite drug-target association network

Experimental

Substructure-drug-target networkbased inference (SDTNBI) Wu, Cheng, et al. (2017)

Drugs

Tripartite substructure-drug-target association network

Retrospective

Network-based Random Walk with Drugs Restart on the Heterogeneous network (NRWRH) Chen, Liu, and Yan (2012)

Heterogeneous network integrating proteinprotein similarity, drug-drug similarity, and known drug-target interaction networks

Retrospective

Wang, Yang, Zhang, and Li (2014)

Drugs

Heterogeneous drug-target-disease association network

Retrospective

DTINet Luo et al. (2017)

Drugs

Heterogeneous network integrating drug, protein, disease, and side-effect nodes

Experimental

Wu, Liu, et al. (2017)

Drugs

Heterogeneous drug-disease pair network

Retrospective

Paolini, Shapland, van Hoorn, Mason, Bioactives and Hopkins (2006)

Simple network in target space representing common active compounds

Retrospective

Similarity Ensemble Approach (SEA) Keiser et al. (2007, 2009)

Bioactives

Simple network in target space representing structural similarity of the ligands

Experimental

Weighted Ensemble Similarity (WES) Zheng et al. (2015)

Bioactives

Simple network in target space representing weighted feature similarity of the ligands

Experimental

Chemical Similarity Network Analysis Bioactives Pull-down (CSNAP) Lo et al. (2015)

Simple network in chemical space representing chemical similarity of compounds

Experimental

Sawada, Iwata, Mizutani, and Yamanishi (2015)

Tripartite drug-target-disease association network

Retrospective

Bioactives

2. THEORETICAL BACKGROUND AND METHODOLOGIES

3 NETWORK-BASED REPRESENTATIONS

TABLE 2

367

Examples of Network-Based Approaches to Drug Repurposing—cont’d Chemical Space Coverage

Network Topology

Validation of Predictions

REMAP Lim et al. (2016)

Bioactives

Bipartite drug-protein association network

Retrospective

Kunimoto and Bajorath (2018)

Bioactives

Tripartite drug-bioactive-target network based on structural similarity and targetcompound association

Retrospective

Molecular Networking Olivon et al. (2017)

Bioactives

Network representing similarity between mass spectra of natural extracts

Experimental

Name and References

3.1 Chemical Space Networks for Drugs Numerous approaches exploiting different kinds of network data structures were suggested, such as network-based models, supervised, and semisupervised machine learning algorithms, including bipartite local models, learning methods based on the Laplacian operator, recommender systems, etc. However, the vast majority of network approaches are based on databases containing rather small sets of compounds, such as DrugBank (Wishart et al., 2018) or KEGG (Kanehisa, Furumichi, Tanabe, Sato, & Morishima, 2017), thus exploiting only a tiny part of the available chemical space. In the simplest form of the network-based analysis, the nodes representing the compounds are connected by edges reflecting their similarities. This approach was used to predict drugtarget interactions using drug side-effect similarity, chemical similarity, and combination of these data (Campillos et al., 2008). The specificity and sensitivity were improved when both chemical and side-effect similarity were taken into account. Overall, 754 drug pairs having dissimilar structures and unrelated indication areas were predicted. More than 20 predicted associations were tested in biological assays, and for 13 associations the drug-target interactions were confirmed by the in vitro binding assays (in 11 cases the inhibition constants were less than 10 μM). One of the most commonly used network topologies is based on the bipartite networks that comprise two distinct sets of nodes representing various drugs and targets whereas the edges reflect the “activity” or “binding” relationship between them (Fig. 3). This approach was extensively exploited to capture the drug pharmacological profiles and to reveal unknown drug-target interactions. In the bipartite model composed of the Food and Drug Administration (FDA)-approved drugs and proteins linked by the binary drug-target associations (Yildirim et al., 2007), the majority of the drugs formed a highly interlinked group of nodes with strong local clustering of drugs of similar types according to their Anatomical Therapeutic Chemical (ATC) classification. The analysis of topological features of this network revealed an overabundance of the “follow-up” drugs targeting already targeted proteins. Conversely, the inclusion of the experimental drugs allowed one to identify a trend toward more functionally diverse targets.

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FIG. 3 Bipartite drug-target graph.

Yamanishi et al. proposed the drug-target interaction inference method as the formalization of a supervised learning problem for a bipartite graph describing the drug-target interaction network (Yamanishi et al., 2008). The known drug-target bipartite network topology was mapped to a unified pharmacological space and a supervised learning framework using a kernel regression model was applied to infer new drug-target interactions. The usefulness of the method was evaluated against four classes of drug-target interaction networks (for enzymes, ion channels, G protein-coupled receptors, and nuclear receptors) using KEGG LIGAND data. This dataset was widely used for benchmarking in subsequent works. Numerous unknown drug-target interactions were predicted by navigation in a unified “pharmacological space.” However, the predictions were not validated experimentally. In a follow-up study this method was combined with the local model approach, transforming edge prediction problems into binary classification problems of labeled points (Bleakley & Yamanishi, 2009). This bipartite local model (BLM) approach was shown to be more efficient in predicting the drug-target interactions on the same benchmark dataset. In BLM the predictions from the drug similarities and target similarities are made separately and then aggregated to give a final prediction. However, the major disadvantage of BLM arises from its learning procedure. BLM for a query drug or target may be trained only on their own interaction profiles and is thus unable to provide good predictions for drugs or targets that have no known interactions. To include such drugs and targets, the BLM algorithm with neighborbased interaction-profile inferring (BLM-NII) was suggested by Mei et al. (2013). Defining neighbors of drugs or proteins as entities that have large similarity to a query drug or protein, the initial weighted interactions for them may be derived from the neighbor interaction profiles. This information is further used to train the model. BLM-NII showed better performance than the original BLM on the same datasets. Cheng et al. proposed three types of inference methods for drug-target interactions: drugbased similarity inference, target-based similarity inference, and network-based inference (NBI) (Cheng, Liu, et al., 2012). Thus, three corresponding types of similarity relationships were exploited: drug-drug chemical structure similarity, target-target sequence similarity, and network topology. Data from KEGG BRITE, BRENDA, SuperTarget, and DrugBank were integrated. Of all the methods, NBI provided the best performance. The new predicted drug-target associations were confirmed in the in vitro assays: five old drugs, namely

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montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features against estrogen receptors or dipeptidyl peptidase IV with half maximal inhibitory or effective concentrations below 10 μM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activity on human MDA-MB-231 breast cancer cells in MTT assays. The NBI approach was further improved by assigning weights to edges or nodes. The edge weights were based on the potency, binding affinity, or inhibitory activity, and node weights were calculated on the basis of node connectivity. Thus two new versions of NBI—edge-weighted EWNBI and node-weighted NWNBI—were developed (Cheng, Zhou, Li, Liu, & Tang, 2012). NWNBI showed better performance in benchmark tests as compared to EWNBI and unweighted NBI. Another approach using an extension of the bipartite networks to the tripartite networks of known drug-target interactions was proposed by Wu, Cheng, et al. (2017). One of the most important features of this approach, named substructure-drug-target network-based inference approach (SDTNBI), is the capability to prioritize potential targets not only for drugs, but also for new chemical entities through the extraction of additional chemical knowledge from the analysis of drug substructures. SDTNBI integrates known drug-target interaction network, drug-substructure linkages, and new chemical entity-substructure linkages to infer new targets. At first a tripartite graph with nodes representing drugs, drug substructures, and targets was constructed. To incorporate new chemical entities in this graph, DrugBank as well as ChEMBL GPCR and kinase subsets were extracted. High performance was achieved in 10-fold and leave-one-out cross validations on four benchmark data sets covering GPCRs, kinases, ion channels, and nuclear receptors. Thousands of new potential interactions were identified by implementing SDTNBI on a global network. SDTNBI was applied to identify novel anticancer indications for nonsteroidal antiinflammatory drugs inhibiting aldo/keto reductase AKR1C3 or carbonic anhydrases CA9 or CA12. Several methods were proposed that exploit a concept of heterogeneous networks, i.e., networks composed of different types of nodes (e.g., drugs, proteins, diseases) and edges representing different types of associations (e.g., drug-protein, drug-disease, protein-disease, see Fig. 4) (Chen et al., 2012; Luo et al., 2017; Wang et al., 2014; Wu, Liu, & Wang, 2017). The Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) approach, suggested by Chen et al. (2012), consists of four steps: (1) a heterogeneous network is constructed based on the protein-protein similarity network, drug-drug similarity network, and known drug-target interaction network; (2) initial probability of random walk is determined to make it start at the given drug nodes and seed target nodes simultaneously; (3) random walk on the heterogeneous network is performed; and (4) the most probable targets FIG. 4

Graphical representation of a heterogeneous network (C, compounds).

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are selected when the probability of the walk is converged. Another heterogeneous network consisting of three types of nodes, namely diseases, drugs, and drug targets, was built by Wang et al. (2014). An iterative updating algorithm that propagates information across the network was developed to predict the missing edges. Both methods were not validated experimentally, though retrospective literature mining was performed in order to support predicted interactions. A heterogeneous network that integrates four types of nodes (drugs, proteins, diseases, and side effects) and six types of edges (drug-protein interactions, drug-drug interactions, drugdisease associations, drug-side effect associations, protein-disease associations, and proteinprotein interactions) was proposed along with a new algorithm called DTINet (Luo et al., 2017). In this approach diverse information from heterogeneous network is integrated using a compact feature-learning algorithm to obtain low-dimensional vector representations of nodes encoding the relational properties, association information, and topological context for each node. The best projection from drug space onto protein space is then searched. This projection is determined by the geometrical closeness of the mapped feature vectors of drugs to their known interacting targets. At the last stage, new interactions for a drug are inferred by ranking its target candidates according to their proximity to the projected feature vector of this drug. The predicted inhibition of cyclooxygenases COX-1 and COX-2 by three drugs (telmisartan, alendronate, and chlorpropamide) was confirmed experimentally. Another work (Wu, Liu, et al., 2017) employs hierarchical integration of heterogeneous data into three layers: the treatment layer reflects drug-drug or disease-disease distances calculated using a drug-disease bipartite graph, the gene layer reflects drug-drug and disease-disease similarities calculated from drug-gene and disease-gene interactions profiles, and the base layer reflects drug-drug chemical structure similarities and disease-disease phenotype similarities. These data were used to construct a weighted drug-disease pair network wherein a node represents a drug-disease pair and an edge represents a node-node relation weighted with the similarity score between the two pairs. Semisupervised graph cut algorithm (SSGC) was applied to find the optimal graph cut of the network, where similar drug-disease pairs are supposed to show similar treatment patterns. The results were verified by comparing the predicted drug-disease pairs with the data from the Comparative Toxicogenomics Database (Grondin et al., 2016) and the literature. Although the network methods based on the drug chemical space were successfully applied to find new drug-target interactions, the use of larger volumes of data is likely to significantly improve the quality of predictions and to enable a deeper understanding of the relationships between the drug and target spaces.

3.2 Chemical Space Networks for Bioactive Compounds A number of approaches to the network analysis of large-scale pharmacology and bioactivity data have been proposed. In broad terms, they can be classified depending on their primary focus on compounds, targets, or their associations. The networks in target space use nodes to represent various biological targets. However, when the edges between them reflect certain relationships between the sets of compounds interacting with each target, such networks implicitly capture some aspects of the

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ligand-target space and can be queried in chemically and pharmacologically relevant ways. This idea was realized by Paolini et al. in one of the first attempts to analyze large-scale pharmacological data (Paolini et al., 2006). The dataset was composed of 4.8 million compound structures, more than 275,000 of which were active against at least one biological target, and more than 600,000 activity data points, including data from Pfizer internal screening files integrated with the commercial screening data, data on approved and investigational drugs, and published medicinal chemistry data from the previous 25 years. A polypharmacology interaction network for human proteins was built wherein the nodes represented human proteins and the edges were created if the two proteins shared common active compounds. In order to predict the pharmacology of compounds, 698 target-specific predictive models were derived using the Laplacian-modified Bayesian classifier. The molecular properties of the ligands were found to correlate with their activity against a certain class of targets. The authors considered this observation as a first step to the foundation of probabilistic approaches to drug discovery. A similar idea was implemented more extensively in the works of Keiser et al. The similarity ensemble approach (SEA) was developed to quantitatively compare biological targets by the structural similarity of the ligands they bind (Keiser et al., 2007, 2009). To compare the ligand sets without biases related to their size and chemical composition, a technique was introduced that corrects for the chemical similarity presumably expected between random ligand sets, based on the BLAST approach. The networks built on the basis of ligand similarity were shown to be substantially different, and often more pharmacologically informative, compared to the networks based on the target protein similarity (Hert, Keiser, Irwin, Oprea, & Shoichet, 2008; Keiser et al., 2007). To predict the targets for a compound, its similarities to their ligand sets are similarly evaluated against the values expected at random. Several dozens of new drug-target associations were discovered and more than 20 of them were validated experimentally, including the antagonistic activity of the serotonin transporter inhibitor fluoxetine against adrenergic β1-receptor and antagonistic activity of the anti-HIV drug delavirdine against H4 histamine receptor (Keiser et al., 2007, 2009). In a follow-up study (Lounkine et al., 2012), SEA was applied to the early versions of ChEMBL (ChEMBL2, ChEMBL10, ChEMBL12) to reveal the activity of 656 marketed drugs against 73 unintended “side effect” targets. Many of the predicted drug-target interactions were confirmed either experimentally or using information from proprietary and public databases, such as GeneGo Metabase, Thompson Reuters Integrity, DrugBank, and GVKBio. An association metric was developed to prioritize new off-targets that explained side effects better than any known target of a given drug. Thus a drug—target—adverse drug reactions network was built. Among the new predicted associations is the association between chlorotrianisene, nonsteroidal estrogen hormone nuclear receptor modulator, the COX-1 enzyme, and the abdominal pain side effect of the chlorotrianisene. It was suggested that this effect was caused by inhibition of COX-1. The importance of the rapidly growing amount of information about compound bioactivities was shown by the example of the SEA ability to predict some drug-target associations (known from the literature) based on the ChEMBL10 and ChEMBL12 databases, but not ChEMBL2. In 2018 SEA, complemented with the calculation of maximum Tanimoto similarity to the nearest bioactive, was applied to predict the biological activity of a purchasable chemical space extracted from the ZINC database (Irwin, Gaskins, Sterling, Mysinger, & Keiser, 2018). Targets were predicted for more than 160 million compounds and a

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retrospective fivefold cross-validation of the ChEMBL bioactivity dataset showed better performance of the proposed approach compared to the naı¨ve Bayesian classifier and the original SEA protocol. In a recent comparison of the publicly available web tools for bioactivity profiling in the context of drug repurposing (Murtazalieva, Druzhilovskiy, Goel, Sastry, & Poroikov, 2017), the SEA approach showed fairly good results. A weighted ensemble similarity (WES) algorithm was proposed as an extension of SEA (Zheng et al., 2015). The main difference between SEA and WES consists in the building of the feature matrix for the ligand set of each protein and weighting of the similarity scores calculated from this matrix. WES was built on the 98,327 drug-target relations extracted from BindingDB, DrugBank, GoPubMed, and PDB, and some of the predicted associations were validated experimentally, such as the discovery of the antagonistic activity of desmopressin against REN receptor. The second group of the network-based approaches involves the networks built only on the basis of chemical similarity, wherein the nodes represent the compounds while the edges are created when their chemical similarity metric exceeds a given threshold. Such networks can be referred to as molecular networks, chemical space networks (CSNs) (Maggiora & Bajorath, 2014; Vogt, Stumpfe, Maggiora, & Bajorath, 2016), or chemical similarity networks (Lo et al., 2015). An example of such network is shown in Fig. 5. The methodological aspects of the construction and analysis of such networks as well as their application to structure-activity relationship exploration were considered in several publications and reviewed by Maggiora & Bajorath (2014) and by Vogt et al. (2016). The

FIG. 5 An example of CSN for the subset of ChEMBL and DrugBank compounds (CEDB). This subset was created as a “toy” example to show some visualization aspects of different chemical space methods. At first all the structures from DrugBank 5.0.3 were extracted. For each DrugBank compound, the compounds with Tanimoto similarity in the range 0.75–0.80 were extracted from ChEMBL 23 (distinct structures). The resulting CEDB set contains 390 compounds in total: 115 ChEMBL compounds and 275 DrugBank compounds for which at least one ChEMBL structure was found. DrugBank compounds are shown in red, ChEMBL compounds are shown in black. (A) Network representation of CEDB with layout created using Kamada-Kawai algorithm. (B) Network representation of the largest component (114 nodes) with layout created using Kamada-Kawai algorithm. The compounds with the largest degree centralities from DrugBank (5-monophosphate-9-β-D-ribofuranosyl xanthine) and ChEMBL (diadenosine tetraphosphate) are shown in cyan and green, respectively.

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authors outlined several advantages of the CSNs: (1) discrete network-based representation matches the inherently discrete nature of chemicals; (2) well-developed methods of network analysis are available (mainly inherited from the social science); and (3) the networks provide a convenient framework to analyze different features of chemical space. As edge creation is the critical stage in network construction, the use of different similarity metrics gives rise to different network topologies. It should be noted that not only symmetrical, but also asymmetrical similarity metrics can be used to define an edge (Bajorath, 2004). The following types of metrics have been used in CSNs (Vogt et al., 2016): fingerprint-based Tanimoto similarity threshold (THR), matched molecular pairs (MMPs), maximum common substructure (MCS)-based Tanimoto similarity, and asymmetrical Tversky (TV) similarity. The CSN analysis was applied to investigation of various aspects of the structure-activity landscapes. In the work (Zwierzyna, Vogt, Maggiora, & Bajorath, 2015) authors characterized a number of chemical datasets with the CSNs using Tanimoto similarity, revealing significant differences in the CSN structure between the subsets of bioactive compounds from ChEMBL and random compounds from ZINC. In the work (Zhang, Vogt, Maggiora, & Bajorath, 2015), the authors built their chemical network using the THR and MCS similarity metrics to explore the distribution of the drug clusters in the network of drugs and compounds with similar activity. One of the key findings in this study was that drugs are much more prone to form clusters in the case of an MCS metric, thus leading to delineating of a drug-like subspace based on the MCS-CSNs. The use of two different similarity metrics, Russel-Rao and Baroni-Urbani, leads to sparser and denser networks compared to the networks obtained from the Tanimoto metric (Lepp, Huang, & Okada, 2009). The analysis of central nodes or hubs was performed in this work, revealing that dual adenosine A1 and A2 receptor antagonists tend to be the hubs. This suggests the suitability of this approach for the identification of molecules with multiple activities. Based on the CSN concept, the CSNAP (Chemical Similarity Network Analysis Pull-down) approach was successfully applied to the target-identification and drug-repurposing tasks (Lo et al., 2015). It builds a CSN subnetwork (cluster) comprising the query compounds and the reference ChEMBL compounds that have known (annotated) targets as well as high Tanimoto similarity to the query compounds. The compounds in a CSN are connected by an edge if their similarity exceeds a specified threshold. Then the target annotations of the reference nodes are assigned to the connecting query nodes using the consensus statistics scores determined by the target annotation frequency shared among the immediate (first-order) neighbors of each query compound in the network. Experimental validation of the CSNAP approach was performed. Targets for 212 antimitotic compounds were predicted and assessed in an in vitro microtubule polymerization assay. CSNAP was used both as a positive selection strategy to identify known compounds associated with three new categories of mitotic targets and as a negative selection strategy to identify novel chemotypes targeting microtubules, a major target in cancer drug discovery. The third group of network-based approaches involves the nodes representing both the compounds and the targets, and possibly other entities such as diseases. In a study by Sawada et al. (2015), apart from the big chemical similarity data, the data on compound-target interactions (defined by the authors as phenotypic effects) were also used. Data were extracted from numerous databases including ChEMBL, DrugBank, BindingDB, KEGG, etc. The

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algorithm could be divided into two logical parts. First, all potential targets for a given drug were predicted based on drug-compound similarity and the compound-target associations inferred from the activities of compounds against targets. Then, based on the target-disease associations, a list of diseases that may be treated by a drug is predicted using either simple matching of target proteins or supervised classification. Finally, a comprehensive drugtarget-disease association network was created for 8270 drugs and 1401 diseases. No experimental validation of this approach was performed; however, some proposed indications for a number of drugs are supported by the retrospective literature data. The REMAP method (Lim et al., 2016) based on the dual-regularized one-class collaborative filtering algorithm was suggested for large-scale off-target predictions. The compoundprotein interaction data were extracted from ChEMBL, DrugBank, and ZINC, giving more than 200,000 unique compound-protein association pairs in which compounds were denoted as “active” and 70,000 pairs in which they were “inactive.” The core idea of the method is adopted from the recommender system methodology: the algorithm determines the users’ preferences and suggests a sorted list of items that they might like. In such a framework the drugs may be considered as users and the targets as listed items. One of the attractive features of this approach is that the negative samples are not required for modeling but may be used as additional data. Internally, a sparse matrix representing the bipartite drug-protein association network is factorized into two low-rank matrices of the drug and protein profiles, additionally taking into account the drug-drug and protein-protein similarities. This approach was validated by retrospective literature analysis, and its easy scalability makes it a promising tool for the in silico drug repurposing. The recently proposed tripartite drug-bioactive-target network approach (Kunimoto & Bajorath, 2018) integrates the MMP-based similarity relationships between the approved drugs and other bioactive compounds, as well as the known compound-target interactions (activities) of drugs and other bioactive compounds from DrugBank and ChEMBL. Additional targets of drugs can be predicted by inference on the basis of their close structural relationships to other drugs or bioactive compounds. Some of the predictions are supported by the retrospective literature data. The network-based analysis may be used not only for individual chemical structures, but also in cases when the structures are not known. Such an approach, called molecular networking, is widely applied to the analysis of the mass spectral data of natural source extracts. The tandem MS/MS spectra of an extract or a set of extracts are used to build a network composed of nodes representing a single consensus spectrum and edges representing cosine similarity between pairs of spectra (Allard et al., 2016; Watrous et al., 2012). Thus, compounds are implicitly grouped based on their MS fragmentation patterns. The molecular networking approach was applied to identify bioactive natural products in extracts from two plant sources, Bocquillonia nervosa and Neoguillauminia cleopatra (Olivon et al., 2017). Inhibitory activity was confirmed for the selected compounds against the Wnt signaling pathway and chikungunya virus replication. The network-based approaches are widely used for drug repurposing tasks. For most of them, the main problem is the sparsity of the drug-target matrix resulting in the scarcity of negative drug-target associations that inevitably leads to biases in machine learning. This problem may be partially solved by incorporating large available chemical interactome data into such approaches. Another disadvantage of the network-based approaches is the poor

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scalability of visualization. While network visualization is very illustrative, it does not allow one to obtain reasonable representations of networks containing thousands of nodes. However, graph embedding algorithms were recently developed for the visualization of network-like data (Goyal & Ferrara, 2018). Although their usability for drug repurposing is yet to be established, at the moment they seem to be promising tools that may complement existing approaches.

4 DIMENSIONALITY REDUCTION METHODS In contrast to the network-based methods that have no coordinate space for the nodes, in a number of chemical space analysis methods the compounds are represented by points in a multidimensional descriptor space (Fig. 2). The position of a point is defined by the vectors composed of values usually referred to as descriptors that can be derived from a wide range of compound properties, including structural features, physico-chemical properties, biological activities, etc. One of the important features of such representations of chemical space is the ability to build its maps by means of the dimensionality reduction algorithms. Using an analogy to geography, such an approach to chemical space analysis was called chemography (Oprea & Gottfries, 2001). In this section, widely used chemographic methods, such as principal component analysis, self-organizing maps, and generative topographic mapping, will be considered.

4.1 Principal Component Analysis One of the simplest methods of chemical space analysis and visualization is the principal component analysis (PCA) (Fig. 6). The main goal of PCA is the transformation of descriptor coordinates to obtain a new set of uncorrelated variables from the initial set of descriptors. The variables in this new basis set are called principal components. These artificial variables are linear combinations of optimally weighted observed variables that account for the maximal amount of variance in the data set. The principal components are selected sequentially in such a way that the first principal component accounts for the maximal amount of total variance in data, the second component is selected to account for a maximal amount of variance not accounted for by the first one, etc. A more accurate and detailed description of the algorithm can be found in Edward Jackson (2005). The main advantage of PCA in comparison with the other dimensionality reduction algorithms (for example, self-organizing maps) is its nonstochastic nature. That means that the same data set and descriptors will provide the same principal components and the same projection of the compounds from initial multidimensional descriptor space into this new latent space. One of the first examples of PCA application to the mechanism of action prediction is presented in Lange et al. (2007). PCA was applied to the binding data for 12 compounds (three drug candidates and nine antipsychotic reference compounds) and 16 receptors related to the schizophrenia treatment (dopaminergic, serotoninergic, muscarinic, adrenergic,

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FIG. 6 PCA plot for the CEDB dataset (DrugBank compounds are shown in red, ChEMBL compounds are shown in black). 2D PCA was performed with FragFP fingerprints implemented in DataWarrior (Sander, Freyss, von Korff, & Rufener, 2015). Drugs and drug candidates are distributed smoothly among ChEMBL analogs. Bioactivity data for ChEMBL compounds in proximity to drugs and drug candidates may be used for drug-target interaction predictions.

histaminergic). The analysis of the PCA plots revealed specific binding profiles of drug candidates compared to known drugs. Not only the original PCA method, but also its modifications can be used for the analysis of chemical space. For example, the ChemGPS method is based on the principles similar to the Global Positioning System (GPS) (Oprea & Gottfries, 2001). Similar to GPS, which exploits the satellites in the Earth’s orbit to find the location of an object on its surface, ChemGPS uses structurally diverse molecules as reference “satellites” that determine the initial chemical space. It allows one to distribute different classes of organic molecules properly on the PCA-based map. In the field of drug discovery, the most interesting compounds are those that have properties similar to drugs, therefore it is important to be able to analyze the region of chemical space occupied by the drug-like compounds. To achieve this, at least one property of each chosen satellite molecule must be far outside the ranges of drug-likeness, so that all the drug-like molecules would be inside the space delimited by the satellites. PCA was performed using mostly physico-chemical and topological descriptors. The ability of the method to properly clusterize compounds was demonstrated on the α-amino acids subset. The new coordinates based on PCs support simple navigation in the chemical space and allow one to project almost any chemical compound (Lee et al., 2012; Muigg, Rosen, Bohlin, & Backlund, 2012; Feng, Campitelli, Davis, & Quinn, 2014; Lauria, Tutone, Barone, & Almerico, 2014). In another approach, ChemMAP, the PCA algorithm was also used for the analysis of the chemical space of the bioactive compounds and drugs (Faller, Ottaviani, Ertl, Berellini, & Collis, 2011).

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A biological target prediction protocol based on PCA for a matrix of known ligand descriptors was proposed, named BIOlogical Target Assignation (BIOTA) (Lauria et al., 2014). The algorithm involves the determination of relative positions in the principal component space for the compounds active against different biological targets, followed by the calculation of barycentric coordinates for each of them. Compound libraries may be projected into the same space, and distances between these compounds and the biological target barycenters may be calculated to predict feasible interactions. The success of this approach depends on the quality of the initial biological data, the choice of molecular descriptors, and the biological target under consideration, but it can undoubtedly be applied for drug repurposing projects. An important feature of the PCA approach is its scalability. Using molecular quantum numbers (MQN) as descriptors, the PCA maps were built for very large subsets of compounds, GDB-13 (970 million compounds containing up to 13 atoms of C, N, O, S, and Cl) (Blum & Reymond, 2009), and GDB-17 (166.4 billion compounds containing up to 17 atoms of C, N, O, S, and halogens) (Ruddigkeit et al., 2012). MQN-based PCA maps were also used for the design of positive allosteric modulators of α3β2 nicotinic acetylcholine receptor (nAChR). By walking through ChEMBL compounds in the MQN space close to known α7 nAChR partial agonist PNU-282987 (quinuclidine 4-chlorobenzamide), several orthohalogenated 3-benzylaminoquinuclidines with a distinctive α3β2 positive allosteric modulator effect were identified (B€ urgi et al., 2014). PubChem compounds were analyzed using MQN-based maps and the MQN-annotated version of PubChem database was also created, allowing fast retrieval of the nearest neighbors of any PubChem molecule (van Deursen, Blum, & Reymond, 2010, 2011).

4.2 Self-Organizing Maps Self-organizing map (SOM, Kohonen network) is a neural network with unsupervised learning (there are also modifications using supervised learning) designed for clustering and visualization of multidimensional data. This method is widely used in drug discovery for dimensionality reduction and subsequent automatic compound clusterization, novel lead compound identification, drug repurposing, and illustrative chemical space visualization (Gasteiger, Teckentrup, Terfloth, & Spycher, 2003; Iyer & Bajorath, 2011; Schneider & Schneider, 2017; Schneider, Tanrikulu, & Schneider, 2009). An algorithm of self-organizing map generation was developed by Teuvo Kohonen in 1980s (Kohonen, 1982) and the first attempts to use this approach in chemistry were made in 1990s by Gasteiger and coworkers (Gasteiger, Li, & Uschold, 1994). The Kohonen map is a grid of rectangular or hexagonal cells, or neurons, which are defined with the weight vector of the length equal to initial descriptor space dimensionality. In the simplest realization, the initial weight vectors are generated randomly. During the learning process they are adjusting to the input data. The algorithm of unsupervised SOM building includes the following steps: (1) choose a datapoint, (2) calculate distances between a data vector and neuron weight vectors and determine the closest “winner neuron,” (3) update “winner neuron” and its neighbors by pulling them closer to the input data vector, and (4) repeat with the next datapoint or terminate (Fig. 7). SOM is suitable for lead discovery, compound library design, diversity analysis, and activity profiling. One of the important features of SOM is the ability to find compounds that

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FIG.

7 SOM for CEDB subset containing 10  10 neurons (DrugBank compounds are shown in red, ChEMBL compounds in black). SOM was trained on FragFP descriptors implemented in DataWarrior (Sander et al., 2015). Bioactivity data for ChEMBL compounds clustered with drugs and drug candidates may be used for drug-target interaction predictions.

possess similar biological activity but have quite different chemotypes from the query compound. In the Reker’s work (Reker, Rodrigues, Schneider, & Schneider, 2014), SOM consensus-based approach was designed and applied to identify new compound-target interactions. Procedure was named “self-organizing map-based prediction of drug equivalence relationships” (SPiDER). The procedure performs target prediction twice using two SOM projections: the first one trained on the CATS (chemically advanced template search) topological pharmacophore descriptors (Reutlinger et al., 2013); the second one on MOE (Molecular Operating Environment, 2018) physicochemical descriptors. A number of mathematical functions to combine the scores evaluated by these SOMs were studied, and the arithmetic mean was found to be the most adequate. The SPiDER procedure was successfully used for off-target prediction in the set of pharmacologically active compounds using COBRA database as a reference set for training SOMs (Schneider & Schneider, 2003). It is noteworthy that the authors considered only the predictions relying on drug pairs with low Tanimoto structural similarity ( 2000 mg, selected from DrugBank, followed by structural similarity with crystal structure ligands of the above proteins (Tanimoto cut-off: 0.5). These selected drugs were docked into the binding cavities of the four proteins using PyRx version 0.9.2 software. Potential binders were further subjected to antimicrobial susceptibility testing using disc diffusion method. Further, the effects of combination of the selected

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drugs with ciprofloxacin and ciprofloxacin alone were investigated. From the initial hit set of 19 drugs with antipseudomonas activity, five drugs (celecoxib [28], fluconazole [29], nitrofurantoin [30], desloratadine [31], and meloxicam [32]) clearly affected the antimicrobial activity of ciprofloxacin. These drugs did not have antipseudomonas activity when tested alone at their original indication therapeutic concentrations. The study is seminal in demonstrating the ARB activity of these drugs against P. aeruginosa. Iwata, Sawada, Mizutani, and Yamanishi (2015) developed a new computational method to generate systematic drug repurposing hypotheses using a framework of supervised network interface. Each drug-disease pair is assigned a descriptor based on the medicinal and side effects profile (phenotypic features) and disease molecular features such as disease-causing genes, pathways, epigenetic factors, etc. This was followed by a statistical model building for predicting new drug-disease associations. The performance of the new method was tested rigorously which, beyond doubt, confirmed the superiority of the method with respect to accuracy and applicability domain when a large-scale prediction using 2349 drugs and 858 diseases was carried out. Notable examples include repurposing of ceftriaxone (33) (used for treating Gram-negative bacterial infections) and vancomycin for treating Whippel’s disease (caused by Tropheryma whippeli, a Gram-negative bacterium). Dean and van Hoek (2015) reported screening of 420 FDA-approved drugs for antibiofilm activity against Francisella novicida, a model organism for virulent Francisella tularensis. These are Gram-negative coccobacilli, which have acquired resistance to fluoroquinolone antibacterials. The authors started the drug repurposing campaign with the aim of targeting expression of virulence factors, rather than the bacterial growth. Several drugs exhibited antibiofilm activity in the screen. Further, chemoinformatics analysis of the structure with activity was performed using Spearman rank correlation and multidimensional scaling (MDS) and hierarchical clustering methods. The results indicated that certain chemical signatures were associated with antibiofilm and growth-inhibitory activities. In all the in vitro and in vivo studies maprotiline (34), a norepinephrine reuptake inhibitor that was hypothesized to target bacterial signal transduction pathways, two-component systems (TCSs), was a clear winner. Orphan sensor kinase QseC was proposed to be the target for maprotiline. The molecular docking studies using predicted F. novicida QseC structure supported the fact that maprotiline binding site would be very close to the predicted norepinephrine binding site. Overall, the combined experimental and chemoinformatics analyses led to successful repurposing of 34 as a treatment option for antibiotic-resistant pathogens. In summary, repurposing of drugs for antibacterial indication can be either in the form of an ARB affecting expression of virulence factors or other related mechanisms, thereby “weakening” the pathogen, which leads to regaining the sensitivity for the otherwise “failing” antibiotics, or the repurposed drug itself has bacteriostatic/bactericidal action. Whether the concentration at which the drug exhibits the repurposing potential is therapeutically relevant to its concentration for the original indication has a key role to play in the successful repurposing of older drugs for newer indications, such as antibacterial action.

3.3 Infections Caused by Mycobacterium tuberculosis The global burden of Mtb is increasing by leaps and bounds. The emergence of XDR- and TDR-TB created panic in the health-care agencies across the world. Developing and underdeveloped nations are looking forward to the scientific community to come up with solutions 3. EXAMPLES AND CASE STUDIES

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FIG. 4 Drugs repurposed for antitubercular indication.

likely to mitigate the serious risk posed by the dreadful disease. It has been almost 40 years since a new anti-TB drug was introduced in the market. Although bedaquiline (35), a new anti-TB drug was approved for use in the United States in 2012, WHO recommended that it should to be reserved for MDR-TB cases where other regimens were ineffective. Several reviews have been published on drug hunting for TB treatment as shown in Fig. 4 (Ekins, Spektor, Clark, Dole, & Bunin, 2017; Maitra et al., 2016; Schaaf, Garcia-Prats, McKenna, & Seddon, 2018). Brindha et al. (2017) started off with a bioinformatics approach for repurposing drugs for TB. A dataset of DrugBank drugs (#1554) was used for structure-based VS against serine/ threonine-protein kinase, pknB of Mtb. The target serves as a house-keeping molecule in Mtb. Glide and AutoDock Vina (rigid docking) were used initially followed by induced fit docking for cherry-picking. Of the top 14 drugs, prioritized based on the docking scores and the ranks in both docking methods, atorvastatin (36) was one hit, which has been shown to possess anti-Mtb activity in vitro. The authors proposed that the top-six drugs could be taken up for experimental screening for repurposing against TB. In a ligand-based pharmacophore modeling study, Choudhury, Priyakumar, and Sastry (2016) performed VS against cyclopropane synthase (CmaA1), a potential Mtb target for drug development. The developed pharmacophore models were validated appropriately using a set of inhibitors and noninhibitors of CmaA1. The VS protocol included a database of 6494 drugs from DrugBank and 709 anti-Mtb and 11,109 anti-HIV compounds from ChEMBL database. Of the 18,239 compounds virtually screened, a total of 12 hits were identified as 3. EXAMPLES AND CASE STUDIES

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potential CmaA1 inhibitors. The interesting hits from the DrugBank database were—taxifolin (37) (polyphenol, experimental drug), myricetin (38) (polyphenol, experimental drug), and 20 -deoxyuridylic acid (39) (nucleoside analog, experimental drug). None of the screened approved drugs could make it to the final list of 12 hits at the end of the fourth cycle of the VS protocol. These hits may further be explored experimentally for their potential inhibitory activity against CmaA1. Ramakrishnan, Chandra, and Srinivasan (2015) presented a novel strategy for target identification by exploring the evolutionary relationship between approved drug targets and Mtb proteins. The concept is quite simple—using sensitive remote-homology detection techniques, explore the evolutionary relationship between proteins from different species and deduce similarities between them with particular emphasis on the small-molecule binding sites. Only drugs acting on human proteins were considered. Around 78 Mtb proteins were identified as potential targets for 130 approved human drugs. Further, molecular docking was employed to understand the precise details of the protein-ligand interaction, which has a great potential for generating drug repurposing hypothesis followed by experimental screening. A few interesting Mtb protein target-drug pairs included: (a) thymidylate synthase thyA (Rv2764c)—sulfadoxine (40); (b) enoyl reductase inhA—triclosan (41); (c) urease alpha subunit ureC—acetohydroxamic acid (42), etc. Overall, the authors provided a generic methodology that could be used for drug repurposing for other antimicrobial indications. In summary, meticulous efforts based on bio- and/or chemoinformatics approaches for antitubercular drug repurposing have not been successful so far. This is not surprising, given the complexity of Mtb as a pathogen. The scientific community is hopeful that these approaches will yield substantial outcomes in curbing the menace of Mtb.

4 DRUG REPURPOSING FOR ANTIVIRAL INDICATION The recent outbreak of Ebola virus disease (EVD) (2014–16) in West Africa shook the entire world. It was the worst outbreak since its first appearance in the mid-1970s. The fatality rate was 50%. No proven treatment options are available for treating EVD. Small-molecule drugs and vaccines are under trial. Similarly, a large ZIKV disease outbreak was reported in 2007, followed by appearances over 2015–17 in various parts of the world, forcing WHO to declare it as “public health emergency of international concern” in February 2016. Dengue fever, a potentially fatal and neglected tropical disease, is yet another addition to the list. Drug treatment is not available, though a vaccine has been approved and is available in several countries. In such a gloomy scenario, various research groups have attempted drug-repurposing approaches (Fig. 5) for these dreadful diseases and excellent reviews are available (Cheng, Murray, & Rubin, 2016; Schuler, Hudson, Schwartz, & Samudrala, 2017; Sweiti, Ekwunife, Jaschinski, & Lhachimi, 2017). In a computational drug repurposing study, Kharkar, Ramasami, Choong, Rhyman, and Warrier (2016) utilized VP35 viral protein for ligand- and structure-based screening of approved drugs (#1135) from DrugBank (https://www.drugbank.ca/). The crystal structure ligand of VP35 was used as a query for initial ligand-based shape- and electrostatic-similarity searches. The top-ranked hits from the ligand-based screen were further investigated for

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FIG. 5 Drugs repurposed for antiviral indication.

binding in the ligand-binding pocket of VP35 using molecular docking. Some of the promising hits included nateglinide (43) (antidiabetic), telmisartan (44) (antihypertensive), ticarcillin (45) (antibiotic), sitaxentan (46) (antihypertensive), tenoxicam (47) (antiinflammatory), among others. Several hits from this computational campaign overlapped with the other anti-Ebola experimental drug-repurposing campaigns reported in the literature, such as alitretinoin (48), nateglinide (43), bexarotene (49), fluvastatin (50), gemfibrozil (51), and others, validating the computational drug repurposing study by Kharkar et al. This should motivate the experimentalists to screen other virtual hits for anti-Ebola activity. Veljkovic et al. (2015) proposed a novel approach for computational screening of molecular libraries for potential hits. The approach uses average quasi valence number (AQVN) and electron-ion interaction potential (EIIP) parameters related to the long-range interactions between biomolecules. The study proposed approved (#267) and experimental (#382) drugs

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from DrugBank cherry-picked by the AQVN/EIIP-based VS, as potential Ebola virus entry inhibitors. The predictive model was developed using a training set, which included 152 drugs experimentally screened and established as in vitro and in vivo Ebola virus infection inhibitors. The control set contained 45 million PubChem (http://www.ncbi.nlm.nih.gov/pccompound) compounds and 49 Ebola virus entry inhibitors from patents and literature. The physical descriptors—AQVN and EIIP—represent the unique physical properties directly related to long-range interactions between biomolecules. In brief, drugs/drug-like molecules with similar AQVN and EIIP are likely to interact with common therapeutic targets, which is directly relevant to the drug-repurposing campaigns attempting to find new uses for old drugs. Some of the drugs selected for treatment of EVD included trihexyphenidyl (52), phenteramine (53), methacholine (54), pregabalin (55), bupivacaine (56), dronabinol (57), and many others. This study provided a simple yet very relevant potential approach for computational drug repurposing that can be extended to other therapeutic areas. Ekins et al. (2015) applied machine-learning methods to a set of 868 molecules from viral pseudotype entry and the Ebola virus replication assays. A cutoff on IC50 < 50 μM was used for an “active” molecule. A large number of molecular descriptors, such as AlogP, MW, #RotlBonds, #Rings, #AroRings, #HBondAcc, #HBondDon, molecular-function class fingerprints of maximum diameter 6 (FCFP_6), and molecular fractional polar surface area, were used. A variety of models—Bayesian, support vector machine (SVM) and recursive partitioning forest (RP-Forest) and single tree—were built in Discovery Studio and compared. The Bayesian model following fivefold cross validation performed the best. This model and other competing models including a previously published pharmacophore model were used for scoring compounds from MicroSource Spectrum set of 2320 compounds. Three selected compounds—pyronaridine, quinacrine and tilorone—along with positive control chloroquine, were tested in vitro. The hits were more potent than chloroquine (58). The combined—computational and experimental—approach led to the successful identification of potent anti-Ebola drugs, supporting its unsurmountable utility in drug discovery. Similar computational and experimental investigations have yielded success for anti-ZIKV therapeutics. Chan et al. (2017) identified 11 drugs from a thorough literature search followed by one of in vitro studies, such as MTT assay, cytopathic effect (CPE) inhibition, virus yield reduction, plaque reduction, and time-of-drug-addition assays. Bromocriptine (59) (dopamine agonist, ergoline derivative) was a clear winner, demonstrating ZIKV NS2B-NS3 protease inhibitory activity. Molecular docking studies exhibited interaction of 59 with several residues of the proteolytic cavity of NS2B-NS3 protease of ZIKV. Moreover, bromocriptine and interferon-α2b worked synergistically in CPE inhibition assay, confirming the potential utility of the hit for ZIKV treatment. Ekins et al. (2016) constructed a homology model of ZIKV envelope protein using dengue crystal structure 4gsx as a template (
60% identity). The complete model of subunit A was further used for docking studies using Discovery Studio. Prestwick Chemical Library (http://prestwickchemical.com/libraries-screening-libraries.html) (#1280) was docked into the homology model binding sites. Three antivirals made it to the Top 10 list—ritonavir (60), indinavir (61), and saquinavir (62), along with colistin, pepstatin A, and five others. These molecules may be taken up for further experimental testing against ZIKV. Wang and Xu (2015) developed DenguePredict, a drug-repurposing system using largescale disease network and unique drug-treatment databases. The authors constructed a novel

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algorithm to prioritize FDA-approved drugs for Dengue treatment. In the initial validation runs, DenguePredict yielded two hits—chloroquine (58) and ivermectin (63)—both of which were tested in clinical trials for curing dengue. The combination of the data from both the diseases and the drugs in a network-based algorithm has great potential in drug repurposing and conventional new drug-discovery programs.

5 DRUG REPURPOSING FOR NEGLECTED TROPICAL DISEASES According to WHO, neglected tropical diseases (NTDs) are a cluster of 17 communicable diseases predominantly found in tropical/subtropical regions, affecting more than one billion people worldwide (http://www.who.int/neglected_diseases/diseases/en/). Flagellated protozoan parasites (trypanosomatids) are responsible for three NTDs, namely human African trypanosomiasis (HAT), Chagas disease, and leishmaniasis. Majority of the current therapeutic trypanosomatid targets are represented by enzymes (especially, eukaryotic protein kinases) or cell-surface receptors. HAT, also known as sleeping sickness, is a vector-borne disease transmitted by the bite of tsetse fly (Glossina genus). It is caused by protozoan parasites Trypanosoma brucei gambiense (T. b. gambiense) and Trypanosoma brucei rhodesiense (T. b. rhodesiense). Another parasite subspecies, Trypanosoma brucei brucei (T. b. brucei), is responsible for animal trypanosomiasis and is widely used for in vitro and in vivo studies because it is noninfective to humans (Keita, Bouteille, Enanga, Vallat, & Dumas, 1997). When the parasite infects the CNS, the typical signs and symptoms of the disease include—neurological disorders such as sleep disturbance, ataxia, psychiatric disorders, coma, and ultimately, if untreated, death (Rodgers, 2009). Chagas disease or American trypanosomiasis is caused by the protozoan parasite Trypanosoma cruzi and is transmitted by the bite of infected triatomine bug, also known as kissing bug (Rassi Jr, Rassi, & Marin-Neto, 2010). If untreated, the disease is fatal following severe myocarditis and, less commonly, meningoencephalitis (Pecoul et al., 2016). Leishmaniasis is caused by protozoans belonging to the genus Leishmania and transmitted by the bite of infected female sandflies. It has been classified as category I: emerging or uncontrolled diseases by the WHO. There are three main forms of the disease: cutaneous leishmaniasis (CL), mucosal leishmaniasis (ML), and visceral leishmaniasis (VL) (Pagliano, Ascione, Di Flumeri, Boccia, & De Caro, 2016). Schistosomiasis is caused by flatworms of the genus Schistosoma (S. mansoni, S. japonicum, S. haematobium, S. intercalatum, and S. mekongi). The current reliance on a single drug (praziquantel) for the treatment and control of the disease calls for the urgent discovery of novel schistosomicidal agents (Cowan & Keiser, 2015; Eissa, Mossallam, Amer, Younis, & Rashed, 2017).

5.1 Current Treatments and Drug Development for Neglected Tropical Diseases Approved drugs candidates used to treat protozoan NTDs possess a definite level of adverse effects, selectivity issues, complex routes of administration, and resistance (Chakravarty & Sundar, 2010). Five drugs are used for the treatment of HAT: pentamidine

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and suramin for the early and peripheral stages and melarsoprol and eflornithine (64) alone or in combination with nifurtimox (NECT) for the CNS stage. Fexinidazole and oxaborole (SCYX-7158) are in clinical trials for the treatment of HAT. Benznidazole and nifurtimox are the two drugs available for the treatment of Chagas disease. However, their use is restricted because of severe side effects. Posaconazole has been taken into phase II trials for the treatment of chronic Chagas disease (Morillo et al., 2017). The old-style treatment for leishmaniasis includes a combination of pentavalent antimony compounds, sodium stibogluconate, and meglumine antimoniate. Also, liposomal amphotericin B was approved by FDA for VL having benefits of low toxicity and shorter duration of treatment whereas the oral miltefosine (65) is an effective FDA-approved treatment against both VL and CL. Pentamidine and azoles (ketoconazole, itraconazole, and fluconazole) are efficient for treating selected cases but they are not FDA approved (Uliana, Trinconi, & Coelho, 2017). WR-279,396 (15% paromomycin + 0.5% gentamicin), 18-methoxycoronaridine, pentoxifylline, and imiquimod are the drug candidates under clinical investigation for CL treatment (Brito et al., 2014; Sosa et al., 2013) Daylight-activated photodynamic therapy (DA-PDT) is also under clinical investigation in treating CL caused by L. major and L. tropical, and it is patient-friendly (Enk et al., 2015). The parasites often change their surface coat (known as antigenic variation) and dodge the immune system, in addition to varying their expressed proteins (Horn, 2014). These characteristics have complicated vaccine development for HAT, although for leishmaniasis and Chagas disease some candidate vaccines are currently under development (Chakravarty et al., 2011; Pereira et al., 2015). Protozoan vaccine development faces a challenge of “transition from animal models to human clinical evaluation” and to date no vaccines have become available (Srivastava, Shankar, Mishra, & Singh, 2016). The development of new drugs and vaccines is needed for the eradication of protozoan NTDs (Field et al., 2017). However, developing drugs and vaccines is a risky task because of low return-on-investment and lack of scientific data. The problem has been somewhat roofed by private-funding institutions and pharmaceutical companies. The Bill & Melinda Gates Foundation and DNDi (The Drugs for Neglected Diseases initiative) support the development of safe and effective treatments to eliminate NTDs (Stolk et al., 2016). The ChEMBL—Neglected Tropical Disease archive (https://www.ebi.ac.uk/chemblntd) is an Open Access repository for screening and medicinal chemistry data sharing. GlaxoSmithKline has a not-for-profit group that develops new drugs and vaccines to eliminate NTDs (Diaz et al., 2014). Recently, nanotechnologies and macrophage-mediated targeted therapy have provided innovative opportunities in the development of unconventional approaches for treating NTDs (Akbari, Oryan, & Hatam, 2017; Branquinho et al., 2017). Biological evaluation and validation processes are the chief steps in defining the antiparasitic activity of a chemical entity. While moving from less to highly complex biological models (target-based to cell-based to animal models), there are a lot of variations in the activity/efficacy profile of the drug candidates. This flow process, so-called “biological scale-up,” represents one of the major issues related to lead identification in drug discovery. To overcome this problem, researchers screen compounds directly in the most complex biological system. For initial screening and hit identification, in protozoan drug discovery, primarily target-based (macromolecular target screening) or cell-based (phenotypic screening) methods are engaged. Not much scientific data is available for validation of the drug target which may obstruct the use of target-based approaches for the lead identification

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process. Phenotypic screening, bearing a certain biological complexity, may be beneficial in finding active and effective compounds since inhibitors may act on unknown mechanisms and multiple pathways, unlike biochemical screens, which depend on known single therapeutic pathways. Overall, phenotypic screening has resulted in more appropriate compounds compared to the target-based assays for effective parasitic infestations (Andriani et al., 2013; Diaz et al., 2014). Most of the operative methods used to find novel drugs for NTDs are target-based and drug repurposing approaches (Naula, Parsons, & Mottram, 2005). The recovery of new drugs may be advantageous, by referring to the established inhibitors of human targets that share a certain level of similarity with parasite targets; this can be a starting point for the development of parasitic inhibitors. There is a library of drugs approved for use in human infections or clinical drug candidates with low potency and selectivity against parasitic targets, and these molecules can be optimized further. Currently many drugs used for the treatment of NTDs are repurposed from other FDA-approved therapeutic indications, as shown in Fig. 6. The anticancer agents miltefosine (65, Fig. 6) and eflornithine (64), antifungal antibiotic amphotericin B, and the antibiotic paromomycin represent effectively repurposed drugs for sleeping sickness (Pollastri & Campbell, 2011). A number of classes of human drugs have been repurposed in trypansomatids. These drug classes include anticancer, antimicrobial, antifungal, antibacterial, antiviral, antihistamines, CNS-active drugs, antiparasitic, and other drugs (Charlton, Rossi-Bergmann, Denny, & Steel, 2018). Andrews, Fisher, and Skinner-Adams (2014) discussed drug repurposing, with a focus on foremost human parasitic protozoan diseases, such as malaria, trypanosomiasis, toxoplasmosis, cryptosporidiosis, and leishmaniasis. Bezerra-Souza, Yamamoto, Laurenti, Ribeiro, and Passero (2016) evaluated butenafine (66) for antileishmanicidal activity against two major species of Leishmania. Butenafine (66) eliminated promastigote (programmed cell death) forms of L. amazonesis and L. braziliensis with efficacy comparable to 65, which is a standard drug, but butenafine was more effective in eradicating intracellular amastigotes for both species. Thompson et al. (2016) reported repositioning of 6-nitro-2,3-dihydroimidazo[2,1-b][1,3] oxazole derivatives (antitubercular agents) for NTDs. This study led to the identification of DNDI-VL-2098 as a potential drug candidate for VL. Additionally, the lead molecule was optimized without conceding activity against VL. A phenylpyridine derivative was found to be more promising than the original preclinical lead candidate in a mouse model of acute Leishmania donovani infection. Neves, Braga, Bezerra, Cravo, and Andrade (2015) reported an in silico, drug-repurposing strategy that involved the identification of novel schistosomicidal drug candidates using similarity between schistosome proteins and known drug targets. In this process, they predicted 115 compounds that could be evaluated for schistosomiasis and the favorable actives discovered could serve as preliminary hits for lead identification. Bellera et al. (2015) reported computer-aided identification of approved drugs: clofazimine (67), benidipine (68), and saquinavir (62, Fig. 5) as potential trypanocidal compounds. This work successfully assimilated molecular and cellular biology with computer-aided drug discovery, confirming the utility of VS to develop knowledge-based drug repurposing. Also, the study recommended rules to select repositioned molecules for advancing to investigational drug

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FIG. 6 Drugs repurposed for antiparasitic indication.

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status and offered a vision on (67) and (68) as drug candidates for treating Chagas disease. da Costa-Silva, Galisteo, Lindoso, Barbosa, and Tempone (2017) investigated the in vitro immunomodulatory potential of buparvauone (69) (BPQ) and evaluated the in vivo efficacy of nanoliposomes (BPQ-LP) in Leishmania infantum-infected hamsters. The subcutaneous route was found to be the most effective for BPQ-LP and this novel formulation could be a key in treating VL. Capparelli, Bricker-Ford, Rogers, McKerrow, and Reed (2017) performed high-throughput screens to identify newer drug candidates to treat parasites. Auranofin (70) (gold (Au)containing compound), which was approved by the FDA for the treatment of rheumatoid arthritis, was effective in vitro and in vivo against Entamoeba histolytica and both metronidazole-sensitive and -resistant strains of Giardia. Bessoff et al. (2014) identified three novel chemical scaffolds derived from the quinolin-8-ol, allopurinol, and 2,4-diaminoquinazoline using a cell-based high-throughput screen (Screening Medicines for Malaria Venture (MMV) Open Access Malaria Box—No longer available at the time to writing this book chapter) against Cryptosporidium parvum. Two of the scaffolds exhibited more rapid inhibition of C. parvum growth than nitazoxanide (71), which could be further improved. The 2,4-diamino-quinazoline and allopurinol-based compounds also inhibited the growth of a related apicomplexan parasite Toxoplasma gondii. Sangenito, d’Avila-Levy, Branquinha, and Santos (2016) demonstrated the repositioning of clinically approved drugs such as nelfinavir (72) and lopinavir (73), aspartic peptidase inhibitors (PIs) currently applied in HIV treatment. The HIV-PIs 72 and 73 were active against T. cruzi in in vitro models of infection and also significantly reduced the number of intracellular amastigotes in mammalian cell lineages. However, the main mechanism of action of 72 and 73 could not be interpreted. Alberca et al. (2016) reported the discovery of novel polyamine analogs with anti-protozoal activity by computer-guided, drug-repositioning approach. In this study, a database of 268compounds containing polyamine analogs with and without inhibitory effect on T. cruzi were screened virtually along with certain conditions to identify antitrypanosomal compounds among drugs already used for other therapeutic indications (i.e., computer-guided drug repositioning). Triclabendazole (74), sertaconazole (75), and paroxetine (76) displayed inhibitory effects on the proliferation of T. cruzi (epimastigotes) and the uptake of putrescine by the parasite. Also, the uptake of other amino acids and the proliferation of infective T. brucei and L. infantum (promastigotes) were prevented. Trypanothione synthetase was ruled out as the molecular target dictating the antiparasitic activity of these compounds. Patterson et al. (2016) investigated delamanid (77) (OPC-67683, an antitubercular nitroimidazole) as a potent inhibitor of L. donovani both in vitro and in vivo. Delamanid (77) was screened as it shares a high degree of structural similarity with (R)-PA-824 (78) and DNDI-VL-2098 (79). It was found that 77 could be potentially repurposed for treating VL. Kaiser, Maes, Tadoori, Spangenberg, and Ioset (2015) described the repurposing of the open-access malaria box for identifying novel chemotypes against trypanosomatids. Also, in vitro and in vivo studies were performed with respect to efficacy, toxicity, and pharmacokinetic parameters. The Open Access Malaria Box initiative was able to create an original open-collaborative working environment useful for drug discovery for neglected diseases within an intellectual property-free framework. Eissa et al. (2017) showed that chlorambucil (80) possessed antischistosomal activity. In vivo efficacy was found to be highest at the juvenile stage of S. mansoni, and was significantly better than PZQ. Further studies are recommended for 80 target identification and improvement in its pharmacokinetic profile. 3. EXAMPLES AND CASE STUDIES

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Cowan and Keiser (2015) reported on the repurposing of anticancer drugs against S. mansoni. Screening was performed on a set of National Cancer Institute (NCI)’s Developmental Therapeutic Program for antischistosomal activity and a total of 11 compounds were found to be active against both stages of S. mansoni (larval and adult stage), out of which five lost the activity in the presence of serum albumin. The highest activity was displayed by trametinib (81) and vandetanib (82). Further investigation on the mechanism of action and SAR can help in improving the potency of the hits. De Rycker et al. (2016) used two small libraries—NIH (Clinical Collection) and Selleck Chemicals (FDA-approved drug library)—for single-point primary screen, followed by potency determination and rate-of-kill assessment against T. cruzi for treatment of Chagas disease. With the help of in vitro CYP51 activity and various in vitro assays, the authors obtained clemastine (83), azelastine (84), ifenprodil (85), ziprasidone (86), and clofibrate (87) as lead molecules that can be optimized further for the treatment of Chagas disease. Hopper et al. (2016) found organic gold (I) compounds 70 and chloro(diethylphenylphosphine)gold(I)) active against T. vaginalis and several strains of trichomonad (Tritrichomonas foetus) by inhibiting thioredoxin reductase (TrxR). Compound 70 was 10-fold more potent than chloro(diethylphenylphosphine)gold(I). When compound 70 was administered orally for 4 days in a murine model, it was found to be active against T. vaginalis with no adverse effect. Hence, 70 can be an alternative for trichomoniasis treatment. Tejman-Yarden et al. (2013) screened a chemical library of 746 approved human drugs and 164 additional bioactive compounds against Giardia lamblia. They found that 56 molecules were active against G. lamblia, out of which 20 molecules were from bioactive compounds, 15 molecules were already reported for the antigiardial activity and 21 molecules had other indications. Auranofin (70) was found to be active in the 4–6 μM concentration range against G. lamblia and metronidazole-resistant strains by blocking an essential enzyme, giardial thioredoxin oxidoreductase. Machado et al. (2015) have evaluated liposomal amphotericin B in 20 patients diagnosed with disseminated leishmaniasis in an open clinical trial. The study continued for 7–14 days with doses ranging from 17 to 37 mg/kg. The authors found that there were 70% and 65% final cure rates after therapy at 3 and 4 months, respectively. Liposomal amphotericin B was effective with a 75% cure rate when more than 30 mg/kg was used with mild adverse effects. Dietrich et al. (2018) identified cisapride (88) as a new inhibitor of putrescine uptake in T. cruzi. ZINC and drug databases were filtered using QSAR models and similarity search, yielding 594 candidates, which were further validated by molecular docking to give four promising hits. Two of these, 88 and [2-(cyclo-pentyloxy)phenyl] methanamine (89), were active where drug 88 inhibited putrescine uptake. Assı´ria Fontes Martins et al. (2015) studied the combination effect of benznidazole (90) and itraconzole (91) for Chagas disease. Benznidazole is a first-line drug for Chagas disease having the limitation of long treatment times and toxicity. Hence, benznidazole/itraconazole combinations were used in T. cruzi Y strain (in mice model). The combination was more (fourfold) effective than each drug used alone for eliminating parasites from the blood. Also, this combination reduced the dose required to obtain trypanocidal effect, thereby reducing the side effects and the cost. Hence, these combinations are alternatives for the treatment of T. cruzi infestation in human and Chagas disease. Gillan, O’Neill, Maitland, Sverdrup, and Devaney (2014) repurposed Hsp90 inhibitors (cancer chemotherapy) as macrofilaricidal agents. In this study, Hsp90 inhibition killed Brugia pahangi (filarial nematodes). Hence, they

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screened several novel Hsp90 inhibitors against brugia adult worms for microfilarial activity. NVP-AUY922 (92) was the most potent and active against female worms by inhibiting microfilariae at >5 nM after 6 days. It was also active against adult worms. Based on these results, it was further tested in in vivo mouse model and found to significantly reduce the recovery of both the adult worm and microfilariae. T. gondii, a ubiquitous apicomplexan parasite, has the ability to infect almost all warmblooded animals, including humans, causing severe disease or death in immunocompromised patients (McFarland et al., 2016). Current treatment options for T. gondii infestation are inadequate and many of them have drawbacks like high toxicity and low tolerability profiles. Pyrimethamine and sulfadiazine treatment are limited to the acute parasitic life stage (tyachyzoite) and allergic reactions to sulfadiazine are very common. For pregnant women, there is no treatment regimen available. Therefore, the development of novel treatment options is needed. For optimistic anti-Toxoplasma compounds, an IC50 value of 0.1, the number of shared compounds between the two proteins is >1 and the number of co-tested compounds for the two targets is > 10. Nodes are colored by gene family. Figure is extracted from Paolini, G. V., Shapland, R. H. B., Van Ntested ij Hoorn, W. P., Mason, J. S., & Hopkins, A. L. (2006). Global mapping of pharmacological space. Nature Biotechnology, 24(7), 805–815, https://doi.org10.1038/nbt1228.

artemisinin after activation generates highly lethal free radicals that interact with molecular targets like PfATPase6 and PfPI3K, as per the known mechanism of action (Cui & Su, 2009) in Plasmodium falciparum, a malarial pathogen. In addition to this, the literature suggests one more reason for the secondary indication of the drug. All of a drug’s pharmacological activities are not properly understood when it is approved for a particular disease, a striking example of this category is thalidomide. Thalidomide was sold as “completely safe” in Germany and England to treat morning sickness in 1957. However, because of its disastrous teratogenic effects it was banned. Later, in 1998, thalidomide was revived and approved under trade name Thalomid for the treatment of erythema nodosumleprosum (ENL) (Ashburn & Thor, 2004).

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1.4 Who Is the Beneficiary The literature review shows that this attractive strategy is now offering more-effective options to patients who are suffering from different types of diseases, such as cellular disease like cancer (Weir, DeGennaro, & Austin, 2012), genetic diseases (Molineris, Ala, Provero, & Di Cunto, 2013), CNS disorders like Alzheimer’s disease and Parkinson’s disease (Caban et al., 2017; Mei et al., 2012), and several neglected and infectious diseases (Ferreira & Andricopulo, 2016; Law, Tisoncik-Go, Korth, & Katze, 2013; Pollastri & Campbell, 2011). NTDs, rare/ orphan diseases, and cancer are major diseases that affect around 415 million people worldwide (Buscaglia, Kissinger, & Ag€ uero, 2015; McGuire, 2016; Tambuyzer, 2010). Therefore these diseases have been selected for a brief discussion of related drug-repurposing opportunities and progress. 1.4.1 Neglected Tropical Diseases NTDs include nearly 18 diverse groups of communicable diseases (http://www.who.int/ neglected_diseases/diseases/summary/en/) that are spread through 149 tropical and subtropical countries (http://www.who.int/neglected_diseases/diseases/en/) and affect more than one billion people (Buscaglia et al., 2015). NTDs pose a high risk to humanity as well as the financial status of the country. Comparing the number of drugs at different clinical stages for NTD versus diseases from Western societies shows a tremendous imbalance; African trypanosomiasis, which results in nearly 1.5 million disability-adjusted life years (DALYs) lost has only four candidates, on the other hand prostate cancer, which is responsible for nearly 1.6 million DALYs lost, has 80 candidate compounds (Pollastri & Campbell, 2011). It has also been found that of the drugs that reached the market between 1975 and 2000, only 1% were for tropical diseases (Trouiller et al., 2002). NTD also presents different challenges like (a) rapid emergence of multidrug-resistant strains against the available regime of drugs, which provides a sense of urgency to identify new drugs; (b) they mostly affect underdeveloped or developing countries with financial limitations making it difficult to provide the best medication; and (c) pharma companies face financial pressures in the development of drugs that can be delivered more cheaply to the affected patients (Feasey, Wansbrough-Jones, Mabey, & Solomon, 2010). With the help of repurposing, new roles for already known drugs are being identified to treat different parasite-borne and bacterial-borne diseases. Table 2 provides a list of drugs that are in different clinical stages. Drugs like harmine, hexadecylphosphocholine (miltefosine) were initially developed as antineoplastic drugs. However, they failed in different clinical phases, but now their antimalarial and antileishmanial activities respectively make them useful in new roles (Bhattacharya et al., 2007; Shahinas, Liang, Datti, & Pillai, 2010). Tamoxifen is a first-generation estrogen receptor modulator and it binds to the estrogen receptors in breast tissues. It has been used as an adjuvant to treat for breast cancer for more than 30 years. Using HTS, the compound was found to have a new indication to treat visceral leishmaniasis (Miguel, Yokoyama-Yasunaka, Andreoli, Mortara, & Uliana, 2007). 1.4.2 Rare or Orphan Diseases Rare or orphan diseases generally gain less attention from academics as well as from pharmaceutical industries because of two reasons: they affect small numbers of individuals

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TABLE 2 Major Drugs That Are Found to Be Useful Against Infectious Diseases: Listed Pathogen-Wise After Repurposing Pathogen

Molecule

Original Use

New Use

References

Bacteria

Amiodarone

To treat cardiac arrhythmias

AntiTrypanosoma cruzi: blocks ergosterol biosynthesis

Oldfield (2010)

Bacteria

Tamoxifen

Antiestrogen

Antiprotozoal: Leishmania amazonensis

Miguel et al. (2007)

Bacteria

Nitazoxanide

Infections caused by Giardia and Cryptosporidium spp.

Antitubercular: multiple potential targets

de Carvalho, Lin, Jiang, and Nathan (2009)

Bacteria

Entacapone and tolcapone

Parkinson’s disease: catechol-Omethyltransferase inhibitors

Antitubercular: inhibits InhA

Kinnings et al. (2009)

Bacteria

Pyrviniumpamoate

Antihelmintic

Antitubercular: antiprotozoal: Trypanosoma brucei

Lougheed, Taylor, Osborne, Bryans, and Buxton (2009) and MacKey et al. (2006)

Parasite

Trimetrexate

Antifolate used in Pneumocystis carinii infection in patients with AIDS

Inhibitor of Trypanosoma cruzi DHFR target protein

Senkovich, Bhatia, Garg, and Chattopadhyay (2005)

Parasite

Astemizole

Nonsedating antihistamine (removed from US market by FDA in 1999)

Antimalarial

Chong, Chen, Shi, Liu, and Sullivan (2006)

Parasite

()-2-amino-3phosphonopropionic acid

Human metabolite, mGluR agonist

Antimalarial: inhibits HSP-90

Shahinas et al. (2010)

Parasite

Acrisorcin

Antifungal

Antimalarial: inhibits HSP-90

Shahinas et al. (2010)

Parasite

Harmine

Anticancer

Antimalarial: inhibits HSP-90

Shahinas et al. (2010)

Parasite

Hexadecylphosphocholine (Miltefosine)

Anticancer

Treatment of visceral leishmaniasis

Bhattacharya et al. (2007)

Fungi

Tamoxifen

Antiestrogen

Cryptococcosis

Butts et al. (2014)

Fungi

Enoxacin

Anti-bacterial

Candidiasis

Breger et al. (2007)

Virus

Niclosamide

Tapeworm infection

Zika virus

Xu et al. (2016)

Virus

Chlorcyclizine

Allergy

Hepatitis C virus

He et al. (2015)

Tamoxifen has been cited twice due to its effects on bacterial or fungi during development but may be used in a different disease.

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(Muthyala, 2011) and pharmaceutical companies find difficulty in generating profit from a drug that is developed using the de novo approach due to the limited market for each indication (Brewer, 2009). As per the US Orphan Drug Act (ODA) (Cheung, Cohen, & Illingworth, 2004), diseases or conditions with a prevalence of less than 200,000 patients are considered as rare. Though one rare disease might affect only a few thousand patients, collectively these diseases influence the lives of nearly 25 million people in the United States alone (Griggs et al., 2009; Tambuyzer, 2010) and nearly 400 million people worldwide. Around 7000–8000 diseases are estimated to come under the rare disease category (https://rarediseases.info. nih.gov/diseases/pages/31/faqs-about-rare-diseases) and this list is ever increasing as new life-threating challenges are growing and knowledge from human genetics and the biology of diseases is also contributing to updates. As only small numbers of people are affected compared with common diseases, such as cancers, cardiovascular disease, and diabetes, the market for drugs developed for rare diseases is also small; the high manufacturing costs associated with the drug-development process make it too costly and unaffordable for most patients (Brewer, 2009). The aforementioned situation discourages most pharmaceutical companies from investing in the drugdevelopment process for these types of disease. Currently, it is shown that only a small pool of medicines are available. Fig. 3 shows the number of medicines that were approved by FDA between 2011 and 2017, but for most of the diseases of this class (around 7000 diseases), no drugs are available for cure (Sun, Sanderson, & Zheng, 2016) (Fig. 4). Figs. 3 and 4 suggest that there is a high demand for an alternative strategy, as well as financial support to speed up the drug-development process for this class of diseases. To encourage and promote pharmaceutical companies to work on rare/orphan products, the legislative bill (ODA) was approved in the United States to provide incentives at various levels, such as financial assistance, market exclusivity, and application fee waivers. After the United States, a similar type of bill was also passed in Japan, Australia, and the European Union (Tambuyzer, 2010).

FIG. 3 Number of drugs approved by the Center for Drug Evaluation and Research (CDER) for rare diseases versus the number of new molecular entities during 2011–17. The Figure shows that approved drugs are near in same proportion in each year but still unable to fulfill the actual need to cues these set of diseases. Data from Drugs@FDA https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugInnovation/.

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FIG. 4 Major rare diseases (89%) have no curative therapies: 281 drugs are approved and 600 are in different phases of clinical trials. Data is taken from https://www.prnewswire.com/news-releases/global-orphan-drug-market-to-reach-us-120billion-by-2018-244195511.html

1.4.3 Cancer Cancer is a complex cellular disease that is characterized by uncontrolled cell proliferation followed by invasion to the adjoining parts of the body, spread to other organs, and resistance to cell death (Hanahan & Weinberg, 2000). Cancer represents more than 100 diseases that are caused by multiple external factors with internal genetic changes (Christiani, 2011). As per the global statistics that are shown in IARC, WHO 2015 report (McGuire, 2016), more than 14 million people are identified as cancer patients and around 8.2 million died because of cancer in 2012. It was also estimated that worldwide more 20 million people will be affected by cancer by 2025. Moreover, malignancies (colorectal, prostate, and breast cancer), which are often incurable in advanced stages with current treatments, will contribute to this increase (McGuire, 2016). Cancer is characterized as a complex disease with many upregulated and downregulated protein expressions and functions, which is further complicated by the specific genetic profile of an individual, making clinically successful outcomes in cancer research remarkably infrequent (Hutchinson & Kirk, 2011). Compared with other therapeutic diseases, drugs tested against cancer account for the highest failure rates in different stages of clinical trials (Begley & Ellis, 2012), making oncology research more challenging than others as the costs of drug development are enhanced with the number of late stage-clinical trial failures (Begley & Ellis, 2012; Jardim, Groves, Breitfeld, & Kurzrock, 2017). The drug-repositioning strategy has identified some anticancer drugs/leads that are in different clinical phases from the many established noncancer drugs, which provides a potential source of options for cancer patients with high unmet medical needs (Table 3). Thalidomide, valproic acid, depakine, and metformin are interesting examples of these types.

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1 INTRODUCTION

TABLE 3

Some Important Drugs Which are Repurposed as Anticancer Agents

Molecule

Original Use

New Use

References

Thalidomide

Nausea, morning sickness

Treatment of multiple myeloma

(Teo, Stirling, & Zeldis, 2005)

Aspirin

Analgesic, antipyretic

Colorectal cancer

(Rothwell et al., 2011)

Valproic acid

Antiepileptic

Antileukemia agents

(Stamatopoulos et al., 2009)

Celecoxib

Osteoarthritis, rheumatoid arthritis

Colorectal cancer, lung cancer

(Steinbach et al., 2000)

Statins

Myocardial infarction

Prostate cancer, leukemia

(Cho et al., 2008)

Metformin

Diabetes mellitus

Breast, adenocarcinoma, prostate, colorectal

( Jiralerspong et al., 2009)

Rapamycin

Immunosuppressant

Colorectal cancer, lymphoma, leukemia

(Recher, 2005)

Methotrexate

Acute leukemia

Osteosarcoma, breast cancer, Hodgkin lymphoma

(Vortherms, Dang, & Doyle, 2009)

Zoledronic acid

Anti-bone resorption

Multiple myeloma, prostate cancer, breast cancer

(Morgan et al., 2010)

Leflunomide

Rheumatoid arthritis

Prostate cancer

(Teschner & Burst, 2010)

Wortmannin

Antiinflammatory

Leukemia

(Workman, Clarke, Raynaud, & van Montfort, 2010)

Minocycline

Severe acne

Ovarian cancer, glioma

(Lokeshwar, 2011)

Vesnarinone

Cardioprotective

Oral cancer, leukemia, lymphoma

(Hanna et al., 2004)

Thiocolchicoside

Muscle relaxant

Leukemia, multiple myeloma

(Reuter et al., 2010)

Nitroxoline

Antibiotic

Bladder, breast cancer

(Shim et al., 2010)

Noscapine

Antitussive, antimalarial, analgesic

Multiple cancer types (NF-kB

(Sung, Ahn, & Aggarwal, 2010)

Gemcitabine

Antiviral

Anticancer agent

(Oettle et al., 2007)

1.4.4 Infectious Diseases Infectious diseases, also known as communicable diseases, contagious diseases, or transmissible diseases, are caused by mainly four types of pathogenic microorganisms, such as bacteria, viruses, parasites, or fungi. These microorganisms infect a host organism and can be spread from one organism to another directly or indirectly. Infectious diseases are responsible for around one-fifth of total worldwide deaths every year (Zheng, Sun, & Simeonov, 2018). From time to time infectious diseases are known to cause significant epidemics in different parts of the world infecting people of different ages and social status. The available regimens of drugs against these diseases are constantly being challenged by different factors, such as the appearance of drug-resistant pathogens (Ashley et al., 2014; McCarthy, 2016), new

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outbreaks (Marini et al., 2017), as well as the reemergence of known diseases (Mercorelli, Palu`, & Loregian, 2018), which challenges physicians and health organizations. The growth and identification of new treatments for drug-resistant microorganisms does not match the current pace at which drug-resistant pathogens are evolving. A timeline-based study of antibiotic production and the prevalence of drug-resistant bacteria show that antibiotics production declined from 16 between 1983 and 1987 to 2 between 2008 and 2012, while resistant strains of Salmonella typhi have increased from 20% to more than 65% between 1999 and 2014 (Zheng et al., 2018). As per FDA records, only 194 approved drugs and 79 approved vaccines are available to treat infectious diseases (Santos et al., 2017; Zheng et al., 2018) and to date no proper treatment is available for some existing and emerging diseases. In an attempt to identify drugs for use against newly (re)emerged infectious diseases, the drug repurposing approach provides an alternative and baseline solution to downgrade the severity of the infection. The drug repurposing approach provides a new opportunity for the large pharmaceutical companies to reinvest in known and abundant drugs by leveraging compounds from their past clinical findings (Pollastri & Campbell, 2011). Examples and case studies of drug repurposing for infectious diseases can be found in the references (Andrews, Fisher, & Skinner-Adams, 2014; Sweiti, Ekwunife, Jaschinski, &Lhachimi, 2017; Ferreira & Andricopulo, 2016; Maitra et al., 2015). 1.4.5 Emerging Disease Like Zika Virus Several new health challenges are emerging, one of which is posed by Zika virus (ZIKV), which is an Aedes aegypti mosquito-borne flavivirus that causes severe neurological complications and Guillain-Barr
e syndrome, especially in newborns (Marini, Guzzetta, Rosa`, & Merler, 2017). The first large outbreak caused by ZIKV was reported in Micronesia in 2007 (Hayes, 2009) and in Brazil in 2014, with 1.5 million estimated to be infected (Tabata et al., 2016). Due to the rapid spread of this virus in other geographical areas in 2016, WHO has declared this disease as a public health emergency of international concern (PHEIC). However, to date not a single drug is approved for the treatment and prevention from ZIKV. As already discussed in above section, drug repurposing has provided safe and better opportunities to treat emerging infections like ZIKV through the rapid identification of effective therapeutics in a short period of time. In 2016 two different groups performed a drugrepurposing screen using a known collection of drugs. The experiment performed by Barrows et al. used 774 FDA-approved drugs and found 20 compounds having antiZIKV activity in a human hepatoma cell line (Barrows et al., 2016). Out of these 20 pools of drugs, some are already known antiflaviviral drugs while some have no antiviral activity (e.g., daptomycin). The selected drugs are further validated for antiZIKV activity in different human cell lines. In a different study using the same approach performed by Xu et al., a collection of  6000 compounds of known approved drugs, which are at different clinical stages, and some pharmacologically active compounds were screened against caspase-3 activity as a primary assay and cell viability assay for confirmation (Xu et al., 2016). This study has identified different drugs inhibiting different target proteins, such as caspase-3 (activation of this by ZIKV infection leads to cell death) and cyclin-dependent kinases, which are involved in replication. Further drug combination has shown increased protection of human neuronal progenitor cells (hNPCs) and astrocytes from ZIKV-induced cell death (Xu et al., 2016).

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2 IDENTIFYING REPURPOSING OPPORTUNITIES: METHODS Comprehensive knowledge of the drug and the scope of the disease should be available before searching for the a indication for a known drug. In comparison with de novo drug discovery and the development pipeline, which is generally divided into seven different stages ranging from target discovery, hit identification, lead optimization, ADMET properties calculation, and then clinical stage submission, the drug-repurposing protocol generally includes only five stages (Fig. 1). This pipeline starts with novel target identification for the known drug. This is done by many ways by applying computational and experimental techniques either alone or in combination (Cavalla, 2013; Jin & Wong, 2014; Sukhai et al., 2011). In the drug-discovery stage, after the identification of validated target-protein information, available in silico techniques can assist in finding initial hits that can be further validated experimentally. Virtual high-throughput screening (vHTS) protocol is well known for putative hits identification by screening libraries containing millions of chemical compounds (Zhu et al., 2013). This technique complements HTS and requires the investment of less time and money ( Joshua Swamidass, 2011). Depending upon the availability of the target structural information, vHTS can be performed either by structure-based screening where the information about a target’s binding-site is exploited in the scoring of each compound and the top-ranked hits are retained for further analysis (Spyrakis et al., 2018) or ligand-based screening, where already known bio-active compounds are used for similarity-based screening (Ripphausen, Nisius, & Bajorath, 2011). This technique is very well suited to drug repurposing by screening known drug collections and promising hits can be further validated experimentally (Ma, Chan, & Leung, 2013). A second technique that is predominantly used for lead-compound identification is SBDD. As the structural information for experimentally validated targets are growing, the scope and contribution of SBDD in the drug-design process are also increasing; SBDD has now become an integral strategy for lead identification and lead optimization processes (Schaffhausen, 2012). SBDD can be used for (1) enhancing underlying interaction energy patterns for improved affinity inhibitor designing (Ealick et al., 1993), (2) utilizing binding-site information for more selective inhibitor designing (Claffey et al., 2012), and (3) fragment-based novel inhibitor designing (Rasina et al., 2016). Several molecular docking programs, such as GLIDE (Halgren et al., 2004), GOLD ( Jones, Willett, Glen, Leach, & Taylor, 1997), FlexX (Rarey, Kramer, Lengauer, & Klebe, 1996), DOCK (Allen et al., 2015), etc., evaluate the poses of the docked ligands by calculating the binding energy based on different scoring function. As structural information of the target protein plays a central role for lead optimization, for drug repurposing, “new partner” for the “old drug” can be identified using the SBDD approach (Sohraby, Bagheri, Aliyar, & Aryapour, 2017). Two techniques that are at the core of the ligand-based drug design (LBDD) are QSAR and pharmacophore modeling. QSAR modeling is used for understanding the structure-activity relationship for known bio-active analogs, which can be further used for predicting the activity of newly synthesized compounds. QSAR modeling is also useful for toxicity prediction, which can be used to improve the toxicity levels of known drugs (Roy, 2015). On the other hand, pharmacophore modeling is useful for features extraction from known drugs that can be utilized for target fishing (AbdulHameed et al., 2012; Ye, Ling, & Chen, 2015), new scaffold identification (Koide et al., 2007), scaffold hopping (Hessler & Baringhaus, 2010), etc.

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TABLE 4 Commercial and Noncommercial Sources of Available Drug Collections Index

Known Drug Collection

Weblink

1

Johns Hopkins University ChemCORE

http://www.hopkinschemcore.org/facility/com_coll.html

2

National Institutes of Health (NIH) clinical collection

https://pubchem.ncbi.nlm.nih.gov/source/NIH% 20Clinical%20Collection

3

NCGC Pharmaceutical Collection (NPC)

https://tripod.nih.gov/npc/

4

Prestwick Chemical Co.: Prestwick Chemical Library

http://www.prestwickchemical.com

5

ChemBank

http://chembank.broadinstitute.org/

6

Microsource: The Spectrum Collection

http://www.msdiscovery.com/spect.html

7

Sequoia

http://www.seqchem.com

8

NIH Brain Bioactive Compound Collection (BIBCC)

http://nihroadmap.nih.gov

9

MedChemExpress

https://www.medchemexpress.com/

2.1 High-Throughput Screening Based Approach Historically the Drugbank collection is mostly serendipity based (Gregori-Puigjan
e et al., 2012), but three major methods have been used in recent times for confirmation; they are compound screening using bioassay, testing in a relevant animal model, and verifying the ADMET properties experimentally (Prasad, Gupta, & Aggarwal, 2016). The same process may be used in case of drug repurposing, where a relevant bioassay in a high-throughput manner can be adopted to screen approved drug collections. This way of drug repurposing has played a central role in the last decade owing to the availability of known drug collections and improved HTS technologies ( Joshua Swamidass, 2011). Table 4 lists known drug and some pharmacologically active compound collections that are available for HTS. Itraconazole, astemizole, and pyrvinium pamoate are some examples of the drugs that have been repurposed with new indications. Two major challenges arise with this approach. First, tested known drugs do not show enough potency for direct clinical application (Sun et al., 2016). Hence established methods associated with medicinal and computational chemistry may be used for the betterment of potency. Second, a direct link between the target(s) and a hit molecule is missing (i.e., target deconvolution), which makes elucidation of the new mode of action a tough job (Laggner et al., 2011; Lu et al., 2012).

2.2 Computational Approach To complement the HTS approach, computer-based methods can also be utilized for drug repurposing by identifying and analyzing the potential drug-target interactions. A computational method such as molecular docking is used to see the interaction pattern after placing the drug compound in the binding site of the probable protein target.

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Once top-ranked hits are identified, candidate compounds can be further validated by in vitro and in vivo experiments. So, in this way, one can find the secondary target of the known drug by systematically screening the compound libraries and filtering nonpromising candidates. Many successful drug repurposing case studies are already reported in the literature but need to be validated experimentally (Kinnings et al., 2011; Liu et al., 2013; Talele, Khedkar, & Rigby, 2010).

2.3 Knowledge Mining The vast majority of data coming from clinical records and literature can be integrated for mining hidden pattern identification useful for drug repurposing. Various databases are established to store specific biological information, such as EntrezGene for genomic, UniProt for proteomic, RCSB, epdb, PDBj for structural, PubChem, DrugBank, ChEMBL for pharmaceutical, GOC for ontology, KEGG, and BioCarta for biochemical pathway data (Loging, Rodriguez-Esteban, Hill, Freeman, & Miglietta, 2011). Many organism-specific databases/ web-servers have been developed that integrate information from the above-mentioned database (Aurrecoechea et al., 2009; Kumar et al., 2014). Mining chemical data can also help to identify any new scaffolds found using data analysis of antituberculosis compounds (Prakash & Ghosh, 2006). Repurposing of antileishmania agent pentamidine for the treatment of renal cell carcinoma (RCC) and imipramine, clomipramine (tricyclic antidepressants) for the treatment of small-cell lung cancers (SCLC) are examples of knowledge mining ( Jahchan et al., 2013; Zerbini et al., 2014). The role of literature mining based-drug repurposing is highlighted in the review by Deftereos et al. (Deftereos, Andronis, Friedla, Persidis, & Persidis, 2011).

2.4 Clinical Indications The successful repurposing of the sildenafil (Viagra®) is an example of postapproval clinical monitoring also known as pharmacovigilance, which provides drug repurposing by identifying secondary indications. The major examples of this approach have been reported in many articles (Blatt & Corey, 2013; Ghofrani, Osterloh, & Grimminger, 2006; Rehman, Arfons, & Lazarus, 2011).

2.5 Novel Target The role of a novel drug target in drug designing and optimization is very well known, and computational techniques aid in this process in many ways (Ripphausen, Nisius, Peltason, & Bajorath, 2010). Before the application of the drug-repurposing protocols, knowledge of the target protein provides a deep understanding of the mechanism of action of any known drug; this knowledge then can be further exploited in many ways, like finding similar functioning proteins, drug-target interaction analysis, local key residues finding, etc. Various computational as well as experimental methods are available that are generally used for novel target identification (Fig. 5) ( Joshua Swamidass, 2011; Vanhaelen et al., 2017).

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15. APPLICATION OF IN SILICO DRUG REPURPOSING IN INFECTIOUS DISEASE

FIG. 5 Key computational techniques involved in drug repurposing.

2.6 Target-Based Screening Target-based screening is a more direct way of validating and finding a novel target for drug repurposing. The functional similarities of the two proteins in two different organisms provide the initial sense of repurposing the known drug in other organisms. Many known drugs that were in the market to cure human diseases, when tested, were also found to be active against many infectious disease-causing organisms. Using this approach, Oprea et al. have found more than six novel targets using known bioactive-based screening (Oprea et al., 2012). In the same line, similar targets are validated in different parasites, such as Plasmodium and Trypanosoma (Beghyn et al., 2011; Bland et al., 2011; Ochiana et al., 2013). Using a molecular-docking approach, Kinnings et al. have repurposed entacapone, a catechol-O-methyl transferase inhibitor used to treat Parkinson’s disease and involved in the breakdown of catecholamine neurotransmitter, by performing the target-binding site similarity search to find different target proteins with the similar binding sites, followed by molecular docking to analyze the physical interactions. Kinnings et al. found that M. tuberculosis enoyl-acyl carrier protein reductase (InhA) is an off-target of entacapone (Kinnings et al., 2009).

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Additional forces driving repurposing applicability are using presently available huge amount of data at various levels and further employ this knowledge for the betterment of humanity. Various comprehensive and systematic search protocols/pipelines have been developed using computational techniques to retrieve, organize, and analyze these resources successfully. Data generated from chemical, genetic, transcriptomic, and phenotypic information along with data coming from the literature, regulatory documents, clinical trials, and electronic health records can further be curated and integrated by academic as well as by R&D professionals to find novel drug-target interactions and thereby explore the computational drug-repositioning possibilities.

3 CHALLENGES OF SELECTIVITY BY IN SILICO Although drug repurposing has provided hope for the production of new medicines at a reduced cost, some challenges remain (Novac, 2013) that should be addressed before the repurposing of existing drugs against infectious diseases. A structure-based, drug-design approach offers a rational way of designing inhibitors exploiting the structural information of protein target. However, sometimes, due to a close homology with the host-target structure, selectivity adds a new dimension to the existing challenges. In infectious diseases, sometimes infectious organisms share the same pathway and target information as the human or other host, targeting these proteins therefore causes cross-reactivity and so downgrades the therapeutic importance of these target proteins. Aurora kinases comprise a multitasking class of enzymes that control several events related to the cell cycle, like mitotic spindle assembly, chromosome separation, and cytokinesis, and developing inhibitors against them could result in the creation of antineoplastic agents; various classes of inhibitors are designed and moved into clinical trials (Borisa & Bhatt, 2017). Related kinases are also found to be important in different parasites, such as Trypanosoma, which causes sleeping sickness, and Plasmodium, which is known for malarial disease (Carvalho, Doerig, & Reininger, 2013; Parsons, Worthey, Ward, & Mottram, 2005); achieving selectivity for the parasite by repurposing aurora kinase drugs over host cells is critical. In an experiment, Ochiana et al. achieved 25-fold selectivity for T. brucei by employing a computational chemistry-based method for generating analogs for the known inhibitor danusertib (Ochiana et al., 2013). Like aurora kinases, cyclic nucleotide-specific phosphodiesterases (PDEs) are well-studied enzymes involved in various cellular response and catalyze the hydrolysis of cGMP and cAMP (Mehats, Andersen, Filopanti, Jin, & Conti, 2002). In humans, 11 isoforms of the PDEs are known and the same PDEs are characterized for protozoan parasites like Trypanosoma (Oberholzer et al., 2007) and Plasmodium (Yuasa et al., 2005). Beghyn et al. used tadalafil, a known drug for cyclic nucleotide PDEs, with 40 analogs and found potent inhibitor targeting plasmodial PDE activity (Beghyn et al., 2011), while Bland et al. used 20 known inhibitors of human PDE for testing activity against TbrPDEB1 and TbrPDEB2 of Trypanosoma brucei and found that human PDE inhibitor piclamilast and its analogues have modest activity against both targets and are able to clear the bloodstream of T. brucei (Bland et al., 2011). Using the same approach eight different inhibitors of human mTOR and/or PI3K, which are at different clinical stages, are tested against orthologous targets in kinetoplastid parasites

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and it was found that compound NVP-BEZ235, which is in the advanced clinical stages, clears the infection of T. brucei rhodesiense in an animal model and shows subnanomolar potency and efficacy against cultured parasites (Diaz-Gonzalez et al., 2011). Some more experimental cases are present in the literature and known drug libraries from different sources have been evaluated using phenotype-based assay and several lead compounds against different organisms have been found (Chong et al., 2006; Lotharius et al., 2014; Miguel et al., 2007). The cases mentioned in above studies show that many known inhibitors/drugs have been repurposed for close homologous protein residing in different organisms and these could serve as important lead compounds; however, to improve the selectivity and reduce the cross-reactivity, target protein structural information is crucial for novel analog design using tested drugs.

3.1 Chemoinformatics-Based Methods The LBDD approach utilizes various types of descriptor information to find similar compounds from the chemical libraries. This strategy is useful in the absence of target-structure information. The rationale behind this strategy is the “structure determines activity” paradigm, where the assumption is that similar chemicals will exert similar biological functions. Pharmacophore modeling, descriptor- and fingerprint-based analogs searching, structureactivity relation analysis, scaffold hopping, and shape-based ligand mapping are the major techniques/tools that come under this approach. In drug repurposing, LBDD offers great opportunities as it can assist in finding and predicting the activity of ligands for a new target. This approach is further supported by known bioactive molecules that are available in public databases such as ChEMBL, DrugBank, BindingDB, and PubChem, and have valuable information on assayed compounds in terms of binding activity, whole cell-based activity, functional and ADME(T) record, retrieved and manually curated from literature data (Gaulton et al., 2017; Gilson et al., 2016; Wang et al., 2017; Wishart et al., 2006). With the availability of the part of known drug collections, these databases can be explored to find similar compounds with corresponding biological targets and activities. This approach has the advantage of tested chemical space in drug repurposing as only PubChem provides more than hundred million compounds, which is far more than the number of crystal structures deposited in the RCSB protein databank (which on April 16, 2018 was less than 140,000) (Burley et al., 2017). Keiser et al. have developed an integrated chemical similarity measure-based systematic protocol, called similarity ensemble approach (SEA), to compute drug-target similarities by relating with the profiles of the binding ligands. With the help of this protocol, 23 new drugtarget associations were correctly predicted (Keiser et al., 2009). Predicted top-ranked offtarget indications were later confirmed by binding assays as well as by in vivo experiment. In another study, Vasudevan et al. (2012) explored the complementary 3D shape-based descriptor information, which in this case was superior in its performance to 2D similarity searching when comparing approved drugs with a set of H1-receptor inhibitors. This analysis found that 13 out of 23 tested drugs had selective inhibition activity against the H1 receptor level (Vasudevan, Moore, Schymura, & Churchill, 2012). The availability of the bioactive compound databases, such as ChEMBL (Gaulton et al., 2017) and PubChem (Wang et al., 2017) provide a great opportunity for identifying novel

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targets that have not been explored. Mervin et al. analyzed around 195 million bioactivity compound records deposited in the ChEMBL and PubChem and found that incorporation of inactive compound information enhances model predictability (Mervin et al., 2015).

3.2 Structure-Based and Simulation As per SCOP rules (Fox, Brenner, & Chandonia, 2014), protein structures with similar biological functions come under the umbrella of “family of the protein” followed by subfamily, etc. So protein structures with the similar function will recognize the similar ligands. Structure-based computational approaches are well known for extracting and analyzing crucial binding-interaction information from the receptor structure. These protocols include binding site-similarity analysis (Haupt, Daminelli, & Schroeder, 2013), binding hotspotcharacterization analysis (Hall, Ngan, Zerbe, Kozakov, & Vajda, 2012), interaction-pattern analysis between receptor-ligand complexes (Furukawa, Konuma, Yanaka, & Sugase, 2016), pose ranking (Wang et al., 2016), and simulation-based receptor dynamics/flexibility analysis (Swegat, Schlitter, Kr€ uger, & Wollmer, 2003; Teague, 2003). In structure-based drug repurposing, proteins are compared at various levels, such as sequence, structure, and binding regions to find the secondary targets corresponding to the approved drug under investigation (Ehrt et al., 2016). Protein sequences are compared to build the phylogenetic tree where each cluster represents the closely related proteins and will recognize the related substrates or ligands. The most popular examples of are kinases and proteases: staurosporine binds many kinases while pepstatin binds to the aspartic proteases (A. Kumar, 2013; Manning, Whyte, Martinez, Hunter, & Sudarsanam, 2002). It has also been seen that protein function also affected by small differences localized at the key positions, which affect the binding of the same compound within the related proteins. Gatekeeper residues of protein kinases are known for determining the ligand specificity for kinases and have a tremendous effect on ligand binding (Huang & Fu, 2015). This suggests that small changes at the key positions in globally similar proteins also have key roles in function determination; the local binding interaction is more important than global similarity, where the role of the binding-site similarities comes into play. In an important study, Keiser et al. showed that using SEA similar ligands were able to bind proteins with distantly related sequences (Keiser et al., 2009). Many studies have shown that binding-site similarity plays a key role in the detection of distant polypharmacological targets and drug repurposing (Haupt, Daminelli, & Schroeder, 2013; Jalencas & Mestres, 2013; Joachim Haupt & Schroeder, 2011). With the known structure of the protein, binding-site information can be extracted using available binding site-detection programs followed by characterization using different descriptors to derive a similarity score. The literature shows various approaches and algorithms are available to compare binding sites; however, each has some limitation (Ehrt, Brinkjost, & Koch, 2016). In a very recent study, large-scale, binding-site knowledge from 400 drug targets was compared with a dataset of around 14,000 cavities associated with 7895 proteins with known structures. The molecular interaction field (MIF) has been used to characterize and compare the binding site. By calculating a statistically significant similarities score followed by docking analysis 140 unique drugs with 1216 unique off-target proteins were identified (Chartier, Morency, Zylber, & Najmanovich, 2017).

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Molecular docking-based studies have been utilized extensively for repurposing known approved drugs (Ma et al., 2013) either by identifying lead molecules against selected molecular targets using virtual screening of the known drugs (Wu et al., 2018; M. Zheng et al., 2017) or evaluating the interaction score of known drugs against the orthologous proteins of different organisms using the similar protein-similar function approach (Beghyn et al., 2011). After finding repurpose candidates, simulation-based studies can be used for absolute binding free energy calculation (Wang, Deng, & Roux, 2006) and lead optimization (De Vivo, Masetti, Bottegoni, & Cavalli, 2016; Michel, 2014). In a very comprehensive analysis, Xie et al. used the chemical system biologybased approach to identify the secondary target of nelfinavir with anticancer effect via multiple kinases involved in the Akt signaling pathway (Xie, Evangelidis, Xie, & Bourne, 2011). Nelfinavir is a competitive inhibitor of HIV aspartyl protease and it was approved by US-FDA as an oral drug in 1997 (Marzolini, Buclin, Decosterd, Biollaz, & Telenti, 2001; Moyle et al., 1998). Xie et al. set up the off-target identification pipeline, which includes MD simulation and MM/GBSA free-energy calculations with ligand binding-site comparison followed by biological network analysis. The computational pipeline predicted multiple protein kinases, involved in the regulation of the carcinogenesis and metastasis pathways. These targets are further validated by kinase activity assay (Xie et al., 2011). To analyze binding-site information across the class of the proteins and automatically identify the common interaction features available in the selected proteins, a software called CliquePharm has been developed in-house (Kaalia, Kumar, Srinivasan, & Ghosh, 2015). CliquePharm uses the MIF information calculated using the GRID molecular discovery program (Goodford, 1985) for each protein’s binding site. CliquePharm is used for generating specificity pharmacophore models based on three probes (Amide, Carboxyl and Hydroxyl) and six target aspartic proteases, four from Plasmodium namely plasmepsin I, II, IV, and plasmepsin vivax and two from humans, namely cathepsin D and pepsin. Selected six features specificity pharmacophore models were validated using a known data set extracted from the ChEMBL; specificity models select compounds that were tested against protease only with 100% true positives (Kumar, Kaalia, Srinivasan, & Ghosh, 2018). To develop novel antimalarials targeting aspartic proteases of the Plasmodium only, the same software with the same input MIF information was used for selectivity pharmacophore features identification and reported two five-featured pharmacophores highly selective towards Plasmodium plasmepsin than human and other proteases (Fig. 6) (Kumar et al., 2018). Virtual screening using docking program is proven to be important for finding offtargets for known drugs (Shoichet, 2004). The potential role of virtual screening protocol has been reported by a number of studies. Wu et al. used the virtual screening protocol against NEDD8-activating enzyme (NAE) target protein, a potential cancer target involved in protein degradation, and found mitoxantrone as a promising hit to inhibit NAE. Cell-free and cell-based experimental validation confirmed the repositioning of the drug (Wu et al., 2018). In the treatment of cancer, Leung et al. also reported the repurposing of darifenacin and ezetimibe drugs as inhibitors of TNF-α through the virtual screening of 3000 FDA-approved compounds followed by experimental analysis (Leung et al., 2011). Some more case studies can be found in the references (Ma et al., 2013).

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FIG. 6 Specificity (left side) and selectivity (right side). Pharmacophore models for acid proteases of Pf as identified by CliquePharm. Both models have the same type of probe combinations (two hydroxyls (OH), two amides (N) and one carboxyl(O)); however, they differ in average energy. The specificity pharmacophore model has 3.94 kcal/mol average clique (interaction energy between the probe and active site) energy while selectivity model has 4.471 kcal/mol average clique energy.

4 GENOMICS AND PROTEOMICS DRIVEN TARGET IDENTIFICATION (ONLY FOR REPURPOSING) Phenotype-based screening has produced many novel drugs (Zheng, Thorne, & McKew, 2013); however, drugs discovered using this approach have one major challenge—target deconvolution—finding target(s) for identified hits (Medina-Franco et al., 2013). Target information not only is crucial for understanding the underlying interaction network associated with that target but also plays a major role in lead optimization and ADMET properties improvization (Ripphausen et al., 2010). The literature has suggested many successful applications for genetic- and proteomic-based approaches for target identification (Wang, Sim, Kim, & Chang, 2004; Wong, Cheng, He, & Chen, 2008; Zheng, Chan, & Zhou, 2004).

4.1 Genetic Analysis Methods Each biological system responds to the external perturbation by upregulating or downregulating the expression level of the associated genes and if this perturbation is associated with a pharmacologically active compound, then the gene product determines the activity of that drug. The Connectivity Map project (Lamb et al., 2006) is one of the most comprehensive and systematic approaches to finding various functional associations among diseases, genetic perturbation, and drug action. Genome-wide transcriptional responses for 1309 compounds were recorded by treating the selected cancer cell lines systematically and based upon which a molecular activity profile for each has been built (Lamb et al., 2006). Two important utilities based on the Connectivity Map model have been utilized for further clustering and identifying the coherent communities of the drug. This study led to the successful identification of the cellular autophagy activity for the rho-kinase inhibitor fasudil

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(Iorio et al., 2010). In another study, Wei et al. compared the drug-signature profiles of the normal state with disease state and found rapamycin (inhibitor of mTOR) profile matched with the signature for glucocorticoid (GC) sensitivity and this hypothesis was proven by experimental study (Wei et al., 2006). In a separate study conducted by Phiel et al. it was found that valproic acid (antiepileptic) activates the same Wnt-dependent gene expression that is known to be activated by lithium, the main therapy for bipolar disorder. Based on this expression, Phiel et al. identified histone deacetylase as a direct target for valproic acid (Phiel et al., 2001). A review published in 2012 discussed the potential role of genome-wide association studies (GWAS) (On beyond GWAS, 2010) for identifying drug-repurposing opportunities and its association with single nucleotide polymorphisms (SNPs) (Sanseau et al., 2012) and highlighted the genetic variations of numerous complex diseases.

4.2 Drug-Target and Disease-Drug Network/Association In systems biology and network pharmacology, network-based approaches are frequently used to visualize, analyze, and understand complex biological systems using different types of biologically relevant interaction data (Pujol, Mosca, Farr
es, & Aloy, 2010). The mathematical framework of the network is well established and can be used to understand the topological properties of the interconnected biological system, such as diseases, genes, proteins, and molecules; this can drive global conclusions and trends useful for drug discovery. In one study, Lin et al. took all FDA-approved drugs (361 NMEs) from 2000 to 2015 and thoroughly analyzed the chemical information data to build a picture of the drug-target relationships using the network-based approach. NMEs for nerve systems have the highest connectivity followed by anticancer and immune-modulating agents. This study also found that NMEs with a multitarget nature are increasing (Lin, Zhang, Yan, Lu, & Hu, 2017). In a very interesting study a computational framework was established to predict new indications based on the large-scale integration of signatures in drug-disease pairs (Sirota et al., 2011). Sirota et al. took a publicly available microarray dataset from the GEO for 100 diseases with gene expression data for 164 drugs from several human cancer cell lines. A developed compendium of the disease-drug relationship was then used to set up connections across all of the available gene-expression measurements. With the help of this study, cimetidine (a histamine H2 receptor antagonist used to inhibit the acid production in the stomach) was found as a possible agent to repurpose for lung adenocarcinoma (Sirota et al., 2011). Some other studies are also reported in the literature for network/associationbased approved-drug analysis, such as the guilt by association (GBA) approach, for predicting novel associations between drugs and diseases (Chiang & Butte, 2009). Recently Iwata et al. developed a predictive statistical model using various descriptor information, such as a phenotypic descriptor to define each drug-disease pair, a molecular descriptor to define the disease and for predicting drug-disease associations for 2349 drugs and 858 diseases (Iwata, Sawada, Mizutani, & Yamanishi, 2015). The same group also developed one more computational model based on the large-scale chemical-protein interactome data and used it for predicting drug-target-disease associations for 8270 drugs and 1401 diseases (Sawada, Iwata, Mizutani, & Yamanishi, 2015). Built upon disease feature

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descriptors, large-scale drug-target and target-disease associations showed performance improvements in predicting new drug-disease links (Iwata et al., 2015; Sawada et al., 2015). In particular it was shown that chemical similarity and phenotypic similarity are complementary to each other and that integrating predictions from both methods is beneficial (Sawada et al., 2015).

4.3 Web-Based Analysis Tools Successful drug repositioning requires a great synergy between all forms of experimental data coming from different sources along with a computational pipeline that can efficiently integrate, analyze, and visualize this experimental information. Although various computational techniques/tools are available that can be utilized for this purpose (Hurle et al., 2013), minor application has been observed due to the limitation of technical knowledge in biology community (Sam & Athri, 2017). Web-based tools are designed to provide computational solutions to the problem and try to fill the gap between wet-lab science and stand-alone computational tools. In last 5 years, many web-servers have been designed with pre-processed experimental information and different inbuilt tools to analyze this information. Table 5 tabulates some of the known web-servers of this type.

4.4 Machine Learning and Artificial Intelligence The role of machine learning in drug discovery is very well established and many successful applications are reported in the literature (Murphy, 2011). The machine learning-based approach utilizes a mathematical framework to learn from the diverse, large datasets and then uses this model for the classification and/or prediction of an unknown dataset (Deo, 2015). The explosive growth in the publicly available chemical and biological datasets has generated huge possibilities for the design, analysis, and application of several learning TABLE 5

Some of the Available Online Databases for Drug Repurposing

Index

Database Name

Web-Link

References

1

DRAR-CPI

https://cpi.bio-x.cn/dpdr/

(Luo et al., 2011)

2

PROMISCUOUS

http://bioinformatics.charite.de/ promiscuous/

(von Eichborn et al., 2011)

3

repoDB Drug Repositioning database

http://apps.chiragjpgroup.org/repoDB/

(Brown & Patel, 2017)

4

RE:fine Drugs

http://drug-repurposing. nationwidechildrens.org/search

(Moosavinasab et al., 2016)

5

RepurposeDB

http://repurposedb.dudleylab.org/

(Shameer et al., 2017)

6

Drug Repurposing Hub

https://clue.io/repurposing

(Corsello et al., 2017)

7

DrugSig

http://biotechlab.fudan.edu.cn/ database/drugsig/

(H. Wu, Huang, Zhong, & Huang, 2017)

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methodologies to identify hidden patterns that are important for drug discovery (Lavecchia, 2015). Literature has reported several models that are used for ranking chemical structures (Agarwal, Dugar, & Sengupta, 2010), toxicity prediction (Mayr, Klambauer, Unterthiner, & Hochreiter, 2016), aqueous solubility prediction for drug-like molecules (Lusci, Pollastri, & Baldi, 2013), virtual screening, and QSAR (Gawehn, Hiss, & Schneider, 2016). Drug repurposing is blessed with multidimensional data that is in the public domain. In 2013, Napolitano et al. developed a predictive drug-repurposing model using machinelearning algorithms. They integrated multiple layers of information, such as the chemical similarity of FDA-approved compounds, the closeness of the target within a protein-protein network, and gene-expression patterns, and they built a classifier that achieved a more than 75% accuracy level (Napolitano et al., 2013). Aliper et al. developed a deep neural network (DNN) using only transcriptional response profiles and showed that the model was capable of classifying drugs to therapeutic categories (Aliper et al., 2016). Along the same lines, one more multilabel classification model has been developed using the transcriptional data produced by the Library of Integrated Network-based Cellular Signatures (LINCS) project (http://www.lincsproject.org/). The generated model has achieved more than 80% training accuracy and approximately 70% validation accuracy. The study reported a set of 98 drugs with high confidence to be repositioned for novel therapeutic purposes (Xie, He, Wen, Bo, & Zhang, 2017). Coelho et al. developed a machine learning-based computational pipeline to identify the putative leads for repurposing using the drug-target interactions network. A random forestbased classification model has achieved a ROC value of 0.91 for classification of test-set data (Coelho, Arrais, & Oliveira, 2016). Some more case studies based on machine learning can be found (Cichonska, Rousu, & Aittokallio, 2015; Hameed, Verspoor, Kusljic, & Halgamuge, 2018; Lim et al., 2016; Wang & Zeng, 2013).

5 OTHER CHALLENGES LIKE SHARING INFORMATION AND RISK Most of the available literature discusses the potential role of drug repurposing and its benefits in the identification of new indications. However, only some articles have discussed the challenges of this potential approach (Novac, 2013; Agrawal, 2015; Kato et al., 2015). For any successful outcome a priori knowledge of challenges is always useful: Sharing data: Gaining access to compounds from all directions, i.e., synthesis, testing, screening, improvements, pharmaceutical data, toxicity data, and tested disease target(s). Only then can industry-academia collaborations effectively start. Sharing animal models: Data accumulated on testing in different animal models can be a resource for analysis and the identification of new therapeutic windows and indications. Synergy between different streams of subjects like biology (all the omics), chemistry (medicinal and computational), pharmacology (molecular), biochemistry (mechanism of action), data mining and statistics need to be developed to fully use the opportunities offered by novel methods of drug design (Korcsmaros, Schneider, & Superti-Furga, 2017). One such example is the application of losartan, which is normally an angiotensin II type 1 receptor blocker used for lowering blood pressure, in Marfan syndrome. It was found that it

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attenuates TGF-beta signaling in rodent models of chronic kidney disease. Using serendipity, the mechanism of pathogenesis of Marfan syndrome provided a path leading to new therapeutic treatment using losartan, which affects the TGF-beta signaling cascade. Availability of all known drug collections: Availability of patent and nonpatent drug collections for investigation for new uses is a major obstacle that can impair the development of a potential new drug (Corsello et al., 2017). Knowledge of targets and mechanism of action: Majority of the drugs found by HTS process lack the relevant targets and/or pathway information, which may also be critical for hypothesis building and formulate the right conclusions from the computational protocol (Gregori-Puigjan
e et al., 2012). Drug development challenges: Drugs available in the bioactive known drug collections do not have the equal scope of repurposing. For example, drugs that were found to be ineffective because of safety reasons need to be removed (Bertolini, Sukhatme, & Bouche, 2015). Financial challenges: This challenge comes from the industry side—Who will support and take the risk? Apart from rare diseases and cancers, no financial incentives are available to repurpose drugs for the treatment of the other diseases (Bertolini et al., 2015).

6 SUMMARY AND FUTURE Drug designing for specific clinical use is a complex process with high financial risks, so reuse of rejected drugs or repurposing of old drugs provides greater opportunities to turn failures into successes, as described in this review. All possible and successful experimental and theoretical methods are mentioned with applicability domain and disease. Some challenges, like the design of the appropriate experiment and selectivity testing, are discussed. Infectious and unmet diseases receive less investment from pharmaceutical companies; the case studies discussed in this review will encourage them to explore the possibility of finding an old drug for a new indication, which will leverage the financial challenges. The management of data flow, sharing all chemical, biological, pharmacological, and clinical information, and risk management to protect the compound for its new application is still a challenging aspect to take care of, above all, enhancement in a collaborative approach between academia and industry to explore these novel opportunities may pave a new path for rational drug design.

References AbdulHameed, M. D. M., Chaudhury, S., Singh, N., Sun, H., Wallqvist, A., & Tawa, G. J. (2012). Exploring polypharmacology using a ROCS-based target fishing approach. Journal of Chemical Information and Modeling, 52(2), 492–505. https://dx.doi.org/10.1021/ci2003544. Agarwal, S., Dugar, D., & Sengupta, S. (2010). Ranking chemical structures for drug discovery: a new machine learning approach. Journal of Chemical Information and Modeling, 50(5), 716–731. https://dx.doi.org/10.1021/ci9003865. Agrawal, P. (2015). Advantages and challenges in drug re-profiling. Journal of Pharmacovigilance, s2. https://dx.doi. org/10.4172/2329-6887.S2-e002. Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016). Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Molecular Pharmaceutics, 13(7), 2524–2530. https://dx.doi.org/10.1021/acs.molpharmaceut.6b00248.

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C H A P T E R

16 In Silico Modeling of FDA-Approved Drugs for Discovery of Anticandida Agents: A Drug-Repurposing Approach Sohini Chakraborti*, Gayatri Ramakrishnan*,†,‡, Narayanaswamy Srinivasan* *

Molecular Biophysics Unit, Indian Institute of Science, Bangalore, India †Indian Institute of Science Mathematics Initiative, Indian Institute of Science, Bangalore, India ‡Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States

1 INTRODUCTION An opportunistic pathogen, Candida albicans is the most prevalent fungal species in a healthy human microbiome. These species survive as harmless commensals, colonizing gastrointestinal, oral, skin, and genitourinary tracts of healthy individuals. However, depending on conditions favoring their growth, such as alterations in host immune response, for instance during stress or the use of immunosuppressant therapies, C. albicans can flourish and cause life-threatening systemic infections (Nobile & Johnson, 2015). The need for new antifungal agents due to increasing incidences of invasive fungal infections, especially in immunocompromised patients, is a growing concern. In spite of the availability of antifungal agents, invasive fungal infections are responsible for an estimated 1.5 million deaths worldwide. This epitomizes more deaths than those caused by tuberculosis or malaria (Armstrong-James, Meintjes, & Brown, 2014; Brown et al., 2012). A study (Pfaller, Moet, Messer, Jones, & Castanheira, 2011) showed that the majority of the bloodstream fungal infections are attributed to C. albicans and most of these are due to the development of highly

In Silico Drug Design. https://doi.org/10.1016/B978-0-12-816125-8.00016-X

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structured biofilms that are difficult to eradicate (De Cremer et al., 2015). C. albicans infection is not only restricted to humans but is also commonly seen in domestic animals ( Jadhav & Pal, 2015; J. Liu et al., 2018). The development of new antifungal drugs is more challenging than antibacterial drugs because, unlike bacteria (prokaryotes), fungi are eukaryotes and several targets of therapeutic relevance share a high degree of similarity with the host proteins, thereby increasing the risks of host toxicity. Moreover, interest in investment for antifungal drug discovery by major pharmaceutical companies is not encouraging (Denning & Bromley, 2015; Roemer & Krysan, 2014). Current therapeutic choices for antifungal agents are mostly restricted to azoles, polyenes, pyrimidine analogues, and echinocandins. Resistance towards many of these drugs has already been reported, which further complicates the treatment of fungal infection. Intrinsic resistance of Candida krusei to fluconazole, Aspergillus terreus to amphotericin B, Cryptococcus spp. to the echinocandins, and Scedosporium spp. to all current antifungals are some of the notable instances of drug resistance among pathogenic fungi. Acquired resistance to azoles and echinocandins among Candida spp. and Aspergillus spp. has also been observed in many clinical isolates (Denning & Bromley, 2015; Ford et al., 2015; Revie, Iyer, Robbins, & Cowen, 2018). One of the approaches to address the emergence of drug-resistant fungal infections is to explore the wealth of existing drugs approved for human use, to find candidates with the desired antifungal potential. In the recent times, interest in drug-repurposing approaches through the integration of various in silico, in vitro, and in vivo techniques has gained popularity, as such approaches ensure the effective utilization of time and resources to accelerate efforts in an attempt to meet any unmet medical needs (MarchVila et al., 2017; Vanhaelen et al., 2017). Attempts have been made in the past by various groups to identify new antifungal agents from the repertoire of Food and Drug Administration (FDA)-approved anticancer (Wakharde, Halbandge, Phule, & Karuppayil, 2018), antipsychotic (Holbrook, Garzan, Dennis, Shrestha, & Garneau-Tsodikova, 2017), antirheumatic (Wiederhold et al., 2017), antihypertensive agents (Kathwate & Karuppayil, 2013), etc. Most of these earlier publications deal with the identification of antifungal agents for which the primary targets are human proteins. This might increase the chances of toxic effects on the host and could possibly compromise on pathogen specificity. With an aim to contribute to the identification of possibly safer anticandida agents with higher chances of pathogen specificity over host, we have developed an in silico drug-repurposing protocol that focuses on identifying potential anticandida agents from the pool of FDA-approved drugs that have not been reported so far to primarily target any human protein or possess any severe side effects. In the current work we have demonstrated the application of an in silico drug-repurposing approach that uses information on evolutionary relationships between protein targets of known drugs and protein targets of a pathogen of interest to infer the possibility of a drug binding to related protein targets. This approach was employed in our earlier studies, which facilitated the identification of FDA-approved drugs that could be repurposed against potential targets of Mycobacterium tuberculosis (Ramakrishnan, Chandra, & Srinivasan, 2015) and Plasmodium falciparum (Ramakrishnan, Chandra, & Srinivasan, 2017). As a proof of concept, much of our predictions in our previous studies corroborate the predictions from several independent in vitro or in vivo studies, justifying the strength of the protocol. The current study attempts to identify “repurposable” drugs with antifungal potential for treatment of fungal infections caused by C. albicans. The term repurposable emphasizes the fact that the predictive analysis attempted in the study was purely based on in silico experiments and therefore such 3. EXAMPLES AND CASE STUDIES

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predictions only hint (and do not guarantee) that the identified drugs probably possess the capability to inhibit or arrest the growth and survival of C. albicans. Thorough experimental follow-up of the results presented in the current study and subsequent clinical studies are required to validate the success of the findings as discussed in this study. The elementary structural motifs (refereed as “folds”) in proteins essential for executing important functions of life are re-sampled by nature across many domains of life. This is the cause behind the limited number of folds (2500) despite the fact that there exists a huge repertoire of proteins (over 100 million as of 2018) (Govindarajan, Recabarren, & Goldstein, 1999). In many instances, conservation of structural folds implies conservation of important functional residues mediating key interactions, such as those required for molecular recognition of a ligand by a protein (Orengo, 1999). This phenomenon of conservation of structural folds among orthologues is exploited in our protocol, which hunts for protein pairs with similar folds and then assesses the possibility of retention of similar molecular recognition profiles between a ligand and the protein pair under study. Such an evaluation forms the basis for prediction of binding of a ligand to a related set of proteins expected to have evolved from a common ancestor. In other words, the possibility of binding of an approved drug to a protein of interest based on its similarity to a known drug target is assessed by considering the knowledge of the binding-site properties in established drug-target complexes. The drugs filtered from such predictions are then subjected to a ligand-centric chemical-similarity search to expand the chemical space of the proposed therapeutic agents. Thus, our in silico drug-repurposing protocol to search for potential antifungal agents is a combination of bioinformatics and chemoinformatics methods.

2 METHODOLOGY The overall workflow of the protocol adopted in this study is summarized in Fig 1. The details regarding dataset preparation and the subsequent analysis required to identify a potential repurposable drug for treatment of candidiasis is discussed below.

2.1 Dataset The inputs for current study were obtained majorly from two databases: (1) DrugBank and (2) UniProt. DrugBank (Wishart et al., 2018) is a comprehensive resource that provides information on approved, vet approved, investigational, withdrawn or illicit small molecule drugs, biotech drugs and nutraceuticals, along with their associated targets. The current version of DrugBank 5.0.11 holds information on 2520 approved drugs and 4912 associated targets/enzymes/transporters/carriers. The information on the protein-target sequences of only those approved drugs not reported to have a known human target were retrieved for the current study from DrugBank version 5.0.10. The idea behind such a selection is to minimize the chances of any off-target effects on the host. Additionally, sequences of all those protein targets that are associated with drugs known to have antifungal properties, as per DrugBank annotation (Table 1), were removed. This step resulted in the formation of “dataset I,” which contained only those protein sequences that are associated with one or multiple approved small-molecule drugs with no known human target or antifungal 3. EXAMPLES AND CASE STUDIES

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FIG. 1

Workflow of the protocol adopted in our in silico study to identify potential repurposable anticandida drugs. The color version of the figure is available online.

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TABLE 1

List of Antifungal Drugs and Their Protein Drug Targets Eliminated From the Study

Drug Name

Fungal Protein Target

Flucytosine

Thymidylate synthase

Miconazole, Oxiconazole, Fluconazole, Bifonazole, Sertaconazole, Ketoconazole, Clotrimazole, Econazole, Posaconazole, Terconazole, Tioconazole, Voriconazole, Luliconazole

Lanosterol 14-alpha demethylase

Griseofulvin

Tubulin beta chain (Beta-tubulin); Tubulin alpha chain

Tavaborole

Cytosolic leucyl-tRNA synthetase

Oxiconazole

Lanosterol synthase

Anidulafungin, Caspofungin, Micafungin

1,3-Beta-glucan synthase component FKS1

Tolnaftate

Squalene monooxygenase

Note: All those approved antifungal agents (such as butenafine, nitroxoline, naftifine, etc.) that are known to target any human protein were eliminated by the initial filter criteria.

properties. “Dataset II,” which comprises sequences of proteins encoded in the C. albicans (strain SC5314/ATCC MYA-2876) genome were retrieved from the Universal Protein (UniProt) (Consortium, 2017) database, which is an extensive resource for structural and functional information on proteins, sourced primarily from literature and other databases. The current release (2018-01) holds information on 6035 proteins of C. albicans, which have been used in this study.

2.2 Chem-Bioinformatics Approach to Identify Potential Drug-Target Associations in C. albicans 2.2.1 Sequence Analyses Each protein in dataset I was queried against all the C. albicans proteins in dataset II to detect evolutionary relationships. This step was facilitated by a sensitive profile-based iterative sequence search program, jackhammer, availed through the HMMER3.0 suite of programs (Finn et al., 2015) by employing an E-value cut-off of 0.0001 and five rounds of iteration. The resultant hits (from C. albicans proteome) detected from the jackhammer search were segregated into two groups: (1) group I—the query proteins for which hits were detected with a query coverage of 70% or more were prioritized for further investigations, three case studies from this group will be discussed in detail; and (2) group II—the query proteins for which hits were detected with a query coverage less than 70% have been placed in this group. The confidence in reliable predictions from this group is anticipated to be low due to lower percentage coverage of alignment between known target proteins and their C. albicans homologues. Drug-target interaction is predominantly governed by satisfactory electrostatics and shape complementarity between the binding partners. Therefore local similarity in the binding site rather than global sequence/structural similarity becomes more important in such cases

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(Anighoro et al., 2015; Jalencas & Mestres, 2013; Keiser et al., 2009). Thus, for the evolutionary relationships recognized for cases in group II, additional criteria such as ensuring presence of at least one functional and/or structural domain in the aligned region (Ramakrishnan et al., 2015, 2017) followed by comparison of the physicochemical properties of ligand-binding pockets (as discussed in the section titled “structural analyses”) between the related proteins, can be employed. This would result in capturing relevant target sequence and structural information in the context of its binding pockets housed in functional domains by eliminating short stretches of alignment. Such an assessment can improve credibility for the predictions made. The percentage sequence identity between a pair of proteins gives a crude estimate of their structural and functional relatedness. Related proteins with sequence identities higher than 35%–40% are very likely to be structurally similar. Structural similarity in pairs with a sequence identity of 20%–35%, often referred to as “twilight zone,” is considerably less common; moreover, it is observed that less than 10% of protein pairs with sequence identity below 25% have similar structures (Friedberg & Margalit, 2002; Krissinel, 2007). Thus, cases where protein pairs have less than 35% sequence identity require careful assessment. Pairwise sequence alignments (Table 2) were arrived at using EMBOSS Water (Rice, Longden, & Bleasby, 2000), an algorithm that performs the local alignment of a sequence to one or more other sequences. Multiple sequence alignments between a query protein and a set of identified target proteins were pursued using Multiple Sequence Comparison by Log-Expectation (MUSCLE), an algorithm that performs alignment of multiple sequences (Li et al., 2015; McWilliam et al., 2013), and visualized through ESPript 3.0 (http://espript.ibcp.fr), an automated web server for the comprehensive analysis of primary to quaternary protein-structure information (Robert & Gouet, 2014). The set of aligned multiple sequences gave us an insight into the extent of conservation of binding-site residues among the proteins of interest. Postsequence analyses, additional measures were taken to ensure that the final set of approved drugs associated with known drug targets, recognized as closely related to proteins of C. albicans, do not result in serious side effects to humans. This was ensured by examining details on each drug pertaining to its description, pharmacodynamics, toxicity, and the organisms affected, as documented in DrugBank. This refinement operation on group I cases finally resulted in the association of 26 unique approved drugs to 31 potential targets of C. albicans (Table 2). Similar refinement of group II cases resulted in the association of 66 unique approved drugs (some of which are also identified in group I) to 98 potential targets of C. albicans (Table 3). 2.2.2 Structural Analyses Structural information for known target proteins were obtained from Protein Data Bank (Berman et al., 2000). Due to the absence of structural information for C. albicans proteins qualified as reliable hits from our sequence analyses, we obtained reliable protein structural models through the Protein Model Portal ( Juergen et al., 2013). Measures were taken to ensure that the structural model encompassed the aligned regions of interest obtained from sequence analyses. These models were then subjected to quality evaluation using ModEval, a web server for evaluating the quality of protein models (https://modbase.compbio.uscf. edu/evaluation//) (Eramian, Eswar, Shen, & Sali, 2008) by means of z-DOPE score. The discrete optimized protein energy (DOPE) score (Shen & Sali, 2006), an atomic distancedependent statistical potential, is regarded as a valid measure to analyze the quality of the model; z-DOPE scores < 0 typically suggest reasonable model quality. It must be noted that

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model quality is imperative in assessing predicted drug-target associations from structural perspective. Different approaches were adopted to determine and evaluate binding sites in potential targets depending on the availability of molecular details, which are summarized in the following section: a. Known target bound to drug of interest: Cases where structural information was available for an established drug-target complex, the sequence of target protein was aligned with that of C. albicans followed by comparison of residues lining the drug-binding pocket (as discussed in case study 2). Additional analyses were conducted for the cases where a reliable comparative model of the C. albicans protein was available. For such cases, the analysis and evaluation of binding sites in the potential target protein of C. albicans is straightforward. The model of the C. albicans protein was superimposed onto the structure of a known drug-target complex followed by binding pocket-similarity assessment. Structural superimposition in this study was performed using TM-align (Zhang & Skolnick, 2005), an algorithm that recognizes local structural matches between a pair of proteins and quantifies the structural similarity between the pair of proteins by assigning a score known as the TM-score. The TM-score typically acquires a value between 0 and 1, wherein a TM-score of > 0.50 indicates structural similarity corresponding to the same fold while a TM-score < 0.30 corresponds to an unconvincing structural match. Inspection of the physicochemical nature and 3D positioning of structurally equivalent residues lining the drug-binding pocket in an optimally superposed pair of protein structures gives an insight into the possibility of retention of key protein-ligand interactions in the fungal target protein. Furthermore, the use of well-established pocket detection and/or comparison tools, such as PocketMatch (Yeturu & Chandra, 2008), ProBiS (Protein Binding Sites) (Konc & Janezic, 2012), CASTp (Computed Atlas of Surface Topography of proteins) (Binkowski, 2003), or PoSSuM (Pocket Similarity Search using Multiple-Sketches) (Ito, Tabei, Shimizu, Tsuda, & Tomii, 2012), can contribute useful information and confer higher confidence in the predictions. Use of one such tool (CASTp) has been demonstrated through case study 1, where the likelihood of binding of the drug to the potential C. albicans protein target with a pose similar to that observed in the experimental structure of known drug-target complexes was assessed. b. Known target bound to an inhibitor that is chemically dissimilar from the drug of interest: In such cases, the possible druggable sites on the modeled structure of C. albicans protein target was determined using SiteMap (Halgren, 2007, 2009), a computational tool availed through the Schr€ odinger suite of programs that identifies potential ligand-binding sites in a protein and rank orders the detected sites using scoring functions (SiteScore). The algorithm further assesses the druggability of such ligand-binding sites and quantifies that with another scoring function (Dscore). The top scoring sites based on their SiteScore (>1.0) and DScore (>0.80) value were selected for subsequent analyses. The residue composition at each selected site location was compared with the structurally equivalent residues of the known target protein and inhibitor complex. The drug of interest was then docked using Glide, a grid-based docking program (Friesner, 2004; Halgren, 2004), into the predicted high-confidence binding site of the C. albicans protein model and potential interactions were analyzed (detailed in case study 3) through Maestro version 11.1.011 (Schr€ odinger Release 2018-1: Maestro, Schr€ odinger, LLC, New York, NY, 2018).

3. EXAMPLES AND CASE STUDIES

470

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 2 Comprehensive List of 26 Shortlisted Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans Along With Relevant Details (the Query Coverage of all the Pairs of Query and Target Protein Is More Than 70%) Information on Known Targets (Query Protein) (Source: DrugBank, UniProt)

Information on Approved Drugs (Source: DrugBank)

Sl. DrugBank No. ID Drug Name 1

DB00336

Nitrofural$

Molecular weight (g/mol) Description

Affected Chemical Class Organism

198.1

Furans

Topical antiinfective agent

UniProt code Protein Name

Organism

Gene name

Pyruvate dehydrogenase [ubiquinone]

Escherichia coli (strain K12)

poxB

P06715

Glutathione reductase

Escherichia coli (strain K12)

gor

P61889

Malate dehydrogenase

Escherichia coli (strain K12)

mdh

Gram-negative P07003 and Grampositive bacteria

2

DB02703

Fusidic acid

516.7

Antibacterial agent

Steroids and steroid derivatives

Enteric bacteria P13551/ Elongation Q5SHN5 factor G and other eubacteria

Thermus thermophillus

fusA

3

DB01051

Novobiocin$

612.6

Antibacterial agent

Coumarins and derivatives

Enteric bacteria P0A0K8 DNA gyrase and other subunit B eubacteria

Staphylococcus aureus

gyrB

4

DB01718

Cetrimonium

284.5

Antiseptics

Organonitrogen N.A. compounds

P9WPB7 Cyclopropane mycolic acid synthase 1 Q79FX6

Mycobacterium tuberculosis cmaA1

Cyclopropane Mycobacterium tuberculosis mmaA2 mycolic acid synthase MmaA2

471

2 METHODOLOGY

Similar Approved Molecules

Information on Predicted Targets in C. albicans

Gene name(source: Uniprot)

Functional Category

Pairwise Seq. Profile Query identity (%) coverage(%) (EMBOSS (JACKHMMER) Water)

Acetolactate synthase

ILV2

Biosynthesis

93.0

25.2

P83779

Pyruvate decarboxylase

PDC11

Degradation/ Utilization/ Assimilation

80.1

19.5

Q59MU3

Phenylpyruvate decarboxylase

ARO10

Degradation/ Utilization/ Assimilation

78.0

20.1

Q59NQ5

Glutathionedisulfide reductase

GLR1

Biosynthesis; 99.1 Generation of precursor metabolites and energy

47.8

Q59RQ6

Dihydrolipoyl dehydrogenase

LPD1

Degradation/ 98.7 Utilization/ Assimilation; Biosynthesis; Generation of precursor metabolites and energy

32.0

P83778

Malate dehydrogenase, cytoplasmic

MDH1

Degradation/ Utilization/ Assimilation; Biosynthesis

99.0

45.6

Q5A5S6

Malate dehydrogenase

MDH1-3

Degradation/ 99.4 Utilization/ Assimilation; Generation of precursor metabolites and energy

44.3

Q5AMP4

Malate dehydrogenase

MDH1-1

Degradation/ 99.0 Utilization/ Assimilation; Generation of precursor metabolites and energy

50.0

Q5AL45

Elongation factor G, MEF1 mitochondrial

Protein synthesis

98.4

45.6

Q5AAV3

Ribosome-releasing factor 2, mitochondrial

MEF2

Protein synthesis

91.5

35.5

A0A1D8PMM1 DNA topoisomerase 2

TOP2

DNA topology maintenance

85.9

25.9

(No other similar aproved drug)

Q5APD4

MTS1

Degradation/ Utilization/ Assimilation

96.9

29.1

98.3

28.1

Decamethonium (DB01245;1.0), Didecyldimethylammonium (DB04221; 0.852), Quaternium-24 (DB13969; 0.821) ,Dioctyldimonium (DB13970; 0.852)

UniProt code

Protein name

A0A1D8PJF9

Sphingolipid C9methyltransferase

Drug Name (DrugBank ID; Similarity score) Nitrofurantoin (DB00698; 0.737)

Ethynodiol diacetate (DB00823; 0.707), Bioallethrin (DB13746; 0.734)

Continued

472

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 2 Comprehensive List of 26 Shortlisted Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans Along With Relevant Details (the Query Coverage of all the Pairs of Query and Target Protein Is More Than 70%)—cont’d Information on Known Targets (Query Protein) (Source: DrugBank, UniProt)

Information on Approved Drugs (Source: DrugBank)

Sl. DrugBank No. ID Drug Name

Molecular weight (g/mol) Description

5

DB01421

Paromomycin

615.6

Antibacterial agent

Organooxygen compounds

Enteric bacteria Q5SHN7 30S ribosomal and other protein S10 eubacteria

Thermus thermophilus (strain HB8/ATCC 27634/DSM 579)

rpsJ

6

DB00560

Tigecycline$

585.7

Antibacterial agent

Tetracyclines

Enteric bacteria P0AG59a 30S ribosomal and other protein S14 eubacteria

Escherichia coli (strain K12)

rpsN

30S ribosomal protein S13

E. coli (strain K12)

rpsM

P0A7U3a 30S ribosomal protein S19

E. coli (strain K12)

rpsS

P0A7X3a 30S ribosomal protein S9

E. coli (strain K12)

rpsI

P0AG59a 30S ribosomal protein S14

E. coli (strain K12)

rpsN

P0A7U3a 30S ribosomal protein S19

E. coli (strain K12)

rpsS

P02359

30S ribosomal protein S7

E. coli (strain K12)

rpsG

P0A7V3 30S ribosomal protein S3

E. coli (strain K12)

rpsC

P0A7W7 30S ribosomal protein S8

E. coli (strain K12)

rpsH

Affected Chemical Class Organism

UniProt code Protein Name

P0A7S9

7

DB09093

Chlortetracyclin$

478.9

Antibacterial agent

Tetracyclines

N.A.

3. EXAMPLES AND CASE STUDIES

Organism

Gene name

473

2 METHODOLOGY

Similar Approved Molecules

Information on Predicted Targets in C. albicans

Gene name(source: Uniprot)

Functional Category

Pairwise Seq. Profile Query identity (%) coverage(%) (EMBOSS (JACKHMMER) Water)

Ribosomal 40S subunit protein S20

RPS20

Protein synthesis

92.4

27.6

Framycetin (DB00452; 1.0), Tobramycin (DB00684; 0.963), Gentamicin (DB00798; 0.811), Kanamycin (DB01172; 0.944), Glucosamine (DB01296; 0.764), Ribostamycin (DB03615; 1.0)

Q5AJZ7

Mitochondrial 37S ribosomal protein MRP2

MRP2

Protein synthesis

91.1

37.0

Q5A357

Putative mitochondrial 37S ribosomal protein SWS2

CAALFM_CR08 400CA Protein synthesis

78.0

40.0

A0A1D8PK22

Ribosomal 40S subunit protein S15

RPS15

Protein synthesis

93.5

34.0

A0A1D8PTL2

Mitochondrial 37S ribosomal protein RSM19

CAALFM_CR07760WA Protein synthesis

83.7

48.1

O94150

37S ribosomal protein S9, mitochondrial

MRPS9

Protein synthesis

93.8

38.3

A0A1D8PCW6 Ribosomal 40S RPS16A subunit protein S16A

Protein synthesis

96.1

36.8

Doxycycline (DB00254; 0.848), Lymecycline (DB00256; 0.824), Clomocycline (DB00453; 0.813), Oxytetracycline (DB00595; 0.838), Demeclocycline (DB00618; 0.813), Tetracycline (DB00759; 0.842), Methacycline (DB00931; 0.829), Minocycline (DB01017; 0.922), Rolitetracycline (DB01301; 0.834), Chlortetracycline (DB09093; 0.79), Meclocycline (DB13092; 0.813)

UniProt code

Protein name

Q5A389

Q5AJZ7

Mitochondrial 37S ribosomal protein MRP2

MRP2

Protein synthesis

91.1

37.0

A0A1D8PTL2

Mitochondrial 37S ribosomal protein RSM19

CAALFM_CR07760 WA

Protein synthesis

83.7

48.1

A0A1D8PK22

Ribosomal 40S subunit protein S15

RPS15

Protein synthesis

93.5

34.0

Q5AG43

Ribosomal 40S subunit protein S5

RPS5

Protein synthesis

72.1

30.2

Q5AK02

Mitochondrial 37S ribosomal protein RSM7

orf19.401 8

Protein synthesis

72.6

28.8

A0A1D8PSV5

Ribosomal 40S subunit protein S3

RPS3

Protein synthesis

73.8

27.2

P0CU35

40S ribosomal protein S22-B

RPS22B

Protein synthesis

97.7

25.2

Q96W54

40S ribosomal protein S22-A

RPS22A

Protein synthesis

97.7

25.2

Drug Name (DrugBank ID; Similarity score)

Doxycycline (DB00254; 0.899), Lymecycline (DB00256; 0.877), Clomocycline (DB00453; 0.953), Tigecycline (DB00560; 0.79), Oxytetracycline (DB00595; 0.919), Demeclocycline (DB00618; 0.856), Tetracycline (DB00759; 0.925), Methacycline (DB00931; 0.878), Minocycline (DB01017; 0.840), Rolitetracycline (DB01301; 0.883), Meclocycline (DB13092; 0.908)

Continued 3. EXAMPLES AND CASE STUDIES

474

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 2 Comprehensive List of 26 Shortlisted Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans Along With Relevant Details (the Query Coverage of all the Pairs of Query and Target Protein Is More Than 70%)—cont’d Information on Known Targets (Query Protein) (Source: DrugBank, UniProt)

Information on Approved Drugs (Source: DrugBank)

Sl. DrugBank No. ID Drug Name

Molecular weight (g/mol) Description

Affected Chemical Class Organism

UniProt code Protein Name

Organism

Gene name

8

DB01321

Josamycin#

828.0

Antibacterial agent

Organooxygen compounds

Enteric bacteria P44345 and other eubacteria

50S ribosomal protein L4

Haemophilus influenzae rplD (strain ATCC 51907/DSM 11121/KW20/Rd)

9

DB01044

Gatifloxacin

375.4

Antibacterial agent

Quinolines and derivatives

Enteric bacteria P0A4L9 and other eubacteria; Mycobacterium Q59961 sp; Chlamydia pneumoniae; Chlamydia trachomatis; Mycoplasma pneumoniae; Legionella pneumophilia; Chlamydia psittaci

DNA gyrase subunit B

Streptococcus pneumoniae serotype 4 (strain ATCC BAA- 334/TIGR4)

gyrB

S. pneumoniae serotype 4 DNA topoisomerase 4 (strain ATCC BAA-334/ TIGR4) subunit B

parE

Isoleucine-tRNA Staphylococcus aureus ligase

ileS

10

DB00410

Mupirocin$

500.6

Antibacterial agent

Fatty acyls

Enteric bacteria P41972 and other eubacteria; Staphylococcus aureus

11

DB01256

Retapamulin

517.8

Topical antibacterial agent

Prenol lipids

Bacteria

12

DB04221

Didecyldimethylam 326.6 monium

N.A.

Organonitrogen N.A. compounds

Q9A1X4a 50S ribosomal protein L3

Streptococcus pyogenes serotype M1

P9WPB5 Cyclopropane mycolic acid synthase 2

Mycobacterium tuberculosis cmaA2

3. EXAMPLES AND CASE STUDIES

rplC

475

2 METHODOLOGY

Similar Approved Molecules

Information on Predicted Targets in C. albicans

UniProt code

Protein name

A0A1D8PQQ9 Mitochondrial 54S ribosomal protein YmL6

Gene name(source: Uniprot)

Functional Category

Pairwise Seq. Profile Query identity (%) coverage(%) (EMBOSS (JACKHMMER) Water)

YML6

Protein synthesis

71.0

28.7

Erythromycin (DB00199; 0.76), Azithromycin (DB00207; 0.717), #, b Nystatin (DB00646; 0.707), #, b Amphotericin B (DB00681; 0.728), b Natamycin (DB00826; 0.706), Clarithromycin (DB01211; 0.777), Spiramycin (DB06145; 0.906), Troleandomycin (DB13179; 0.766)

DNA topology maintenance

85.3

26.0

85.5

27.2

Moxifloxacin (DB00218; 0.909), Grepafloxacin (DB00365; 0.795), Pefloxacin (DB00487; 0.759), Ciprofolxacin (DB00537; 0.803), Lomefloxacin (DB00978; 0.78), Norfloxacin (DB01059; 0.759), Levofloxacin (DB01137; 0.875), Ofloxacin (DB01165; 0.875), Sparfloxacin (DB01208; 0.792), Fleroxacin (DB04576; 0.737), Besifoxacin (DB06771; 0.737), Finafloxacin (DB09047; 0.708)

A0A1D8PMM1 DNA topoisomerase TOP2 2

Drug Name (DrugBank ID; Similarity score)

A0A1D8PSG5

Isoleucine–tRNA ligase

CAALFM_CR03400WA Biosynthesis

99.7

36.0

Q59RI1

Isoleucine-tRNA ligase

ILS1

Biosynthesis

90.8

27.6

A0A1D8PS12

Leucine-tRNA ligase CDC60

Biosynthesis

74.9

20.5

A0A1D8PRP6

Mitochondrial 54S ribosomal protein YmL9

CAALFM_CR00490WA Protein synthesis

96.6

42.0

(No other similar aproved drug)

Q5APD4

Sphingolipid C9methyltransferase

MTS1

93.1

27.9

Pentolinium (DB01090; 0.800), Decamethonium (DB01245; 0.852), Cetrimonium (DB01718; 0.852), Quaternium-24 (DB13969; 0.964), Dioctyldimonium (DB13970; 1.0)

Degradation/ Utilization/ Assimilation

(No other similar aproved drug)

Continued

3. EXAMPLES AND CASE STUDIES

476

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 2 Comprehensive List of 26 Shortlisted Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans Along With Relevant Details (the Query Coverage of all the Pairs of Query and Target Protein Is More Than 70%)—cont’d Information on Known Targets (Query Protein) (Source: DrugBank, UniProt)

Information on Approved Drugs (Source: DrugBank)

Sl. DrugBank No. ID Drug Name

Molecular weight (g/mol) Description

Affected Chemical Class Organism

UniProt code Protein Name

Organism

Gene name

Escherichia coli O157:H7

rplV

13

DB01369

Quinupristin#

1022.2

Antibacterial agent

Peptidomimetics Enteric bacteria P61177 and other eubacteria; Enterococcus faecalis

14

DB00256

Lymecycline

602.6

Antibacterial agent

Tetracyclines

Enteric bacteria P0A7X3a 30S ribosomal protein S9 and other eubacteria

Escherichia coli (strain K12) rpsI

15

DB00453

Clomocycline

508.9

Antibacterial agent

Tetracyclines

Enteric bacteria P0A7X3a 30S ribosomal and other protein S9 eubacteria

Escherichia coli (strain K12) rpsI

3. EXAMPLES AND CASE STUDIES

50S ribosomal protein L22

477

2 METHODOLOGY

Similar Approved Molecules

Information on Predicted Targets in C. albicans

Gene name(source: Uniprot)

Functional Category

Pairwise Seq. Profile Query identity (%) coverage(%) (EMBOSS (JACKHMMER) Water)

Mitochondrial 54S ribosomal protein YmL22

orf19.3367

Protein synthesis

90.0

26.7

(No other similar aproved drug)

37S ribosomal protein S9, mitochondrial

MRPS9

Protein synthesis

93.8

38.3

Doxycycline (DB00254; 0.912), Tigecycline (DB00560; 0.824),

A0A1D8PCW6 Ribosomal 40S RPS16A subunit protein S16A

Protein synthesis

96.1

36.8

Oxytetracycline (DB00595; 0.932), Demeclocycline (DB00618; 0.898), Tetracycline (DB00759; 0.945), Methacycline (DB00931; 0.891), Minocycline (DB01017; 0.864), Rolitetracycline (DB01301; 0.966), Chlortetracycline (DB09093; 0.877), Meclocycline (DB13092; 0.865)

MRPS9

Protein synthesis

93.8

38.3

A0A1D8PCW6 Ribosomal 40S RPS16A subunit protein S16A

Protein synthesis

96.1

36.8

Doxycycline (DB00254; 0.912), Lymecycline (DB00256; 0.902), Tigecycline (DB00560; 0.813), Oxytetracycline (DB00595; 0.932), Demeclocycline (DB00618; 0.976), Tetracycline (DB00759; 0.945), Methacycline (DB00931; 0.891), Minocycline (DB01017; 0.858), Rolitetracycline (DB01301; 0.908), Chlortetracycline (DB09093; 0.953), Meclocycline (DB13092; 0.921)

UniProt code

Protein name

A0A1D8PLT6

O94150

O94150

37S ribosomal protein S9, mitochondrial

Drug Name (DrugBank ID; Similarity score)

Continued

3. EXAMPLES AND CASE STUDIES

478

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 2 Comprehensive List of 26 Shortlisted Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans Along With Relevant Details (the Query Coverage of all the Pairs of Query and Target Protein Is More Than 70%)—cont’d Information on Known Targets (Query Protein) (Source: DrugBank, UniProt)

Information on Approved Drugs (Source: DrugBank)

Sl. DrugBank No. ID Drug Name

Molecular weight (g/mol) Description

Affected Chemical Class Organism

UniProt code Protein Name

Organism

Gene name

16

DB00595

Oxytetracycline$

460.4

Antibacterial agent

Tetracyclines

Enteric bacteria P0A7X3a 30S ribosomal and other protein S9 eubacteria

Escherichia coli (strain K12) rpsI

17

DB00618

Demeclocycline

464.8

Antibacterial agent

Tetracyclines

Enteric bacteria P0A7X3a 30S ribosomal and other protein S9 eubacteria

Escherichia coli (strain K12) rpsI

3. EXAMPLES AND CASE STUDIES

479

2 METHODOLOGY

Similar Approved Molecules

Information on Predicted Targets in C. albicans

Gene name(source: Uniprot)

Functional Category

Pairwise Seq. Profile Query identity (%) coverage(%) (EMBOSS (JACKHMMER) Water)

MRPS9

Protein synthesis

93.8

38.3

A0A1D8PCW6 Ribosomal 40S subunit protein S16A

RPS16A

Protein synthesis

96.1

36.8

O94150

MRPS9

Protein synthesis

93.8

38.3

RPS16A

Protein synthesis

96.1

36.8

UniProt code

Protein name

O94150

37S ribosomal protein S9, mitochondrial

37S ribosomal protein S9, mitochondrial

A0A1D8PCW6 Ribosomal 40S subunit protein S16A

Drug Name (DrugBank ID; Similarity score) Doxycycline (DB00254; 0.978), Lymecycline (DB00256; 0.932), Clomocycline (DB00453; 0.932), Tigecycline (DB00560; 0.838), Demeclocycline (DB00618; 0.935), Tetracycline (DB00759; 0.986), Methacycline (DB00931; 0.948), Minocycline (DB01017; 0.886), Rolitetracycline (DB01301; 0.939), Chlortetracycline (DB09093; 0.919), Meclocycline (DB13092; 0.913) Doxycycline (DB00254; 0.914), Lymecycline (DB00256; 0.898), Clomocycline (DB00453; 0.976), Tigecycline (DB00560; 0.813), Oxytetracycline (DB00595; 0.935), Tetracycline (DB00759; 0.948), Methacycline (DB00931; 0.912), Minocycline (DB01017; 0.872), Rolitetracycline (DB01301; 0.904), Chlortetracycline (DB09093; 0.956), Meclocycline (DB13092; 0.943)

Continued

3. EXAMPLES AND CASE STUDIES

480

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 2 Comprehensive List of 26 Shortlisted Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans Along With Relevant Details (the Query Coverage of all the Pairs of Query and Target Protein Is More Than 70%)—cont’d Information on Known Targets (Query Protein) (Source: DrugBank, UniProt)

Information on Approved Drugs (Source: DrugBank)

Sl. DrugBank No. ID Drug Name

Molecular weight (g/mol) Description

Affected Chemical Class Organism

UniProt code Protein Name

18

DB01301

Rolitetracycline

527.6

Antibacterial agent

Tetracyclines

N.A.

P0A7X3a 30S ribosomal protein S9

Escherichia coli (strain K12) rpsI

19

DB00479

Amikacin$

585.6

Antibacterial agent

Organooxygen compounds

Enteric bacteria P0A7S3a 30S ribosomal and other protein S12 eubacteria

Escherichia coli (strain K12) rpsL

20

DB00684

Tobramycin

467.5

Antibacterial agent

Organooxygen compounds

Enteric bacteria P0A7S3a 30S ribosomal and other protein S12 eubacteria

Escherichia coli (strain K12) rpsL

21

DB00919

Spectinomycin$

332.3

Antibacterial agent

Dioxanes

Enteric bacteria P0A7S3a 30S ribosomal and other protein S12 eubacteria

Escherichia coli (strain K12) rpsL

22

DB00955

Netilmicin

475.6

Antibacterial agent

Organooxygen compounds

Enteric bacteria P0A7S3a 30S ribosomal protein S12 and other eubacteria

Escherichia coli (strain K12) rpsL

23

DB01172

Kanamycin$

484.5

Antibacterial agent

Organooxygen compounds

Enteric bacteria P0A7S3a 30S ribosomal and other protein S12 eubacteria

Escherichia coli (strain K12) rpsL

3. EXAMPLES AND CASE STUDIES

Organism

Gene name

481

2 METHODOLOGY

Similar Approved Molecules

Information on Predicted Targets in C. albicans

Gene name(source: Uniprot)

Functional Category

Pairwise Seq. Profile Query identity (%) coverage(%) (EMBOSS (JACKHMMER) Water)

MRPS9

Protein synthesis

93.8

38.3

A0A1D8PCW6 Ribosomal 40S RPS16A subunit protein S16A

Protein synthesis

96.1

36.8

UniProt code

Protein name

O94150

37S ribosomal protein S9, mitochondrial

Drug Name (DrugBank ID; Similarity score) Doxycycline (DB00254; 0.918), Lymecycline (DB00256; 0.966), Clomocycline (DB00453; 0.908), Tigecycline (DB00560; 0.834), Oxytetracycline (DB00595; 0.939), Tetracycline (DB00759; 0.952), Methacycline (DB00931; 0.890), Minocycline (DB01017; 0.870), Chlortetracycline (DB09093; 0.883), Meclocycline (DB13092; 0.865)

Q5AA46

Putative mitochondrial 37S ribosomal protein MRPS12

orf19.2438

Protein synthesis

87.1

55.2

Spectinomycin (DB00919;0.702), Arbekacin (DB06696; 0973), # Hyaluronic acid (DB08818; 0.756)

Q5AA46

Putative mitochondrial 37S ribosomal protein MRPS12

orf19.2438

Protein synthesis

87.1

55.2

Framycetin (DB00452; 0.963), Gentamicin (DB00798; 0.839), Kanamycin (DB01172; 0.962), Glucosamine (DB01296; 0.762), Paromomycin (DB01421; 0.963), Ribostamycin (DB03615; 0.963), Arbekacin (DB06696; 0.703)

Q5AA46

Putative mitochondrial 37S ribosomal protein MRPS12

orf19.2438

Protein synthesis

87.1

55.2

Amikacin (DB00479; 0.702), Gentamicin (DB00798; 0.735), Streptomycin (DB01082; 0.733) Arbekacin (DB06696; 0.703)

Q5AA46

Putative mitochondrial 37S ribosomal protein MRPS12

orf19.2438

Protein synthesis

87.1

55.2

(No other similar aproved drug)

Q5AA46

Putative mitochondrial 37S ribosomal protein MRPS12

orf19.2438

Protein synthesis

87.1

55.2

Framycetin (DB00452; 0.944), Tobramycin (DB00684; 0.962) Gentamicin (DB00798; 0.808), Glucosamine (DB01296; 0.760), Paromomycin (DB01421; 0.944), Ribostamycin (DB03615; 0.944)

Continued 3. EXAMPLES AND CASE STUDIES

482

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 2 Comprehensive List of 26 Shortlisted Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans Along With Relevant Details (the Query Coverage of all the Pairs of Query and Target Protein Is More Than 70%)—cont’d Information on Known Targets (Query Protein) (Source: DrugBank, UniProt)

Information on Approved Drugs (Source: DrugBank)

Sl. DrugBank No. ID Drug Name

Molecular weight (g/mol) Description

24

DB06696

Arbekacin

552.6

Antibacterial agent

Organooxygen compounds

Enteric bacteria P0A7S3a 30S ribosomal and other protein S12 eubacteria; Escherichia coli; Staphylococcus aureus; Acinetobacter sp

Escherichia coli (strain K12) rpsL

25

DB00698

Nitrofurantoin$

238.2

Antibacterial agent

Azolidines

Gram-negative and Grampositive bacteria

Escherichia coli (strain K12) rpsJ

26

DB06145

Spiramycin#

843.1

Antimicrobial Organooxygen agent compounds

Affected Chemical Class Organism

UniProt code Protein Name

P0A7R5

30S ribosomal protein S10

Bacteria; Q9A1X4a 50S ribosomal Bacteria protein L3 and protozoa; Corynebacterium diphtheriae; S. pyogenes; H. influenzae; Streptococcus viridans

a

Organism

S. pyogenes serotype M1

Gene name

rplC

Query proteins associated with more than one approved drug. Known approved antifungal drugs. # Drugs with molecular weight above 700 g mol 1. Notes: $Drugs approved for both human and veterinary treatment; entries without “$” symbol indicates drugs approved for human treatment only. N.A.: Information not available in DrugBank. b

3. EXAMPLES AND CASE STUDIES

483

2 METHODOLOGY

Similar Approved Molecules

Information on Predicted Targets in C. albicans

Gene name(source: Uniprot)

Functional Category

Pairwise Seq. Profile Query identity (%) coverage(%) (EMBOSS (JACKHMMER) Water)

Putative mitochondrial 37S ribosomal protein MRPS12

orf19.2438

Protein synthesis

87.1

55.2

Amikacin (DB00479; 0.973), Tobramycin (DB00684; 0.703) Gentamicin (DB00798; 0.722), Spectinomycin (DB00919; 0.703), # Hyaluronic acid (DB08818; 0.767)

Q5A389

Ribosomal 40S subunit protein S20

RPS20

Protein synthesis

97.1

27.5

Nitrofural (DB00336; 0.737) Dantrolene (DB01219; 0.720)

A0A1D8PRP6

Mitochondrial 54S ribosomal protein YmL9

CAALFM_CR00490WA Protein synthesis

96.6

42.0

Erythromycin (DB00199; 0.701), #, b Nystatin (DB00646; 0.724), #, b Amphotericin B (DB00681; 0.746), b Natamycin (DB00826; 0.723), Clarithromycin (DB01211; 0.717), Josamycin (DB1321; 0.906)

UniProt code

Protein name

Q5AA46

3. EXAMPLES AND CASE STUDIES

Drug Name (DrugBank ID; Similarity score)

484

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 3 Comprehensive List of 66 Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans (the Query Coverage of all the Pairs of Query and Target Protein Is Less Than 70%) Information on Drug With No Human Target (Source: DrugBank)

Information on Known Drug Target (Source: DrugBank) Query Protein UniProt ID Protein name

Sl. No.

Drug Name (DrugBank ID) Description

1

Rifapentine (DB01201)

Antitubercular agent

P9WGY7 DNA-directed RNA polymerase subunit beta

rpoC

Mycobacterium tuberculosis

2

Fusidic acid@ (DB02703)

Antibacterial agent

P13551

fusA

Thermus thermophilus

Elongation factor G

3. EXAMPLES AND CASE STUDIES

Gene name

Organism name

485

2 METHODOLOGY

Query Coverage From Jackhmmer Search*

Hits from jackhmmer search#

Information on Potential Target in C. albicans (Source: UniProt)

qlen

hmm from

hmm to

qcov (hmm)

Target protein Uniprot ID

env from

env to

tien

Target Protein name

Gene name

1316

7

166

12.082067

A0A1D8PUA6

11

163

1728

DNA-directed RNA polymerase subunit

RP021

1316

301

590

21.960486

217

542

1728

1316

706

943

18.009119

610

941

1728

1316

986

1052

5.0151976

1042

1148

1728

1316

1109

1164

4.1793313

1299

1381

1728

1316

1201

1266

4.9392097

1377

1458

1728

1316

7

127

9.118541

7

138

1448

DNA-directed RNA polymerase subunit

Orf19.3103

1316

305

591

21.732523

224

568

1448

1316

710

914

15.50152

669

949

1448

1316

949

1063

8.662614

1032

1167

1448

1316

1111

1165

4.1033435

1270

1357

1448

1316

1198

1267

5.2431611

1349

1430

1448

1316

8

124

8.8145897

9

121

1665

DNA-directed RNA polymerase subunit

RPA190

1316

289

910

47.18845

301

1055

1665

1316

969

1065

7.2948328

1118

1269

1665

1316

1113

1168

4.1793313

1514

1599

1665

1316

1206

1266

4.5592705

1595

1661

1665

691

628

673

6.512301

Q5AAV3

740

800

807

Ribosome-releasing factor 2, mitochondrial

MEF2

691

7

170

23.589001

Q5A0M4

9

244

842

Elongation factor 2

EFT2

691

270

686

60.202605

Q5A0M4

328

815

842

691

5

158

22.141823

A0A1D8PH71

138

323

1022

U5 snRNP GTPase

SNU114

691

270

507

34.298119

457

759

1022

691

531

688

22.720695

801

1016

1022

691

3

169

24.023155

7

199

1044

GTPase

RIA1

691

316

508

27.785818

503

769

1044

691

530

679

21.562952

691

9

164

22.431259

Translation factor GUF1, mitochondrial

GUF1

691

302

499

28.509407

A0A1D8PMS1

A0A1D8PEX3

Q59WB7

Q59P53

812

988

1044

50

233

654

226

455

654

Continued

3. EXAMPLES AND CASE STUDIES

486

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 3 Comprehensive List of 66 Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans (the Query Coverage of all the Pairs of Query and Target Protein Is Less Than 70%)—cont’d Information on Drug With No Human Target (Source: DrugBank)

Sl. No.

Drug Name (DrugBank ID) Description

Information on Known Drug Target (Source: DrugBank) Query Protein UniProt ID Protein name

Gene name

Organism name

3

Etravirine (DB06414/ DB07191)

Antiretroviral agent

P03366

Gag-Pol polyprotein

gag-pol HIV-1

4

Dapsone (DB00250)

Antibacterial; leprostatic; antimalarial agent

P0C0X1

Dihydropteroate synthase 1

folP1

Mycobacterium leprae (strain TN)

5

Dapsone (DB00250)

Antibacterial; leprostatic; antimalarial agent

P0C0X2

Inactive dihydropteroate synthase 2

folP2

Mycobacterium leprae (strain TN)

6

Nitrofurantoin

Antibacterial agent

P52647

Probable pyruvate-flavodoxin oxidoreductase

ydbK

Escherichia coli (strain K12)

7

Cetrimonium

Antiseptic

Q79FX6

Cyclopropane mycolic acid synthase mmaA2 Mycobacterium MmaA2 tuberculosis

@

@

(DB00698)

(DB01718)

3. EXAMPLES AND CASE STUDIES

487

2 METHODOLOGY

Query Coverage From Jackhmmer Search*

Hits from jackhmmer search#

Information on Potential Target in C. albicans (Source: UniProt)

qlen

hmm from

hmm to

qcov (hmm)

Target protein Uniprot ID

env from

env to

tien

Target Protein name

Gene name

691

10

153

20.694645

P0CY35

2

182

458

Elongation factor 1-alpha 1

TEF1

691

10

153

20.694645

Q59QD6

2

182

458

Elongation factor 1-alpha 2

TEF2

691

11

158

21.273517

A0A1D8PRL2

132

326

570

Ribosome dissociation factor GTPase

orf19.7144

691

10

146

19.681621

Q5ABC3

28

185

426

Elongation factor Tu

TUF1

691

10

159

21.562952

Q59YE8

288

478

721

Translation termination factor GTPase eRF3

SUP35

691

10

187

25.615051

Q5AGF6

100

311

534

Translation initiation factor elF2 subunit gamma

GCD11

691

15

168

22.141823

Q5A782

413

616

1017

Translation initiation factor elF5B

FUN12

691

15

152

19.826339

Q5A5B3

230

365

767

Translation initiation factor 2

IFM1

1447

608

875

18.45197

A0A1D8PC70

75

346

653

Pol93p

POL93

1447

1266

1351

5.8742225

331

440

653

1447

390

435

3.1098825

5

52

175

mRNA-binding translational activator

GIS2

1447

386

432

3.178991

46

103

175

1447

388

430

2.902557

100

156

175

284

4

151

51.760563

464

633

788

Folic acid synthesis protein fol1

FOL1

284

143

270

44.71831

632

787

788

291

15

168

52.57732

462

633

788

Folic acid synthesis protein fol1

FOL1

291

159

270

38.14433

630

787

788

1174

280

346

5.6218058

157

329

1437

Sulfite reductase (NADPH) subunit beta

ECM17

1174

928

1162

19.931857

372

670

1437

1174

238

332

8.0068143

Sulfite reductase subunit alpha

MET10

1174

453

586

11.32879

287

7

164

54.703833

287

50

170

287

37

287

57

Q59YJ9

A0A1D8PN03

A0A1D8PN03

A0A1D8PHL8

A0A1D8PIF6

247

364

1094

385

615

1094

074198

63

244

376

Sterol 24-C-methyltransferase

ERG6

41.811847

Q5A4P2

112

262

327

Ubiquinone biosynthesis O-methyltransferase, mitochondrial

COQ3

144

37.28223

Q5A943

11

181

339

Protein-arginine omega-N methyltransferase

HMT1

133

26.480836

Q5AKW7

45

131

326

rRNAadenineN(6)-methyltransferase

DIM1

Continued

3. EXAMPLES AND CASE STUDIES

488

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 3 Comprehensive List of 66 Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans (the Query Coverage of all the Pairs of Query and Target Protein Is Less Than 70%)—cont’d Information on Drug With No Human Target (Source: DrugBank)

Sl. No.

8

Drug Name (DrugBank ID) Description

Bismuth subcitrate (DB09275)

Information on Known Drug Target (Source: DrugBank) Query Protein UniProt ID Protein name

Antiinfective agents; Antiulcer 025926 agents; Antacids

ATP-dependent Clp protease ATPbinding subunit ClpX

3. EXAMPLES AND CASE STUDIES

Gene name

clpX

Organism name

Helicobacter pylori (strain ATCC 700392/ 26695)

489

2 METHODOLOGY

Query Coverage From Jackhmmer Search*

Hits from jackhmmer search#

env from

env to

Information on Potential Target in C. albicans (Source: UniProt)

qlen

hmm from

hmm to

qcov (hmm)

Target protein Uniprot ID

287

64

167

35.888502

A0A1D8PLX3

232

371

287

59

169

38.327526

Q59VY2

84

245

446

139

344

45.964126

Q59WG3

237

451

826

446

140

373

52.242152

495

767

826

446

141

353

47.533632

252

476

766

446

139

334

43.721973

504

737

766

446

139

386

55.381166

217

471

827

446

141

312

38.340807

546

757

827

446

140

349

46.860987

302

549

796

446

117

348

51.793722

561

795

796

446

125

347

49.775785

464

708

1090

446

142

326

41.255605

765

956

1090

446

194

293

22.197309

937

1071

1090

446

135

308

38.789238

527

776

1157

446

140

325

41.479821

837

1067

1157

446

126

219

20.852018

287

402

5035

446

108

189

18.161435

602

753

5035

446

140

217

17.264574

1073

1175

5035

446

135

188

11.883408

1275

1438

5035

446

131

187

12.556054

1732

1844

5035

446

140

353

47.757848

Q5A6S2

196

425

446

140

335

43.721973

Q5A0L8

167

446

138

362

50.224215

A0A1D8PIV7

446

140

334

43.497758

446

140

331

42.825112

446

141

324

41.03139

Target Protein name

Gene name

444

Putative RNA methyltransferase

orf19.1300

312

Methyltransferase-like protein

ABP140

AAA family ATPase

CDC48

AAA family ATPase

CAALFM_CR08590WA

Putative AAA family ATPase

RIX7

AAA family ATPase

SEC18

AAA family ATPase peroxin 1

PEX1

AAA family ATPase peroxin 6

PEX6

Midasin

MDN1

428

Proteasome regulatory particle base subunit

RPT4

396

411

Proteasome regulatory particle base subunit

PR26

199

435

441

Proteasome regulatory particle base subunit

RPT2

Q5AJC2

291

515

795

AAA family ATPase

AFG3

A0A1D8PI93

154

376

401

Proteasome regulatory particle base subunit

RPT6

Q5A2A0

210

431

444

Proteasome regulatory particle base subunit

RPT1

Q5A331

A0A1D8PND0

A0A1D8PFN6

A0A1D8PR43

Q59ZE6

A0A1D8PL61

tien

Continued

3. EXAMPLES AND CASE STUDIES

490

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 3 Comprehensive List of 66 Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans (the Query Coverage of all the Pairs of Query and Target Protein Is Less Than 70%)—cont’d Information on Drug With No Human Target (Source: DrugBank)

Gene name

Antibacterial agent

P0A7S9

rpsM

Escherichia coli (strain K12)

Antiseptic

P9WPB7 Cyclopropane mycolic acid synthase 1

cmaAl

Mycobacterium tuberculosis

Sl. No.

Drug Name (DrugBank ID) Description

9

Tigecycline

10

Cetrimonium

@

(DB00560)

@

(DB01718)

Information on Known Drug Target (Source: DrugBank) Query Protein UniProt ID Protein name

30S ribosomal protein S13

3. EXAMPLES AND CASE STUDIES

Organism name

491

2 METHODOLOGY

Query Coverage From Jackhmmer Search*

Hits from jackhmmer search#

qlen

hmm from

hmm to

qcov (hmm)

Target protein Uniprot ID

446

119

357

53.363229

446

140

336

43.946188

446

139

309

446

142

357

446

118

446

142

446

Information on Potential Target in C. albicans (Source: UniProt)

env from

env to

tien

Target Protein name

Gene name

A0A1D8PG80

8

294

362

Replication factor C subunit 5

RFC5

A0A1D8PMQ4

197

428

430

Proteasome regulatory particle base subunit

RPT5

38.116592

Q5AD10

382

593

846

M-AAA protease subunit

orf19.2057

48.206278

Q5A283

240

444

444

Bifunctional AAA family ATPase chaperone/translocase

CAALFM_CR05970CA

314

43.946188

A0A1D8PNV1

14

249

347

Replication factor C subunit 3

RFC3

341

44.618834

A0A1D8PME0

252

481

687

l-AAA protease

YME1

118

338

49.327354

A0A1D8PQP2

28

303

363

Replication factor C subunit 2

RFC2

446

140

333

43.273543

A0A1D8PU31

565

768

820

Putative AAA family ATPase

YTA6

446

138

330

43.049327

Q5AG40

155

367

439

Vacuolar protein sorting-associated protein 4

VPS4

446

113

228

25.784753

Q96UX5

93

363

812

Heat shock protein 78, mitochondrial

HSP78

446

127

223

21.524664

493

678

812

446

117

227

24.663677

ChaperoneATPase

HSP104

446

135

224

19.955157

446

118

301

41.03139

446

135

217

18.38565

446

139

209

15.695067

2776

2909

4161

446

137

314

39.686099

Q5A0W7

53

289

446

137

303

37.219731

Q5A889

209

446

138

303

36.995516

A0A1D8PHI0

359

446

132

248

26.008969

Q59YV0

662

836

446

134

281

32.959641

Q5AGZ9

51

253

446

135

281

32.735426

A0A1D8PCY7

172

408

446

135

228

20.852018

Q5A6N1

512

657

446

133

223

20.179372

A0A1D8PNC0

119

311

446

133

198

14.573991

A0A1D8PCM6

693

788

118

2

56

45.762712

A0A1D8PQQ5

14

118

63

109

38.983051

287

4

164

55.749129

A0A1D8PTP9

151

337

899

571

800

899

A0A1D8PU93

14

247

323

Replication factor C subunit 4

RFC4

A0A1D8PK53

2442

2562

4161

Dynein heavy chain

DYN1

458

RuvB-like helicase 1

RVB1

451

853

Ctfl8p

CTF18

595

888

Replication factor C subunit 1

RFC1

1258

Lon protease homolog 2, peroxisomal

CAALFM_C305360CA

498

RuvB-like helicase 2

RVB2

678

SsDNA-dependent ATPase

orf19.3019

1078

Lon protease homolog, mitochondrial

PIM1

564

Origin recognition complex subunit 4

ORC4

1274

Roalp

ROA1

75

145

Ribosomal 40S subunit protein S18B

RPS18

86

145

145

62

244

376

Sterol 24-C-methyltransferase

ERG6

074198

Continued

3. EXAMPLES AND CASE STUDIES

492

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 3 Comprehensive List of 66 Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans (the Query Coverage of all the Pairs of Query and Target Protein Is Less Than 70%)—cont’d Information on Drug With No Human Target (Source: DrugBank)

Information on Known Drug Target (Source: DrugBank)

Sl. No.

Drug Name (DrugBank ID) Description

Query Protein UniProt ID Protein name

11

Sulfacetamide (DB00634)

Antiinfective agent

P0C002

Dihydropteroate synthase type-1

sull

Escherichia coli

12

Rosoxacin (DB0081)

Antibacterial agent

P0AES6

DNAgyrase subunit B

gyrB

Escherichia coli (strain K12)

13

Mupirocin

(DB00410)

Antibacterial agent

P41972

Isoleucine-tRNA ligase

ileS

Staphylococcus aureus

14

Nitrofural

(DB00336)

Topical antiinfective agent

P06715

Glutathione reductase

gor

Escherichia coli (strain K12)

@

@

3. EXAMPLES AND CASE STUDIES

Gene name

Organism name

493

2 METHODOLOGY

Query Coverage From Jackhmmer Search*

qlen

hmm from

hmm to

qcov (hmm)

287

5

170

287

30

166

287

50

287

Hits from jackhmmer search#

Information on Potential Target in C. albicans (Source: UniProt)

Target protein Uniprot ID

env from

env to

tien

Target Protein name

Gene name

57.491289

Q59VY2

44

246

312

Methyltransferase-like protein

ABP140

47.38676

Q5A943

10

226

339

Protein-arginine omega-N methyltransferase

HMT1

170

41.811847

Q5A4P2

112

250

327

Ubiquinone biosynthesis O-methyltransferase, mitochondrial

COQ3

64

165

35.191638

Q5AD01

148

296

323

Protein-lysine N-methyltransferase

orf19.2066

287

89

169

27.874564

A0A1D8PSY8

83

202

303

Crglp

CRG1

287

56

135

27.526132

Q5AKW7

26

137

326

rRNAadenineN(6)-methyltransferase

DIM1

287

62

167

36.585366

A0A1D8PLX3

209

372

444

Putative RNA methyltransferase

orf19.1300

287

64

166

35.54007

Q5A386

16

166

265

tRNA (Carboxymethyluridine(34)-50)-methyltransferase

TRM9

287

37

164

44.250871

Q59TS3

65

221

752

Btalp

BTA1

279

4

132

45.878136

A0A1D8PN03

Folic acid synthesis protein fol1

FOL1

279

192

265

26.164875

804

28

536

63.18408

917

17

248

25.19084

917

292

865

62.486369

917

56

381

35.441658

917

447

589

917

594

805

917

42

109

7.306434

917

45

225

19.629226

917

527

772

26.717557

917

549

724

19.083969

Q5AL46

917

44

200

17.011996

A0A1D8PP21

131

333

450

91

337

54.666667

Q5AEC9

168

479

450

98

337

53.111111

A0A1D8PQ66

221

538

450

165

231

14.666667

A0A1D8PDU9

1748

1846

450

123

327

45.333333

450

116

316

44.444444

469

632

788

637

788

788

A0A1D8PMM1

92

630

1461

DNA topoisomerase 2

TOP2

A0A1D8PHR0

144

414

1119

Valine-tRNA ligase

VAS1

411

1033

1119

1

393

861

Leucine-tRNA ligase

NAM2

15.485278

388

558

861

23.009815

583

841

861

A0A1D8PS12

20

162

1097

Leucine-tRNA ligase

CDC60

A0A1D8PP08

9

188

577

Methionine-tRNA ligase

MSM1

220

555

577

373

579

781

Cysteine-tRNA ligase

orf19.4931

748

Methionine-tRNA ligase

MES1

574

NADH-ubiquinone reductase (H(+)translocating)

NDE1

622

Ymx6p

YMX6

2126

Glutamate synthase (NADH)

GUI

Thioredoxin reductase

TRR1

A0A1D8PQ56

Q5AG89

1840

2104

2126

65

319

320

Continued

3. EXAMPLES AND CASE STUDIES

494

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 3 Comprehensive List of 66 Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans (the Query Coverage of all the Pairs of Query and Target Protein Is Less Than 70%)—cont’d Information on Drug With No Human Target (Source: DrugBank)

Sl. No.

Drug Name (DrugBank ID) Description

Information on Known Drug Target (Source: DrugBank) Query Protein UniProt ID Protein name

Gene name

Organism name

15

Adefovir Dipivoxil (DB00718)

Antiretroviral agent

P24024

DNA polymerase/reverse transcriptase

P

HBV-D

16

Cidofovir (DB00369) Foscarnet (DB00529)

Antiviral agents

P08546

DNA polymerase catalytic subunit

UL54

HHV-5

17

Ethionamide (DB00609)

Antibacterial agent; antitubercular agent

P9WIE5 Cata lase-peroxidase

katG

Mycobacterium tuberculosis

3. EXAMPLES AND CASE STUDIES

495

2 METHODOLOGY

Query Coverage From Jackhmmer Search*

Hits from jackhmmer search#

qlen

hmm from

hmm to

qcov (hmm)

Target protein Uniprot ID

450

9

40

6.8888889

A0A1D8PR96

450

129

264

30

450

8

204

43.555556

450

4

149

32.222222

env from

env to

Information on Potential Target in C. albicans (Source: UniProt)

tien

Target Protein name

Gene name

Ssp96p

SSP96

5

101

415

114

319

415

Q5A927

10

299

463

N,N-dimethylaniline monooxygenase

orf19.3307

Q59T35

15

226

503

Osmlp

OSM1

450

115

187

16

A0A1D8PH88

65

233

249

NADPH-adrenodoxin reductase

ARH2

450

6

55

10.888889

Q5AMQ8

2

79

477

Polyamineoxidase

orf19.4589

450

4

51

10.444444

A0A1D8PMP1

64

144

650

Glycerol-3-phosphate dehydrogenase

GUT2

450

7

47

8.8888889

Q5AKX2

10

101

648

Fumarate reductase

OSM2

832

498

634

16.346154

A0A1D8PC70

194

347

653

Pol93p

POL93

1242

124

606

38.808374

A0A1D8PR64

40

515

1038

DNA polymerase

POL3

1242

693

858

13.285024

512

677

1038

1242

897

1023

10.144928

675

810

1038

1242

1167

1217

4.0257649

860

926

1038

1242

209

267

4.6698873

201

275

1630

DNA polymerase

REV3

1242

351

599

19.967794

837

1085

1630

1242

700

853

12.318841

1089

1268

1630

1242

901

1014

9.0982287

1265

1398

1630

1242

205

290

6.8438003

384

534

1470

DNA polymerase

POLI

1242

370

446

6.1191626

586

750

1470

1242

681

854

13.929147

818

991

1470

1242

903

1025

9.8228663

988

1138

1470

1242

377

442

5.2334944

344

451

2211

DNA polymerase epsilon catalytic subunit

POL2

1242

794

853

4.7504026

796

872

2211

963

1034

2211

30

148

291

Putative heme-binding peroxidase

CCP2

Cytochrome c peroxidase, mitochondrial

CCP1

A0A1D8PKE9

A0A1D8PK28

A0A1D8PTD0

1242

962

1019

4.589372

740

83

191

14.594595

740

235

277

5.6756757

142

197

291

740

644

734

12.162162

195

288

291

740

91

193

13.783784

85

211

366

Q59X94

Q5AEN1

Continued

3. EXAMPLES AND CASE STUDIES

496

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 3 Comprehensive List of 66 Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans (the Query Coverage of all the Pairs of Query and Target Protein Is Less Than 70%)—cont’d Information on Drug With No Human Target (Source: DrugBank)

Information on Known Drug Target (Source: DrugBank)

Sl. No.

Drug Name (DrugBank ID) Description

Query Protein UniProt ID Protein name

18

Sulfadoxine (DB01299)

Antiinfective agent; antimalarial

P13922

Bifunctional dihydrofolate reductase- N.A. thymidylate synthase

19

Famciclovir (DB00426) Acyclovir (DB00787)

Antiviral agents

P09252

DNA polymerase catalytic subunit

ORF28 HHV-3

20

Rosoxacin (DB00817) Delafloxacin (DB11943) Ozenoxacin (DB12924)

Antibacterial agents

P0AFI2

DNA topoisomerase 4 subunit A

parC

Escherichia coli (strain K12)

21

Retapamulin @ (DB01256) Spiramycin @ (DB06145)

Topical antibacterial agent

Q9A1X4 50S ribosomal protein L3

rpIC

Streptococcus pyogenes serotype Ml

22

Didecyldimethylammoniu@ N.A. m(DB04221)

23

Quinupristin

24

25

Gene name

Organism name

Plasmodium falciparum (isolate Kl/Thailand)

Antimicrobial agent

@

P9WPB5 Cyclopropane mycolic acid synthase cmaA2 Mycobacterium 2 tuberculosis rpIV

Antibacterial agent

P61177

50S ribosomal protein L22

Nitazoxanide (DB00507)

Antimicrobial; antiviral

P94692

Pyruvate-flavodoxin oxidoreductase por

Besifloxacin (DB06771) Gatifloxacin @ (DB01044)

Antibacterial agent

P72524

DNA gyrase subunit A

3. EXAMPLES AND CASE STUDIES

gyrA

Escherichia coli 0157-.H7 Desulfovibrio africanus

Streptococcus pneumoniae serotype 4 (strain ATCCBAA-334/ TIGR4)

497

2 METHODOLOGY

Query Coverage From Jackhmmer Search*

Hits from jackhmmer search#

Target protein Uniprot ID

Information on Potential Target in C. albicans (Source: UniProt)

qlen

hmm from

hmm to

qcov (hmm)

env from

env to

tien

740

234

278

740

646

732

5.9459459

206

262

366

11.621622

259

346

366

608

324

607

46.546053

P12461

4

315

608

38

66

4.6052632

Q5A5E0

10

608

97

189

15.131579

1194

317

619

25.293132

1194

644

954

25.963149

1194

395

954

46.81742

1194

391

628

19.849246

1194

665

953

24.120603

1194

337

464

10.636516

1194

758

817

1194

903

959

752

10

192

24.202128

208

92

177

40.865385

208

180

204

11.538462

311

345

389

302

20

121

33.443709

074198

58

189

110

42

108

60

Q59TE0

81

1232

9

192

14.853896

A0A1D8PIF6

1232

248

550

24.512987

1232

282

400

9.5779221

Target Protein name

Gene name

315

Thymidylate synthase

TMP1

47

192

Dihydrofolate reductase

DFR1

44

155

192

205

508

1038

DNA polymerase

POL3

499

813

1038

A0A1D8PK28

563

1203

1470

DNA polymerase

POLI

A0A1D8PKE9

827

1091

1630

DNA polymerase

REV3

1089

1388

1630

249

430

2211

DNA polymerase epsilon catalytic subunit

POL2

4.9413735

794

872

2211

4.6901173

945

1037

2211

A0A1D8PMM1

726

922

1461

DNA topoisomerase 2

TOP2

Q59LS1

151

293

389

Ribosomal 60S subunit protein L3

RPL3

376

Sterol 24-C-methyltransferase

ERG6

153

185

Ribosomal 60S subunit protein L17B

RPL17B

41

249

1094

Sulfite reductase subunit alpha

MET10

252

570

1094

170

336

1437

Sulfite reductase (NADPH) subunit beta

ECM17

365

694

1437

723

931

1461

DNA topoisomerase 2

TOP2

1232

866

1160

23.863636

822

8

202

23.600973

A0A1D8PR64

A0A1D8PTD0

A0A1D8PHL8

A0A1D8PMM1

Continued 3. EXAMPLES AND CASE STUDIES

498

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 3 Comprehensive List of 66 Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans (the Query Coverage of all the Pairs of Query and Target Protein Is Less Than 70%)—cont’d Information on Drug With No Human Target (Source: DrugBank)

Sl. No.

Drug Name (DrugBank ID) Description

Information on Known Drug Target (Source: DrugBank) Query Protein UniProt ID Protein name

Gene name parC

Streptococcus pneumoniae serotype 4 (strain ATCCBAA-334/ TIGR4)

topA

Staphylococcus aureus

DNA topoisomerase 4 subunit A

Organism name

26

Besifloxacin (DB06771) Gatifloxacin @ (DB01044)

Antibacterial agent

P72525

27

Novobiocin @ (DB01051)

Antibacterial agent

Q06AK7 DNA topoisomerase 1

28

Delafloxacin (DB11943)

Antibacterial agent

P0AES4

DNA gyrase subunit A

gyrA

Escherichia coli (strain K12)

29

Tigecycline @ (DB00560) Arbekacin @ (DB06696) Kanamycin @ (DB01172) Netilmicin @ (DB00955) Spectinomycin @ (DB00919) Tobramycin @ (DB00684) Amikacin @ (DB00479)

Antibacterial agent

P0A7S3

30S ribosomal protein S12

rpsL

E. coli (strain K12)

30

Penciclovir (DB00299) Famciclovir (DB00426) Foscarnet (DB00529) Acyclovir (DB00787) Valaciclovir (DB00577)

Antiviral agent

P04293

DNA polymerase catalytic subunit

UL30

HHV-1

31

Besifloxacin (DB06771) Ozenoxacin (DB12924) Gemifloxacin (DB01155)

Antibacterial agent

P43700

DNA gyrase subunit A

gyrA

Haemophilusinfluenzae (strain ATCC51907 / DSM11121/KW20/Rd)

32

Besifloxacin (DB06771)

Antibacterial agent

P43702

DNA topoisomerase 4 subunit A

parC

Haemophilus influenzae (strain ATCC51907/ DSM 11121/KW20/Rd)

33

Dalfopristin (DB01764)

Antibacterial agent

P50870

Streptogramin A acetyltransferase

vatD

En terococcus faecium

34

Sulfamethizole (DB00576) Sulfacytine (DB01298) Sulfacetamide (DB00634) Sulfamethazine (DB01582) Sulfaphenazole (DB06729) Sulfamerazine (DB01581) Su Ifa meter (DB06821) Sulfanilamide (DB00259) Sulfisoxazole (DB00263)

Antibacterial agent Antibacterial agent Antibacterial agent Antiinfective Antibacterial agent Antibacterial agent Antibacterial agent Antibacterial agent Antibacterial agent

P0AC13 Dihydropteroate synthase

folP

Escherichia coli (strain K12)

3. EXAMPLES AND CASE STUDIES

499

2 METHODOLOGY

Query Coverage From Jackhmmer Search*

Hits from jackhmmer search#

Information on Potential Target in C. albicans (Source: UniProt)

qlen

hmm from

hmm to

qcov (hmm)

Target protein Uniprot ID

env from

env to

tien

Target Protein name

Gene name

823

27

189

19.684083

A0A1D8PMM1

728

920

1461

DNA topoisomerase 2

TOP2

689

50

517

67.77939

Q5ANG9

74

593

629

DNA topoisomerase

orf19.5934

875

3

203

22.857143

A0A1D8PMM1

720

927

1461

DNA topoisomerase 2

TOP2

124

24

107

66.935484

A0A1D8PDU3

24

143

145

Ribosomal 40S subunit protein S23B

RPS23A

1235

353

641

23.319838

A0A1D8PR64

213

511

1038

DNA polymerase

POL3

1235

689

995

24.777328

507

810

1038

1235

414

987

46.396761

A0A1D8PK28

565

1213

1470

DNA polymerase

POLI

1235

425

638

17.246964

A0A1D8PKE9

DNA polymerase

REV3

1235

700

988

23.319838

1235

363

495

10.688259

DNA polymerase epsilon catalytic subunit

POL2

1235

791

1000

880

6

747

817

1085

1630

1090

1392

1630

A0A1D8PTD0

267

537

2211

16.923077

A0A1D8PTD0

791

1037

2211

202

22.272727

A0A1D8PMM1

721

926

1461

DNA topoisomerase 2

TOP2

9

199

25.435074

A0A1D8PMM1

722

922

1461

DNA topoisomerase 2

TOP2

209

104

172

32.535885

A0A1D8PKI3

217

303

307

Acetyltransferase

orf!9.7437

282

13

166

54.255319

A0A1D8PN03

460

636

788

Folic acid synthesis protein fol1

FOL1

Continued

3. EXAMPLES AND CASE STUDIES

500

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 3 Comprehensive List of 66 Potential Repurposable Drugs With Their Known Targets and Predicted Targets in Candida albicans (the Query Coverage of all the Pairs of Query and Target Protein Is Less Than 70%)—cont’d Information on Drug With No Human Target (Source: DrugBank)

Sl. No.

Drug Name (DrugBank ID) Description

Information on Known Drug Target (Source: DrugBank) Query Protein UniProt ID Protein name

Gene name

Organism name

35

Sulfadoxine (DB01299) Sulfoxone (DB01145) Sulfamoxole (DB08798) Sulfathiazole (DB06147) Sulfametopyrazine (DB00664) Sulfadiazine (DB00359)

Antiinfective; antimalarial agent Antiinfective agent; antileprosy Antibacterial agent Antiinfective agent Antiinfective; antimalarial agent Antiinfective; antimalarial agent

Q27738

Dihydropteroate synthetase

N.A.

Plasmodium falciparum

36

Efavirenz (DB00625) Delavirdine (DB00705) Emtricitabine (DB00879) Stavudine (DB00649) Nevirapine (DB00238) Zalcitabine (DB00943) Tenofovir alafenamide (DB09299) Bictegravir (DB11799) Sennosides (DB11365) Tenofovir disoproxil (DB00300)

Antiretroviral agent Antiretroviral agent Antiretroviral agent Antiretroviral agent Antiretroviral agent Antiretroviral agent Antiretroviral agent Antiretroviral agent Cathartics and laxatives; antiretroviral agent Antiretroviral agent

Q72547

Reverse transcriptase/RNaseH

pol

Human immunodeficiency virus 1

37

Etravirine (DB06414)

Antiretroviral agent

P04585

Gag-Pol polyprotein

gag-pol HIV-1

Note: @ indicates drugs that have been identified in group 1 as well (Table 2)*: “qlen” stands for length of query sequence; “hmm from” and “hmm to” indicates the endpoints of the reported local alignment with respect to query; “q cov(hmm)” represents query coverage based on the endpoints of local alignment through jackhammer search. #: “env from” and “env to” define the envelope of the domain’s location on the target sequence. As explained in the HMMER user’s guide by Sean R. Eddy, Travis J. Wheeler and the HMMER development team (version 3.1b2; feb. 2015), envelope represents a subsequence that encompasses most of the posterior probability for a given homologous domain, even if precise endpoints are only fuzzily inferable.

3. EXAMPLES AND CASE STUDIES

501

2 METHODOLOGY

Query Coverage From Jackhmmer Search*

qlen

hmm from

hmm to

qcov (hmm)

282

152

214

282

212

272

370

41

219

48.108108

370

207

274

370

330

367

566

10

241

40.812721

1435

596

863

18.606272

1435

1254

1339

5.9233449

1435

391

435

3.0662021

1435

386

432

1435

390

430

Hits from jackhmmer search#

Target protein Uniprot ID

Information on Potential Target in C. albicans (Source: UniProt)

env from

env to

tien

21.985816

630

712

788

21.276596

708

787

788

449

636

788

18.108108

634

742

788

10

741

787

788

A0A1D8PC70

76

380

A0A1D8PC70

75

Target Protein name

Gene name

Folic acid synthesis protein fol1

FOL1

653

Pol93p

POL93

346

653

Pol93p

POL93

334

440

653

4

52

175

mRNA-binding translational activator

GIS2

3.2055749

46

107

175

2.7874564

101

157

175

A0A1D8PN03

Q59YJ9

3. EXAMPLES AND CASE STUDIES

502

16. DRUG-REPURPOSING AGAINST C. ALBICANS

2.3 Augmenting Chemical Space within the Domain of Approved Drugs Structurally similar molecules tend to have similar physical properties and are expected to exhibit similar biological activities (Martin, Kofron, & Traphagen, 2002; Patterson, Cramer, Ferguson, Clark, & Weinberger, 1996). Therefore molecules structurally similar to the shortlisted candidates were identified with the assumption that some (if not all) of the shortlisted drugs predicted from our study would exhibit satisfactory antifungal potential under physiological conditions and small molecules structurally similar to such drugs might also show the desired biological activity. Similarity-based ligand screening was performed by querying the shortlisted repurposable drugs (Table 2) against the pool of all approved molecules in DrugBank (with a similarity score cut-off of 0.7). This chemical similarity-based search was facilitated by ChemQuery interface of DrugBank (Wishart et al., 2008), which converts all the structures to SMILES strings and employs a substring-matching program to identify similar structures. The scoring scheme is based on the number of character matches for the longest matching substring. It is to be noted here that in-depth analyses of the safety and toxicity profiles available for the resultant drugs has not been carried out in the current study (as has been done for the 26 shortlisted drugs).

2.4 Proof of Concept We executed our study without consideration of any prior information regarding antifungal potential of the shortlisted drugs. To evaluate the strength of the protocol a literature survey was conducted in order to check whether the proposed drugs have ever been reported to show an inhibitory effect on the growth and survival of C. albicans in any earlier studies. It is worth mentioning here that, although our protocol is focused on identifying drugs capable of acting against the pathogen of interest, it does not explicitly take into account the interaction profiles of drugs with-cytochrome P450, other metabolic enzymes or transporters. It is well known that an administered drug often becomes involved in the interactions with various proteins within human host, which influences the bioavailability and pharmacokinetics of the drug, resulting in altered pharmacodynamics. Such interactions include binding to carrier proteins and/or transporters to reach the site of action in the cell and enzyme metabolism by cytochrome P450/other enzymes, which are not the primary biological targets of the drug. In many cases a drug may induce or inhibit certain metabolizing enzymes and transporters, which in turn could result in unwanted effects. Explicit consideration of such details (wherever available in DrugBank) might aid in pruning further undesirable drug candidates.

3 RESULTS AND DISCUSSION 3.1 Identification of Potential Repurposable Drugs A total of 26 FDA-approved small-molecule drugs have been identified as repurposable candidates from group I cases that could be taken up for further experimental investigations to check their antifungal potential against 31 C. albicans target proteins. The 26 shortlisted drugs could be grouped into 12 chemical classes based on the chemical taxonomy of

3. EXAMPLES AND CASE STUDIES

3 RESULTS AND DISCUSSION

503

FIG. 2

A plot showing the number of predicted targets for each of the 26 shortlisted drugs. The bars are color-coded based on the chemical class to which the drug belongs. The color version of the figure is available online.

molecules as detailed in DrugBank (Fig. 2). While the majority of these drugs are antibacterial, a few of them are also general antiseptics or broad-spectrum antimicrobials. Sixteen out of these 26 drugs could be associated with 16 C. albicans targets, whereas the rest were predicted to bind to multiple C. albicans proteins that are functionally related in most of the cases. These multitarget drugs have the potential to act as polypharmacological agents that can simultaneously arrest the activities of several proteins (Table 4). Interest in such polypharmacological agents is steadily growing, not only in the field of antimicrobial drug discovery, but also for oncological drug-discovery programs to tackle the emerging problem of drug resistance (Bourne, 2014; Xie & Bourne, 2015). Developing resistance against such multitarget antimicrobial agents is in general challenging, as the concerned pathogen must devise multiple defence strategies to render the drug ineffective. An early filter in this study resulted in the removal of all those polypharmacological agents capable of binding to both the host and pathogen proteins, thereby eliminating undesirable polypharmacological drugs. On the other hand, we also encountered instances where multiple drugs could be associated with a single C. albicans target (Table 5). Such drugs, especially those belonging to diverse chemical scaffolds, might serve as effective tools for designing combinatorial therapy strategies to achieve

3. EXAMPLES AND CASE STUDIES

504

16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 4 List of Potential Polypharmacological Agents Identified in the Study

Chemical Class

Drug Name

1.

Furans

Nitrofural

ILV2, PDC11, ARO10, GLR1, LPD1, MDH1, MDH1-3, MDH1-1

8

2.

Steroids and steroid derivatives

Fusidic acid

MEF1, MEF2

2

3.

Tetracycline

Lymecycline

MRPS9, RPS16A

2

4.

Clomocycline

MRPS9, RPS16A

2

5.

Oxytetracycline

MRPS9, RPS16A

2

6.

Demeclocycline

MRPS9, RPS16A

2

7.

Rolitetracycline

MRPS9, RPS16A

2

8.

Tigecycline

MRP2, CAALFM_CR08400CA, RPS15, CAALFM_CR07760WA, MRPS9, RPS16A

6

9.

Chlortetracycline

MRP2, CAALFM_CR07760WA, RPS15, RPS5, orfl9.4018, RPS3, RPS22B, RPS22A

8

Mupirocin

CAALFM_CR03400WA, ILS1, CDC60

3

10.

Fatty acyls

Gene Names of the Predicted Potential Targets

No. of Predicted Targets

Sl. No.

TABLE 5 List of “One Target: Many Drugs” Associations Predicted in the Study That Could Serve as Effective Tools in Combinatorial Therapy for Treatment of Candida albicans Infections

SI. No.

UniProt Code of Candida albicans protein

1.

A0A1D8PMM1

2.

Q5APD4

Protein Name

Gene Name

No. of Predicted Predicted Drug Drugs Name

DNA topoisomerase 2

TOP2

2

Sphingolipid C9methyltransferase

MTS1

2

Chemical Class

Novobiocin

Coumarins and derivatives

Gatifloxacin

Quinolines and derivatives

Didecyldimethyl ammonium

Organonitrogen compounds

Cetrimonium 3.

Q5A389

Ribosomal 40S subunit protein S20

RPS20

2

Paromomycin

Organooxygen compounds

Nitrofurantoin

Azolidines

3. EXAMPLES AND CASE STUDIES

505

3 RESULTS AND DISCUSSION

TABLE 5 List of “One Target: Many Drugs” Associations Predicted in the Study That Could Serve as Effective Tools in Combinatorial Therapy for Treatment of Candida albicans Infections—cont’d

SI. No.

UniProt Code of Candida albicans protein

4.

Q5AJZ7

5.

6.

7.

A0A1D8PK22

A0A1D8PTL2

094150

Protein Name

Gene Name

No. of Predicted Predicted Drug Drugs Name

Mitochondrial 37S ribosomal protein MRP2

MRP2

2

Ribosomal 40S subunit protein S15

RPS15

Mitochondrial 37S ribosomal protein RSM19

CAALFM_C R07760WA

2

37S ribosomal protein S9, mitochondrial

MRPS9

6

Tigecycline

Chemical Class Tetracycline

Chlortetracycline 2

Tigecycline

Tetracycline

Chlortetracycline Tigecycline

Tetracycline

Chlortetracycline Rolitetracycline

Tetracycline

Tigecycline Lymecycline Clomocycline Oxytetracycline Demeclocycline

8.

A0A1D8PCW6

Ribosomal 40S subunit protein S16A

RPS16A

6

Rolitetracycline

Tetracycline

Tigecycline Lymecycline Clomocycline Oxytetracycline Demeclocycline

9.

10.

A0A1D8PRP6

Q5AA46

Mitochondrial 54S ribosomal protein YmL9

CAALFM_C R00490WA

2

Putative mitochondrial 37S ribosomal protein MRPS12

Orf19.2438

6

Retapamulin

Prenol lipids

Spiramycin

Organooxygen compounds

Kanamycin

Organooxygen compounds

Arbekacin Amikacin Tobramycin Netilmicin Spectinomycin

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Dioxanes

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synergistic effects and minimize the chances of the emergence of resistance (Carrillo-Mun˜oz, 2014; De Cremer et al., 2015). A chemical structure-based, ligand-centric similarity search for each of the 26 shortlisted drugs led to the identification of an additional 33 (nonredundant) approved small molecules (Table 2). These additional molecules, by virtue of their structural similarity with the primary set of shortlisted drugs, are also expected to exhibit antifungal activities that can be confirmed through further investigations. Studies in the past suggest that the intact fungal cell can be permeated freely by compounds of a molecular weight not greater than about 700 g mol 1 due to the barrier presented by the small pore size of the fungal-cell wall (De Nobel, 1990; Scherrer, London, & Gerhardt, 1974). Interestingly, the molecular weight of 23 out of the 26 repurposable drugs identified in this study are less than 700 g mol 1 (Table 2), which promotes the likelihood of these identified molecules exhibiting the desired antifungal properties. Two of the identified molecules that have a molecular weight greater than 700 g mol 1 belong to the group of macrolide antibiotics (Josamycin and Quinupristin) and are structurally similar to three approved macrocyclic polyene antifungal agents, namely, amphotericin B, natamycin, and nystatin. These polyene antifungal agents have been demonstrated to bind to fungal bilayer to form a complex with ergosterol leading to pore formation, which increases the adsorption of the drug molecules and disruption of the cell membrane, thereby causing leakage of the cell content (Campoy & Adrio, 2017). Albeit of heavier molecular weight (>700 g mol 1), amphotericin B and nystatin can thus produce the desired chemotherapeutic effect through interactions with the fungal-cell wall.

3.2 Identification of Potential Drug Targets in Candida albicans The potential C. albicans targets identified in this study are found to be homologous to known targets (of the associated drug/s) with reasonable sequence identities (>35%), implying the possibility of high structural and functional similarities between the two (Table 2). The predicted C. albicans targets in the high-priority group (Group I, see Methodology 2.2) could be broadly classified under two functional categories: (a) proteins involved in metabolic pathways and (b) proteins involved in vital life processes (such as DNA topology maintenance and protein synthesis). The protein targets participating in metabolic pathways can be further divided into three subtypes based on the underlying biological principle as annotated in Candida Genome Database (Arnaud et al., 2005) namely (a) biosynthesis, (b) degradation/utilization/assimilation, and (c) generation of precursor metabolites and energy (Table 2; Fig.3). Targeting such proteins required for survival and growth of C. albicans can lead to effective management of the disease. Currently, there are no approved antifungal agents against the majority of the targets prioritized in this study. Successful repurposing of drugs against these targets can pave the way for a new generation of antifungal agents with novel mechanism of actions that can help to combat the global problem of resistance. Careful case-by-case evaluation (as discussed in methodology) of the drug-target associations listed in Table 3 is likely to expand both the repurposable drug and C. albicans target space, which could be considered for experimental investigation.

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FIG. 3 Distribution of biological functions of the predicted Candida albicans targets that are likely to interact with 26 shortlisted candidates. The color version of the figure is available online.

3.3 Case Study 1: Cetrimonium—A Potential Repurposable Drug Candidate Against MTS1 Cetrimonium (DB01718) is a quaternary ammonium cation whose salts are used as antiseptics. It is currently used in many topical dermatological preparations and consumer products such as shampoos and hand-wash. The precise molecular mechanism of action of this compound responsible for its antiseptic property is not clearly understood. However, at a cellular level it is known that the cationic part of cetrimonium and other quaternary ammonium compounds (QACs) is responsible for causing injury to the cell wall of bacteria leading to leakage of vital cell constituents (Resuggan, 1952). Benzethonium (DB11125), an approved small-molecule drug that does not have an established molecular target listed in DrugBank, is a synthetic quaternary ammonium salt with biocidal properties and is shown to be effective in mediating its antimicrobial action against bacteria, fungi, mould, and viruses. The molecular targets of cetrimonium, as listed in DrugBank, are cyclopropane mycolic acid synthase mmaA2 (organism: M. tuberculosis; gene name: mmA2) and cyclopropane mycolic acid synthase 1 (organism: M. tuberculosis; gene name: cmaA1) based on the information available from the crystal structures of cetrimonium bound to the mentioned enzymes (Protein Data Bank [PDB] ID: 1TPY (Smith and Sacchettini, to be published according to the PDB entry); PDB ID: 1KPG (Huang, Smith, Glickman, Jacobs, & Sacchettini, 2002)). The M. tuberculosis genes mentioned, mmaA2 and cmaA1, are involved in the mycolic-acid biosynthesis pathway, which is part of the lipid metabolism in M. tuberculosis. Both of these proteins when queried independently against the entire C. albicans proteome have picked up sphingolipid-C9-methyltransferase as a reliable hit indicating that cyclopropane mycolic-acid

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FIG. 4

Drug target networks of four drugs from the set of 26 shortlisted candidates. In each panel (A: cetrimonium; B: fusidic acid; C: nitrofural/nitrofurazone; D: mupirocin), the drug is shown as yellow, the known targets are indicated in green and the predicted targets in Candida albicans are indicated in blue. The drug-target network diagrams were generated using Gephi 0.9.2 (Bastian, Heymann, & Jacomy, 2009). The color version of the figure is available online.

synthase mmaA2 and cyclopropane mycolic acid synthase 1 of M. tuberculosis are close relatives of C. albicans sphingolipid-C9-methyltransferase (Table 2; Fig. 4). Sphingolipid-C9methyltransferase (gene name: MTS1) is involved in the sphingolipid metabolism pathway, which is part of the lipid metabolism process in C. albicans. Experimental studies have suggested that deletion of MTS1 may affect cell-wall synthesis in C. albicans during hyphal growth (Oura & Kajiwara, 2010), thereby highlighting the importance of sphingolipid-C9methyltransferase in cellular morphology of the pathogen. A reliable protein model of C. albicans sphingolipid-C9-methyltransferase being deposited by SWISSMODEL (Biasini et al., 2014) was obtained from Protein Model Portal. The crystal structure of mmA2 from M. tuberculosis (PDB ID: 1TPY) co-crystalized with cetrimonium was obtained from PDB. The key interactions (Table 6) between cetrimonium and the binding-site residues of M. tuberculosis cyclopropane synthase mmaA2 were analyzed using Protein Ligand Interaction Profiler (PLIP) (Salentin, Schreiber, Haupt, Adasme, & Schroeder, 2015). Analyses of the structurally equivalent interacting residues in cyclopropane synthase mmaA2 and C. albicans sphingolipid-C9-methyltransferase revealed that the key interacting residues

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3 RESULTS AND DISCUSSION

TABLE 6 Comparative Analysis of Top-Ranked Pocket in mmaA2 (Mycobacterum tuberculosis) and MTS1 (Candida albicans) Proteins Cyclopropane mycolic acid synthase mmaA2 (M. tuberculosis) and cetrimonium interaction profile

Does the top ranked pocket (predicted by CASTp; area: ˚ 2, volume: 606.4 A ˚ 3) in mmaA2 296.3 A protein (M. tuberculosis) contain the residue listed in column A? (D)

Does the top ranked pocket (predicted by ˚ 2, CASTp; area: 533.3 A ˚ 3) in volume: 252.9 A MTS1 protein (C. albicans) contain the residue listed in column C? (E)

Residues of mmaA2 protein (Mycobacterium tuberculosis) involved in interaction with cetrimonium (A)

Type of interaction (B)

Residues in Sphingolipid C9-methyltransferase (C. albicans) which are structurally equivalent to residues in column A (C)

Glu 140

Hydrophobic

Glu 338

Yes

Yes

Ile 169

Hydrophobic

Tyr 366

Yes

No

Phe 200

Hydrophobic

Phe389

Yes

Yes

Pro 205

Hydrophobic

Ser395

Yes

Yes

Tyr 232

Hydrophobic

Tyr 422

Yes

Yes

Leu 236

Hydrophobic

Leu 426

Yes

Yes

Trp 239

Hydrophobic

Trp 429

Yes

Yes

Tyr 265

Hydrophobic

Phe 455

Yes

Yes

Phe 273

Hydrophobic

Ser 463

Yes

Yes

Tyr 33

Cation-π

Tyr 222

Yes

Yes

are highly conserved across the two organisms (i.e., M. tuberculosis and C. albicans). The presence of a few nonconserved residues in the potential cetrimonium binding pocket of C. albicans protein are speculated not to have any effect on the binding affinity of the drug to C. albicans sphingolipid-C9-methyltransferase, since the equivalent set of residues in the known target are reportedly involved in nonspecific hydrophobic interactions (Table 6; Fig. 5). Tyr33 in M. tuberculosis cyclopropane synthase mmaA2 is involved in specific interactions (cation-π) between the drug and the protein. The residue corresponding to Tyr33 (of the M. tuberculosis protein) is Tyr222 in C. albicans sphingolipid-C9-methyltransferase, which may be engaged in cation-π interaction with cetrimonium. To gain further confidence in the binding-site similarity of the two proteins of interest, the location of potential pockets in both the proteins was detected using the online tool, CASTp. Comparison of the area and volume as well as the constituting residues of the top-ranked pocket in both the proteins revealed that they are highly similar (Table 6), implying that the concerned pockets in the two proteins are likely to bind similar molecules. As evident from the crystal structure of cetrimoniummmA2 protein complex (PDB ID: 1TPY), the top-ranked pocket in the M. tuberculosis protein as predicted by CASTp was identified to house the binding site for cetrimonium. A knowledge-based hypothetical drug-protein complex of cetrimonium bound to C. albicans sphingolipid-C9-methyltransferase was built and that was energy minimized using the

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FIG. 5 Cetrimonium binding site analysis in Mycobacterium tuberculosis mmaA2 protein and Candida albicans MTS1 protein. (A) Superposition of C. albicans MTS1 protein model (cyan cartoon) on to the crystal structure of M. tuberculosis mmaA2 protein (green cartoon) co-crystallized with cetrimonium (represented as ball and stick where the carbon atoms are shown in green). High structural similarities could be seen between the two proteins near the cetrimonium binding site. The secondary structural elements are well superposed on each other while the loops (encircle in red) show variation in conformation. (B) Superposed binding site residues interacting with cetrimonium (based on PLIP report as discussed in text) in crystal structure of M. tuberculosis mmaA2 (green) and structurally equivalent residues in C. albicans MTS1 (cyan). (C) Superposition of the hypothetical complex of crystal cetrimonium and C. albicans MTS1protein model before and after minimization. The input protein model for minimization is shown in cyan and cetrimonium (whose crystal coordinates have been retrieved from cetrimonium co-crystallized with M. tuberculosis mmaA2 protein in PDB entry: 1TPY) is shown in green. The minimized drug-receptor complex is depicted in pink, which shows minor conformational variation with respect to input structure. (D) 2D chemical structure of cetrimonium; the image has been obtained from DrugBank. In all the panels, charged nitrogen atom of cetrimonium is shown in blue. The images in panel A, B, and C have been generated from Maestro, availed through Schr€ odinger suite of programs. The color version of the figure is available online.

YASARA force field (Krieger et al., 2009). The energy-minimized model of cetrimonium and C. albicans sphingolipid-C9-methyltransferase thus obtained reveals trivial alterations in the conformation of cetrimonium and binding-site residues compared to the input hypothetical model (Fig. 5). More importantly, mutual geometries and distance between the cation 3. EXAMPLES AND CASE STUDIES

3 RESULTS AND DISCUSSION

511

(in cetrimonium) and π electron clouds (in Tyr222) satisfies the establishment of a cation-π interaction between the drug and the protein, which is important for biological activity of the drug. The overall analysis suggests that cetrimonium and other cationic QACs have the potential to bind to C. albicans sphingolipid-C9-methyltransferase. Experimental studies in the past have shown fungicidal action of QACs against C. albicans, A. niger and many other fungal species (Terleckyj, 1987). Chloride salt of didecyldimethylammonium (DB04221), an approved QAC used as an antiseptic, has also been found to inhibit C. albicans growth (Hammer, Mucha, & Hoefer, 2012). Excitingly, didecyldimethylammonium is one of the shortlisted potential repurposable anticandida agents in our study (Table 2).

3.4 Case Study 2: Fusidic Acid—A Potential Repurposable Drug Candidate Against MEF1 and MEF2 Fusidic acid (DB02703) is an antibiotic isolated from the fermentation broth of Fusidium coccineum. Fusidic acid and its sodium salt is often used in topical preparations to treat bacterial infections of skin and eyes, but it is also administered systemically through oral or intravenous routes. Fusidic acid has a good safety profile and is used in combination with other antibiotics to prevent the emergence of resistance (Huttner & Harbarth, 2017). Fusidic acid is a universal protein synthesis inhibitor in both prokaryotic and eukaryotic cell-free systems that acts by interfering with translocation elongation factor G (EF-G) (Bodley, 1970). EF-G, the bacterial homolog of eukaryotic eEF2, is an ancient translational GTPase. EF-G/EF2 is present in all domains of life and plays a central role in the elongation stage of protein synthesis by facilitating the translocation of peptidyl-transfer RNA (tRNA) from the A to the P site of the ribosome. The final stage of protein synthesis requires EF-G, the ribosome recycling/releasing factor (RRF), and initiation factor 3 (IF3) (Atkinson & Baldauf, 2011). One of the molecular targets of fusidic acid, as listed in DrugBank, is elongation factor G (gene name: fusA) from T. thermophilus. When T. thermophilus EF-G was queried against C. albicans proteome, it yielded elongation factor G, mitochondrial (gene name: MEF1) and ribosome-releasing factor 2 (RRF2), mitochondrial (gene name: MEF2) of C. albicans, as reliable hits (Table 2; Fig. 4). Analysis of the fusidic acid binding-pocket residues in the crystal structure of ribosome trapped with EF-G in the posttranslocational state (PDB ID: 4V5F; Chain: AY; organism: T. thermophilus (strain HB8/ATCC 27634/DSM 579)) (Gao et al., 2009) revealed the set of residues important for molecular recognition of fusidic acid by EF-G (Fig. 6). Multiple sequence alignment of EF-G from T. thermophilus, mitochondrial EF-G and mitochondrial RRF-2 from C. albicans showed that the residues lining the fusidic acid-binding pocket in the former are largely conserved in both the C. albicans proteins (Fig. 6). This indicates that in the absence of any large-scale conformational change in the concerned C. albicans proteins compared to that of EF-G from T. thermophilus, binding of fusidic acid to the C. albicans proteins (with the retention of key interactions as observed in T. thermophilus EF-G) is highly likely. The only dissimilarity observed in the binding pocket residues is at the position corresponding to I462 of T. thermophilus EF-G. While Ile is an aliphatic hydrophobic residue, C. albicans EF-G has a Tyr residue (aromatic polar) at the corresponding position. This dissimilarity can be further probed for developing a C. albicans specific EF-G inhibitor over a bacterial EF-G inhibitor, which in turn can minimize the chances of cross-resistance. 3. EXAMPLES AND CASE STUDIES

512 16. DRUG-REPURPOSING AGAINST C. ALBICANS

3. EXAMPLES AND CASE STUDIES

FIG. 6 Comparison of fusidic acid binding sites of Thermus thermophilus fusA protein with C. albicans MEF1 and MEF2 proteins. (A) Fusidic acid (FUA) binding pocket (represented in its electrostatics potentials) in T. thermophilus fusA protein (PDB ID: 4V5F). The image has been generated using the 3D ligand interaction viewer tool available in RCSB PDB website. (B) 2D chemical structure of fusidic acid. (C) Multiple sequence alignment of the bacterial (Uniprot code: Q5SHN5) and the fungal proteins (Uniprot code: Q5AAV3 and Q5AL45). Only the blocks containing the residues lining the fusidic acid binding pocket in the bacterial protein are shown. The corresponding residues in the fungal proteins that could possibly interact with fusidic acid are highlighted in black rectangles. A high degree of residue conservation in the highlighted region among the three proteins can be noted. The color version of this figure is available online.

3 RESULTS AND DISCUSSION

513

Our analysis suggests fusidic acid could be a potential inhibitor of protein synthesis machinery in C. albicans. Reports of sensitivity of C. albicans to silver fusidate exist in literature (Hamilton-Miller & Shah, 1996). Fusidic acid has also been shown to stabilize EF2-ribosome complex of S. cerevisiae ( Justice et al., 1998). In several separate studies, it has been noted that fusidic acid considerably inhibits the protein synthesis mechanism of C. albicans (Domı´ nguez, 1999; Justice et al., 1998). On the contrary, in another study it has been observed that fusidic acid has no activity against C. albicans (CCUG 5594) (Alsterholm, Karami, & Faergemann, 2010). In this context, it is worthwhile mentioning here that even though the drug targets are same in different species, or even in different strains belonging to the same species, sensitivity to the drug may vary due to differences in genetic profiles. This implies chemogenomic profiles of same drug may vary significantly across different strains and species of an organism (Chen et al., 2018). One of the important outcomes of such differences in the chemogenomic profile is the varying rate of adsorption of a small molecule by a pathogen, which can be affected even by a slight change in the chemical composition of the outer cell layer of the organism and many other factors as reviewed in the references (Russell, 2003). Consideration of such factors is not within the purview of the current study. Thus the validity of our prediction pertaining to the potential of fusidic acid (as well as all other identified drugs) to act as a static/cidal agent against C. albicans (strain SC5314/ATCC MYA-2876), either in monotherapy or combinatorial therapy, can be ascertained only thorough experimental investigations using the same strain of the pathogen.

3.5 Case Study 3: Nitrofural—A Potential Repurposable Drug Candidate Against Multiple Metabolic Enzymes in C. albicans Nitrofural or nitrofurazone (DB00336) is a topical antiinfective agent effective against Gram-negative and Gram-positive bacteria. It is used for treating superficial wounds, burns, ulcers, and skin infections. Nitrofurazone is the first among the nitrofurans to be approved for therapeutic uses. Other nitrofurans like nitrofurantoin (DB00698), antibacterial, and furazolidone (DB00614), antibacterial and antiprotozoal, have been in clinical use for a long time. Two other nitrofuran moiety-containing drugs (which have not been approved by the FDA), such as nifuratel (Hamilton-Miller & Brumfitt, 1978) and nifuroxime (Kim, Zilbermintz, & Martchenko, 2015), are known to possess antifungal properties and inhibit the growth of C. albicans. The precise molecular mechanism of action by which nitrofurazone exhibits its antibacterial activity is not well understood. Nitrofurazone inhibits several bacterial enzymes, especially those involved in the aerobic and anaerobic degradation of glucose and pyruvate. The molecular targets of nitrofurazone as enumerated in DrugBank are pyruvate dehydrogenase [ubiquinone], glutathione reductase, malate dehydrogenase, and [citrate [pro-3S]-lyase] ligase, all of which belong to Escherichia coli (strain K12). The first three of the four mentioned enzymes have picked up multiple C. albicans proteins as reliable hits in this study (Table 2; Fig. 4). Malate dehydrogenase, cytoplasmic (gene name: MDH1) is one such C. albicans protein target that has been inferred to be homologous to E. coli malate dehydrogenase (gene name: mdh). MDH1 of C. albicans is known to possess antigenic properties and triggers a human immune response in systemic candidiasis patients suffering from

3. EXAMPLES AND CASE STUDIES

514

16. DRUG-REPURPOSING AGAINST C. ALBICANS

hematological malignancies (Pitarch et al., 2004). Inhibition of C. albicans MDH1 protein in such patients can thus be of significant therapeutic interest. A reliable model of C. albicans malate dehydrogenase, cytoplasmic, deposited by SWISSMODEL, was retrieved from Protein Model Portal. This model was built using human mitochondrial malate dehydrogenase as the template (PDB ID: 4WLE (Eo, Han and Ahn, to be published according to the PDB entry); chain A; residues: 3–336; sequence identity: 53%; z-DOPE_chainA: 1.774; z-DOPE_chainB: 1.842). The crystal structure of E. coli malate dehydrogenase with the inhibitor 6DHNAD (PDB ID: 5KKA) (Beaupre et al., 2016) was used as reference for predicting the inhibitor binding site in C. albicans’ protein-target model. Computation of the pairwise percent sequence identities post multiple-sequence alignment of human mitochondrial malate dehydrogenase, E. coli malate dehydrogenase and C. albicans malate dehydrogenase, cytoplasmic, showed that human mitochondrial malate dehydrogenase shares about 58% and 49% sequence identities with the E. coli and C. albicans malate dehydrogenase, respectively. Despite such high sequence identities between bacterial and human malate dehydrogenases, nitrofurazone binding is selective to bacterial protein over human host in the therapeutic dosage window, which can be ascertained from the fact that no serious side effects (allergic contact dermatitis is the most frequently reported adverse effect, occurring in approximately 1% of patients treated: https://www.drugbank.ca/drugs/ DB00336) upon administration of nitrofurazone have been reported to date, and the drug has been in clinical use for more than half a century. SiteMap was used to predict the probable drug-binding sites in the C. albicans cytoplasmic malate dehydrogenase protein model (preceded by protein preparation using protein preparation wizard (Sastry, Adzhigirey, Day, Annabhimoju, & Sherman, 2013)). The top-ranked site with a SiteScore and Dscore of 1.048 and 0.932, respectively, was selected for further analyses. Superposition of C. albicans cytoplasmic malate dehydrogenase protein model on to the E. coli malate dehydrogenase crystal structure bound to the inhibitor 6DHNAD revealed that the proteins share a high degree of structural similarity (TM-score: 0.962) and the topranked SiteMap predicted site in the C. albicans protein corresponds to the inhibitor binding site in the bacterial protein (Fig. 7). A receptor grid that encompassed the top-ranked site in the C. albicans protein model was generated and nitrofurazone was docked into said site using both Glide SP and Glide XP docking mode (with default settings). The 3D structure of nitrofurazone used for docking was built from its 2D structure, which was drawn using the 2D-sketcher tool availed through Maestro and subsequently optimized using LigPrep (Schr€ odinger Release 2018-1: LigPrep, Schr€ odinger, LLC, New York, NY, 2018). OPLS3 force field (Harder et al., 2016) was used during protein preparation, ligand preparation, and the entire docking simulation. Both SP (DockScore: 6.098 kcal/mol) and XP (DockScore: 4.531 kcal/mol) docking mode yielded the same docked pose for nitrofurazone in this study. Analysis of the noncovalent interactions (default settings were used to detect the noncovalent interactions in Maestro 11.1.011; Table 7) between the docked nitrofurazone and C. albicans cytoplasmic malate dehydrogenase protein model showed that a number of residues in the C. albicans protein are engaged in hydrogen bonding, salt-bridge formation, and aromatic hydrogen bonding with nitrofurazone (Fig. 8). Two of these interacting residues, which are hydrogen bonded to nitrofurazone in its docked pose, are structurally equivalent to E. coli malate dehydrogenase residues known to be essential for substrate (such as Asn119 in E. coli, which corresponds to Asn124 in C. albicans) and NAD binding

3. EXAMPLES AND CASE STUDIES

3 RESULTS AND DISCUSSION

515

FIG. 7

Comparison of possible nitrofurazone binding sites in mdh protein (Escherichia coli) and MDH1 protein (Candida albicans). (A) Superposition of C. albicans protein model (yellow cartoon) on to the crystal structure of mdh protein in E. coli (pink cartoon) co-crystallized the inhibitor 6DHNAD (represented as ball and stick where the carbon atoms are shown in pink). (B) SiteMap predicted top scoring druggable site (shown as mesh and highlighted with black rectangles) in C. albicans MDH1 protein (yellow cartoon) superposed on mdh protein of E. coli (pink cartoon). The yellow region in the mesh denotes the hydrophobic region while the blue and red region indicates the distribution of the hydrogen-bond donor and acceptor respectively in the predicted site. (C) Docked pose of nitrofurazone in MDH1 protein model (C. albicans). (D) Superposition of the docked pose of nitrofurazone (yellow) in MDH1 protein model (C. albicans) on to the mdh protein (E. coli), which shows that nitrofurazone is likely to occupy the inhibitor binding pocket of mdh protein (E. coli). Pink ball and stick models represent 6DHNAD. The E. coli protein has not been displayed for visual clarity. The images have been generated from Maestro availed through Schr€ odinger suite of programs. The color version of the figure is available online.

(such as Asn94 in E. coli, which corresponds to Asn99 in C. albicans) (Bell et al., 2001; Hall & Banaszak, 1993; Hall, Levitt, & Banaszak, 1992). This suggests the likeliness of nitrofurazone in competitively inhibiting the function of malate dehydrogenase in C. albicans. The multiple sequence alignments of C. albicans MDH-1 protein with its other isoforms (which are also identified to be closely related to E. coli malate dehydrogenase), such as MDH1-1 and ˚ of the predicted binding pose of MDH1-3 proteins, show that the residues within 5A nitrofurazone in C. albicans MDH1 are conserved (Fig. 9) across all the paralogues. The MDH1 protein residues involved in noncovalent interactions with nitrofurazone in its

3. EXAMPLES AND CASE STUDIES

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16. DRUG-REPURPOSING AGAINST C. ALBICANS

TABLE 7 Criteria Used to Detect the Noncovalent Interactions Between the Docked Pose of Nitrofurazone and MDH-1 Protein of Candida albicans HYDROGEN BOND Hydrogen bonds involve four atoms: the donor hydrogen atom (H), the donor atom (D) bonded to H, the acceptor atom (A), and another neighbour atom (B) bonded to A, D–H... A–B. The following cut-offs for each criteria (which are the default values Maestro, version 11.1.011 criteria) have been used: Maximum distance between H atom and acceptor atom (H... A)

˚ 2.8 A

Donor minimum angle (D-H... A angle)

120°

Acceptor minimum angle (H... A-B angle)

90°

SALT BRIDGE Salt bridges are defined by oppositely charged atoms that are within a specified distance and are not directly hydrogen bonded. Maximum distance between two oppositely charged atoms

˚ (default) 5A

AROMATIC HYDROGEN BOND Aromatic hydrogen bonds involve four atoms just like in hydrogen bonds. The criteria are different for 0 and N atoms as acceptors. Only sp2 nitrogen atoms are considered for these types of bonds. Here also, the default cut-off values for each parameter in Maestro version 11.1.011 have been used. Maximum distance from the H atom to an 0 acceptor atom

˚ 2.8 A

Maximum distance from the H atom to an sp2 nitrogen atom

˚ 2.5 A

Donor minimum angle (D–H... A angle) when 0 acceptor is involved

90°

2

108°

2

Donor maximum angle (D–H... A angle) when sp N atom is involved

130°

Acceptor minimum angle (H... A–B angle)

90°

Donor minimum angle (D–H... A angle) when sp N atom is involved

predicted binding pose are also fairly conserved across all the other C. albicans proteins obtained as reliable hits (Table 2) against E. coli pyruvate dehydrogenase [ubiquinone] (gene name: poxB) and glutathione reductase (gene name: gor) (Fig. 10). This indicates a high chance of nitrofurazone-mediated inhibition of multiple metabolic enzymes in C. albicans. Experimental studies have shown when nitrofurazone is used alone, it fails to cause any effect on C. albicans growth ( Johnson, Johnston, & Kuskowski, 2012). However, nitrofurazone when administered in combination with an antifungal protein purified from the hemolymph of third instar larvae of flesh fly shows synergistic repression of C. albicans growth in each other’s presence (Iijima, Kurata, & Shunji, 1993).

3.6 Other Shortlisted Cases Independent experimental observations against each of the three case studies discussed above in detail justifies the strength of our computational protocol to identify potential

3. EXAMPLES AND CASE STUDIES

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517

FIG. 8

Binding mode of nitrofurazone in MDH1 protein of Candida albicans. The images in panel (A) and (B) were generated in the same frame of reference. Comparison of these images show that nitrofural (shown in yellow CPK model in panel A) and 6DHNAD (shown in pink CPK model in panel B) bind to similar cavities. Binding of 6DHNAD, which is a larger molecule, to E. coli protein is more compact than nitrofurazone, the latter being a smaller molecule. This also suggests the availability of room for further modification of nitrofurazone to develop better analogues in terms of selectivity and binding affinity. (C) Interaction profile of docked pose of nitrofurazone (shown as yellow ball and stick models) with C. albicans MDH1 binding site residues (shown as grey thin sticks). The orange, pink, and cyan broken lines represent hydrogen bonds, salt bridge, and aromatic hydrogen bond, respectively. The labels of ˚ of the predicted binding mode of interacting residues are highlighted in bold while the other residues within 5 A nitrofurazone are shown in regular text. The nitrogen and oxygen atoms are shown in blue and red in all the panels. The color version of the figure is available online.

repurposable candidates against C. albicans, as we have seen in the past for our antimalarial and antitubercular drug repurposing projects (as discussed earlier). A literature survey revealed that experimental evidence for many of the other high-priority cases (apart from the three case studies discussed here), as identified in this study (Table 2), also exists in the public domain. Few such examples are discussed below.

3. EXAMPLES AND CASE STUDIES

FIG. 9 Multiple sequence alignment of malate dehydrogenase paralogues in C. albicans (MDH1, MDH1-1 and MDH1-3 proteins). Only the blocks ˚ of the predicted binding mode of nitrofurazone in MDH1 protein are shown. The corresponding aligned residues containing the residues within 5 A in the other isoforms, which could possibly interact with nitrofurazone, are highlighted in black rectangles. A high degree of residue conservation in the highlighted region among the three proteins is noted. The columns containing the residues of MDH1 protein found to interact with nitrofurazone in its docked pose are indicated with stars (the color coding is same as that explained in legend of Fig. 8). The color version of the figure is available online.

3 RESULTS AND DISCUSSION

FIG. 10

519

Multiple sequence alignment of MDH1 protein with all the Candida albicans proteins predicted to bind to nitrifurazone. Only the blocks containing the residues predicted to interact with the docked pose of nitrofurazone in MDH1 are shown. The columns containing such residues are indicated with stars (the color coding is the same as that explained in the legend of Fig. 8) and highlighted with black rectangles. The residues in some of these columns are found to be fairly conserved. The color version of the figure is available online.

3. EXAMPLES AND CASE STUDIES

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3.7 Aminoglycosidic Antibiotics—Potential Repurposable Anti-Candida Agents The growth inhibitory effect of paromomycin (DB01421, an aminoglycoside antibiotic that has been identified as a potential repurposable anticandida agent in this study) in combination with chromate has been demonstrated in several pathogenic fungal species, including C. albicans (Moreno-Martinez, Vallieres, Holland, & Avery, 2015). The antifungal potential of modified aminoglycosides have been discussed in other reports as well (Chang et al., 2010; Shrestha, Grilley, Fosso, Chang, & Takemoto, 2013). This suggests that the remaining aminoglycosides (amikacin, tobramycin, spectinomycin, netilmicin, kanamycin, spiramycin, and arbekacin) shortlisted in our study might also hold anticandida activity. Experimental evidence for antifungal activities of some of these aminoglycosides has also been noted in earlier published literatures (Ikeda, Suegara, Abe, & Yamaguchi, 1999; Shrestha, Fosso, Green, & Garneau-Tsodikova, 2015).

3.8 Mupirocin—A Potential Repurposable Anti-Candida Agent Mupirocin (DB00410), a fatty acyl chemical scaffold containing antibiotic shortlisted in our study, is bacteriostatic at low concentrations and bactericidal at high concentrations. It acts by binding to isoleucyl-tRNA ligase and thus interferes with the incorporation of isoleucine into bacterial proteins. Multiple protein targets in C. albicans have been inferred to be homologous to bacterial isoleucyl-tRNA ligase. Isoleucine-tRNA ligase (gene name: ILS1) in C. albicans is one of the predicted targets of mupirocin (Table 2; Fig. 4). The antifungal activity of mupirocin against the mentioned target has been demonstrated in an experimental study (Nicholas, 1999). Another predicted target for mupirocin in C. albicans is leucine-tRNA-ligase, which is encoded by an essential gene CDC60 (Beckera, Kauffmana, & Hausera, 2010). Tavaborole (DB09041), which is a novel boron-based topical antifungal agent (approved for clinical use in the year 2014) is known to act by inhibiting fungal leucine-tRNA-ligase (Baker et al., 2006; Elewski et al., 2015). Tavaborole and mupirocin belong to diverse chemical scaffolds. Thus we propose tavaborole and mupirocin could form an interesting combination to achieve synergy and avoid resistance by inhibiting tRNA charging pathway and thereby affecting the protein synthesis mechanism of the pathogen in a different way from other available protein-synthesis inhibitors.

3.9 Gatifloxacin—A Potential Repurposable Anti-Candida Agent Gatifloxacin (DB01044), a fluoroquinolone antibiotic, known to inhibit the bacterial DNA gyrase and topoisomerase IV enzymes has been predicted to have the potential to bind to C. albicans DNA topoisomerase 2 (gene name: TOP2) in our study. An experimental study by Shams et al. in 2014 (Shams et al., 2014) proved the antifungal effect of gatifloxacin and copper ion combination.

3.10 Tetracycline Analogues—Potential Repurposable Anti-Candida Agents Several tetracycline analogues shortlisted in our study and also other chemically modified tetracyclines have been reported in many experimental studies to possess antifungal activity

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either alone or in combination with known antibacterial or antifungal agents (Gu, Yu, Yu, & Sun, 2017; Lew, Beckett, & Levin, 1977; Liu et al., 2002). These experimental studies complement our predictions to some extent and strongly demand further thorough experimental investigations to explore the anticandida potential of tetracycline analogues both as candidates for monotherapy as well as combinatorial therapy.

4 CONCLUSION The concept of within-target-family selectivity of small molecules pursued in this study has led to the identification of 26 approved drugs (and 33 additional chemically similar drugs), which could be taken up for further experimental investigations to explore their antifungal potentials against 31 protein targets in C. albicans and other closely related species. Evaluation of the cases enumerated in Table 3 is likely to expand the investigational scope both with respect to drug and target space. For several drugs we found encouraging experimental evidence regarding their antifungal potentials. Interestingly, the current study was conducted without any a priori knowledge of existence of such experimental indications. Albeit many of the earlier published reports revealed the antifungal potentials of the drugs shortlisted in our study, most of those studies do not give any insight into the targets that mediate the drug action. To the best of our knowledge, the current study might be one of the very few studies that provide a comprehensive list of repurposable drugs and their targets in C. albicans. Besides identifying potential repurposable antifungal agents against a set of C. albicans targets, this study also gives important insight into the chemical scaffolds that are likely to bind to the predicted targets. This insight may pave the way to the formulation of structure-guided strategies (with integration of receptor and ligand-based design approaches) for discovery of new antifungal agents. The potential FDA-approved repurposable drugs against C. albicans identified in this study are likely to be pathogen specific (with the assumption that the shortlisted drugs are not known to primarily target any human proteins) and thereby would have better safety profiles than many of the existing antifungal drugs. Taking into consideration the cost- and timesaving benefits of drug-repurposing approaches together with the possibility of a better safety profile of the 26 shortlisted drugs (which include potential polypharmacological agents and molecules that could be used in combinatorial therapy), we believe this study can contribute to alleviate the life-threatening situations of patients suffering from invasive and superficial fungal infections. C. albicans infections are common not only in humans but also in animals. However, most studies focuses only on human disorders. Since many of the drugs we have identified are approved for both human and veterinary treatment, this study can also help to improve the conditions for farm animals and birds suffering from C. albicans infections. In conclusion, systematic experimental investigations under clinical setups, considering the route of administration of the drug, its dosage regimen, and several other established factors, are needed to validate the predictions made in this study. Careful assessment of the risk versus benefit ratio for an indication during treatment of fungal infections caused by C. albicans must be exercised, to ensure the safety of any re-use of potential antifungal agents identified in our study.

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Acknowledgments The authors would like to thank Prof. R. Sowdhamini for her generous support in providing access to Schr€ odinger suite of programs. The authors would also like to thank Dr. Sreenivas Chavali for useful discussions. SC acknowledges the financial support by Department of Science & Technology towards her research through DST/INSPIRE Fellowship/2016/IF160629. This research is supported by Mathematical Biology program and FIST program sponsored by the Department of Science and Technology and also by the Department of Biotechnology, Government of India in the form of IISc-DBT partnership programme. Support from UGC, India—Centre for Advanced Studies and Ministry of Human Resource Development, India is gratefully acknowledged. NS is a J. C. Bose National Fellow.

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Roemer, T., & Krysan, D. J. (2014). Antifungal drug development: challenges, unmet clinical needs, and new approaches. Cold Spring Harbor Perspectives in Medicine, 4(5). https://dx.doi.org/10.1101/cshperspect.a019703. Russell, A. D. (2003). Similarities and differences in the responses of microorganisms to biocides. The Journal of Antimicrobial Chemotherapy, 52(5), 750–763. https://dx.doi.org/10.1093/jac/dkg422. Salentin, S., Schreiber, S., Haupt, V. J., Adasme, M. F., & Schroeder, M. (2015). PLIP: fully automated protein-ligand interaction profiler. Nucleic Acids Research, 43(W1), W443–W447. https://dx.doi.org/10.1093/nar/gkv315. Sastry, G. M., Adzhigirey, M., Day, T., Annabhimoju, R., & Sherman, W. (2013). Protein and ligand preparation: parameters, protocols, and influence on virtual screening enrichments. Journal of Computer-Aided Molecular Design, 27(3), 221–234. https://dx.doi.org/10.1007/s10822-013-9644-8. Scherrer, R., London, L., & Gerhardt, P. (1974). Porosity of the yeast cell wall and membrane. Journal of Bacteriology, 118, 534–540. Shams, S., Ali, B., Afzal, M., Kazmi, I., Al-Abbasi, F. A., & Anwar, F. (2014). Antifungal effect of Gatifloxacin and copper ions combination. Journal of Antibiotics (Tokyo), 67(7), 499–504. https://dx.doi.org/10.1038/ja.2014.35. Shen, M. Y., & Sali, A. (2006). Statistical potential for assessment and prediction of protein structures. Protein Science, 15(11), 2507–2524. https://dx.doi.org/10.1110/ps.062416606. Shrestha, S., Grilley, M., Fosso, M. Y., Chang, C. T., & Takemoto, J. Y. (2013). Membrane lipid-modulated mechanism of action and non-cytotoxicity of novel fungicide aminoglycoside FG08. PLoS One, 8, e73843. https://dx.doi.org/ 10.1371/journal.pone.0073843.g001. Shrestha, S. K., Fosso, M. Y., Green, K. D., & Garneau-Tsodikova, S. (2015). Amphiphilic tobramycin analogues as antibacterial and antifungal agents. Antimicrobial Agents and Chemotherapy, 59(8), 4861–4869. https://dx.doi. org/10.1128/AAC.00229-15. Terleckyj, B. A., & D.A. (1987). Quantitative neutralization assay of fungicidal activity of disinfectants. Antimicrobial Agents and Chemotherapy, 31, 794–798. Vanhaelen, Q., Mamoshina, P., Aliper, A. M., Artemov, A., Lezhnina, K., Ozerov, I., … Zhavoronkov, A. (2017). Design of efficient computational workflows for in silico drug repurposing. Drug Discovery Today, 22(2), 210–222. https://dx.doi.org/10.1016/j.drudis.2016.09.019. Wakharde, A. A., Halbandge, S. D., Phule, D. B., & Karuppayil, S. M. (2018). Anticancer drugs as antibiofilm agents in Candida albicans: potential targets. Assay and Drug Development Technologies, https://dx.doi.org/10.1089/ adt.2017.826. Wiederhold, N. P., Patterson, T. F., Srinivasan, A., Chaturvedi, A. K., Fothergill, A. W., Wormley, F. L., … LopezRibot, J. L. (2017). Repurposing auranofin as an antifungal In vitro activity against a variety of medically important fungi. 8, 138–142. https://dx.doi.org/10.1080/21505594.2016.1196301. Wishart, D. S., Feunang, Y. D., Guo, A. C., Lo, E. J., Marcu, A., Grant, J. R., … Wilson, M. (2018). DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Research, 46(D1), D1074–D1082. https://dx.doi.org/ 10.1093/nar/gkx1037. Wishart, D. S., Knox, C., Guo, A. C., Cheng, D., Shrivastava, S., Tzur, D., … Hassanali, M. (2008). DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Research, 36(Database issue), D901–D906. https://dx.doi.org/10.1093/nar/gkm958. Xie, L., & Bourne, P. E. (2015). Developing multi-target therapeutics to fine-tune the evolutionary dynamics of the cancer ecosystem. Frontiers in Pharmacology, 6, 209. https://dx.doi.org/10.3389/fphar.2015.00209. Yeturu, K., & Chandra, N. (2008). PocketMatch: a new algorithm to compare binding sites in protein structures. BMC Bioinformatics, 9, 543. https://dx.doi.org/10.1186/1471-2105-9-543. Zhang, Y., & Skolnick, J. (2005). TM-align: a protein structure alignment algorithm based on the TM-score. Nucleic Acids Research, 33(7), 2302–2309. https://dx.doi.org/10.1093/nar/gki524.

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17 In Silico Modeling of FDA-Approved Drugs for Discovery of Anticancer Agents: A Drug-Repurposing Approach Mengzhu Zheng*, Lixia Chen†, Li Hua*,† *

Hubei Key Laboratory of Natural Medicinal Chemistry and Resource Evaluation, School of Pharmacy, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China †Wuya College of Innovation, School of Traditional Chinese Materia Medica, Key Laboratory of Structure-Based Drug Design & Discovery, Ministry of Education, Shenyang Pharmaceutical University, Shenyang, China

1 INTRODUCTION Malignant tumors have become one of the leading causes of human death. Therefore cancer-associated research has always been at the forefront of medical science. Although significant progress has been made in the field of antitumor drug development, due to high costs and low success rates it has been difficult to bring new drugs from preclinical screening to clinical trials. The discovery of new drugs is a process of high input and low output. Usually the discovery of a novel drug takes 10–17 years from the establishment to the listing, and the total R&D expenditure is more than US $1 billion. In the past 10 years, fewer and fewer new chemical entities (NCEs) have been approved by the Food and Drug Administration (FDA); however, research funding has been gradually increasing. Despite continuing investment in drug development and biomedical research, the NCEs being developed by pharmaceutical companies and passed into clinical trials or the market have gradually declined. Some new drugs have had to be withdrawn from the market due to adverse reactions, causing huge economic losses (Hernandez et al., 2017; Ma, Chan, & Leung, 2013; Shim & Liu, 2014). For

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example, Pfizer’s Thelin, used to treat pulmonary arterial hypertension, was withdrawn because of its severe toxicity causing liver damage; Bayer Medical’s hypolipidemic drug, cerivastatin, was withdrawn from the market because of hundreds of deaths from rhabdomyolysis; and Astimizol by Janssen was withdrawn due to severe cardiotoxicity. At present, the FDA is increasingly scrutinizing the toxic side effects of new drugs and the review cycle has become longer and longer, making new drugs more difficult to the market. Due to the short development cycle, low risk, and high success rates, drug repositioning has received increasing attention in recent years. The advantages of drug repositioning are that these types of drugs may enter clinical trials faster and more cheaply due to the validation of their pharmacokinetics, toxicology, and safety data (Sukhai et al., 2011). Once the new drug is developed with new indications, it can rapidly enter phase II clinical, which will reduce research and development expenses by about 40% (Gupta, Sung, Prasad, Webb, & Aggarwal, 2013) and shorten the development cycle from 3 to 12 years (Pantziarka, Bouche, Meheus, Sukhatme, & Sukhatme, 2015). New molecular targets for known drugs can be used to develop new indications that are different from the initial one. Drug repositioning overcomes the potential clinical applicability risks of drug marketing, accelerates discovery of new drugs, and reduces the overall risk of drug development. In recent years, with the development of molecular biology, the deepening understanding of cancer biology, and the application of high-throughput screening, computational virtual ligand screening, and genetic engineering technology, the field of antitumor drug discovery has been greatly advanced, especially for the discovery of novel anticancer drugs through drug repositioning. Along with the advancement of IT technology and bioinformatics, in silico modeling of FDA-approved drugs for the discovery of anticancer agents has become more successful since a large amount of information on the structure of proteins and pharmacophores has been accumulated over the past few decades. Most of pharmaceutical companies have employed in silico models to discover lead compounds from different chemical spaces. Structure-based drug repositioning is a powerful technology that has some advantages over activity-based drug repositioning. According to the statistics, there are more than 60 kinds of molecular-docking tools, including free software and commercial software (Pagadala, Syed, & Tuszynski, 2017). Common commercial software tools include Schrodinger-Glide, ICM-Pro, Gold, MOE-Dock, etc.; free software are AutoDOCK Vina, LeDock, AutoDock, etc. The following are the most representative and comprehensive evaluations of Schrodinger-Glide, ICM-Pro, and Autodock Vina. The Autodock Vina scoring function is primarily evaluated by (i) spatial interactions, (ii) hydrophobic interactions, (iii) hydrogen-bond energy, and (iv) number of rotatable bonds in the ligand. ICM-Pro is a fast and accurate molecular-docking software that supports the docking of proteins with small molecules, ligands, or proteins (Abagyan, Totrov, & Kuznetsov, 1994). The protocol followed by the molecular docking is that the ligands are continuously and elastically docked with receptors that are represented by grid interaction-potential energy, and the scores are given according to internal coordinate mechanics (ICM). Glide is a docking tool in the Schrodinger software package that enables precise ligand and receptor molecular docking (Alogheli, Olanders, Schaal, Brandt, & Karlen, 2017). Glide’s scoring function can fully consider hydrophobicity, metal coordination, hydrogen bonding, steric hindrance, unfavorable bond rotation, etc., effectively increasing the enrichment rate of compounds and reducing the number of false positives. 3. EXAMPLES AND CASE STUDIES

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Structure-based virtual ligand screening as a new approach for the discovery of new drugs can quickly screen large numbers of compounds in a short period of time to find possible candidates with low cost and high success rates. There have been many successful examples in recent years. In this chapter, we focus on new therapeutic applications for known drugs in the field of cancer treatment and explain their possible mechanisms of action. Drug repositioning may help to find more effective anticancer drugs, including drugs that selectively target cancer stem cells.

2 EXAMPLES OF STRUCTURE-BASED VIRTUAL LIGAND SCREENING OF FDA-APPROVED DRUGS FOR DISCOVERY OF ANTICANCER AGENTS In 2000 Douglas Hanahan and Robert A. Weinberg wrote a review entitled “The Hallmarks of Cancer” in Cell, which explained the six basic characteristics of tumor cells, namely: selfsufficiency in growth signals, insensitivity to antigrowth signals, apoptosis evasion, limitless replicative potential, sustained angiogenesis, and tissue invasion and metastasis (Douglas Hanahan, 2000). On March 4, 2011, they republished an updated version of the review entitled “Hallmarks of Cancer: The Next Generation.” The entire review of 29 pages briefly described hotspots and advances in oncology over the last 10 years (e.g., autophagy, cancer stem cells, tumor microenvironment, etc.) and they increased the previous six features to ten. The four new characteristics are: avoiding immune destruction, tumor promotion inflammation, deregulating cellular energetics, and genome instability and mutation. In addition, evading apoptosis, from the previous list was updated to, resisting cell death (Hanahan & Weinberg, 2011) In this chapter, we will classify anticancer targets based on the characteristics of cancer cells, and enumerate examples of structure-based virtual ligand screening of FDA-approved drugs for the discovery of anticancer agents.

2.1 Reprogramming Energy Metabolism Under aerobic conditions, normal cells process glucose, first to pyruvate via glycolysis in the cytosol and thereafter to carbon dioxide in the mitochondria; under anaerobic conditions, glycolysis is favored and relatively little pyruvate is dispatched to the oxygen-consuming mitochondria. Otto Warburg first observed an anomalous characteristic of cancer cell energy metabolism (Warburg, 1956): even in the presence of oxygen, cancer cells can reprogram their glucose metabolism and thus their energy production, by limiting their energy metabolism largely to glycolysis, leading to a state that has been termed “aerobic glycolysis.” Since Otto Warburg’s pioneering work on aerobic glycolysis (Warburg, 1956) was published, glucose has become a focus for cancer metabolic research. We have collected a number of targets related to tumor metabolism and elaborated them separately.

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EXAMPLE 1 BENSERAZIDE, A DOPADECARBOXYLASE INHIBITOR, SUPPRESSES TUMOR GROWTH BY TARGETING HEXOKINASE 2 The Warburg effect is the most fundamental metabolic alteration in tumor development and progression, especially in a solid tumor like colorectal cancer, breast cancer, liver cancer, etc. (Hanahan & Weinberg, 2011; Krasnov, Dmitriev, Lakunina, Kirpiy, & Kudryavtseva, 2013; Mathupala, Ko, & Pedersen, 2009). HK2 is the rate-limiting enzyme in the first reaction of glycolysis in cancer cells, and it plays a crucial role in the glycolytic pathway. Moreover, the expression level of HK2 is significantly elevated in various solid tumors and thus distinguishes cancer cells from normal cells; this makes HK2 a promising therapeutic target for cancer treatment (Patra et al., 2013; Ros & Schulze, 2013; Tan & Miyamoto, 2015). To search potential HK2 inhibitors, structure-based virtual ligand screening was performed by ICM 3.8.2 modeling software (MolSoft LLC, San Diego, CA) with the crystal structure of human HK2 (PDB code: 2NZT) as the model from ZINC Drug Database (Totrov & Abagyan, 1997). Benserazide (Benz) was identified as a possible HK2 inhibitor, which was further confirmed by enzymatic inhibition and microscale thermophoresis (MST) assay. Benserazide could specifically bind to HK2 with a certain binding affinity (Kd ¼ 149  4.95 μM) and significantly inhibit HK2 enzymatic activity in vitro with a combined mechanism that was both competitive and noncompetitive. Molecular docking results revealed that benserazide occupied the binding site of the substrate glucose and its pyrogallol part adopted a similar conformation as the glucose. Six hydrogen bonds were predicted between benserazide and HK2, they were 2-carbonyl and Gly681, 3-amino and Thr680, 200 -hydroxyl and Asn656, 300 -hydroxyl and Asn656, 400 -hydroxyl and Thr620, 400 -hydroxyl, and Glu708 (Fig. 1). Benserazide showed cytotoxicity to SW480 cells and without notable cytotoxicity

FIG. 1 H-bond interactions between benserazide and HK2 residues. Benserazide is depicted as ball-andstick model.

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to normal cells. In addition, benserazide reduced glucose uptake, lactate production, and intracellular ATP levels, and could cause cell apoptosis and an increased loss of MMP as well. In vivo study indicated that intraperitoneal (ip) injection of benserazide at 300 and 600 mg/kg suppressed cancer growth in tumor-bearing mice and no toxicity was shown. This study provides a new insight for the development of benserazide and its derivatives as novel antitumor agents (Li et al., 2017).

EXAMPLE 2 STRUCTURE BASED DISCOVERY OF CLOMIFENE AS A POTENT INHIBITOR OF MUTANT IDH1 Isocitrate dehydrogenase (IDH) mutations are present in nearly 75% of glioma and 20% of acute myeloid leukemia (Dang et al., 2009; Hartmann et al., 2009; Parsons et al., 2008; Wang et al., 2013; Xu et al., 2011). All of mutant IDH proteins, including IDH1R132H and IDH1R132C, demonstrate the concomitant gain of a neomorphic function that reduces α-KG to D-2-hydroxyglutaricacid (D-2HG) using NADPH as the cofactor (Popovici-Muller et al., 2012). As a result of mutations in IDH, high cellular concentration of D-2HG may cause global methylation of histone and DNA, which may lead to tumorigenesis (Zheng et al., 2013). A structure-based virtual ligand screening was conducted to identify small-molecule inhibitors of mutant IDH1 by using the X-ray structure of the IDH1R132H homodimer (PDB: 4UMX) (Yang, Tang, Habermehl, & Iczkowski, 2010; Yang, Zhong, Peng, Lai, & Ding, 2010; Zeng et al., 2016) as the molecular model. Clomifene was found to be an IDH1R132H inhibitor that can selectively suppress mutant enzyme activities in vitro and in vivo in a dose-dependent manner. The molecular docking indicated that clomifene occupied the allosteric site of the mutant IDH1 (Fig. 2). In contrast, for the known inhibitor AGI-5198, enzyme kinetics demonstrated that clomifene inhibited mutant enzymes in a noncompetitive manner. Knockdown

FIG. 2 Binding mode of clomifene with mutant IDH1. Molecular docking predicted that clomifene fitted the allosteric site of mutant IDH1 well with an extended conformation and the binding site of AGI-5198 with mutant IDH1 is close to the pocket of active center.

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of mutant IDH1 in HT1080 cells decreased sensitivity to clomifene. In vivo studies indicated that clomifene significantly suppressed the tumor growth of HT1080-bearing CB-17/Icr-scid mice with oral administration of 100 mg/kg and 50 mg/kg per day. These findings indicated clomifene may have clinical potential in tumor therapies as a mutant IDH1 inhibitor (Zheng, Luan, et al., 2017; Zheng, Sun, et al., 2017).

EXAMPLE 3 REPOSITIONING PROTON PUMP INHIBITORS AS ANTICANCER DRUGS BY TARGETING FATTY ACID SYNTHASE Fatty acid synthase (FASN) is an enzyme responsible for the de-novo synthesis of free fatty acids. FASN expression is associated with the formation, maintenance, and progression of many types of cancer (Liu, Liu, Wu, & Zhang, 2010). FASN is essential for the survival of cancer cells and contributes to drug resistance and poor prognosis (Liu, Liu, & Zhang, 2008). However, it is not expressed in most nonfat normal tissues. Therefore FASN is an ideal target for drug discovery for many types of human cancers with high FASN expression. Although different FASN inhibitors have been identified, none have been successfully transferred to clinical use. Zhang et al. performed virtual ligand screening of FDA-approved drugs to search FASN inhibitors by using the crystal structure of FASN thioesterase (TE) domain (PDB code: 3TJM) as the model (Zhang et al., 2011). They found that proton pump inhibitors (PPIs) agents for the treatment of various acid-related diseases of digestive system, effectively inhibit FASN TE activity. In order to further elucidate the binding mode of each PPI within FASN TE, the AMBER 12 suite of programs were used to perform molecular dynamics (MD) simulations for each PPI docked in the active site of FASN TE. Omeprazole shows potential for the formation of a strong hydrogen bond between the active site serine residue (Ser 2308) of the catalytic triad of TE and the sulfoxide moiety of omeprazole, which may prevent Ser 2308 from nucleophilically attacking a substrate. Further examination showed that PPIs inhibit lipid synthesis and disturb the binding between serine hydrolase probes and FASN. It also inhibits cancer cell proliferation by inducing apoptosis. Thus PPIs may exert anticancer activity in part by targeting and inhibiting TE activity of human FASN (Fako, Wu, Pflug, Liu, & Zhang, 2015).

2.2 Inducing Angiogenesis Like normal tissues, tumors require sustenance in the form of nutrients and oxygen as well as an ability to evacuate metabolic wastes and carbon dioxide. During tumor progression, an “angiogenic switch” is almost always activated and remains on, causing normally quiescent vasculature to continually sprout new vessels that help sustain expanding neoplastic growths (Hanahan & Folkman, 1996). Vascular endothelial growth factor (VEGF) gene expression can be upregulated both by hypoxia and by oncogene signaling (Mac & Popel, 2008). Abnormal angiogenesis, a process by which new blood vessels sprout from preexisting vessels, is well recognized as a common characteristic of various cancer types (Marme, 2018). By increasing

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the number of capillaries into the expanding tumor tissues, tumor-associated neo-vasculature is induced to accelerate tumorigenesis through aiding the nutrient supply and metastasis of tumor cells (Ocana, Martinez-Poveda, Quesada, & Medina, 2018). VEGF/vascular endothelial growth factor receptor-2 (VEGFR2) signaling has been widely accepted for its proangiogenic role by dominating all steps of angiogenesis including survival, proliferation, migration, and capillary-like tube formation of endothelial cells (Chung, Lee, & Ferrara, 2010). Therefore inhibition of VEGFR2 activity emerges as a potential therapy strategy against tumor-induced angiogenesis (Granci, Dupertuis, & Pichard, 2010).

EXAMPLE 1 DISCOVERY OF VEGFR2 INHIBITORS BY INTEGRATING NAIVE BAYESIAN CLASSIFICATION, MOLECULAR DOCKING AND DRUG-SCREENING APPROACHES VEGFR2 acts as a central modulator of angiogenesis and is therefore an important pharmaceutical target for developing antiangiogenic agents. Ligand-based naive Bayesian (NB) (Bender, 2011) models and structure-based molecular docking were combined to develop a virtual screening (VS) for identifying potential VEGFR2 inhibitors from 1841 FDA-approved drugs. By identifying eight FDA-approved antiangiogenic agents, the integrated VS pipeline was validated for its excellent predictive accuracy. The crystal structure of VEGFR2, complexed with axitinib, was retrieved from the Protein Data Bank (PDB ID: 4AGC) as the docking model (McTigue et al., 2012). The molecular docking studies were performed in Discovery Studio 2016. Using the optimal model NB-c and molecular-docking module LibDock, 1841 FDA-approved drugs were sequentially screened. To analyze the results of VS, biological validation was performed on nine top-ranked drugs. VEGFR2 kinase assay results demonstrated that flubendazole, rilpivirine, and papaverine were found to inhibit the enzymatic activities of VEGFR2. Flubendazole was identified as the most potent inhibitor in this study, with a IC50 value of 0.47 μM against VEGFR2. The reference compound axitinib and the selected compounds were docked into the binding site by utilizing the LibDock and CDOCKER modules. It is speculated that flubendazole, rilpivirine, and papaverine formed Pi-cation interaction with Lys868. Flubendazole formed three hydrogen bonds with Asp1046, Gly922, and Cys919, and one carbon hydrogen bond with Lys920. Rilpivirine formed one hydrogen bond with Cys919 and one carbon hydrogen bond with Glu917. Papaverine was found to interact with Asp1046 via one hydrogen bond, as well as Cys919, Glu917 and Glu885 via carbon hydrogen bonds. In summary, three FDA-approved drugs were identified as novel VEGFR2 inhibitors that could be used as leads to design and develop new antiangiogenic agents (Kang et al., 2018).

EXAMPLE 2 PREDICTING NEW INDICATIONS FOR APPROVED DRUGS USING A PROTEOCHEMOMETRIC METHOD Using the “train, match, fit, streamline” (TMFS) method, Byers et al. performed extensive molecular-fit computations on 3671 FDA-approved drugs across 2335 human-protein crystal structures. They predicted that anti-hookworm medication mebendazole could inhibit VEGFR2 activity and angiogenesis. It was confirmed that mebendazole binds directly to VEGFR2 and affects VEGFR2

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kinase activity with an IC50 of 3.6 μM. Besides this, mebendazole inhibits angiogenesis in a HUVEC cell-based assay. Mebendazole significantly inhibited network formation with an IC50 of 8.8 μM, which was implicated by the lack of cellular migration, alignment, and branching. Overall, it was predicted and experimentally validated that the anti-hookworm medication mebendazole can inhibit VEGFR2 activity and angiogenesis (Dakshanamurthy et al., 2012).

2.3 Evading Growth Suppressors In addition to the hallmark capability of inducing and sustaining positively acting growthstimulatory signals, cancer cells must also circumvent powerful programs that negatively regulate cell proliferation. The cyclin-dependent kinase (CDK) pathway is an important and established target for cancer treatment (Mariaule & Belmont, 2014; Morgan, 1997; Murray, 2004; Sherr & Roberts, 1999). It has been reported that cyclin-dependent kinase 2 (CDK2), one of the serine/threonine protein kinases, is overexpressed in numerous types of human neoplasia, including colorectal, ovarian, breast, and prostate cancers (Robb et al., 2018; Webster, 1998); it is responsible for the transition from the G1 to S phase of the cell cycle, and its deregulation is a hallmark of cancer.

EXAMPLE 1 IN SILICO IDENTIFICATION AND IN VITRO AND IN VIVO VALIDATION OF THE ANTIPSYCHOTIC DRUG FLUSPIRILENE AS A POTENTIAL CDK2 INHIBITOR AND A CANDIDATE ANTICANCER DRUG In this study, the free and open-source protein ligand-docking software idock was used to screen 4311 FDA-approved small-molecule drugs against CDK2. Nine compounds were identified using the idock score and selected for further study. Among them, the antipsychotic drug fluspirilene showed the highest antiproliferative effect in human hepatoma HepG2 and Huh7 cells. Structural analysis predicted that fluspirilene bound inside the ATP-binding pocket of CDK2 (PDB ID: 1GZ8) (Li, Leung, Ballester, & Wong, 2014; Li, Leung, Nakane, & Wong, 2014) and interacted with CDK2 mainly through hydrogen bonds, hydrophobic contacts, and cation-π interactions. All these bindings were spread over the head, middle, and tail fragments of fluspirilene, thereby firmly holding fluspirilene at its predicted position and orientation. It also revealed that fluspirilene treatment increased the percentage of cells in the G1 phase and decreased the expression of CDK2, cyclin E, and Rb, as well as the phosphorylation of CDK2. In vivo results show that oral fluspirilene treatment significantly inhibits tumor growth in nude mice xenografted with Huh 7 cells. Fluspirilene (15 mg/kg) showed strong antitumor activity, which was comparable to the leading cancer drug 5-fluorouracil (10 mg/kg). Combined therapy with fluspirilene and 5-fluorouracil showed the highest therapeutic effect. These results demonstrate that fluspirilene is a potential CDK2 inhibitor and could be a candidate for the treatment of human hepatocellular carcinoma (Shi et al., 2015a, 2015b).

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EXAMPLE 2 ADAPALENE INHIBITS THE ACTIVITY OF CYCLINDEPENDENT KINASE 2 IN COLORECTAL CARCINOMA Among the nine top compounds identified by the idock score, adapalene exhibited the highest antiproliferative effects in LOVO and DLD1 human colon cancer cell lines. Adapalene (ADA) was predicted to reside in the adenosine triphosphate-binding site of CDK2 and interact with CDK2 mainly through hydrophobic contacts with Phe82, Ile10, Leu134, Lys33, and His84. The study assessed the effects of ADA on the viability and cell cycle of colorectal cancer cells, as well as the expression of CDK2, cyclin E, and retinoblastoma protein (Rb), and the phosphorylation of CDK2 (on Thr-160) and Rb (on Ser-795). Furthermore, ADA was evaluated in vivo in a BALB/C nude mouse xenograft model using a DLD1 human colorectal cancer cells alone or in combination with oxaliplatin. ADA (20 mg/kg orally) exhibited marked antitumor activity, comparable to that of oxaliplatin (40 mg/kg), and dosedependently inhibited tumor growth, while combined administration of ADA and oxaliplatin produced the highest therapeutic effect. As ADA is an FDA-approved drug, its clinical use is facilitated compared with that of novel drugs; therefore, its potential use as a drug for the treatment of human colorectal cancer, particularly in combination with oxaliplatin, should be further investigated.

Overall, the powerful synergy of drug repositioning combined with in-silico structurebased VS (Bernard, 1993), where, by targeting CDK2, two FDA-approved drugs fluspirilene and adapalene have been rediscovered as anticancer agents in vitro and in vivo for the treatment of hepatocellular and colorectal carcinomas, respectively (Shi et al., 2015a, 2015b).

2.4 Sustaining Proliferative Signaling Cancer cells, by deregulating the production and release of growth-promoting signals, become masters of their own destinies. The enabling signals are conveyed in large part by growth factors that bind cell-surface receptors, typically containing intracellular tyrosine kinase domains. Mutations in the catalytic subunit of phosphoinositide 3-kinase (PI3-kinase) isoforms are being detected in an array of tumor types, which serve to hyperactivate the PI3-kinase signaling circuitry, including its key Akt/PKB signal transducer (Yuan & Cantley, 2008). mTOR activation results, via negative feedback, in the inhibition of PI3K signaling. Thus when mTOR is pharmacologically inhibited in such cancer cells (such as by the drug rapamycin), the associated loss of negative feedback results in increased activity of PI3K and its effector Akt/PKB, thereby blunting the antiproliferative effects of mTOR inhibition (Sudarsanam & Johnson, 2010). The phosphatidylinositol-3-kinase (PI3K)/AKT signaling pathway plays a key role in many cellular processes, including proliferation, survival, and differentiation of lung cancer cells. Therefore PI3K is a promising therapeutic target for the treatment of lung cancer.

EXAMPLE 1 ECONAZOLE NITRATE INHIBITS PI3K ACTIVITY AND PROMOTES APOPTOSIS IN LUNG CANCER CELLS A free and open-source protein-ligand docking software was applied to screen 3167 FDA-approved small molecules in order to identify putative PI3Kα inhibitors. The antifungal agent econazole nitrate was found to show the highest activity of reducing cell viability in pathological

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types of nonsmall-cell lung cancer (NSCLC) cell lines including H661 and A549. Econazole was predicted to bind to PI3Kα (PDB ID: 4JPS) (Vansteenkiste et al., 2015) by forming a hydrogen bond with Ser854, a halogen bond with Asp810, and hydrophobic contacts with Ile848 and Ile932. Econazole specifically inhibited AKT phosphorylation and Bcl-2 gene expression but had no effects on the phosphorylation level of ERK. It inhibited cell growth and promoted apoptosis in lung cancer cells in a dose-dependent manner. In addition, the combination of econazole and cisplatin produced additive effects in H661 and A549 lung cancer cell lines, respectively. Finally, econazole significantly inhibited A549 tumor growth in nude mice. These results suggested that econazole was a new PI3K inhibitor and a candidate for the treatment of lung cancer (Dong et al., 2017).

EXAMPLE 2 IN SILICO PREDICTION AND IN VITRO AND IN VIVO VALIDATION OF ACARICIDE FLUAZURON AS A POTENTIAL INHIBITOR OF FGFR3 AND A CANDIDATE DRUG FOR BLADDER CARCINOMA Bladder cancer (BC) is the ninth most common cause of cancer worldwide, so there is an urgent need to develop new therapeutic methods. Due to tumor recurrence and resistance, surgical resection, conventional chemotherapy, and radiotherapy eventually fail. The fibroblast growth factor receptor (FGFR) family represents an attractive therapeutic target in oncology that is attracting more and more attention. Fibroblast growth factor receptor 3 (FGFR3) is an important target for BC therapy. A free and open-source protein ligand-docking software idock (Li, Leung, Ballester, et al., 2014; Li, Leung, Nakane, et al., 2014) together with the binding affinity prediction software RF-Scire-v3 (Li, Leung, Wong, & Ballester, 2015) was used to prospectively identify potential inhibitors of FGFR3 from 3167 globally recognized small-molecule drugs. The molecular visualization tool iview (Li, Leung, Ballester, et al., 2014; Li, Leung, Nakane, et al., 2014) was used to inspect and analyze putative interactions. The X-ray structure of FGFR3 bearing the ATP-binding site (PDB code: 4K33) (Mir et al., 2018) was chosen to generate a molecular model. Six high-score compounds were tested in vitro. Among them, the acaricide drug fluazuron showed the highest antiproliferative effects in human BC cell lines. Structural analysis revealed that fluazuron formed three hydrogen bonds with Lys508, a hydrogen bond with Gly484, and a hydrophobic contact with Phe483. Further studies showed that fluoxetine treatment significantly increased the percentage of apoptotic cells and decreased the phosphorylation of FGFR3. The in vivo antitumor results showed that fluazuron given orally (80 mg/kg) significantly inhibited tumor growth in BALB/C nude mice transplanted with RT112 cells. These results demonstrate that fluazuron is a potential inhibitor of FGFR3 and is a candidate drug for the treatment of BC (Ke et al., 2017).

EXAMPLE 3 IN SILICO IDENTIFICATION OF NOVEL KINASE INHIBITORS TARGETING WILD-TYPE AND T315I MUTANT ABL1 FROM FDA-APPROVED DRUGS The proto-oncoprotein ABL1, a member of the nonreceptor tyrosine kinase (TK) family, is ubiquitously expressed in various mammalian cells (Laneuville, 1995). It participates in the cell cycle and apoptosis through integrating extracellular and intracellular signals. The constitutively active fusion 3. EXAMPLES AND CASE STUDIES

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protein BCR-ABL1 is the major cause of chronic myelogenous leukemia (CML), and the selective inhibition of ABL1 is a promising method for the treatment of CML. The fusion protein kinase can then stimulate numerous signal pathways including JAK-STAT, PI3K/AKT, Ras/MAPK (An et al., 2010; Kantarjian, Giles, Quintas-Cardama, & Cortes, 2007), and NF-kB (Cortes et al., 2007; Quintas-Cardama & Cortes, 2009), leading to uncontrolled cell proliferation and suppression of apoptosis. The reported drugs, such as imatinib, dasatinib, nilotinib, and bosutinib work well in clinical practice. However, resistance has manifested due to mutations within the kinase domain undermining the interaction between imatinib and ABL1, particularly T315I-gated mutations. Therefore there is an urgent need for broad-spectrum drugs that target ABL1. Two X-ray crystal structures of ABL1 were obtained from the RCSB protein databank (Berman et al., 2000): the wild-type (PDB code 2HYY, in complex with imatinib) (Cowan-Jacob et al., 2007) and the T315I mutant (PDB code 3QRJ, in complex with DCC2036) (Cortes et al., 2007). Molecular docking was performed by UCSF DOCK (version 6.5) (Lang et al., 2009). To screen for potential drugs targeting the wild-type ABL1 and T315I mutant ABL1, 1408 FDA-approved small-molecule drugs were screened by molecular docking. Following MD simulations and MM/GBSA combined with free energy calculations and energy decomposition, chlorhexidine and sorafenib were identified as potential “new use” drugs targeting wild-type ABL1, while nicergoline and plerixafor were identified to target T315I ABL1. At the same time, residues located at the ATP binding site and the A ring motif were found to play key roles in the interaction with ABL1. These findings could not only serve as an example for the repositioning of existing approved drugs, but also inject new vitality into ABL1-targeted antiCML therapeutics (Xu et al., 2014).

2.5 Resisting Cell Death The apoptotic regulators are divided into two major circuits, one receiving and processing extracellular death-inducing signals (the extrinsic apoptotic program, involving, for example, the Fas ligand/Fas receptor), and the other sensing and integrating a variety of signals of intracellular origin (the intrinsic program). Each culminates in activation of a normally latent protease (caspases 8 and 9, respectively). The archetype, Bcl-2, along with its closest relatives (Bcl-xL, Bcl-w, Mcl-1, A1) are inhibitors of apoptosis, acting in a large part by binding to and thereby suppressing two proapoptotic triggering proteins (Bax and Bak); the latter are embedded in the mitochondrial outer membrane.

EXAMPLE 1 REPOSITIONING OF AMPRENAVIR AS A NOVEL EXTRACELLULAR SIGNAL-REGULATED KINASE-2 INHIBITOR AND APOPTOSIS INDUCER IN MCF-7 HUMAN BREAST CANCER Studies have shown that ERK 1/2 acts as an extracellular signal-regulated kinase that mediates the phosphorylation of the Ser69 site of BimEL, thereby enhancing proteasomal degradation of BimEL (Luciano et al., 2003) or reducing its association with pro-survival molecules such as Mcl-1, Bcl-xL Bcl-2 (Ewings et al., 2007); so it promotes the survival of cancer cells. Therefore the identification of compounds that can inhibit ERK1/2 kinase activity is of considerable therapeutic importance for breast cancer. 3. EXAMPLES AND CASE STUDIES

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Since ERK1 has only one X-ray structure reported, but several high-resolution crystal structures of ERK2 complexed with selective inhibitors were available, a library of 1447 FDA-approved smallmolecule drugs was screened in silico to search for inhibitors of extracellular signal-regulated kinase 2 (ERK2) with the PDB code 2OJJ as the model (Aronov et al., 2007). A HIV-1 protease inhibitor, amprenavir was predicted to bind in the ATP-binding site via hydrophobic interactions with Ile18, Ala22, Val26, Ala39, Ile43, Ile71, and Leu143. In vivo kinase assays showed that amprenavir inhibited ERK2-mediated phosphorylation of BimEL at the Ser69 site. Amprenavir can inhibit this phosphorylation in MCF-7 cells, which may further promote the binding of BimEL to several prosurvival molecules. In addition, amprenavir inhibits the ERK2-BimEL signaling pathway, which may contribute to its antiproliferation and apoptosis-inducing activity in MCF-7 cells. Finally, in vivo tumor growth and immune-histochemical studies confirmed that amprenavir significantly inhibited tumor proliferation and induced apoptosis in MCF-7 xenograft models. In conclusion, amprenavir can effectively inhibit the kinase activity of ERK2, thereby inducing apoptosis in vitro and in vivo, and inhibiting tumor growth of human MCF-7 cancer cells; therefore it would be a promising candidate for future anticancer therapies ( Jiang, Li, et al., 2017; Jiang, Xing, et al., 2017).

EXAMPLE 2 IN SILICO SCREENING FOR DNA-DEPENDENT PROTEIN KINASE (DNA-PK) INHIBITORS: COMBINED HOMOLOGY MODELING, DOCKING, MOLECULAR DYNAMIC STUDY FOLLOWED BY BIOLOGICAL INVESTIGATION DNA-dependent protein kinase (DNA-PK), a serine/threonine nuclear kinase, belonging to the phosphatidylinositol-3 (PI-3) kinase-like kinase (PIKK) family (Abramenkovs & Stenerlow, 2017; Davis, Chen, & Chen, 2014) plays an essential role in protecting genome stability and is considered as the key enzyme in the nonhomologous DNA end-joining (NHEJ) repair pathway. Targeted inhibition of DNA-PK will provide a valuable option for cancer treatment. An enzyme homology model was validated and subsequently used as the model for dockingbased VS of FDA-approved drug databases. The results identified co-crystal structures of truncated mTOR with mLST8 subunit (4JSP) (Yang et al., 2013) as the best template candidate, which has 31% of sequence identities to DNA-PK. The nominated highest-ranking compounds, praziquantel and dutasteride, were investigated biologically. Praziquantel displayed the ability to interact with Ala-3730, Lys-3753, Thr-3809, and Asn-3926 by direct hydrogen bonding, while the amino acid Phe-3928 was shown to be able to form a water-bridge with praziquantel. Furthermore, praziquantel interacts with the key amino acids Leu-3751 (16% hydrophobic) and Lys-3753 (4% ionic). In addition, MD studies were conducted to explore the binding modes. The results of the biological evaluation showed that the two compounds inhibited DNA-PK enzyme activity at relatively high levels of concentration, the IC50 of praziquantel was 17.3 μM, and the IC50 of dutasteride was more than 20 μM. In addition, these two drugs enhanced the antiproliferative effects of doxorubicin and cisplatin on breast cancer (MCF7) and lung cancer (A549) cell lines. These results indicated that these two hits were good candidates as DNA-PK inhibitors and deserve further structural modifications to enhance their activities (Tarazi, Saleh, & El-Awady, 2016).

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EXAMPLE 3 DISCOVERY OF NOVEL BRD4 INHIBITORS BY DRUG REPURPOSING OF NITROXOLINE AND ITS ANALOGUES The bromodomain and extra-terminal (BET) family of brominated domain (BRDs)-containing proteins is thought to be a promising drug target for therapeutic intervention in many diseases, including cancer, inflammation, and cardiovascular disease. BRD4 is the most widely studied in hematology and solid tumors in the BRD family ( Jung, Gelato, Fernandez-Montalvan, Siegel, & Haendler, 2015; Zuber et al., 2011). BRD4 can recruit positive transcription elongation factor b to the promoter, stimulate RNA phosphorylation of RNA polymerase II, and regulate transcription of the famous carcinogenic driver c-Myc (Itzen, Greifenberg, Bosken, & Geyer, 2014; Jang et al., 2005; Yang et al., 2005). The localization of BRD4 on chromatin greatly attenuates the expression of c-Myc, cyclindependent kinase 6, and BCL-2, and leads to cell-cycle arrest on G1 phase and apoptosis (Delmore et al., 2011). These findings conformed that BRD4 is a promising drug target for therapeutic intervention. Therefore there is a great demand for new chemical forms of BRD4 inhibitors. Using a drugrepositioning strategy, a VS based on BRD4 specificity scores was performed in the internal drug library, followed by an ALPHA screening assay test. The protein structure of BRD4_BD1 (PDB Code: 4GPJ) was prepared by Protein Preparation Wizard in Maestro 9.1 (Schr€ odinger, LLC, New York, NY, 2010). The FDA-approved antioxidant nitroxoline exhibited potent inhibition and could significantly disrupt the binding between BRD4_BD1 and the acetylated H4 peptide on the ALPHA screen with an IC50 of 0.98 μM. Nitroxide inhibited all BET proteins’ selectivity. The crystal structure of nitroxoline-BRD4_BD1 complex provided that nitroxoline formed a crucial hydrogen-bonding interaction with N140 and occupied the substrate pocket in a stable manner through multiple hydrogen-bond interactions. Effective hydrophobic interactions with residues, including P82, L92, V87, L94, Y97, Y139, C136, and I146, also contributed to the binding affinity of nitroxoline. Nitroxoline could effectively inhibit the proliferation of mixed-lineage leukemia (MLL) cells by inducing cell-cycle arrest and apoptosis. This is due to the inhibition of BET and the downregulation of target-gene transcription. Overall, nitroxoline was found as a BRD4 inhibitor and can be used for the clinical translation of BET family-related diseases ( Jiang, Li, et al., 2017; Jiang, Xing, et al., 2017).

EXAMPLE 4 PANTOPRAZOLE, AN FDA-APPROVED PROTONPUMP INHIBITOR, SUPPRESSES COLORECTAL CANCER GROWTH BY TARGETING T-CELL-ORIGINATED PROTEIN KINASE T-LAK cell-originated protein kinase (TOPK, also known as PBK or PDZ-binding kinase) is a serine-threonine kinase belonging to the MAPKK family (Abe, Matsumoto, Kito, & Ueda, 2000; Gaudet, Branton, & Lue, 2000), which is highly expressed in various cancer cells. TOPK plays a critical role in the early stages of mitosis, it could phosphorylate histone H3 at Ser10 in vitro and in vivo and mediate its growth promoting effect by histone H3 modification (Li et al., 2016). Previous studies show that TOPK could link PDZ-containing proteins to signal-transduction pathways that regulate the cell cycle or cellular proliferation (Kim et al., 2012). TOPK promotes the resistance of cancer cells to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor gefitinib by

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phosphorylating c-Jun, while inhibiting the expression of TOPK can restore the sensitivity of cancer cells to gefitinib. TOPK inhibitors HI-TOPK-032 (Kim et al., 2012) and OTS964 (Matsuo Y, 2014), were reported on in 2012 and 2014, respectively. However, neither of them has been put into clinical trials because of their toxicities or poor pharmacokinetic properties. The structure-based virtual ligand screening method was employed to screen the FDA-approved drug databases. A homology model of human TOPK was constructed using the X-ray structure of IRAK-4 kinase (PDB code: 2NRU) as the template and the docking was performed using ICM 3.8.1 modeling software (MolSoft LLC, San Diego, CA). With the best docking score, pantoprazole (PPZ) was identified to be a potential TOPK inhibitor from the FDA-approved drug databases. From the generated docking model two hydrogen bonds were predicted between pantoprazole with K213 and Y264, and a π-π stacking interaction was predicted between the compound and the benzene ring of F197 (Fig. 3). Further studies indicated that pantoprazole inhibited TOPK activities by directly binding with TOPK in vitro and in vivo. Pantoprazole inhibited TOPK activities in HCT 116 colorectal cancer cells, and the knockdown of TOPK in HCT 116 cells decreased their sensitivities to pantoprazole. In vivo study also demonstrated that pantoprazole effectively suppressed cancer growth in the xenograft model of HCT116 colorectal cancer. In short, pantoprazole can suppress the growth of colorectal cancer cells as a TOPK inhibitor both in vitro and in vivo (Zeng et al., 2016).

FIG. 3 Low-energy binding conformations of pantoprazole bound to TOPK generated by virtual ligand docking. Pantoprazoleis depicted as the ball-and-stick model. Hydrogen bonds are represented in dotted lines.

EXAMPLE 5 PROTON PUMP INHIBITOR ILAPRAZOLE SUPPRESSES CANCER GROWTH BY TARGETING T-CELLORIGINATED PROTEIN KINASE Inspired by the results of pantoprazole, other PPIs were speculated to be developed as potential TOPK inhibitors. Another six PPIs in clinical use were screened against TOPK using the virtual

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ligand-screening method, the homology model of human TOPK was constructed using the X-ray structure of the mixed-lineage kinase MLK1 (PDB code: 3DTC) as the template, which has 29% of sequence homology identities to the human TOPK. Among these PPIs, liaprazole was identified to be a potent TOPK inhibitor. From the generated docking model of ilaprazole, two hydrogen bonds were predicted between the methoxyl oxygen of ilaprazole and Gly223 and pyridine nitrogen and Arg155, respectively. π-π stacking interactions also were predicted to form between the pyridine ring of ilaprazole and the pyrrole ring of Pro169, and the pyrrole ring of ilaprazole and the pyrrole ring of Pro154. In vitro studies confirmed that ilaprazole inhibited TOPK activities in HCT116, ES-2, A549, and SW1990 cancer cells. At the same time, knockdown of TOPK in these cells decreased their sensitivities to ilaprazole. In vivo study also demonstrated that gavage of ilaprazole effectively suppressed cancer growth in the xenograft model of HCT116 colorectal cancer. The TOPK downstream signaling molecule phospho-histone H3 in tumor tissues was also decreased after ilaprazole treatment (Zheng, Luan, et al., 2017; Zheng, Sun, et al., 2017).

EXAMPLE 6 IN SILICO PREDICTION OF NEW INHIBITORS FOR THE NUCLEOTIDE POOL SANITIZING ENZYME, MTH1, USING DRUG REPURPOSING MTH1, a homologue of bacterial mutT, is a nucleotide pool sanitizing enzyme that converts oxidative nucleotides such as 8-oxo-dGTP or 2-OH-dATP into their corresponding monophosphates 8-oxod-GMP or 2-OH-dAMP, respectively (Burton & Rai, 2015; Fujikawa et al., 1999; Gad et al., 2014). Recent studies reported that MTH1 play an important role in maintaining tumor-cell survival. FDA-approved drug datasets were docked with MTH1 and then used consensus scores to screen for more effective compounds. The selected hit compounds, such as rolapitant and nilotinib, exhibited higher binding free energies than the co-crystalized inhibitor. From the docking results, Phe27 and Trp117 were found to be important in π-π stacking and hydrophobic interactions, while Asp119, Asp120, Glu77, Lys23, and Tyr7 could form hydrogen bonds with the hit compounds. These residues of MTH1 played major roles in the binding of hit compounds with the enzyme (Sohraby, Bagheri, Javaheri, & Aryapour, 2017).

EXAMPLE 7 COULD THE FDA-APPROVED ANTI-HIV PR INHIBITORS BE PROMISING ANTICANCER AGENTS? AN ANSWER FROM ENHANCED DOCKING APPROACH AND MOLECULAR DYNAMICS ANALYSES The FDA-approved HIV-1 protease inhibitor (PI) nelfinavir (NFV) was reported to have anticancer activities. However, the mechanism of its anticancer effects have not yet been confirmed. It has been speculated that the anticancer activities of NFV are ascribed to its inhibitory effects on the heat shock protein 90 (Hsp90), a promising target for anticancer therapy. In order to investigate the potential anticancer activity of all other FDA-approved HIV-1 PIs against human Hsp90, the VS

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method was used to elucidate the mechanism of binding and the relative binding affinity for FDAapproved HIV-1 PIs against HSP90. Homology modeling was performed to create its subsequent simulated 3D structure. Hsp90 MD from Homo sapiens (PDB Code: 3PRY) and Hsp90 CTD from Leishmania major (PDB Code: 3HJC) were selected as structural templates. The results showed that NFV had better binding affinity than other PIs, with the reasonable experimental data (IC50 3.1 μM). Indinavir, saquinavir, and ritonavir had similar affinities with NFV. The dissociation analysis of the interaction energy per residue showed that hydrophobic interactions with the active site residues Trp598, Met602, Tyr596, Val522, Met620, and Leu533, and hydrogen bonding with active site residues Gln523 and Tyr596 seem to play major roles in ligand-enzyme binding. This finding prompts researchers from different scientific domains to further investigate the potential applications of current FDA-approved HIV-1 PIs as dual antiHIV-1 and anticancer drugs (Arodola & Soliman, 2015).

2.6 An Enabling Characteristic: Tumor-Promoting Inflammation Inflammation is in some cases evident at the earliest stages of neoplastic progression and is demonstrably capable of fostering the development of incipient neoplasias into full-blown cancers. Additionally, inflammatory cells can release chemicals, notably reactive oxygen species, that are actively mutagenic for nearby cancer cells, accelerating their genetic evolution toward states of heightened malignancy.

EXAMPLE 1 PREDICTING NEW INDICATIONS FOR APPROVED DRUGS USING A PROTEOCHEMOMETRIC METHOD Predicted by the TMFS method and confirmed by surface plasmon resonance, dimethyl celecoxib (DMC) and the antiinflammatory drug celecoxib can bind cadherin-11 (CDH11), an adhesion molecule present in rheumatoid arthritis and the source of a poor prognosis in malignancy, which has no current targeted therapies. The growth inhibition assay of the MDA-MB-231 invasive breastcancer cell line by celecoxib and DMC were performed. The results confirmed that celecoxib and DMC cause growth inhibition with an IC50 of 40 and 36 μM, respectively (Dakshanamurthy et al., 2012).

EXAMPLE 2 SUBSTRUCTURE-DRUG-TARGET NETWORKBASED INFERENCE: AN INTEGRATED NETWORK AND CHEMOINFORMATICS TOOL FOR SYSTEMATIC PREDICTION OF DRUG-TARGET INTERACTIONS AND DRUG REPOSITIONING Substructure-drug-target network-based inference (SDTNBI) was used to predict potential targets for old drugs, failed drugs, and NCEs. Previous studies have suggested that COX-2, a well-known primary target for nonsteroidal antiinflammatory drugs (NSAIDs), plays a crucial role in cancer (Subbaramaiah & Dannenberg, 2003). In addition, inhibition of COX-2 by NSAIDs has potential anticancer indications for colorectal cancer (Carothers, Davids, Damas, & Bertagnolli, 2010) and breast cancer (Harris, Alshafie, Abou-Issa, & Seibert, 2000).

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Several potential targets were chosen to explore new potential anticancer indications for NSAIDs via SDTNBI. For instance, AKR1C3 was predicted as a novel potential antitumor target for several NSAIDs. Several previous studies reported that AKR1C3 played a crucial role in prostate cancer (Yang, Tang, et al., 2010; Yang, Zhong, et al., 2010). Previous preclinical studies demonstrated that multiple NSAIDs (Inoue et al., 2013; John-Aryankalayil et al., 2009; Soriano-Hernandez et al., 2012; Wechter et al., 2000), had potential antiprostate-cancer indications. Thus the predicted interactions of diclofenac-AKR1C3 and ibuprofen-AKR1C3 via SDTNBI were consistent with pharmacological experiments and co-crystal structure data (Lovering et al., 2004). Carbonic anhydrases, which were associated with breast cancer, were found to be potential anticancer targets for several NSAIDs (Watson et al., 2003). Previous studies have reported that NSAIDs were potent carbonic anhydrase inhibitors and had potential antibreast-cancer effects (Innocenti, Vullo, Scozzafava, & Supuran, 2008). Collectively inhibiting AKR1C3 carbonic anhydrases with NSAIDs may provide a new strategy for cancer chemoprevention. Also, CDK2 was identified to be targeted by carprofen, etodolac, and rofecoxib via SDTNBI. Previous studies demonstrated that CDK2 plays a crucial role in several cancer types (Shapiro, 2006). Overall SDTNBI is a powerful approach to predict potential targets for NCEs on a large scale in drug repositioning (Wu et al., 2017).

3 CONCLUSION Compared to the ever-increasing failure rates, high cost, and limited efficacy of the traditional drug-screening approaches, drug repurposing via the analysis of FDA-approved drugs is an effective method to identify therapeutic opportunities in cancer and other human diseases. Structure-based virtual ligand screening is a computational method that docks small molecules into the structures of macromolecular targets and scores their potential complementarity to binding sites. Along with great advances in both computational algorithms and computer-processing power, this approach is widely used in hit identification and lead optimization. Thus, the combination of structure-based virtual ligand screening and drug repositioning represents an efficient approach to accelerate drug discovery. Because of the verified bioavailability and safety evaluation of approved drugs, the obtained hits have higher probability to enter clinical trials than a NCE.

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18 Tackling Lung Cancer Drug Resistance Using Integrated Drug-Repurposing Strategy Nivya James, V. Shanthi, K. Ramanathan Department of Biotechnology, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore, India

1 INTRODUCTION: HISTORICAL PERSPECTIVE, TARGETS AND THERAPIES OF LUNG CANCER 1.1 Emergence, Cause and Statistics Lung cancer remains a major cause of cancer-related death globally with 1.38 million deaths recorded in 2008 (Alberg & Samet, 2003). The 2012 GLOBOCAN estimates on worldwide cancer mortality and incidence rates also depict lung cancer as the most common cancer with 1.6 million deaths (Ferlay et al., 2015). It revealed that in 2012 there were 1.8 million lung cancer cases diagnosed, of which 58% occurred in less developed regions of the world (Ferlay et al., 2015). Interestingly, the reports highlighted an estimated lung-cancer incidence and mortality rate of 70,725 and 63,759, respectively, in India. These statistics rank lung cancer as the third most common cause of cancer-related deaths in India, after cervical and breast cancer (Noronha, Pinninti, Patil, Joshi, & Prabhash, 2016). According to literature dating back to the early 1400s, lung-cancer incidences where found to be common in miners working along the border of the Czech Republic and Germany, and sufferers died due to a pulmonary disease termed as bergkrankheit. Further investigations identified the pulmonary disease to be squamous cell lung carcinoma, which was hypothesized to be caused by dust inhalation. Later the epidemiological studies carried out by Sir Richard Doll and Austin Hill in 1950 described the strong evidence between cigarette smoking and lung cancer (Alberg & Samet, 2003; Hill & Doll, 1999; Sun, Schiller, Spinola, & Minna, 2007). Due to an increase in cigarette smoking since the 1930s the incidence and mortality of lung cancer had increased steadily.

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Thus it transformed itself from a rare disease into an issue of global concern (Alberg & Samet, 2003). The epidemiological studies carried out in the 1950s illustrated a landmark in the association of cigarette smoking to lung cancer (Doll & Hill, 1950; Wynder, 1997). To date cigarette smoking remains the primary causative agent for lung cancer. It accounts for about 85% of deaths related to lung cancer (Doll & Peto, 1976). Of note is the fact that nonsmokers are at an increased risk of developing lung cancer through passive smoking (Wald, Nanchahal, Thompson, & Cuckle, 1986). Apart from passive smoking, lung cancer in nonsmokers can be attributed to nontobacco carcinogens (Hubaux et al., 2012). The three prominent nontobacco carcinogens are radon, asbestos, and arsenic. Out of these, radon gas exposure is the most frequent cause of lung cancer in nonsmokers (Hubaux et al., 2012) and the second leading cause of all lung cancer deaths (Ettinger et al., 2010). Radon-222 is an odorless, invisible, and tasteless radioactive gas that is ubiquitously found in the natural decay of uranium (Lantz, Mendez, & Philbert, 2013). It is an inert gas that is mainly present in rock and soil. When the radon atoms decay they produce radioactive substances called radon progeny. The radon progeny upon entry into the human body during respiration releases alpha particles. It damages the DNA of the lung cells and lays the foundation for lung malignancy (Lackey & Donington, 2013; Yoon, Lee, Joo, & Kang, 2016). On the other hand, asbestos is a group of minerals that occur as a bundle of fibers in soil and rocks. It is estimated that asbestos-related cancer incidences account for about 3%–4% of all lung cancer cases. Upon inhalation, asbestos induces the release of radical, which ultimately induces DNA damage and apoptosis of the alveolar epithelial cells of the lungs (Aljandali et al., 2001; Panduri, Weitzman, Chandel, & Kamp, 2004). The third important carcinogen is arsenic, which is a naturally occurring metalloid in the earth’s crust. Similar to asbestos, arsenic also induces radical formation thus laying a base for lung-cancer development ( Jomova et al., 2011; Shi, Shi, & Liu, 2004). Other putative risk factors that cause lung cancer include exposure to nickel, polycyclic aromatic hydrocarbons, chromium, and bis(chloromethyl) ether (Fraumen Jr, 1975; Janerich et al., 1990). According to WHO, lung cancer can be categorized as small-cell lung cancer (SCLC) and nonsmall-cell lung cancer (NSCLC) based on its prognosis, histology, and therapy. Among them, NSCLC accounts for approximately 80%–85% and SCLC for 10%–15% (D’addario et al., 2010). SCLCs are more prominent in smokers (Ettinger, 2006). Reports show that the development of SCLC is related to the duration and intensity of smoking and mostly heavy smokers are diagnosed with SCLC (Govindan et al., 2006; Van Meerbeeck, Fennell, & De Ruysscher, 2011). On the other hand, NSCLC is seen mostly in the nonsmoking population, particularly in the Asian countries and affecting women more than men (Subramanian & Govindan, 2007; Yano, Haro, Shikada, Maruyama, & Maehara, 2011). Its occurrence in nonsmoking population is mainly due to second-hand smoking, occupational exposures to carcinogens, diet, family history, hormonal factors, and preexisting lung diseases (Subramanian & Govindan, 2007; Yano et al., 2011). Based on histology, NSCLC is further classified as squamous cell carcinoma and nonsquamous cell carcinoma, including large-cell carcinoma, adenocarcinoma, and other cell types. Adenocarcinoma is reported to be the predominant histopathological subtype of NSCLC in nonsmokers (Subramanian & Govindan, 2007). It contributes to about 40% of all lung cancers (Corvalan & Wistuba, 2010). Even though smoking cessation is the best way to lessen the risk of lung cancer, genotyping has resulted in the discovery of genetic abnormalities that could be targeted in lung cancer. 3. EXAMPLES AND CASE STUDIES

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1.2 Lung Cancer Targets Many oncogenic driver mutations have been identified that are responsible for the initiation and sustainment of lung cancer. They are genetic mutations that can constitutively activate signaling pathways resulting in uncontrolled cellular proliferation and survival. Moreover, targeting these aberrations has increased the response rate and progression-free survival of patients. Thus recent therapeutic strategies have particularly focused on molecular targets for eliminating lung cancer. Table 1 lists some major drug targets in lung cancer. 1.2.1 ErbB/HER Family of Proteins Owing to their role in cell differentiation and proliferation, this family of receptors comprise the most extensively studied protein-tyrosine kinases (Roskoski Jr, 2014; Wieduwilt & Moasser, 2008). The human epidermal growth factor receptor (HER) family contains four members namely: HER1 (EGFR, ErbB1), HER2 (Neu, ErbB2), HER3 (ErbB3), and HER4 (ErbB4) (Cappuzzo, 2014; Wieduwilt & Moasser, 2008). The HER family receptors are made TABLE 1 Lists of Oncogenic Driver Mutations in Nonsmall-Cell Lung Cancer, Its Location in the Chromosome and the Drugs Available for Them Oncogenic Driver Mutations

Chromosome Number

Inhibitors

Epidermal growth factor receptor (EGFR) family EGFR

7p11

Gefitinib, Erlotinib, Afatinib, Osimertinib, Rociletinib, Dacomitinib, cetuximab, HM61713, ASP8273, EGF816, PF-06747775, AZD9291, EKB-569, HKI-272, CI-1033, ZD6474, AC 0010, ASP 8273

Human epidermal growth factor receptor (HER)2

17q12

Afatinib, Neratinib, Dacomitinib, Lapatinib

HER3

12q13

Afatinib, Patritumab

HER4

2q34

Afatinib,

ALK

2p23

Crizotinib, Ceritinib, Alectinib

MET

7q31

Crizotinib, Tivantinib, Cabozantinib, Foretinib, Onartuzumab

ROS

6q22

Ceritinib, Cabozantinib, Entrectinib, And Lorlatinib

RET

10q11

Alectinib, Vandetanib, Sorafenib

BRAF

7q34

Dabrafenib, Vemurafenib, Sorafenib

KRAS

12p12

Paclitaxel, Carboplatin, Erlotinib

FGFR-1

8p12

AZD4547, Docetaxel, BGJ398, LY2874455

DDR2

1q23

Dasatinib, Imatinib, Nilotinib

PI3K

3q26

BKM120, GDC0941, BEZ235, BYL719, PF-04691502.

VEGF

6p21

Ramucirumab, Nintedanib

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of an extracellular domain, a single transmembrane segment, an intracellular portion, a protein kinase domain, and a carboxyterminal tail (Cappuzzo, 2014). Several malignancies, including lung cancer, are associated with the overexpression or mutation of these family members (Hsieh & Moasser, 2007). Most importantly, epidermal growth factor receptor (EGFR) aberrations have been reported in about 10%–15% of Caucasian patients and 50% of the Asian patient population (Chan & Hughes, 2015). Additionally, HER2 overexpression was found in about 2.4%–38% of NSCLC (Mar, Vredenburgh, & Wasser, 2015). The HER3 protein is considered to be “kinase-dead” as it lacks tyrosine kinase (TK) activity (Roskoski Jr, 2014). Nevertheless, HER3 was found to be responsible for drug resistance in HER2 and EGFR tumor cells. On the other hand, overexpression of HER4 is shown to have a significant role in lung cancers. 1.2.2 Anaplastic Lymphoma Kinase Anaplastic lymphoma kinase (ALK) is a transmembrane protein made up of 1620 amino acids (Roskoski Jr, 2013; Soda et al., 2007). Many fusion genes have been discovered in ALK. However, in 2007 an activating fusion of ALK was discovered in a subset of NSCLC (Roskoski Jr, 2013). This fusion contained a somatic gene arrangement between ALK and echinoderm microtubule-associated protein-like 4 (EML4) gene (Roskoski Jr, 2013). It was later found that this gene arrangement is present in about 3%–5% of NSCLC cases (Facchinetti et al., 2016; Roskoski Jr, 2013). The fusion between EML4 and ALK occurs due to an inversion in the short arm of the 2nd chromosome (Facchinetti et al., 2016; Roskoski Jr, 2013). During the inversion, 5’ end of EML4 gene becomes ligated to the 3’ end of the ALK gene. This translocation creates an altered ALK TKR with an N-terminal portion of EML4 fused to the kinase domain of ALK (Facchinetti et al., 2016; Roskoski Jr, 2013). As a result, the TKR will be constitutively activated. Moreover, ALK translocations cause an increased TK activity, and thus lead to an increased cell proliferation, survival, and ultimately tumorigenesis. 1.2.3 Mesenchymal Epithelial Transition Factor Mesenchymal epithelial transition (MET) factor is a proto-oncogene that encodes hepatocyte growth factor receptor (HGFR), a transmembrane TK that was first discovered in the mid-1980s (Ichimura, Maeshima, Nakajima, & Nakamura, 1996; Siegfried et al., 1998). Later, in the 1990s, it was found to be associated with lung cancer (Ichimura et al., 1996; Siegfried et al., 1998). It is a 190 kDa transmembrane TK receptor that is activated by HGF-ligand binding (Organ & Tsao, 2011; Skead & Govender, 2015). MET signaling occurs via RAS/ERK/ MAPK, Wnt/b-catenin, PI3K/AKT and STAT signaling pathways and plays a pivotal role in neuronal, embryonic, and muscle development (Skead & Govender, 2015). However, in NSCLC, MET signaling dysregulation influences cancer-cell survival leading to its proliferation, growth, and invasiveness. For instance, MET amplification is diagnosed in about 2%– 5% of NSCLC cases and is found to be the reason for TK inhibitor (TKI) resistance against EGFR in about 20% of NSCLC patients (Salgia, 2017). 1.2.4 BRAF BRAF (v-RAF murine sarcoma viral oncogene homolog B1) is a proto-oncogene that encodes a serine/threonine kinase protein. It is a member of the RAS family: A-RAF, B-RAF, and RAF-1 (Paik et al., 2011; Rolfo & Caparica, 2016; Tissot et al., 2016). It is a downstream

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target in the RAS-RAF-MEK-ERK signaling pathway that promotes cell survival and proliferation. Of note is the fact that BRAF mutations are recognized in 2%–4% of NSCLCs and often evidenced in smokers (Paik et al., 2011; Rolfo & Caparica, 2016; Tissot et al., 2016). 1.2.5 KRAS v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog or KRAS is a member of the RAS gene family consisting of HRAS and NRAS. KRAS mutations are detected in 25%–30% of lung adenocarcinoma and they are found to occur at a higher frequency in the tumors of smokers than those of nonsmokers (Cagle, Allen, & Olsen, 2013; Girard, 2013). It is a GTPase located usually on cell membrane, which gets inactivated when GTP is converted to GDP thus regulating cell proliferation, growth, and survival (Dienstmann, Martinez, & Felip, 2011). Mutations that occur in their GTPase activity block this conversion leading to uncontrolled growth of tumor cells (Dienstmann et al., 2011). Most importantly, these mutations occur in adenocarcinomas and squamous-cell carcinomas, but have not been detected in SCLCs. 1.2.6 Rearranged During Transfection Rearranged during transfection (RET) is a proto-oncogene that belongs to the TK superfamily. It was initially identified in 1985 and is phylogenetically related to fibroblast growth-factor receptor (Dugay et al., 2017; Gainor & Shaw, 2013). It is expressed in neurons and adrenal medullary cells and plays a crucial role in the development of the nervous system and kidneys (Dugay et al., 2017; Gainor & Shaw, 2013). Most importantly, RET undergoes autophosphorylation upon binding to ligands and engages in several downstream pathways, such as PI3K/AKT, RAS/MAPK/ERK and phospholipase C-γ (Dugay et al., 2017; Gainor & Shaw, 2013). Subsequently it leads to cellular migration, differentiation, and proliferation. Moreover in late 2011, RET rearrangements with various fusion partners (CCDC6, KIF5B, NCOA4) were discovered in approximately 1%–2% of NSCLCs (Dugay et al., 2017; Gainor & Shaw, 2013). 1.2.7 ROS1 ROS1 is a transmembrane receptor that belongs to the insulin subgroup of receptor TKs (RTKs) (Bubendorf et al., 2016; Gainor & Shaw, 2013; Rossi et al., 2017). Despite having the largest extracellular domains among all RTKs in humans, no ligands have been identified in humans. Moreover, the role of ROS1 as an oncogene was discovered in 1987 and its role in NSCLC was identified in 2007 (Bubendorf et al., 2016; Rossi et al., 2017). Interestingly, rearranged ROS1 is prevalent in about 1%–2% cases of NSCLC, even though normal lung tissues lack ROS1 protein (Bubendorf et al., 2016; Rossi et al., 2017). Rearrangement of ROS1 occurs with various fusion partners (CCD6, CD74, TPM3, EZR, FIG1, LRI3, SDC4, SLC34A2, KDELR2, and TPD52L1) in which the 3’ end of ROS1 fuse with the 5’ end of the partner gene (Bubendorf et al., 2016; Rossi et al., 2017). This rearrangement will lead to constitutive activation of downstream signals leading to cellular proliferation, growth, and tumor formation. 1.2.8 Fibroblast Growth Factor Receptors The fibroblast growth factor receptors (FGFR) consist of four TK receptors: FGFR1–FGFR4. FGFR-1 is a cell-surface TKR and is the most common target for squamous-cell lung cancer (Chae et al., 2016; Weeden, Solomon, & Asselin-Labat, 2015). Moreover, in approximately 20%

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of squamous-cell cancers FGFR1 amplification are diagnosed. FGFR-1 signaling pathways play a fundamental role in regulating angiogenesis, embryogenesis, and various cell functions, including cell differentiation, apoptosis, proliferation, migration, etc. (Chae et al., 2016; Weeden et al., 2015). Upon binding to its ligand, fibroblast growth factor (FGF), there is activation of various downstream pathways, such as RAS/MAPK, PI3K/AKT leading to deregulated cell proliferation, angiogenesis, and survival (Chae et al., 2016; Weeden et al., 2015). These deregulated FGFR pathways lead to cancer progression, especially NSCLC. 1.2.9 Discoidin Domain Receptor 2 The discoidin domain receptor 2 (DDR2) encodes type I transmembrane RTK and is usually expressed in human tissues (Payne & Huang, 2014; Xu et al., 2015). They play important roles in cellular proliferation, migration, adhesion, and survival and are diagnosed in about 4% of SCC (Payne & Huang, 2014; Xu et al., 2015). In particular, collagen activates DDR2, which results in the activation of significant signaling molecules, such as JAK, ERK 1/3, PI3K, SHC, and SRC. DDR2 mutations have been diagnosed in various cancers, including lung cancer (Payne & Huang, 2014; Xu et al., 2015). The mutated DDR2 kinase regulates the interaction of tumor cells to their collagen matrix leading to cancer-cell metastasis and progression (Payne & Huang, 2014; Xu et al., 2015). 1.2.10 Phosphatidyl 3-Kinase Phosphatidyl 3-kinase (PI3K) is an intracellular TK that is involved in the early developmental stages of lung cancer (Courtney, Corcoran, & Engelman, 2010; Samuels et al., 2004; Yamamoto et al., 2008). It plays a significant role in the proliferation and survival of many cancers including NSCLC. Interestingly, amplification in the gene PIK3CA that encodes the TK has been diagnosed in 12%–17% of NSCLC cases and PIK3CA gene mutations have been reported in 2%–13% of NSCLC cases (Courtney et al., 2010; Janku, Garrido-Laguna, Petruzelka, Stewart, & Kurzrock, 2011; Samuels et al., 2004; Yamamoto et al., 2008). These aberrations lead to defective PI3K activity that activates the signals through downstream components mTOR and AKT leading to lung cancer (Courtney et al., 2010; Samuels et al., 2004; Yamamoto et al., 2008). 1.2.11 Vascular Endothelial Growth Factor Vascular endothelial growth factor (VEGF) is an important target in NSCLC that belongs to the VEGF family ( Janku et al., 2011; Pinho, Mendes, Rodrigues, Estrela, & Teixo, 2015). The family consists of six proteins namely, VEGF-A (commonly referred as VEGFR), placenta growth factor (PlGF) -1 and -2, VEGF-B, VEGF-C, VEGF-D, and VEGF-E (Barr et al., 2015; Villaruz & Socinski, 2015). VEGF-A is the important member and exerts its biological effects by normally binding to VEGFR-2 RTK (Barr et al., 2015; Villaruz & Socinski, 2015). They play an important role in normal as well as pathological angiogenesis and are involved in the formation of new blood vessels within a tumor (Barr et al., 2015; Villaruz & Socinski, 2015). Additionally, the VEGFR pathway induces proliferation and self-survival in tumor cells (Barr et al., 2015; Villaruz & Socinski, 2015). In most human tumors, including NSCLC, VEGFR is linked to increased metastasis, tumor recurrence, and death (Barr et al., 2015; Villaruz & Socinski, 2015).

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1.3 Therapies for Lung Cancer Many treatment strategies exist for lung-cancer patients, such as surgery, radiation therapy, and chemotherapy. Nevertheless, the progress made in lung-cancer research and personalized therapy has given rise to targeted therapies and immunotherapies. Moreover, surgery serves as an option for early stage NSCLC to remove the cancer whereas SCLC is rarely treated using surgery (Lackey & Donington, 2013). In addition, patients are prone to relapse even after surgical removal of their cancers. These patients are treated with a combination of radiation therapy and chemotherapy. While radiation therapy uses particles or high-energy rays such as X-rays to kill cancer cells, chemotherapy uses chemical drugs to kill the tumor. Interestingly, SCLC has shown extremely high response rates to radio and chemotherapies, with a few patients being cured by chemoradiotherapy (Crivellari, Monfardini, Stragliotto, Marino, & Aversa, 2007). Furthermore, chemotherapy is the present hallmark treatment strategy for SCLC. In particular, chemotherapeutic agents such as topotecan, vinorelbine, irinotecan, and gemcitabine have proven to be effective for treating SCLC. Although radiotherapy is used for NSCLC, it is curative for only a small proportion of patients (Bleehen & Cox, 1985). Unfortunately, for most NSCLC cases the disease will have advanced beyond cure and is unsuitable for surgery. Until recently radiation therapy combined with chemotherapy was the standard treatment for advanced NSCLC, but with limited treatment outcomes. Moreover, chemotherapy and radiation therapy possess intolerable side effects as the treatment does not differentiate between normal and cancerous cells. However, targeted therapies and immunotherapies now play an important role in the treatment of these advanced NSCLCs and SCLCs. They have a milder toxicity profile and are generally better tolerated by patients than conventional chemotherapy (Padma, 2015). The primary basis of targeted therapy is to use inhibitors that can target specific signaling molecules or the receptors that are responsible for cancer-cell proliferation and growth (Padma, 2015). These inhibitors include small molecules or monoclonal antibodies. For instance, crizotinib, ceritinib, and alectinib are small-molecule inhibitors targeting ALK, afatinib targets EGFR family of proteins, and vemurafenib targets BRAF kinase (Rossi et al., 2009). Most commonly, these targeted agents are administered through oral routes instead of the intravenous route. During targeted therapy the drugs are released at the disease site thus minimizing the side effects caused to normal tissues. The use of monoclonal antibodies as targeted agents is called immunotherapy. It has been the focus of research from the 1970s (Disis, 2014; Ruiz, Hunis, & Raez, 2014). This functions by stimulating the body’s own immune system to detect and destroy cancer cells. Immunotherapeutic agents overcome the mechanisms by which the tumor escapes and suppresses the immune response (Disis, 2014; Ruiz et al., 2014). Immunotherapy has shown good response rates against both NSCLC and SCLC (Disis, 2014; Li et al., 2016; Ruiz et al., 2014). It also has a low toxicity profile thus improving the quality of survival in patients. MAGE-A3, TG4010, anti-CTLA-4, nivolumab, pembrolizumab, and ipilimumab are some of the monoclonal antibodies used against NSCLC and SCLC (Disis, 2014; Li et al., 2016; Ruiz et al., 2014). Nevertheless, the emergence of resistance to these agents is leading the search for stable and successful treatment strategies.

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2 DRUG RESISTANCE PATTERN IN LUNG CANCER Drug resistance is the attenuation of effectiveness towards a medication in curing a condition or a disease. According to World Health Organization (WHO), a tumor is said to be drug resistant when there is only a partial response in patients post chemotherapy treatment. It is a significant problem in the treatment of cancer patients (Sˇkarda, Hajdu´ch, & Kolek, 2008). For instance, the tumors that respond well to a therapy initially become incurable due to the emergence of drug resistance. Most importantly, tumors have become resistant to agents to which there has been no prior exposure. The complete mechanism of drug resistance towards chemotherapy and small-molecule inhibitors in lung cancer is not fully understood. Nevertheless, some mechanisms identified to date include ineffective drug delivery to the tumor, target modifications, shortened half-life of the drug due to increased metabolism, drug inactivation, and drug efflux. Some of the major mechanisms contributing to the lung-cancer drug resistance are explained in the upcoming sections.

2.1 Drug Resistance Towards Chemotherapy 2.1.1 Drug Transporters One of the primary reasons for lung-cancer drug resistance is due to the drug transporters that are involved in the efflux of the chemotherapeutic drugs. Among the different drug transporters, the ATP binding cassette (ABC) group of proteins play the pivotal role in drug resistance. The ABC family consists of P-glycoprotein (P-gp), major vault protein (MVP), also known as lung resistance-related protein (LRP), and multidrug resistance-associated proteins (MRPs). In particular, P-gp expression is shown to be higher in NSCLCs than SCLCs. They are upregulated in these tumors and act as drug efflux pumps (Abe et al., 1996; Sˇkarda et al., 2008; Volm, Mattern, & Samsel, 1991). MRP proteins have been implicated in the NSCLC drug-resistance mechanism. There are seven isoforms (MRP1–MRP7) and MRP3, MRP2, and MRP1 were found to be expressed in higher levels in NSCLC than in SCLC (Shanker, Willcutts, Roth, & Ramesh, 2010; Young, Campling, Cole, Deeley, & Gerlach, 2001). Of note is the fact that the expression of MRP1 and MRP3 was correlated with reduced doxorubicin sensitivity (Young et al., 2001). Moreover, MVP/LRP transporters were first discovered in doxorubicin-resistant NSCLC cell lines (Shanker et al., 2010). They cause drug resistance by redistributing the drugs from the nucleus to the cytoplasm. Literature studies have shown that these transporters are expressed mainly in squamous cell and adenocarcinomas of NSCLC than in SCLC (Awasthi et al., 2003). 2.1.2 Drug Inactivation Drug inactivation occurs when a drug gets conjugated with antioxidants, such as superoxide dismutase (SOD), and sulfur containing macromolecules, such as glutathione-s-hydroxylase (GSH) and metallothioneins (MTs) (Kasahara et al., 1991; Kelley et al., 1988; Mattern & Volm, 1992; Vandier et al., 2000). For instance, MTs bind to cisplatin and cause decreased sensitivity in SCLC. Additionally, MTs demonstrate doxorubicin resistance in SCLC (Kasahara et al., 1991; Kelley et al., 1988; Mattern & Volm, 1992; Vandier et al., 2000). Of note, MTs are involved in cell

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protection against metal toxicity and oxidative stress. Furthermore, high expression of MTs was observed in squamous cell and adenocarcinoma in NSCLC (Kasahara et al., 1991; Kelley et al., 1988; Mattern & Volm, 1992; Vandier et al., 2000). Glutathine-S-transferases (GSTs) are other inactivating enzymes that are upregulated in drug-resistant cell lines. GSTs conjugate chemotherapeutic agents to GSH and detoxify them (Kasahara et al., 1991; Kelley et al., 1988; Mattern & Volm, 1992; Vandier et al., 2000). Moreover, it was found that the isoforms of GSTs play a role in regulating the MAPK pathway. Of note is the fact that numerous chemotherapeutic drugs function by inducing tumor cell apoptosis by activating the MAPK/JNK pathway (Kasahara et al., 1991; Kelley et al., 1988; Mattern & Volm, 1992; Vandier et al., 2000). Therefore inhibition of this pathway by GSTs leads to decreased sensitivity to chemo drugs thus conferring drug resistance (Kasahara et al., 1991; Kelley et al., 1988; Mattern & Volm, 1992; Vandier et al., 2000). Over the overexpression of antioxidants was also responsible for drug resistance. For instance, chemotherapeutic drugs cause oxidative damage to the cells by releasing hydrogen peroxide and superoxide moieties, thus leading to cancer cell death (Kasahara et al., 1991; Kelley et al., 1988; Mattern & Volm, 1992; Vandier et al., 2000). However, SOD and GSH overexpression will induce the neutralization of this drug-induced oxidative stress thus leading to drug resistance (Kasahara et al., 1991; Kelley et al., 1988; Mattern & Volm, 1992; Vandier et al., 2000). 2.1.3 DNA-Repair Pathways Most chemotherapeutic agents against lung cancer, induce apoptosis by forming DNA adducts and causing oxidative damage to the cancer cell DNA. However, the DNA-repair mechanisms will be overexpressed in tumor cells. For instance, the nucleotide excision-repair (NER) pathway was found to be involved in platinum-based drug resistance. Excision repair cross-complementation group 1 (ERCC1) is a key enzyme present in the pathway, which will remove DNA-adducts and cause DNA polymerase to synthesize or repair new DNA strand. This will cause the normal proliferation of tumor cells. Interestingly, the cancer cells sometimes use homologous-recombination (HR) pathway along with NER pathway to repair the damage caused by chemotherapeutic drugs. Another DNA repair pathway called as the base excision-repair (BER) pathway is also found to be responsible for chemotherapy resistance. This pathway is initiated by DNA glycosylases, which recognize and remove damaged DNA bases. Further, the gaps in DNA are filled, preventing chemotherapy-induced apoptosis in cancer cells. Additionally, nonhomologous end-joining (NHEJ) pathway is one of the major pathways responsible for repairing DNA with double-stranded breaks (DSBs). The NHEJ pathway uses Ku proteins to repair the double-stranded breaks in DNA causing resistance to ionizing radiations and multiple chemotherapeutic drugs. 2.1.4 Loss of Intracellular Death Mechanisms Cancer cells can cause drug resistance due to the loss of their apoptotic and antiapoptotic intracellular mechanisms. Moreover, drugs such as taxol, cisplatin, gemcitabine, and etoposide activate the intrinsic and extrinsic apoptotic pathways and cause cancer cell death (Los et al., 1997; Tanabe et al., 2004; Yang et al., 2003). Importantly, it was found that cancer cells encounter drug resistance to paclitaxel due to the overexpression of Bcl-2. In addition,

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cisplatin-resistant NSCLC cells have been shown to decrease caspase 9 and caspase 3 expression (Los et al., 1997; Tanabe et al., 2004; Yang et al., 2003). Furthermore, overexpression of antiapoptotic proteins, namely survivin and IAP (inhibitor of apoptosis protein), were found to be implicated in NSCLC drug resistance (Los et al., 1997; Tanabe et al., 2004; Yang et al., 2003).

2.2 Drug Resistance Towards Small Molecule Inhibitors 2.2.1 Primary Resistance A tumor cell is said to have primary resistance when it occurs prior to any given treatments and does not respond to the treatment from the start. It can be mainly due to tumor intrinsic factors or patient-/drug-specific factors. Tumor intrinsic primary resistance can be due to target mutation and also coexistent genetic alterations in the signaling genes or the target gene itself. For example, EGFR exon 20 insertion mutations are found to be responsible for the lack of clinical response to gefitinib and erlotinib (Girard et al., 2010; Lovly & Shaw, 2014; Oxnard et al., 2013). Interestingly, T790M mutations were found in the germlines of nonsmokers and were found to be associated with primary resistance. Also, coexistent alterations in other signaling genes, for instance de-novo MET amplifications, were also found to be associated with EGFR-TKI primary resistance in NSCLCs (Girard et al., 2010; Lovly & Shaw, 2014; Oxnard et al., 2013). More importantly, patient-specific pharmacokinetic factors, such as absorption, distribution, metabolism, and excretion (ADME) properties, drug levels, and kinetics of drug exposure, may influence targeted therapy efficiency and can lead to primary resistance. Additionally, drug-drug interactions may also result in primary resistance. For instance, when erlotinib is administered with fenofibrate there occurs a decreased level of erlotinib in the plasma (Girard et al., 2010; Lovly & Shaw, 2014; Oxnard et al., 2013). 2.2.2 Acquired Resistance Acquired resistance to targeted therapy occurs after its initial application to tumor cells. Interestingly, acquired resistance can also occur while the patient is still receiving the drugs. It can occur due to target modification, bypass signaling, histological transformation, lack of CNS metastasis, and drug toxicity. In particular, target modifications can occur by gene amplifications and second-site mutations. Gene amplifications will stimulate target amplifications leading to an “outcompetition” of the drug (Lovly & Shaw, 2014). For instance, it was found that amplification of EGFR and EML4-ALK acquired resistance to gefitinib/erlotinib and crizotinib. Additionally, secondary mutations of the target also lead to acquired resistance to targeted therapies (Lovly & Shaw, 2014). In particular, L1196M, G1269A, S1206Y, C1156Y, G1202R, and 1151Tins mutations present in the ATP-binding kinase domain of ALK; EGFR T790M “gatekeeper” mutations (in almost 50% of patients); V600E mutations in BRAF; and KRAS mutations in codons 12 or 13 (8%–24% of NSCLC patients) are a few examples of secondary mutations responsible for the occurrence of acquired resistance towards targeted therapies (Lovly & Shaw, 2014).

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Tumor cells undergo different mechanisms for survival of which bypass signaling is one of the most interesting. After a kinase inhibitor is administered against a particular target, there occurs a mechanism to evade the inhibited kinase (Lovly & Shaw, 2014). This will result in the activation of another target and thereby the continuation of downstream signaling pathways for tumor proliferation, even when the inhibitor is still being administered. Presently, these bypass mechanisms are found in ALK- and EGFR-positive NSCLCs. In the case of crizotinibresistant ALK-positive NSCLC, increased phosphorylation of EGFR TK and KIT amplification were found in 44% and 15% of the tumor samples, respectively (Lovly & Shaw, 2014). Analogously, amplification of MET and HER2 receptors were found in about 5% and 12% of tumors having EGFR TKI-acquired resistance (Lovly & Shaw, 2014). Furthermore, the histological transformation of tumor cells has been shown to demonstrate acquired resistance to EGFR TKIs. It occurs as a result of epithelial-to-mesenchymal transformation. Moreover, there are other mechanisms of acquired resistance found in lung tumors. For instance, increased hepatocyte growth factor (HGF) (ligand for MET) production has been reported as being the cause of drug resistance towards EGFR TKI as well as in vemurafenibresistant BRAF-tumors (Lovly & Shaw, 2014). Additionally, pharmacological resistance also plays an important role in acquired resistance to TKIs. In particular, crizotinib was found to have poor central nervous system (CNS) activity (Wu, Savooji, & Liu, 2016). Thus it was not able to tackle brain metastasis and therefore relapses occurred originating from the CNS. Incidences of drug resistance due to adverse effects in patients have also been reported towards crizotinib (Wu et al., 2016).

3 RESOURCES FOR DRUG REPURPOSING IN LUNG CANCER Many comprehensive databases or resources are available for drug-repurposing studies in lung cancer. These resources allow for rapid access to various sources of clinical and molecular data on lung cancer for use in drug repurposing. Table 2 shows a list of some online resources that can be used for drug-repurposing studies in lung cancer. For instance, DrugBank is a freely available online database containing molecular information about drugs and their targets. It also contains data on the mechanisms of drugs and their influence on gene and protein expression and metabolite levels (Wishart et al., 2017). The Connectivity Map (CMap) is an online portal that is used to discover the unexplored connections among small molecules, diseases, and pathways that connect them. In previous studies, CMap has been successfully applied for the identification of inhibitors for lung adenocarcinoma. For instance, rapamycin, LY-294002, prochlorperazine, resveratrol, imipramine, and promethazine have been identified against lung adenocarcinoma using CMap analysis (Subramanian et al., 2017). Gene expression omnibus (GEO database) is a public repository that freely distributes and archives next-generation sequencing (NGS), microarrays, and other high-throughput genomic data (Barrett et al., 2010). The cBio Cancer Genomics Portal (cBioPortal) was developed at Memorial Sloan-Kettering Cancer Center (MSKCC). It is an open-access resource for the exploration, analysis, and downloading of large-scale genomics data (Cerami et al., 2012; Gao et al., 2013).

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TABLE 2 Drug-Repurposing Resources for Lung Cancer Studies Resources

Content

Web URL

References

DrugBank

A total of 11,061 drug entries, including 2527 approved small molecules, 5122 experimental drugs, 4923 nonredundant protein, and 112 nutraceutical

https://www. drugbank.ca/

Wishart et al. (2017)

Connectivity Map

A library containing over 1.5 million gene expression profiles from approximately 5000 small-molecule compounds, and 3000 genetic reagents

http:// broadinstitute.org/ cmap/

Subramanian et al. (2017)

Gene Expression Omnibus (GEO)

Contains over 40,000 gene expression data for over 2200 organisms submitted by over 15,000 laboratories from around the world. Also contains nonexpression data including genome methylation, genome-protein interactions, and chromatin structure

https://www.ncbi. nlm.nih.gov/geo/

Barrett et al. (2010)

cBioPortal

Contains more than 5000 tumor samples from over 20 cancer studies

http://cbioportal. org

Cerami et al. (2012), Gao et al. (2013)

Oncomine

To date, it contains 715 datasets and 86, 733 samples

www.oncomine.org

Rhodes et al. (2004)

CellMiner

As of 2012, it contains transcripts for 22,379 genes, 360 microRNAs along with activity reports for 20,503 chemical compounds including 102 FDA-approved drugs

https://discover. nci.nih.gov/ cellminer

Reinhold et al. (2012)

Cancer Genome Atlas (TCGA)

TCGA dataset contains 2.5 petabytes of data describing tumor tissue and matched normal tissues from more than 11,000 patients

https:// cancergenome.nih. gov/

Tomczak, Czerwi
nska, and Wiznerowicz (2015)

Cancer Cell Line Encyclopedia (CCLE)

To date, has a compilation of gene expression, chromosomal copy number, and parallel sequencing data from 1457 cancer cell lines

http://www. broadinstitute.org/ ccle

Barretina et al. (2012)

Kinase Map (KMap)

Contains protein kinase receptors and kinase inhibitors for drug repurposing strategy

http://tanlab. ucdenver.edu/ kMap/kMapv2.0/

Kim, Yoo, Kang, and Tan (2013)

MANTRA 2.0

Contains gene expression data and drug-induced transcriptional profiles

mantra.tigem.it/

Carrella et al. (2014)

PharmGKB

As of 2013, there are 5000 variant annotations, 900 genes related to drugs and over 600 drugs related to genes

https://www. pharmgkb.org/

Thorn, Klein, and Altman (2013)

STITCH

Contains 1.6 billion interactions for over 0.5 chemicals and over 9.6 million proteins in 2031 species

stitch1.embl.de/

Szklarczyk et al. (2015)

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Oncomine is a cancer microarray database and web-based data-mining platform that is aimed at facilitating drug discovery from genome-wide expression analyses. The users can visualize and assess differential expression data of a chosen gene from all the available differential expression analysis and available datasets (Rhodes et al., 2004). The CellMiner is a web-based application that retrieves and integrates molecular and pharmacological datasets for NCI-60 cell lines. NCI-60 panel has 60 human cancer cell lines including lung, breast, renal, colorectal, ovarian, and melanomas (Reinhold et al., 2012). The Cancer Genome Atlas (TCGA) is a publicly available resource that provides cancer genomic alterations. It aims to catalogue and discover major cancer-causing genomic alterations and create a comprehensive “atlas” of cancer genomic profiles (Tomczak et al., 2015). The Cancer Cell Line Encyclopedia (CCLE) is a publicly accessible resource that provides information for the visualization and analysis of mRNA expression, DNA copy number, mutation data for individual cell lines, gene methylation data, etc., for over 1000 cancer cell lines (Barretina et al., 2012). Kinase Map (K-Map) is a web-based user-friendly resource that can systematically find kinase inhibitors for query kinases. It can also reveal novel interactions between kinase inhibitors and kinases and help in drug repurposing (Kim et al., 2013). The Mode of Action by NeTwoRk Analysis (MATRA) is an online tool that is based on nonparametric statistics and network theory on gene-expression data. It helps to analyze the mode of action of new drugs and also in the identification of approved drugs for drug repurposing (Carrella et al., 2014). The Pharmacogenomics Knowledge Base (PharmGKB), provides pharmacogenomics relationships integrated as pathway representations and is available for manual inspection or to download for further analyses. It also provides information on how genetic variations affect drug response (Thorn et al., 2013). In addition, the Search Tool for Interacting Chemicals (STITCH) is an online database of known and predicted interactions between proteins and chemicals. The chemicals are linked to other chemicals and the proteins are linked from experiments, databases, and the literature (Szklarczyk et al., 2015).

4 DRUG REPOSITIONING: A CASE STUDY WITH HER PROTEINS OF NSCLC 4.1 Materials and Methods The X-ray coordinates of native HER1 (2ITY) and HER4 (3BBT) proteins were extracted from Protein Data Bank (PDB) (Berman et al., 2000) and used as an input for our study. The representation of the workflow carried out for the present case study is shown in Fig. 1. Since structures with an Rfree value of >0.40 may indicate structural defects in the protein, PDB structures with Rfree values of
4.56 kcal/mol) for EGFR and eight hit molecules (>
6852 kcal/mol) for HER4. Further, these molecules were then taken for MCS and PASS analysis. The docking scores of the screened hit compounds with the reference ligand are illustrated in Table 5. 4.2.3 MCS and PASS Analysis The comparison of chemical-structure similarity has become one of the standard techniques to identify compounds with similar biological activities. Additionally, PASS algorithm predicted the anticancer activity of the hit molecules. Moreover, MCS was computed for all the hits in comparison with afatinib. The mean of the MCS values was taken as the threshold cut off for the study. Further, the hits exhibiting only antineoplastic property were retained. The two analyses demonstrated that for HER4, out of the eight hits only two, namely eslicarbazepine acetate and rucaparib showed highest similarities with an MCS size of 13 and 15, respectively (Table 6). Moreover, it was found that these two hits possessed antineoplastic activities with rucaparib having specific activity against NSCLC (Table 7). As for EGFR, out of 144 hits, only 26 hit molecules were found to possess antineoplastic activity (Table 8). These 26 hits were screened with an MCS threshold of 10. The results showed that 10 hit molecules had an MCS size higher than the threshold (Table 9). Note that, rucaparib was identified in both sets (EGFR and HER4) of hit molecules and was screened out as the final hit molecule. 4.2.4 Protein-Ligand Interaction Analysis The potential binding conformations of the screened hit molecules were visualized using the ligand interaction diagram (LID) of the Schr€ odinger suite. The purple lines indicated the formation of hydrogen bonds, red lines indicated the existence of salt bridge respectively.

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TABLE 6 Maximum Common Substructure Size for Hit Drug Molecules Against Human Epidermal Growth Factor Receptor 4 S. No

DrugBank ID

MCS Size

1.

DB04908

10

2.

DB08941

10

3.

DB01182

10

4.

DB00373

6

5.

DB09119

13

6.

DB00358

9

7.

DB01214

10

8.

DB12332

15

TABLE 7 Prediction of Activity Spectrum of Human Epidermal Growth Factor Receptor 4 Hits by PASS Algorithm S.No

DrugBank ID

Drug Name

Biological properties

Pa

Pi

1

DB09119

Eslicarbazepine acetate

Antineoplastic

0.238

0.195

2

DB12332

Rucaparib

Antineoplastic (nonsmall-cell lung cancer)

0.316

0.020

TABLE 8 Pass prediction of Hit Molecules Against Epidermal Growth Factor Receptor S. No

DrugBank ID

Drug Name

Biological Property

Pa

Pi

1.

DB00420

Promazine

Antineoplastic (NSCLC)

0.189

0.093

2.

DB01142

Doxepin

Antineoplastic (NSCLC)

0.197

0.083

3.

DB00553

Methoxsalen

Antineoplastic (NSCLC)

0.434

0.009

4.

DB00170

Menadione

Antineoplastic (NSCLC)

0.295

0.033

5.

DB00998

Frovatriptan

Antineoplastic (NSCLC)

0.165

0.130

6.

DB01403

Methotrimeprazine

Antineoplastic (NSCLC)

0.177

0.111

7.

DB00661

Verapamil

Antineoplastic (NSCLC)

0.176

0.112

8.

DB00458

Imipramine

Antineoplastic (NSCLC)

0.223

0.056

9.

DB01242

Clomipramine

Antineoplastic (NSCLC)

0.166

0.130

10.

DB12332

Rucaparib

Antineoplastic (NSCLC)

0.316

0.020

11.

DB01125

Anisindione

Antineoplastic (NSCLC)

0.185

0.099

12.

DB00321

Amitriptyline

Antineoplastic (NSCLC)

0.175

0.115

13.

DB01089

Deserpidine

Antineoplastic (NSCLC)

0.205

0.073

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4 DRUG REPOSITIONING: A CASE STUDY WITH HER PROTEINS OF NSCLC

TABLE 8 Pass prediction of Hit Molecules Against Epidermal Growth Factor Receptor— cont’d S. No

DrugBank ID

Drug Name

Biological Property

Pa

Pi

14.

DB00776

Oxcarbazepine

Antineoplastic (NSCLC)

0.237

0.045

15.

DB01428

Oxybenzone

Antineoplastic (NSCLC)

0.235

0.047

16.

DB01165

Ofloxacin

Antineoplastic (NSCLC)

0.270

0.032

17.

DB00609

Ethionamide

Antineoplastic (NSCLC)

0.197

0.082

18.

DB06708

Lumefantrine

Antineoplastic (NSCLC)

0.183

0.101

19.

DB00206

Reserpine

Antineoplastic (NSCLC)

0.213

0.065

20.

DB00724

Imiquimod

Antineoplastic (NSCLC)

0.155

0.149

21.

DB01558

Bromazepam

Antineoplastic (NSCLC)

0.206

0.071

22.

DB00662

Trimethobenzamide

Antineoplastic (NSCLC)

0.226

0.053

23.

DB00918

Almotriptan

Antineoplastic (NSCLC)

0.161

0.139

24.

DB01136

Carvedilol

Antineoplastic (NSCLC)

0.234

0.047

25.

DB00953

Rizatriptan

Antineoplastic (NSCLC)

0.202

0.077

TABLE 9 Maximum Common Substructure Analysis for Hit Drug Molecules Against Epidermal Growth Factor Receptor S.No

DrugBank ID

MCS Size

1.

DB01136

16

2.

DB00724

12

3.

DB00662

16

4.

DB12332

15

5.

DB00776

13

6.

DB00661

11

7.

DB00458

13

8.

DB01242

13

9.

DB01403

12

10

DB00420

12

Fig. 2 illustrates the binding of rucaparib to EGFR and HER4 and its corresponding LID. The atomic interactions of the hit molecules are shown in Table 10. Most importantly, rucaparib is found to interact with Met 793 of EGFR protein. EGFR protein structure analysis has revealed that this residue is present in the kinase hinge region. The hinge region is crucial as it connects C-lobe and N-lobes of the kinases and is the primary 3. EXAMPLES AND CASE STUDIES

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18. TACKLING LUNG CANCER DRUG RESISTANCE USING INTEGRATED DRUG-EPURPOSING STRATEGY

FIG. 2 The LID and image of Rucaparib superimposed with the binding sites of (A) epidermal growth factor receptor (B) human epidermal growth factor receptor 4.

target for the majority of kinase inhibitors (Aertgeerts et al., 2011; Sun et al., 2009; Urich et al., 2013). Moreover, the EGFR protein is phosphorylated when the adenine moiety of ATP forms hydrogen bonds with this region. This is one of the primary steps for EGFR activation and its downstream signaling (Aertgeerts et al., 2011; Sun et al., 2009; Urich et al., 2013). Hence it can be assumed that rucaparib is able to interrupt the phosphorylation and therefore activate EGFR kinase, thus leading to inhibition of tumor formation. 3. EXAMPLES AND CASE STUDIES

569

5 CONCLUSION

TABLE 10

Interaction Analysis of Rucaparib

Target Name

Ligand ID

Number of Bonds

Details of Interaction

˚) Distance (A

Human epidermal growth factor receptor (HER)4

Rucaparib

4

Met774…Lig(O)

1.82933

Lig(NH+2 )…Asn823

1.72094

Lig(NH+2 )…Asp836

2.30567

Lig(NH+2 )…Asp818

4.19494

Met793…Lig(O)

1.94148

Asp800…Lig(N)

1.80982

Epidermal growth factor receptor

Rucaparib

2

Additionally, rucaparib also exhibited interactions with Met 774 and Asp 836 residues present in the kinase domain of HER4 protein (Sahu, Patra, Yadav, & Varma, 2017). Interestingly, Met 774 was found to be present in the αC-helix of the protein. αC-helix is a regulatory element present in the N-lobe of the HER4 protein. Of note is the fact that the position of αC-helix relative to the ATP-binding region will determine the activation state of the kinase (Aertgeerts et al., 2011; Sun et al., 2009). For instance, “αC-helix in” conformation will denote the kinase to be active and vice versa (Aertgeerts et al., 2011; Sun et al., 2009). This conformation is required for the kinase to be active (Aertgeerts et al., 2011; Sun et al., 2009). Therefore interrupting αC-helix conformation will lead to distortion of the kinase active conformation rendering the protein inactive. Overall, the interaction analysis of rucaparib reveals that the drug could be an efficient blocker of EGFR and HER4 proteins in lung cancer and can eliminate the pharmacological resistance. However, caution is required in such an interpretation and experimental study will be necessary to confirm the conclusions.

5 CONCLUSION This case study summarizes the efforts carried out for the discovery of EGFR and HER4 inhibitors using a drug-repurposing approach. For this purpose, FDA-approved drugs from DrugBank were downloaded, cleaned, and subjected to QikProp analysis. They were then screened for their CNS and HOA activities. The screened molecules were taken for molecular docking analysis to measure their binding affinities towards EGFR and HER4. Subsequent MCS metric analysis led to the elimination of hits that did not have a structural similarity to the reference molecule afatinib. Additionally, the PASS algorithm predicted the antineoplastic activities of the drug molecules. Further, analysis of the results revealed that rucaparib could inhibit both EGFR and HER4. Finally, the interaction profile of rucaparib showed the presence of crucial interactions with the kinase domains of EGFR and HER, that are required for the essential inhibition of both the receptors. Although this compound must be further explored as a potent inhibitor of EGFR and HER4 in vitro and in vivo, we believe this study offers insight into the discovery of novel inhibitors with better CNS and low toxicity for the same.

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18. TACKLING LUNG CANCER DRUG RESISTANCE USING INTEGRATED DRUG-EPURPOSING STRATEGY

Acknowledgments The authors are grateful to Department of Science and Technology-Science and Engineering Research Board (DSTSERB) for funding the research project (File No. EMR/2016/001675) and the management of Vellore Institute of Technology, Vellore for providing the facilities to carry out this work.

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Further Reading Chackalamannil, S., Rotella, D., & Ward, S. (2017). Comprehensive medicinal chemistry III. UK: Elsevier. Cohen, B. D., Green, J. M., Foy, L., & Fell, H. P. (1996). HER4-mediated biological and biochemical properties in nih 3t3 cells evidence for her1-her4 heterodimers. Journal of Biological Chemistry, 271, 4813–4818. Eccles, S. A. (2011). The epidermal growth factor receptor/Erb-B/HER family in normal and malignant breast biology. International Journal of Developmental Biology, 55, 685–696. Lionta, E., Spyrou, G. K., Vassilatis, D., & Cournia, Z. (2014). Structure-based virtual screening for drug discovery: principles, applications and recent advances. Current Topics in Medicinal Chemistry, 14, 1923–1938. Nishio, K., Nakamura, T., Koh, Y., Suzuki, T., Fukumoto, H., & Saijo, N. (1999). Drug resistance in lung cancer. Current Opinion in Oncology, 11, 109. Politi, K., & Herbst, R. S. (2015). Lung cancer in the era of precision medicine. Clinical Cancer Research, 21, 2213–2220. Samet, J. M. (1991). Health benefits of smoking cessation. Clinics in Chest Medicine, 12, 669–679. Shelley, J. C., Cholleti, A., Frye, L. L., Greenwood, J. R., Timlin, M. R., & Uchimaya, M. (2007). Epik: a software program for pK a prediction and protonation state generation for drug-like molecules. Journal of Computer-Aided Molecular Design, 21, 681–691. Wang, Y., Xing, J., Xu, Y., Zhou, N., Peng, J., Xiong, Z., … Zheng, M. (2015). In silico ADME/T modelling for rational drug design. Quarterly Reviews of Biophysics, 48, 488–515.

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C H A P T E R

19 In Silico Modeling of FDA-Approved Drugs for Discovery of Anti-Cancer Agents: A Drug-Repurposing Approach Anu R. Melge, K. Manzoor, Shantikumar V. Nair, C. Gopi Mohan Bioinformatics and Computational Biology Lab, Center for Nanosciences and Molecular Medicine, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Kochi, India

1 INTRODUCTION Drug discovery and development is an expensive and prolonged process. There is severe attrition of the drug-like molecules during the preclinical discovery stages, due to safety and efficacy issues (Scannell, Blanckley, Boldon, & Warrington, 2012). In the past, several drugs have been repurposed by serendipity. However, presently much consideration is being given to this field for its systematic and progressive development. The drug-development process begins with the identification of an aberrant protein causing disease followed by the lead molecule identification and optimization. The lead molecules undergo preclinical assessment, wherein animal models are used to test its safety and efficacy. Following the preclinical testing the clinical phases begin, which are expensive and time-consuming processes involving human subjects. Clinical trials involve three different phases. The final lead molecule has to pass through these phases with good safety and efficacy in order to be titled as a candidate drug. The whole process of this drug-discovery program might take 7–10 years. Some clinical trials are even terminated between different phases due to drug-toxicity concerns

In Silico Drug Design. https://doi.org/10.1016/B978-0-12-816125-8.00019-5

577 # 2019 Elsevier Inc. All rights reserved.

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19. IN SILICO DRUG REPURPOSING AGAINST CANCER

(Scannell et al., 2012). Thus in order to overcome these complications, an effective strategy has to be devised in order to reduce the time and costs involved in developing novel drugs. Drug repurposing is a promising approach that identifies new indications for Food and Drug Administration (FDA)-approved drugs. In other words, the drug proposed for the treatment of a particular disease could have a more potent activity with less adverse events for a different disease. For example, thalidomide was prescribed as a pregnancy antiemetic but was withdrawn due to its teratogenic effects. However, now it has been repurposed and approved for multiple myeloma (Ashburn & Thor, 2004; Zhou, Wang, Hsieh, Wu, & Wu, 2013). Repurposing of existing drugs will certainly reduce the costs associated with its assessment since the pharmacokinetics and efficacy of these drugs are already well established. Several successful drug-repurposing studies are listed in Table 1. TABLE 1 List of Drugs Identified to Have Repurposed Ability for Their Use in Cancer Name of Drug

Native Indication

Repurposed Indication

References

Mebendazole

Helminthes infection

Cancer especially metastatic adrenocortical carcinoma; refractory metastatic colon cancer

(Dobrosotskaya, Hammer, Schteingart, Maturen, & Worden, 2011; Mukhopadhyay, Sasaki, Ramesh, & Roth, 2002; Nygren & Larsson, 2014; Pantziarka, Bouche, Meheus, Sukhatme, & Sukhatme, 2014)

Thalidomide

Pregnancy antiemetic

Multiple myeloma

(Palumbo et al., 2008)

Itraconazole

Antifungal

Angiogenesis inhibition in basal cell carcinoma, small-cell lung cancer

(Aftab, Dobromilskaya, Liu, & Rudin, 2011; Chong et al., 2007; Ekins, Williams, Krasowski, & Freundlich, 2011)

Fumagillin

Antiamoebic

Cancer especially colorectal cancer (antiangiogenic)

(Hou et al., 2009)

Gemcitabine

Antiviral

Cancers

(Toschi, Finocchiaro, Bartolini, Gioia, & Cappuzzo, 2005)

IFN alpha

Hepatitis B and C

Cancers

(Rosenberg et al., 1989)

Nelfinavir

HIV antiprotease

Cancers

( Jensen et al., 2017)

Orlistat

Obesity

Cancers especially triple-negative breast cancer

(Paulmurugan et al., 2016)

Raloxifene

Osteoporosis

Postmenopausal breast cancer

(Muchmore, 2000)

Retinoic acid

Acne

Promyelocytic leukemia

(Tallman et al., 1997)

Ritumaxib

Rheumatoid arthritis

Cancer

(Witzig et al., 2005)

Simvastatin and Ketoconazole

Cholesterol lowering agents

Breast cancer

(Cheng et al., 2012)

3. EXAMPLES AND CASE STUDIES

TABLE 1

List of Drugs Identified to Have Repurposed Ability for Their Use in Cancer—cont’d

Name of Drug

Native Indication

Repurposed Indication

References

Sunitinib and dasatinib

Gastrointestinal, pancreatic neuroendocrine tumors and chronic myelogenous leukemia (CML)

Breast cancer linked to brain metastases

(Zhao et al., 2013)

Sirolimus

Antifungal

Acute lymphoblastic leukemia with dexamethasone resistance

(Lamb et al., 2006; Xia et al., 2013)

Cimetidine

Blocks stomach acid production

Lung cancer

(Lussier & Chen, 2011; Sirota et al., 2011)

Daunorubicin

Acute myeloid leukemia (AML), acute lymphocytic leukemia (ALL), chronic myeloid leukemia (CML), and Kaposi’s sarcoma

Breast cancer

(Nguyen et al., 2018)

Everolimus

Immunosuppressant

Pancreatic neuroendocrine tumors

(Yao et al., 2011)

Imatinib

CML

Gastrointestinal stromal tumors

(Dagher et al., 2002)

Trastuzumab

HER2-positive breast cancer

HER2-positive metastatic gastric cancer

(Rose & Bekaii-Saab, 2011)

Telmisartan

Hypertension

Colon cancer

(Su & Sanger, 2017)

Phylloquinone

Dietary supplement

Reduced risk of cancer

(Su & Sanger, 2017)

Aliskiren

Hypertension

Gastric and renal cancers

(Su & Sanger, 2017)

Pentamidine

Antiparasitic

Renal cancer

( Jones et al., 2005)

Imatinib alone or in combination with emurafenib and flucytosine

CML

Triple negative breast cancer

(Vitali et al., 2016)

Olsalazine

Antiinflammatory

Cancer

(M
endez-Lucio, Tran, MedinaFranco, Meurice, & Muller, 2014)

Pranlukast

Asthma

Cancer metastasis

(Zhao & Li, 2012)

Benzthiazide

Hypertension

Lung cancer

(Lee et al., 2012)

Plerixafor

Immunostimulant

NonHodgkin’s lymphoma

(Guney, Menche, Vidal, & Bara´basi, 2016)

Drospirenone and levonorgestrel

Birth control pills

Endometrial cancer

(Allen et al., 2015)

Sertraline

Antidepressant

Nonsmall-cell lung cancer

( Jiang et al., 2018)

Metformin

Type 2 diabetes

Cancer

(Kasznicki, Sliwinska, & Drzewoski, 2014)

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19. IN SILICO DRUG REPURPOSING AGAINST CANCER

Current anticancer drugs are associated with several adverse effects, which add to the patient’s illness. Thus employing a repositioning strategy in this area could result in a new drug being developed with potentially fewer side effects (Hernandez et al., 2017). Noncancer-repurposed drugs with fewer side effects and efficacy better than or similar to the native drugs would be preferable to the existing cancer drugs. Since cancer involves multiple aberrant pathways, a repurposed drug having activity against any of the other aberrant targets within the pathway would definitely have an effect on the progression of cancer. Several techniques are adopted to identify drugs that can be repurposed, for example, in silico studies, literature mining, phenotypic screening, proteomics and genomics, in vitro experiments, in vivo assays, observations from clinical trials, and patient studies. There have also been attempts towards personalized drug repurposing due to person to person or even intra-disease heterogeneity. Disease heterogeneity is observed in rare diseases like cancer, where the target mutational rates are very high. Although a few drugs are found to be effective in certain populations, they may be ineffective in a certain percentage of the patient categories. In such cases, analyzing the gene expression and genome sequence data would definitely help in identifying better and more effective repurposed drugs (Gottlieb, Stein, Ruppin, & Sharan, 2011; Li & Jones, 2012). There are also several governmental and non-governmental pharmaceutical company initiatives to cure rare and critical diseases, where no other better treatment options are available (Frail et al., 2015). The in silico-based drug-repurposing strategy is gaining importance and involves computer-based tools that predict the effects of novel inhibitors on biological systems. These methods provide a low cost, more rapid repurposing analysis. The computational repurposing of drugs is made possible by the development of efficient algorithms using enormous amounts of data obtained from proteomics, genomics, and phenomics. It also uses the algorithms for the generation of databases to collect and organize available drug data. Traditional drug-repurposing techniques possess vague information regarding different biological processes, thereby making it difficult to analyze the repurposing ability of the drug against a particular target. Tapping into the computational power for gathering, classifying, and analyzing the vast amount of information obtained from genomics, proteomics, drugs, and disease data will certainly aid in better prediction of drug repurposability. In this chapter, we will discuss the various tools and strategies that have been developed using advanced computational methodology in an attempt to study the repurposing of drugs against cancer.

2 BASIC IN SILICO REPURPOSING STRATEGY WORKFLOW Different in silico strategies have varied approaches and algorithms for the performance of drug repurposing, as shown in Fig. 1. This workflow can be divided into three major divisions: (i) big scientific data collection and integration, (ii) scientific algorithms developed for repurposing, and (iii) in silico validation techniques to understand the significance and robustnesss of the data generated and model developed.

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2 BASIC IN SILICO REPURPOSING STRATEGY WORKFLOW

Big Data collection and integration Transcriptional signatures Networks based Ligand-based

Different Algorithms for drug repurposing

Structurebased Multiple in silico strategies

Literature/ Text mining

Validation using different in silico techniques

FIG. 1 The basic workflow of the drug-repurposing methodology using in silico techniques.

2.1 Big Data Collection and Integration Big-data mining and integration is an important task for analyzing and drawing conclusions about drug repurposability. Accumulating big data of different types and from a variety of sources improves the predictive ability. Including more and more relevant data regarding the drug-target interactions will indeed help in the making of more precise predictions with fewer false positives (Hodos, Kidd, Shameer, Readhead, & Dudley, 2016). Important data are collected and stored in different types of databases from which users can access them as required. There are different types of databases such as proteomics, genomics, pathways, and phenotype data, including 3D structural information about small molecules and drugs shown in Table 2. The integration of this data in different databases and tools and connecting them can lead to meaningful drug-target-disease associations. DrugSig is a fully drug responsive gene-expression database of 500 upregulated and 500 downregulated genes. It contains 1300 FDA-approved drugs, 7000 microarray expression data entries, and 800 druggable targets, which will enormously help in drug-repurposing studies (Wu et al., 2017). Brown et al. have created a database called repoDB for setting up gold standard benchmarks in drug repurposing by gathering several true positives (FDAapproved drugs) and false positives (failed drugs), respectively (Brown & Patel, 2017). The information contained in these databases can be used to build algorithms to integrate the relevant data to understand the relationship between drugs and disease (Chen, Zhang, Zhang, Cao, & Tang, 2015; Dai et al., 2015; Dudley, Deshpande, & Butte, 2011; Gottlieb et al., 2011; Hu & Agarwal, 2009; Sirota et al., 2011), disease and genes (Menche et al., 2015), drugs and genes (Iorio et al., 2010), and drugs and proteins, respectively (Cheng et al., 2012; Lee et al., 2012). Along with the interaction of drugs at a molecular horizon, more complex connections do exist between drug, target, and disease.

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19. IN SILICO DRUG REPURPOSING AGAINST CANCER

TABLE 2 Different Types of Databases Useful for Drug Repurposing Category

Name

Website

References

3D structure of target

RCSB Protein Data Bank (PDB)

http://www.rcsb.org

(Berman et al., 2002)

OCA

http://oca.weizmann.ac.il/ oca-bin/ocamain

(Prilusky, 1996)

ProtCID

http://dunbrack2.fccc.edu/ protcid/

(Xu & Dunbrack, 2010)

OPM

http://opm.phar.umich.edu

(Lomize, Pogozheva, Joo, Mosberg, & Lomize, 2012)

ProteinLounge

http://www.proteinlounge. com/

(Besaw, 2013)

Proteopedia

http://proteopedia.org

SWISS-MODEL Repository

https://swissmodel.expasy. org/repository/

(Bienert et al., 2016)

TOPSAN

http://www.topsan.org

(Krishna et al., 2010)

DrugBank

http://www.DrugBank.ca/

(Wishart et al., 2006)

PubChem

http://pubchem.ncbi.nlm.nih. gov

(Kim et al., 2015)

Therapeutic Target Database (TTD)

http://bidd.nus.edu.sg/ group/cjttd/

(Yang et al., 2015)

SweetLead

https://simtk.org/home/ sweetlead

(Novick, Ortiz, Poelman, Abdulhay, & Pande, 2013)

Pharmacogenetics Knowledge Base (PharmGKB)

http://www.pharmgkb.org/

(Hewett et al., 2002)

Collaborative Drug Discovery Vault

https://www. collaborativedrug.com

(Hohman et al., 2009)

ChemSpider

http://www.chemspider.com

(Pence & Williams, 2010)

ChemDB

http://www.chemdb.com

(Chen, Linstead, Swamidass, Wang, & Baldi, 2007)

Chemicalize (ChemAxon)

http://www.chemicalize.org

(Swain, 2012)

DistilBio

http://distilbio.com

ChEMBL

https://www.ebi.ac.uk/ chembldb/

(Gaulton et al., 2011)

ChemMaps

https://www.chemmaps.com

(Borrel, Kleinstreuer, & Fourches, 2018)

NCI Pathway Interaction Database (NCI-PID)

http://pid.nci.nih.gov/

(Schaefer et al., 2008)

Kyoto Encyclopedia of Genes and Genomes (KEGG)

http://www.genome.jp/ kegg/

(Kanehisa & Goto, 2000)

Ligand structure

Pathways

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2 BASIC IN SILICO REPURPOSING STRATEGY WORKFLOW

TABLE 2

Different Types of Databases Useful for Drug Repurposing—cont’d

Category

Clinical trials and adverse effects

Gene expression

Disease

Name

Website

References

DrugMap Central (DMC)

http://r2d2drug.org/index. html

(Fu et al., 2013)

Reactome

http://www.reactome.org

(Fabregat et al., 2018)

BioCarta

http://www.biocarta.com

PathwayCommons

http://www. pathwaycommons.org/about/

Clinicaltrial.gov

http://clinicaltrials.gov

FAERS (US FDA

http://www.fda.gov/Drugs/

SIDER

http://sideeffects.embl.de/

(Kuhn, Campillos, Letunic, Jensen, & Bork, 2010)

DrugMap Central (DMC)

http://r2d2drug.org/index. html

(Fu et al., 2013)

Iowa Drug Information Service (IDIS)

http://itsnt14.its.uiowa.edu

NCBI-GEO

http://www.ncbi.nlm.nih. gov/geo/

(Barrett et al., 2013)

ArrayExpress

http://www.ebi.ac.uk/ arrayexpress/

(Parkinson et al., 2010)

cMap

https://portals.broadinstitute. org/cmap/

(Lamb et al., 2006)

DrugSig

http://biotechlab.fudan.edu. cn/database/drugsig/

(Wu, Huang, Zhong, & Huang, 2017)

GXA

https://www.ebi.ac.uk/gxa/ home/

(Petryszak et al., 2014)

OncoMine

https://www.oncomine.org/

(Rhodes et al., 2007)

Magic Tool

http://www.bio.davidson. edu/MAGIC/

(Heyer et al., 2005)

NFFinder

http://nffinder.cnb.csic.es

(Setoain et al., 2015)

MalaCards

https://www.malacards.org/

(Rappaport et al., 2016)

KEGG DISEASE Database

http://www.genome.jp/ kegg/disease/

(Kanehisa & Goto, 2000)

Comparative Toxicogenomics

http://ctdbase.org/

(Davis et al., 2016)

DisGeNET

http://www.disgenet.org/ web/DisGeNET/

(Pin˜ero et al., 2016)

(Cerami et al., 2011)

Continued

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TABLE 2 Different Types of Databases Useful for Drug Repurposing—cont’d Category

Mutation

Drug-target

Target interaction

Validation of drug repositioning

Name

Website

References

Online Mendelian Inheritance in Man (OMIM)

http://www.omim.org/

(McKusick, 2007)

Pharos

https://pharos.nih.gov/idg/ index

( Nguyen et al., 2016)

dbSNP

http://www.ncbi.nlm.nih. gov/projects/SNP/

ALFRED

https://alfred.med.yale.edu/ alfred/index.asp

HGMD

http://www.hgmd.cf.ac.uk/ ac/index.php

Therapeutic Target Database (TTD)

http://bidd.nus.edu.sg/ group/cjttd/

(Yang et al., 2015)

MATADOR

http://matador.embl.de

(G€ unther et al., 2007)

STITCH

http://stitch.embl.de/

(Szklarczyk et al., 2015)

BindingDB

http://www.bindingdb.org/ bind/index.jsp

(Nicola, Liu, Hwang, & Gilson, 2012)

DrugMap Central (DMC)

http://r2d2drug.org/index. html

(Fu et al., 2013)

DrugBank

http://www.DrugBank.ca/

(Wishart et al., 2006)

HRPD

http://www.hprd.org/

STRING

http://string-db.org/

(Szklarczyk et al., 2014)

MIPS

http://mips.helmholtzmuenchen.de/proj/ppi/

(Pagel et al., 2004)

BioGRID

http://www.thebiogrid.org

(Chatr-Aryamontri et al., 2014)

IntAct

http://www.ebi.ac.uk/intact/

(Orchard et al., 2013)

repoDB

http://apps.chiragjpgroup. org/repoDB/

(Brown & Patel, 2017)

(Osier et al., 2001)

Cancer and other disease-signaling pathways are subjected to several drug reactions and drug resistance (Dorel, Barillot, Zinovyev, & Kuperstein, 2015). Pathway-based drugrepurposing studies involve the integration of different pathways that are activated on exposure to a drug (Pan, Cheng, Wang, & Bryant, 2014; Zeng, Qiu, & Cui, 2015). Han et al. also developed pathway-prediction databases, which have the ability to predict the pathway based upon its interactions with the target (Han & Kim, 2008). Even though there are several

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databases containing massive amounts of data, the algorithms developed experience biases based on the type of data input that can result in false positives leading to wrong pathway predictions. In order to overcome this bias, Liu et al. suggested including signaling networks with known positive interactions and known negative interactions for this type of in silico validation (Liu, Sun, Guan, Zheng, & Zhou, 2015).

2.2 Different Algorithms for Drug Repurposing Drug repurposing can be achieved using different in silico strategies, which include transcriptional signatures, networks-based, ligand-based, and target-based (Fig. 2). A short description about each technique is provided in Table 3. It should be noted that a combination of these techniques would be more informative than using individual in silico strategies (Alaimo, Giugno, & Pulvirenti, 2016; Hodos et al., 2016). These techniques basically take into account the similarity criteria that are used as metrics to study drugs, targets, or pathways matching these criteria. This will give a list of hits complying with the similarity criteria. In order to select the most significant hit, a probability (P) value was computed that gives a statistically significant score relating to the predicted and validated target of a drug. The P value is considered a particular characteristic of the drug and a secondary similarity is developed based on this property. Different in silico strategies for drug repurposing are given below. FIG. 2 A diagrammatical representation of the various in silico approaches mainly developed for drug repurposing.

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TABLE 3 Different In Silico Repurposing Strategies Adopted in Drug Discovery In Silico Strategy

Description

References

Literature mining

This technique basically explores the knowledge available regarding the existing drugs and develops various drug-target and target-disease associations and identifies new indications for existing drugs.

(Cohen, Widdows, Schvaneveldt, Davies, & Rindflesch, 2012; Su & Sanger, 2017)

Transcriptional signatures

Transcriptional signatures are unique for different drugs thus comparing their signatures in order to identify similar expression patterns and then develop novel associations between the drug, target, and diseases.

(Huang, Chang, et al., 2014; Huang, Li, et al., 2014; Jahchan et al., 2013; Jones et al., 2005; Lee, Kang, & Kim, 2016; Nagaraj et al., 2018)

Networks

Network-based techniques help in integrating enormous amounts of biological data so as to make meaningful connections and thus identifying putative novel drugs or targets for an indication.

(Aliper et al., 2016; Guney et al., 2016; Regan, Payne, & Li, 2017; Vitali et al., 2016)

Ligand-based

Ligand-based approaches mostly make use of the chemical structural similarity. The basic idea is that molecules with similar structure will possess similar activity too by binding to same target.

(Keiser et al., 2007, 2009)

Machine learning

It employs different algorithms, Bayesian classifiers, Hidden Markov models, Radial Basis Function, K-means, and Support Vector Machines in order to predict new interactions by providing as input existing information regarding a drug or target or disease and training the models based on the information provided.

(Bender et al., 2007; Crisan, Avram, & Pacureanu, 2017; Gregori-Puigjan
e & Mestres, 2008; Mestres, Martı´n-Couce, GregoriPuigjan
e, Cases, & Boyer, 2006; Yamanishi, Araki, Gutteridge, Honda, & Kanehisa, 2008; Yang & Agarwal, 2011)

Structure-based

Targets with similar binding sites have the capacity to occupy different drugs binding to targets with similar active sites. This approach also uses docking to perform virtual screening runs to identify novel drugs for the targets.

(Dakshanamurthy et al., 2012; Li, An, & Jones, 2011)

2.3 Scientific Literature Mining Strategy The scientific data in biological/pharmaceutical/clinical literature is exponentially growing. This data can be used to analyze different relationships between the drug and the disease and the drug and the target. It has become increasingly difficult to accumulate this big data with respect to a particular target. Drug repurposing requires the integration of all the abovementioned big data in order to complete and analysis and make meaningful predictions. The more varied the types of data included, the better the judgment in the case of drug

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repositioning ( Jensen, Saric, & Bork, 2006). Thus, considering the large volume of big data, computational automation techniques would be of great help to link this data to interpret the masked connectivity among them and bring out useful information regarding the drug repurposability. Cohen et al. discussed the open and closed mode of literature mining in situations where terms that cannot co-exist can be found by including a third linking term that exists for each of the other terms. They used predication-based semantic indexing (PSI) to search for discovery patterns, i.e., sequences of relationships ("a" drug affects protein "b" and "b" causes certain disease "c") extracted from scientific literature using SemRep system (natural language processing system). In the literature based discovery system, there are two modes of drug discovery which include (i) an open mode, which has a disease term along with a few related intermediates and (ii) a closed mode, which has the relationship between a drug and the target of its area of interest. Thus, an advanced PSI type discovery was developed by the authors which treat the relationships and concepts in the scientific literature between sentences as vectors in a hyper-dimensional space. This technique has the ability to identify meaningful treatment options for diseases across huge volumes of knowledge using SemRep system (Cohen et al., 2012). 2.3.1 Case Studies Su and Sanger recently performed a big-data mining study on ClinicalTrials.gov (website with information on >220,000 clinical trials) to identify drug-repurposing opportunities based on the occurrence of adverse effects in the treatment arm in comparison to that of the control arm (Su & Sanger, 2017). From this data-mining study they identified three different repurposable drugs: (i) telmisartan, which is a hypertension drug, was found to have activity against colon cancer; (ii) phylloquinone (vitamin K1) had the potential to lower the risk of cancer; and (iii) aliskiren, an another hypertension drug, was known to be effective against gastric and renal cancers, respectively.

2.4 Transcriptional Signatures-Based Predictions for Anticancer Drug Repurposing Transcriptional signatures of the drug and disease relate to a favorable outcome of the drug against the disease. The in silico strategy towards the gene expression patterns was successfully reported in complex diseases, i.e., cancer, Alzheimer’s disease, and inflammatory bowel disease. Several transcriptional signature databases were created in an attempt to support the drug-repurposing research. The Connectivity Map (CMap) (https://portals.broadinstitute. org/cMap/) (Lamb et al., 2006) was the first huge genomic-expression database and contains information regarding the gene-expression patterns after treatment with several thousand bioactive molecules. To browse the database, they built a pattern-comparing tool depending on the gene set enrichment analysis (GSEA), which can also assess the results statistically (Subramanian et al., 2005). GSEA matches the expression data of gene groups classified based on their roles in the same biological pathways, co-expression, and proximal presence in chromosomes. CMap is helpful in identifying drugs with similar effects revealing similar transcriptional expression patterns. This key finding can be very useful in the drug-

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repurposing strategy, since, if two or more drugs have similar gene-expression patterns, there is a high probability they can be used to treat the same disease. ssCMap is an advancement of CMap and uses a similar algorithm to CMap coded in java. It contains a reference dataset that makes it easier to include the user-defined data for its execution (Zhang & Gant, 2009). The mode of action by network analysis (MANTRA) tool is another processed version of the CMap dataset (Iorio et al., 2010). This makes use of drug similarity based on whether these drugs induce similar gene expression. Fortney et al. developed a metaanalysis approach called cCMapBatch that analyses simultaneously several gene signatures of a disease and compiles these results elegantly (Fortney et al., 2015). 2.4.1 Case Studies Using 21 patients Jones et al. identified the top 100 overexpressed and top 100 underexpressed genes in the tumor transcriptome. Using GSEA and CMap, they identified drugs against these differential expressed genes. In this study, nearly 30% to 40% of genes were seen to be overlapping in 21 patients. From this analysis high-scoring consensus drugs included pentamidine (antiparasitic drug), amitriptyline (antidepressant), oligomycin (antibiotic), yohimbine (alpha-2-adrenergic blocker), phenanthridinone (immunosuppressive), oxaprozin (NSAID), dopamine (vasoconstrictor), and exemestane (aromatase inhibitor). Pentamidine showed an increased apoptosis in renal cancer cells and was successful in in vivo studies, where the renal cancer xenograft tumors showed a significant growth reduction. Further, to assess the CMap-based prediction of pentamidine to reverse the cancer-cell transcriptome to normality it was tested by treating the drug for the renal cancer cell line. It was observed that the key genes downregulated by this drug were associated with metastasis, differentiation, and cell signaling. Thus this repurposing study using a transcriptional signature was successful in identifying the drugs with varied purposes to be effective against renal cancer ( Jones et al., 2005). Huang et al. performed drug repurposing by network analysis to identify drugs having the potential to inhibit early and late stage nonsmall-cell lung cancer (NSCLC) in order to reduce the difference in the test and control samples. The cancerous and noncancerous samples were taken from the same patient. Differentially expressed upregulated and downregulated genes were identified and also classified based on the early- or late-stage NSCLC genes. These differentially expressed genes were provided as a query to GSEA for identifying its pathways. The enrichment analysis report showed that the pathways of both early- and late-stage NSCLC were different. Consequently, to identify the drugs with activity against NSCLC, the gene-expression data was given to CMap and validated using wet-lab experiments (Huang, Chang, et al., 2014; Huang, Li, et al., 2014). Lee et al. employed transcriptional signatures in order to identify potential repurposed drugs for glioblastoma, lung cancer, and breast cancer. Approximately 20,000 drug-induced gene-expression profiles in multiple cancer cell lines were used in this study. They predicted 14 high-scoring drug-repurposed candidates against different cancers. Among them, eight drugs showed a much better antiproliferative activity against glioblastoma than the remaining six repurposed drugs (Lee et al., 2016). Jahchan et al. screened gene-expression profiles to identify drugs with the potential to inhibit small-cell lung cancer (SCLC) and neuroendocrine tumors. Tricyclic antidepressants were identified that potently induce apoptosis in chemonaive and

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chemoresistant SCLC cells. In vitro and in vivo efficacy of the drug was found to activate stress pathways, and thereby cause cell death in these cancerous cells ( Jahchan et al., 2013). Nagaraj et al. designed the DrugPredict tool to quickly identify repurposed candidates for epithelial ovarian cancer (EOC). This technique identified nonsteroidal antiinflammatory drugs (NSAIDs) to be scored very close to the currently used EOC drugs. Epidemiological studies also report the effectiveness of NSAIDs against ovarian cancer. DrugPredict uses a profile-based drug-approach that does not mainly rely on the existing drugs and helps in identifying the novel drug-disease associations. The input can be disease-associated genes, mutation phenotype data, and drug genetics. The output of this tool is a list of drugs ranked on its close association with the disease (Nagaraj et al., 2018). Gayvert et al. developed a novel strategy by targeting the transcription factors (TF) by small molecules to modulate their actions. They developed a repositioning strategy called CRAFTT, a computational drugrepositioning approach for targeting TF activity. This approach compiles the expression and ChIP-seq data of several TFs on drug induction. Further, based on this data they built networks connecting the drugs with their affected TFs. Finally, dexamethasone was identified to inhibit ERG-TF activity, which was usually seen to be overexpressed in cancers (Gayvert et al., 2016).

2.5 Network-Based Drug Repurposing for Anti-Cancer Drug Discovery A network-based drug-repurposing strategy involves the collection of the huge amounts of biologically and pharmacologically important data. Thus in order to gather information from these resources we need to integrate them into a network to better understand their relatedness in therapy. In a biological network the main elements are represented as nodes and the connectivity between them is represented as edges. Networks simplify the understanding of the relation between several entities by visual analysis of the topological architecture of network associations. The nodes in a network can represent the genes, proteins, drugs, and diseases, while the edges could be the experimental activity of a drug towards a protein or the similarity index linking two similar drugs. These network associations can be given weights to provide more importance to a specific type of connection and to avoid other undesired outcomes. There are several primary, secondary, and tertiary databases developed that provide information regarding the relationship between protein/nucleic acid and drug, protein and protein, drug and disease, and, finally, disease and protein. Some of the databases, including the DisGeNET database, provide information regarding the connection between human genes, gene variants, and diseases (Pin˜ero et al., 2016). Another key database was the Therapeutic Target database, which contains information regarding the drug and its targets (Yang et al., 2015). BindingDB stores information regarding the experimental interactive content of several inhibitor molecules and their targets (Nicola et al., 2012). STITCH is another database similar to the BindingDB and contains both the experimental and predicted data regarding the protein and chemical interactions. The database also has additional text-mining advantages and the option to view the connectivity between small molecule and target as networks (Szklarczyk et al., 2015). STRING is a database containing protein-protein interaction data based on the experimental evidence integrated over the tree of life and used to provide key information at the systems biology level (Szklarczyk et al., 2014).

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2.5.1 Case Studies Different research groups successfully applied this network-based strategy for drugrepurposing studies. Vitali et al. adopted this strategy in poorly understood breast-cancer type cells (triple-negative breast cancer). They identified several aberrant genes involved in this disease and also queried the STRING database for any protein-protein co-expression seen in this type of cancer. Using this information, they constructed a network topology in order to identify the potential target proteins involved in this disease. In order to identify multitargeted drugs from the network, they used the topological score of drug synergy. Eighteen pathways were identified from the KEGG-pathway database involving triplenegative breast cancer. DrugBank and comparative Toxicogenomics databases were then queried to identify drugs that bind to the potential targets that emerged from the network. Finally, it was found that imatinib alone or in combination with emurafenib and flucytosine was effective against triple-negative breast cancer (Vitali et al., 2016). This work promisingly shows the importance of the network-based strategy for drug repurposing. Network-based strategies are successful in identifying multitargeted and combination drugs in order to overcome the drug resistance in cancer. Some advanced tools, like DrugComboRanker or SynGeNet, combine transcriptomics with network-mining algorithms and can be used to identify the best drug combinations (Huang, Chang, et al., 2014; Huang, Li, et al., 2014; Regan et al., 2017). In an attempt to identify drug combinations in melanoma using SynGeNet, Regan et al. developed a melanoma signaling network with information gathered on genes expressed in normal skin and in BRAFV600E/K mutant melanoma. The top-ranked genes were designated as nodes to identify their role in melanoma. In addition, data containing the activity of FDA-approved drugs against the BRAF-mutant A375 melanoma cell line was included from L1000 transcriptomics database. The drugs were ranked using the connectivity score, following which its synergism was determined by grouping the top-ranked drugs based on their role in certain pathways (Regan et al., 2017). Guney et al. developed a disease-drug proximity measure using network-based associations between the disease, drug, and target; 238 drugs against 78 diseases were used in this study. The proximity measure was found to help in identifying the therapeutic effect of the drugs and differentiating effective from palliative ones. It also helps to predict the novel drugdisease associations and identify new therapeutic indications for the existing drugs (Guney et al., 2016). Daminelli et al. created a comprehensive network of 147 promiscuous drugs, their 553 protein targets, and 44 diseases. To identify a drug-target-disease association in such large networks, they searched for bi-cliques, which are small subnetworks where every drug is related to every target and disease. Most of the identified bi-cliques were incomplete and they were completed by predicting drug-target-disease associations based on prior knowledge (literature mining and target binding site similarity). This approach helped them to identify several novel associations. One among them was the prediction of PIK3CG, which is a novel target overexpressed in cancers, for the drug resveratrol used as an antioxidant (Daminelli, Haupt, Reimann, & Schroeder, 2012). Jiang et al. gathered information from DrugBank, PharmGKB, and Therapeutic Target Database, to develop a drug-gene interactome (DGI). They also generated a disease-gene associations (DGA) model by integrating OMIM, HuGE Navigator, PharmGKB, and Comparative Toxicogenomics databases. Finally, to determine new indications for the existing drugs, they combined the DGI and DGA models to draw novel drug-disease associations based on these networks. From their analysis, they identified sertraline, which is an antidepressant used for 3. EXAMPLES AND CASE STUDIES

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the treatment of NSCLC disease ( Jiang et al., 2018). The network topological similarity-based inference method (NTSIM) and the network topological similarity-based classification method (NTSIM-C) was proposed by Zhang et al. to predict the novel unobserved drugdisease association using bipartite network and linear neighborhood-similarity techniques respectively (Zhang et al., 2018).

2.6 Ligand-Based Approach to Drug Repurposing for Anti-Cancer Drug Discovery The ligand-based approach involves different chemoinformatics techniques, which include molecular modeling, chemical databases, quantitative structure-activity relationship models (QSAR), pharmacophore models, and machine learning (ML). There are several chemical databases containing small molecules that are FDA approved, investigational, withdrawn, and under research categories. Some of the well-known databases include DrugBank and Sweetlead containing information regarding FDA-approved or investigational drugs while PubChem, ChEMBL, ChemSpider contain information regarding these drugs along with the other drug-like small molecules. DrugBank contains information regarding the drug’s chemical structure, dosage, route, generic products, mixture products, unapproved products, pharmacodynamics, mechanism of action, elimination, targets, etc. (Wishart et al., 2006). PubChem, ChEMBL, ZINC, and ChemSpider databases contain information regarding the structure, different names, notation, physicochemical properties, vendors, etc. (Bolton, Wang, Thiessen, & Bryant, 2008; Gaulton et al., 2011; Irwin & Shoichet, 2005; Pence & Williams, 2010). Sweetlead is a highly curated database of globally approved drugs. It searches across several chemical databases to finally list the correct chemical structure and associated details (Novick et al., 2013). All these databases provide excellent tools to study the repurposing of drugs by comparing the similarity index within chemical structures. Ma et al. found that drugs with similar chemical structures will exhibit similar effects on the target (Ma, Chan, & Leung, 2013). Wu et al. established the indication similarity ensemble approach (iSEA) that correlates the anatomical therapeutic chemical (ATC) classes with the structural similarity (Wu, Ai, Liu, Wang, & Fan, 2013). 2.6.1 Case Studies Keiser et al. performed association studies by grouping proteins and ligands with similar chemical structure and established a connection between them. From these studies, they identified new indications for existing drugs, i.e., methadone, emetine, and loperamide inhibit muscarinic M3, alpha2 adrenergic, and neurokinin NK2 receptors, respectively, which impede cancer progression (Keiser et al., 2007). In another similar study, they identified several new drug indications for the existing FDA-approved and investigational drugs using the chemical similarity criterion. They identified 23 new drug-target associations, which were proved experimentally, and five of these drugs were highly potent against the predicted target (Keiser et al., 2009). Recently, a few small-molecule databases containing important information regarding repurposed or withdrawn drugs, their activity, and therapeutic indications have been developed (Brown & Patel, 2017; Shameer et al., 2017). QSAR technique is a popular ligand-based approach in which molecular descriptors play an important role in predicting structure-activity relationships (SARs). The main objective of 3. EXAMPLES AND CASE STUDIES

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QSAR is that molecules with similar structures and/or physicochemical properties have similar biological activities. This technique was used to repurpose drugs with similar structure or physicochemical properties. Yang and Agarwal built robust naive Bayes model to predict indications (repurpose) using side effects as a feature and QSAR models to predict the side effects based on the compound structure. They combined these models with the disease models to predict new drug therapeutic indications (Yang & Agarwal, 2011). Pharmacophore models serve as the standard geometric occupancy of the small molecule bound in the active site of its target so as to inhibit its action. Pharmacophore models can be used to screen the drug databases in order to find similar drugs that fit the model and thereby show similar inhibitory action as the native drug. Crisan et al. developed a pharmacophore model of glycogen synthase kinase-3 inhibitors and performed drug repurposing by screening 1510 approved drugs from DrugBank to find drugs matching the pharmacophoric features of GSK-3 inhibitors. From their study, they identified 30 drugs that were used to treat leukemia, irritable bowel syndrome, nausea, and heart disease, which have the ability to inhibit GSK-3 (Crisan et al., 2017). Several ML techniques were used in the drug-repurposing arena (Bender et al., 2007; GregoriPuigjan
e & Mestres, 2008; Mestres et al., 2006). Different algorithms in ML techniques include Bayesian classifiers, Hidden Markov models, Radial Basis Function, K-means, Support Vector Machines, etc. ML algorithms use approaches based on chemical similarity to build a classification or prediction model. They identify the true relationships and make predictions about ones between the chemical feature and that entity. Several ML methods have been developed that can be successfully employed in a drug-repurposing strategy. Nidhi et al. developed multiplecategory Bayesian models using chemogenomics data to predict the target of small molecules (Nidhi, Davies, & Jenkins, 2006). Another group of researchers have built ML models to test the association between the chemical features of small molecules and gene-expression data (Fernald & Altman, 2013). PredicT-ML is an ML-based tool developed to make predictions about drug-disease associations and it is very effective in a drug-repurposing strategy (Gottlieb et al., 2011). Yamanishi et al. generated the kernel regression method to identify the novel interactions for different drug-target entities. Kernel regression is a supervised learning technique that integrates the chemical data with the target data within a pharmacological space. In this space, the drugs and targets were mapped using the model and the targets that were predicted to be closer than the threshold were identified as new targets (Yamanishi et al., 2008). Dai et al. developed a novel ML method of identifying putative drug-disease connectivities by building a gene-interaction network and also taking information for predicting drug-gene and disease-gene interactions (Dai et al., 2015). van Laarhoven et al. employed the bipartite local models (BLMs), which consider similarity information in the form of kernels (Mei, Kwoh, Yang, Li, & Zheng, 2012; van Laarhoven, Nabuurs, & Marchiori, 2011). This technique has an advantage over the others, in that it can take multiple pieces of information and use them for making drug-target predictions (Sch€ olkopf, Tsuda, & Vert, 2004). Mei et al. built a modified BLM method called the BLM-NII (neighbor-based interaction profile inferring), which predicts the drug-target interactions that are new and have high reliability. The NII methods additionally account for the learning ability of the neighbors for making predictions (Mei et al., 2012). Alaimo et al. developed a network-based interference algorithm that can predict novel drug-target associations based on the drug and target similarities (Alaimo et al., 2016). These varied ligand-based techniques will contribute in the future to effective drug-repurposing studies.

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2.7 Structure-Based Approaches in Drug Repurposing A structure-based approach is a popular technique for use in a drug-repurposing strategy and it includes molecular docking, structure-based pharmacophore modeling, sequence alignment, mapping target structure similarities, etc. A docking tool is basically used in drug repurposing for the virtual screening (VS) of ligands to a single target or to multiple targets, by assessing the mode of binding at the active site regions of the druggable target (Kitchen, Decornez, Furr, & Bajorath, 2004). Several drug-repurposing studies have successfully applied docking techniques in order to identify novel therapeutic indications for the repurposed drug (Dakshanamurthy et al., 2012; Li et al., 2011). Molecular docking identifies the right binding orientation of the small molecule towards the biological target. It is an experimentally validated tool for predicting the preference of the drug’s binding to its target. The structure-based pharmacophore technique uses the information regarding the binding sites, residue types, and its geometry to develop a model that can be used to search chemical databases to identify inhibitors. This in silico strategy has been very useful in drug repurposing. When only the structure of the target is known, the structure-based pharmacophore model development method can be employed in order to identify potential small-molecule inhibitors that have a binding affinity towards the target (Hall, Kozakov, Whitty, & Vajda, 2015). The structure-based pharmacophore model can be searched across FDA-approved databases like DrugBank to identify drugs that have similar chemical groups and geometry as defined by the model. The structure-based repurposing technique takes into account the structural similarity of protein targets. Proteins with similar structure will have the ability to bind to the same drugs, a concept called chemoisosterism ( Jalencas & Mestres, 2013). This concept can be used to develop interconnected networks to link protein and drug-structure similarities (Ehrt, Brinkjost, & Koch, 2016; Haupt, Daminelli, & Schroeder, 2013; Salentin, Haupt, Daminelli, & Schroeder, 2014). Some of the drugs identified to have repurposing capabilities using a structure-based approach are listed in Table 4. Inhester et al. developed a novel tool, PELIKAN, for searching large protein databases using geometrical shape as queries. This in silico strategy can be greatly explored in drug repurposing and polypharmacology studies in order to identify secondary targets of a known drug (Inhester, Bietz, Hilbig, Schmidt, & TABLE 4

List of Drugs Identified Using Structure-based Repurposing Approach

Name of Drug

Native Indication and Target

Repurposed Indication and Target

References

Nilotinib

CML (BCR-ABL)

Rheumatoid arthritis (MAPK14)

(Li et al., 2011)

Chlorhexidine

Anti-infective

Paromomycin

Antibiotic

CML (with wild type BCR-Abl kinase domain)

(Sohraby, Bagheri, Aliyar, & Aryapour, 2017)

Defroxamine

Iron chelating agent

Chlorhexidine

Anti-infective

(Sohraby et al., 2017)

Ritonavir

HIV (Antiviral)

CML (with T315I mutated BCR-Abl kinase domain)

Dasatinib

CML (BCR-Abl)

Breast and prostate cancer (ACK1)

(Phatak & Zhang, 2013)

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Rarey, 2017). Manning et al. catalogued the kinome for the comprehensive analysis of protein phosphorylation and kinase families. Phylogenetic trees were developed using protein sequences showing the relationship between closely and distantly related proteins (Manning, Whyte, Martinez, Hunter, & Sudarsanam, 2002). Proteins belonging to the same family having similar binding sites, are prone to bind to the same drugs. For example, Dasatinib binds and inhibits both Src and Lyn that belong to the same protein family (Lombardo et al., 2004). Pairwise and multiple sequence alignment of protein sequences also identifies similar proteins by identifying the conserved regions of sequence similarity or identity using tools such as BLAST/CLUSTALω software. These sequence alignment tools need to be carefully scrutinized, since globally conserved sequences with difference in the key binding residues can also seriously affect ligand binding and cause drug resistance. Consequently, local similarities play a much important role in conserving the key residues required for binding (Anighoro et al., 2015; Ehrt et al., 2016). Some inhibitors, namely imatinib, nilotinib, and ponatinib, have inhibitory activity towards both BCR-ABL and PDGFR targets. Although these proteins belong to different families, they have more local secondary structural similarities, which in turn helps in the identification of drugs to make critical interactions with these multitargets (Broekman, Giovannetti, & Peters, 2011). 2.7.1 Case Studies Imatinib has been successfully approved for CML through its inhibition of target BCRABL. It was also repurposed for gastrointestinal stromal tumors through its inhibition of target c-kit and the drug molecular mechanisms were identified to be a chemoisosteric type inhibition (Dagher et al., 2002; McKie, 2016; Peng, Lin, Guo, & Huang, 2012). Using structural biology, the mode of binding of imatinib with BCR-ABL and c-kit protein was explored and is presented in Fig. 3. The computational repurposing approach using the high-throughput docking-based VS of the DrugBank database was developed by Li et al. This was to gain an understanding of the existing drug-target interactions and also to determine the novel interactions. Nilotinib, which is an FDA-approved drug for CML, was identified to bind with MAPK14 and show antiinflammatory properties in this study, thereby making it a candidate for repurposing for rheumatoid arthritis disease (Li et al., 2011). A similar VS study was carried out by Sohraby et al. on wild-type and T315I-mutated BCR-ABL protein. Their work identified chlorohexidine, paromomycin, and deferoxamine as having high binding energy towards wild-type BCR-ABL, while chlorohexidine and ritonavir had good inhibitory potential against T315I mutated BCR-ABL (Sohraby et al., 2017). A structure-based 3D bioactive pharmacophore model was developed by Paniker et al. for wild type and mutant EGFR and VS against three chemical databases including FDAapproved drugs was undertaken for NSCLC disease. The pharmacophore model and docking-based VS approach lead to the identification of the ML-167 compound, which is a selective inhibitor of Cdc2-like kinase 4. This compound was repurposed and identified to have inhibitory activity towards EGFR kinase and its mutants in our work, and its molecular mechanism of action is presented in Fig. 4. Thus integrating the structure-based docking technique with other in silico methods, as explained above, will rapidly accelerate drug discovery using the repurposing strategy (March-Vila et al., 2017; Panicker et al., 2017).

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FIG. 3 The concept of chemoisosterism is illustrated where in imatinib (pink sticks) binds and interacts with the active-site residues of both (A) BCR-ABL(in CML), PDB Id: 2HYY; (B) c-kit (in gastrointestinal stromal tumors), PDB Id: 1T46. Figure was reproduced using PyMOL software.

2.8 Multiple In Silico Strategy-Based Approach Different in silico strategies have their own difficulties in predicting associations between drug, disease, and target. Every predictive model for drug repurposing has to be validated experimentally in order to finally make a clinical decision. Integrating different in silico approaches can help in improving the predictions in repurposing drugs. This further depends upon the quality, amount of data, and the type of association required. There are several drug repurposing tools that are available online that integrate the data on gene expression, protein-

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FIG. 4 Structure-based (pharmacophore and docking) repurposing strategy to identify potent drugs against mutant EGFR kinase (L858R and T790M). Pharmacophore model of (A) gefitinib bound to EGFR kinase; (B) ML-167 (repurposed molecule) fitting to (A); (C) binding pose of gefitinib and ML-167 at the active site of EGFR identified using docking studies (Panicker, Melge, Biswas, Keechilat, & Mohan, 2017) (Copyright with permission from Elsevier).

protein interaction, pathways, chemical structure of small molecules, and structure of macromolecules. They make use of the different algorithms to make predictions and score the drug-disease and drug-target associations. There are different tools available for drug repositioning as listed in Table 5.

3. EXAMPLES AND CASE STUDIES

TABLE 5

List of Various Resources and Tools Available for Drug Repositioning

Name

Description

Reference/Website

Drug Repurposing Hub

This information resource contains extensive curated annotations for each drug, including details about commercial sources of all compounds

https://clue.io/repurposing-app (Corsello et al., 2017)

DPDR-CPI

A server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome

https://cpi.bio-x.cn/dpdr/ (Luo et al., 2016)

DRUGSURV

A resource for repositioning approved and experimental drugs in oncology based on the patient survival information; it covers both approved drugs as well as experimental drugs

http://www.bioprofiling.de/GEO/ DRUGSURV/index.html (Amelio et al., 2014)

DIGEP-Pred

In silico prediction of drug-induced geneexpression profiles based on structural formula

http://www.way2drug.com/GE/ (Lagunin, Ivanov, Rudik, Filimonov, & Poroikov, 2013)

DrugMap Central

Provides a comprehensive technical platform for online querying and visualization of multidimensional drug data and information for drug-repositioning studies

http://r2d2drug.org/index.html (Fu et al., 2013)

DRAR-CPI

Server for predicting Drug Repositioning and Adverse Reaction via Chemical-Protein Interactome

https://cpi.bio-x.cn/drar/ (Luo et al., 2011)

MANTRA

Analysis of the mechanism of action (MOA) of novel drugs and the identification of known and approved candidates for “drug repositioning”

http://mantra.tigem.it/ (Iorio et al., 2010)

DrugComboRanker

Predicts drug combinations targeting multiple signaling modules of cancer-specific networks. DrugComboRanker selects combinations targeting the alternative and complementary signaling modules of disease. It can provide insights into MOA of drug combinations by mapping the predicted drug targets on the disease-signaling network

(Huang, Chang, et al., 2014; Huang, Li, et al., 2014)

SynGeNet

Compiles transcriptomics data analyzing disease and drug z-score profiles with network mining algorithms to predict the synergistic drug combinations

(Regan et al., 2017)

PredicT-ML

Predicts drug-disease association using a machine-leaning approach

(Gottlieb et al., 2011)

PELIKAN

Searches for a variety of geometrical queries in large protein-structure collections to identify secondary targets for a drug

http://www.zbh.uni-hamburg.de/ pelikan (Inhester et al., 2017)

gene2drug

Therapeutic target gene—a prioritization of potential effective drugs by assessing their impact on the transcription of genes in the pathway(s) including the target

http://gene2drug.tigem.it (Napolitano et al., 2017)

GeneXpharma

GeneXpharma is designed to predict repurposability of drugs against diseases based on the expression data of druggable gene targets

http://genexpharma.org/ (Turanli, Gulfidan, & Arga, 2017)

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2.8.1 Case Studies M
endez-Lucio et al. performed drug repositioning of olsalazine, an antiinflammatory drug as a DNA hypomethylating agent by integrating different drug repurposing strategies. DNA hypomethylation similar to DNA hypermethylation is most generally associated with cancer. They initially employed a ligand-based repurposing strategy by performing a chemical similarity search against DrugBank using a known hypomethylating agent, NSC14778. Olsalazine showed a better structural similarity value w.r.t NSC14778. Since NSC14778 is a DNMT1 inhibitor, olsalazine, due to its structural similarity should act as a DNMT1 inhibitor. The authors employed a structure-based repositioning approach by performing a docking study using AutoDock software in order to understand the molecular mechanism of action of the repurposed drug olsalazine with DNMT1 and DNMT3b enzymes. Olsalazine was found to bind in a similar manner to NSC14778, which is a known inhibitor of DNMT1. The binding sites and the binding scores of both these small molecules (NSC14778 and olsalazine) were similar. Docking analysis revealed that the important hydrogen-bonding interactions between the drug and the key residues required for its biological activity could be the reason for its inhibition. This in silico prediction was validated using HeLa cells and identified gene-expression patterns of GFP-tagged DNMT1 in the presence of olsalazine. Promisingly this drug showed decreased expression of DNMT1 and a hypomethylation pattern, thereby supporting the in silico drug-repurposing prediction (M
endez-Lucio et al., 2014). Another structure-based study by Dakshanamurthy et al. employed the proteochemometric method called “train, match, fit, streamline” (TMFS) pipeline, wherein the ligand- and structure-based repurposing strategies were combined to discover new drug-target associations. The TMFS technique involves the descriptors associated with the shape and topology of proteins and ligands, chemical features, docking score, and key interactions for target contact, in order to identify novel drug-target interactions. These multiple in silico studies identified an antiparasitic drug, mebendazole, to have inhibitory potential towards vascular endothelial growth factor receptor 2 (VEGFR2), which is involved in angiogenesis. The antiinflammatory agent celecoxib binds to cadherin-11 target and it is approved for rheumatoid arthritis. This drug was repurposed for cancers with poor prognosis where there were no targeted therapies (Dakshanamurthy et al., 2012). A three modal in silico approach was adopted by Sharangdhar et al. to identify the ACK1 inhibitors, since ACK1 is seen to be overexpressed in breast and prostate cancers. They employed network, structure, and ligand-based approaches to identify repurposed drugs against ACK1 target. Initially, a bipartite graph was developed based on the data available in DrugBank, Protein Data Bank (PDB), and Protein Knowledge Base (UniProt) databases. Next a VS was performed of small molecules from DrugBank in addition to chemical similarity of ACK1 inhibitor (ligand-based) and genome-based similarity of ACK1 proteins and their drugs using the bipartite graph to identify small molecules against ACK1. Finally, a selection of drugs based on the commonness in all the three prongs was computed. In this study, dasatinib was identified as a repurposed drug against ACK1 protein. Further, experimental analysis of testing dasatinib activity of ACK1 inhibition revealed potent anticancer activity, suggesting a successful validation of the in silico strategy (Phatak & Zhang, 2013).

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3 VALIDATION TECHNIQUES FOR IN SILICO REPURPOSING STUDY The degree of in silico predictability was assessed using certain statistical metrices in order to verify the algorithm used. Some of the common metrics used to evaluate the predictive power include area under the ROC curve (AUC), specificity, sensitivity, and positive predictive value (Alaimo et al., 2016; Chen et al., 2015; Yamanishi et al., 2008). Other metrics include the precision-recall curve, which calculates the ratio of true positives in all positive predictions and the area under precision recall curve (AUPR). This judges the separation between the predicted scores of true interactions and false-positive interactions (Mei et al., 2012; van Laarhoven et al., 2011). In ML techniques, cross-validation is performed using a test set with the known values. Once a new relationship is predicted it has to also be validated by referring to the existing literature searching for any association regarding the prediction obtained (Chen et al., 2015). Zhao et al. predicted that antiasthma drug pranlukastto would have antimetastatic activity by developing a module comCIPHER using the Bayesian partition method. They validated their study by comparing the ROC of drug-gene-disease association using two other types of prediction modules. The ROC of comCIPHER was comparatively higher than the other two methods in different complexity levels, depicting the significance of the superiority of comCIPHER in making drug-gene-disease associations. (Zhao & Li, 2012). The tripartite database named PharmDB, which integrates information about disease, drugs, and targets and also predicts their associations, was developed by Lee et al. (2016). A shared neighborhood scoring (SNS) algorithm was developed by them in order to predict and score disease-drug-target associations. The performance of SNS was validated by plotting the ROC curves for various predicted associations. The ROC curve values were above 0.85 in drug-protein, drug-disease, and protein-disease relations. Using PharmDB and SNS, they identified benzthiazide, which is approved for hypertension, as a repurposed inhibitor of lung cancer progression by showing promising experimental activity (Lee et al., 2012). Sirota et al. developed a systematic computational approach using information regarding the gene-expression signatures from 100 diseases and 164 drugs, in order to establish novel drug-disease associations. From this approach, they identified the antiulcer drug cimetidine as having potent activity against lung adenocarcinoma. This result was validated both in vitro and in vivo, wherein the drug (cimetidine) demonstrated anticancerous activity (Sirota et al., 2011). Thus to make any conclusive decisions from an in silico, drug repurposing-based drugdiscovery program experimental validation studies are essential.

4 SUMMARY AND FUTURE PROSPECTIVES The drug-repurposing strategy aims at establishing a new relationship between the drugs and diseases. Several studies have successfully proven the importance of drug repurposing in identifying novel indications for existing drugs. Multiple in silico strategies has an advantage over individual strategies, especially when dealing with enormous amounts of data. Combining the massive amounts of this big data, connecting them, and developing meaningful and

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novel associations are a challenging concept in drug repurposing. In spite of the advancements in different in silico techniques, some disjointed data still exists, which has to be combined so that useful information can be extracted. Another major difficulty is the development of the complex algorithms and their optimization for big-data analysis. A small dataset might not result in a model predicting a correct association. Moreover, prediction varies based on the dataset used to generate the local model. Thus to overcome such instabilities robust statistical validations need to be performed. Another challenge in drug repurposing is the existence of prodrugs whose molecular mechanisms of action are more complex and dynamic within the system. Such drugs need to be carefully scrutinized, i.e., their metabolite form has to be considered while performing the repurposing studies. The pharmacokinetics of drug metabolites also can vary from patient to patient due to the polymorphic nature of the metabolizing enzymes. Further, the development of algorithms and generation of high-quality experimental/theoretical data are required to establish a better association between the drug, disease, and target. This indeed will provide a huge impetus to this novel field to set up better indications for the existing drugs.

Acknowledgments We acknowledge gratefully Centre for Nanosciences and Molecular Medicine, Amrita Institute of Medical Sciences and Research Centre, Amrita Vishwa Vidyapeetham, Kochi for the infrastructure support. ARM is thankful to CSIRNew Delhi (09/963(0042)2K18 EMR-I) for awarding senior research fellowship. CGM is thankful to Department of Biotechnology-New Delhi (BT/PR5711/BID/7/406/2012) for funding towards Computational & Bioinformatics lab and ICMR-New Delhi for awarding Senior Biomedical International Research Fellowship (INDO/FRC/452(S-37)/ 2016-17-IHD).

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Turanli, B., Gulfidan, G., & Arga, K. Y. (2017). Transcriptomic-guided drug repositioning supported by a new bioinformatics search tool: geneXpharma. Omics: A Journal of Integrative Biology, 21(10), 584–591. https://doi.org/10. 1089/omi.2017.0127. van Laarhoven, T., Nabuurs, S. B., & Marchiori, E. (2011). Gaussian interaction profile kernels for predicting drugtarget interaction. Bioinformatics, 27(21), 3036–3043. https://doi.org/10.1093/bioinformatics/btr500. Vitali, F., Cohen, L. D., Demartini, A., Amato, A., Eterno, V., Zambelli, A., & Bellazzi, R. (2016). A network-based data integration approach to support drug repurposing and multi-target therapies in triple negative breast cancer. PLoS One, 11(9). https://doi.org/10.1371/journal.pone.0162407. Wishart, D. S., Knox, C., Guo, A. C., Shrivastava, S., Hassanali, M., Stothard, P., … Woolsey, J. (2006). DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Research, 34, D668–D672. https://doi.org/10.1093/nar/gkj067. Witzig, T. E., Vukov, A. M., Habermann, T. M., Geyer, S., Kurtin, P. J., Friedenberg, W. R., … Fitch, T. R. (2005). Rituximab therapy for patients with newly diagnosed, advanced-stage, follicular grade I non-Hodgkin’s lymphoma: a phase II trial in the North Central Cancer Treatment Group. Journal of Clinical Oncology, 23(6), 1103–1108. https://doi.org/10.1200/JCO.2005.12.052. Wu, H., Huang, J., Zhong, Y., & Huang, Q. (2017). DrugSig: a resource for computational drug repositioning utilizing gene expression signatures. PLoS One, 12(5). https://doi.org/10.1371/journal.pone.0177743. Wu, L., Ai, N., Liu, Y., Wang, Y., & Fan, X. (2013). Relating anatomical therapeutic indications by the ensemble similarity of drug sets. Journal of Chemical Information and Modeling, 53(8), 2154–2160. https://doi.org/10.1021/ ci400155x. Xia, G., Zhou, C. Y., Qiang, L., Ju, G., Zhu, Y. P., Ling, G., & Gui, Z. (2013). 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suppression and up-regulation and activation of glucocorticoid receptor. Biomedical and Environmental Sciences, 26 (5), 371–381. https://dx.doi.org/10.3967/0895-3988.2013.05.006. Xu, Q., & Dunbrack, R. L. (2010). The protein common interface database (ProtCID)—a comprehensive database of interactions of homologous proteins in multiple crystal forms. Nucleic Acids Research, 39, D761–D770. https://doi. org/10.1093/nar/gkq1059. Yamanishi, Y., Araki, M., Gutteridge, A., Honda, W., & Kanehisa, M. (2008). Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics, 24(13), i232–i240. https://doi.org/10. 1093/bioinformatics/btn162. Yang, H., Qin, C., Li, Y. H., Tao, L., Zhou, J., Yu, C. Y., … Chen, Y. Z. (2015). Therapeutic target database update 2016: enriched resource for bench to clinical drug target and targeted pathway information. Nucleic Acids Research, 44 (D1), D1069–D1074. https://doi.org/10.1093/nar/gkv1230. Yang, L., & Agarwal, P. (2011). Systematic drug repositioning based on clinical side-effects. PLoS One, 6(12). https:// doi.org/10.1371/journal.pone.0028025. Yao, J. C., Shah, M. H., Ito, T., Bohas, C. L., Wolin, E. M., Van Cutsem, E., … De Vries, E. G. (2011). Everolimus for advanced pancreatic neuroendocrine tumors. New England Journal of Medicine, 364(6), 514–523. https://doi. org/10.1056/NEJMoa1009290. Zeng, H., Qiu, C., & Cui, Q. (2015). Drug-Path: a database for drug-induced pathways. Database, 2015. https://doi. org/10.1093/database/bav061. Zhang, S. -D., & Gant, T. W. (2009). sscMap: an extensible Java application for connecting small-molecule drugs using gene-expression signatures. BMC Bioinformatics, 10(1). https://doi.org/10.1186/1471-2105-10-236. Zhang, W., Yue, X., Huang, F., Liu, R., Chen, Y., & Ruan, C. (2018). Predicting drug-disease associations and their therapeutic function based on the drug-disease association bipartite network. Methods, 145, 55–59. https://doi. org/10.1016/j.ymeth.2018.06.001. Zhao, H., Jin, G., Cui, K., Ren, D., Liu, T., Chen, P., … Rodriguez, A. (2013). Novel modeling of cancer cell signaling pathways enables systematic drug repositioning for distinct breast cancer metastases. Cancer Research, 73(20), 6149–6163. https://doi.org/10.1158/0008-5472.CAN-12-4617. Zhao, S., & Li, S. (2012). A co-module approach for elucidating drug–disease associations and revealing their molecular basis. Bioinformatics, 28(7), 955–961. https://doi.org/10.1093/bioinformatics/bts057. Zhou, S., Wang, F., Hsieh, T. -C., Wu, J., & Wu, E. (2013). Thalidomide–a notorious sedative to a wonder anticancer drug. Current Medicinal Chemistry, 20(33), 4102–4108. https://doi.org/10.2174/09298673113209990198.

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C H A P T E R

20 Drug Repurposing by Connectivity Mapping and Structural Modeling Jameel Iqbal*,†,a, Tony Yuen*,a, Neeha Zaidi‡, Se-Min Kim*, Samir Zaidi§, Alberta Zallone*, Mone Zaidi*, Li Sun*,b, Shozeb Haider¶,b *

Department of Medicine, Mount Sinai School of Medicine, New York, NY, United States Department of Pathology, James J. Peters VA Medical Center, New York, NY, United States ‡ Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD, United States § Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States ¶School of Pharmacy, University College, London, United Kingdom †

1 INTRODUCTION Osteoporosis affects 200 million people worldwide, who suffer from 2.4 million fractures each year (International Osteoporosis Foundation, 2011). Bisphosphonates are the gold standard for osteoporosis therapy with established efficacy in reducing fractures and attenuating bone loss, primarily by inhibiting bone resorption (Pazianas, Epstein, & Zaidi, 2009). For the same reason, they are utilized in cancer-induced bone disease, as well as the rare skeletal disease, such as Paget’s bone disease and osteogenesis imperfecta (Tanimoto, Matayoshi, Yagasaki, Takeuchi, & Kami, 2011; Ward et al., 2011). Low absorption rates and issues with patient compliance make oral bisphosphonates less desirable in actual clinical practice (Liberman, 2006). In fact, the therapeutic armamentarium for preventing bone loss pales in comparison to available treatment options for equally profound public health hazards, such as cancer, diabetes, and hypertension. a

Jameel Iqbal and Tony Yuen are joint first authors.

b

Li Sun and Shozeb Haider are joint senior authors.

In Silico Drug Design. https://doi.org/10.1016/B978-0-12-816125-8.00020-1

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Commonalities in signaling mechanisms between antiosteoporosis and anticancer drugs have long been long suspected from in vitro studies. For example, c-src and Akt have established functions both in oncogenesis and bone resorption (Sun, Iqbal, Singh, Sun, & Zaidi, 2010). There is growing evidence that bisphosphonates, prominently zoledronic acid, inhibit tumor growth and metastasis in addition to their potent inhibitory effects on resorption (Sun et al., 2010). We recently showed that bisphosphonates inhibit nonsmall cell lung cancer and breast cancer growth by interacting directly with the kinase domain of the human epidermal growth factor receptor (EGFR) (Stachnik et al., 2014; Yuen et al., 2014). Conversely, certain anticancer drugs have antiresorptive properties, and hence could be utilized to treat cancer as well as accompanying bone disease. Attempts at drug discovery have traditionally utilized high-throughput screening of small molecule libraries. In contrast, the C-MAP (www.broad.mit.edu/cmap) is a rapid in silico method of studying connections between drug actions, genetic perturbations, and disease states (Lamb et al., 2006). Superseding gene profiling that was used to elucidate biological pathways (DeRisi, Iyer, & Brown, 1997) reveal cryptic disease subtypes (Golub et al., 1999), and predict cancer prognosis (Pomeroy et al., 2002); this approach employs a drug or disease gene signature to query the C-MAP database comprising gene expression profiles derived from human cells (Lamb et al., 2006). Nonparametric, rank-based pattern-matching using the Kolmogorov-Smirnov (KS) statistic examines for shared mechanisms of action to reveal both mimics and antimimics of a given compound or disease (Lamb et al., 2006). These compounds can subsequently be tested for biological activity, potentially as lead molecules.

2 GENOMIC C-MAPPING OF THE BISPHOSPHONATE GENE SIGNATURE Genomic C-MAPping provides us with a purposeful strategy for the rapid identification of new osteoprotective actions of anticancer agents, and anticancer actions of bisphosphonates. To uncover novel compounds with antiosteoclastic activity, we interrogated C-MAP with a gene signature developed from cultured human osteoclasts exposed to the two most commonly used oral bisphosphonates, alendronate and risedronate (Pazianas et al., 2009). We then demonstrated that a selection of the hits, including a PARP inhibitor, 1,5-isoquinolinediol, and a firstgeneration EGFR tyrosine kinase inhibitor tyrphostin AG-1478, inhibited osteoclastogenesis in vitro. In silico computational modeling showed that bisphosphonates bind directly to the EGFR by docking into its kinase domain. In vitro, the drugs attenuated the viability of nonsmall cell lung cancer (NSCLC) cells either overexpressing wild type EGFR (H1666) or those driven by an activating EGFR4E746-A750 mutation (HCC827). C-MAP was developed at the Broad Institute at MIT (www.broad.mit.edu/cmap) (Lamb et al., 2006; Lamb, 2007) as a compendium of genome-wide gene expression data obtained from cultured human cells treated with a range of small molecules. It allows for the assessment of functional connections between drugs, genes, and diseases. The strategy relies on the initial validation of a drug or disease gene signature (query signature), which is then used to interrogate C-MAP. Drug-drug or drug-disease matching is performed by a nonparametric, rank-based algorithm using the Kolmogorov-Smirnov (KS) statistic. Gene set enrichment

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analysis (GSEA) examines shared mechanisms of action to reveal both mimics and antimimics of a given compound or disease. The interrogation yields connectivity scores (C score), which are composed from the set of instances (relative value between +1 and 1); it represents the relative strength of a given signature in an instance from the total set of instances calculated. A high positive C score indicates that the perturbing agent induced the expression of the query signature, and vice versa. A zero or null C score indicates that the agent had no self-consistent effect upon expression of the query signature. The KS statistic is computed for the set of t instances in the list of all n instances in a result ordered in descending order of C score, giving an enrichment score ks0. Null P values are assigned to instances where t ¼ 1, where the mean of the C scores for the set of t instances is zero, or where the nonnull percentage for the set of t instances is 99%), the high maximum doses used for some of the previous indications (up to 4 g/day for the treatment of inflammatory bowel disease) may compensate its low trypanocidal potency and high plasma protein binding. However, it should be remembered that sulfasalazine behaves as a prodrug that is converted in vivo to sulfapyridine and

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5-aminosalicylic acid; accordingly, which chemical species is responsible for the observed in vivo activity must be studied. The authors emphasized that the simultaneous trypanocidal and antiinflammatory properties of sulfasalazine could be beneficial to treat not only the infection but the complications of Chagas disease, and they note that the drug has been previously applied to treat inflammatory cardiomyopathy in ankylosing spondylitis.

2.2 The Similarity Ensemble Approach The similarity ensemble approach (SEA) is a ligand-centric approach that quantitatively groups and relates proteins based on the set-wise molecular similarity among their ligands (Keiser et al., 2007). The underlying notion behind SEA is that with some valuable exceptions (the activity cliffs, see Cruz-Monteagudo et al., 2014) similar molecules are likely to bind the same group of proteins even if these targets appear unrelated by some bioinformatics metrics. To develop their approach, the authors compared the ligand sets of 246 targets from the MDDR database, consisting of more than 65K unique ligands. Inspired by BLAST theory, they implemented a statistical model of the similarity that would be expected at random for sets drawn from the same database of ligands, which allowed comparing sets without size or chemical composition bias. All pairs of ligands between any two sets were compared by a pair-wise similarity metric, which in the initial application consisted of calculating the Tanimoto coefficient using Daylight fingerprints of 2–7 length (other fingerprinting systems were later implemented). For set comparisons, pair-wise similarity coefficients between elements across sets were calculated, and those above a threshold (0.57) were summed, giving a raw score for those two sets. The threshold was chosen so that the resulting statistics best fit an extreme value distribution. From such scores, Z-scores and expectation values were computed, in a similar way to those used for BLAST protein or gene comparisons. The authors emphasized some aspects of the SEA approach: for each ligand set, the approach retrieves only a few others; many targets related to their ligand sets’ chemical similarities would be missed if the linking criterion was chemical identity, and no biological information (e.g., sequence similarity) was used to make the connections. Further analysis of their results revealed that many targets displayed sequence similarity with no ligand similarity (which would result in false positives if sequence comparison was used to repurpose drugs), whereas others showed high-ligand similarity but weak sequence similarity (which would result in false negatives). In other words, an important degree of “disagreement” was observed between cheminformatic and bioinformatic techniques. As will be discussed in Section 3, this probably arose from the bioinformatic techniques used to establish similarities between targets at that time. Remarkably, sequence similarity depends on evolutionary history: two proteins that evolved from a common ancestor through speciation may retain commonalities between their amino-acid sequences and, at the same time, present different ligand specificities. In contrast, cheminformatic similarity could have other origins, such as binding site similarity and the state of the art of medicinal chemistry. Interestingly, the authors also performed a VS campaign, using 12K PubChem compounds as individual queries, retrieving 30 hits with very low expectation values against pharmacological categories genuinely unrelated to those already annotated in MDDR. Two of these predictions were validated experimentally.

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A very similar approach (the indication similarity ensemble approach, or iSEA) was later developed by Wu et al., who chose to relate therapeutic indications instead of relating molecular targets by their drug set similarities (Wu, Ai, Liu, Wang, & Fan, 2013). Other implementations of the SEA model have been realized, including those that integrate the Z-score by Bayesian networks and multivariate kernel approach to make predictions (Zheng et al., 2015) or those that try other molecular descriptors to establish the molecular similarity (Wang et al., 2016). Unfortunately, we have not been able to find examples of SEA application in the field of NTDs, even when the basic approach is freely available online at http://sea.bkslab.org (accessed 29 April 2018).

2.3 Molecular Topology and Promiscuity Determinants as Predictors of Drug Repurposing Drifting away from the “one target, one drug” paradigm that dominated the drug discovery arena by the end of the 20th century, the drug discovery community later expressed a renewed interest in promiscuous (or polyspecific) drugs (Chaudhari et al., 2017; Reddy & Zhang, 2013). There are many reasons behind this change of paradigm. Systems pharmacology/polypharmacology may provide better therapeutic opportunities for complex disorders and, moreover, it has been observed that the universe of approved drugs is enriched in promiscuous scaffolds in comparison to (nonapproved) bioactive compounds (Hu & Bajorath, 2010). The prediction of promiscuity may also help anticipating undesired off-target interactions. At last, the off-target interactions of a drug may be exploited for drug repurposing ends, expanding its indications to unforeseen areas of medicine (off-target, highly innovative drug repurposing). Consequently, a lot of attention has been paid to the identification of molecular determinants of promiscuity or polypharmacology. Establishing such determinants would help in anticipating promiscuity or, its counterpart, selectivity, at the early stages of drug discovery. One or the other may be preferred depending on the scenario (promiscuous chemotypes and scaffolds may be preferred if searching for a therapeutic agent for complex disorders or for repurposing opportunities, whereas they would be disregarded if looking for highly selective agents). Furthermore, polyspecific antiinfective agents are believed to be less prone to elicit drug resistance issues. Molecular size (Cases & Mestres, 2009; Hu et al., 2014; Sturm et al., 2012), lipophilicity (Lesson & Springthorpe, 2007; Yang et al., 2010), acid base behavior (Lesson & Springthorpe, 2007), and molecular complexity (Sturm et al., 2012) are some of the molecular properties that seem to have an impact in promiscuity. Yang et al. provided one early piece of evidence on the correlation between promiscuity and molecular topology of small molecules (Yang et al., 2010). Using bioactivity data of more than 2000 compounds from the BioPrint database tested against a panel of more than 200 diverse protein targets, they demonstrated a direct correlation between promiscuity and the fraction of molecular framework (fMF), valid for fMF above 0.65 (fMF being defined as the number of nonhydrogen atoms in the molecular framework, a.k.a. Bermis-Murcko scaffolds, divided by the total number of heavy atoms in the molecule (Bermis and Murcko, 1996)). The fact that for high fMF a direct correlation between promiscuity and fMF is observed suggests that, in that fMF range, side chains contribute to selectivity. 3. EXAMPLES AND CASE STUDIES

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Furthermore, Yang and coworkers distinguished four different topological classes according to the number of terminal ring systems in a molecule, and the presence or absence of chemical bridges between them. The “one terminal ring system” (1TR) class comprises molecules with only one ring system. The “two terminal ring systems” (2TR) comprise molecules with two ring systems directly linked to each other. The 2TR + B class consists of molecules with two terminal ring systems connected through a molecular bridge. Finally, the 3TR + B class covers molecules with three terminal ring systems and a molecular bridge. Promiscuity showed an uptrend from simpler (1TR) to more complex (2TR + B) topologies; however, the topology class did not impact on promiscuity for compounds with logP values ranging from 1 to 3. The previous observation suggests that promiscuity cannot be explained univocally by a single molecular determinant. On the contrary, it is the result of a complex interplay between several molecular properties related to promiscuity. Therefore, the impact of promiscuity determinants should be considered in a comprehensive manner. Another significant contribution on the association between molecular topology and promiscuity may be found in the work by Hu and Bajorath (2010). Using data on bioactive compounds retrieved from ChEMBL and BindingDB, the authors demonstrated that 33 chemotypes with distinct topology (i.e., particular molecular frameworks) (Fig. 2) comprehend all the molecules active against more than three targets. They also observed that specific scaffolds seemed to be related to particular sets of target families. Based on the previous data, Morales et al. recently resorted to descriptive statistics to explore the distribution of molecular topology descriptors and other promiscuity predictors across different therapeutic categories (Morales et al., 2018). They applied different clustering algorithms to group 770 compounds distributed across 34 different therapeutic categories. Interestingly, a 3-cluster clustering scheme based on molecular descriptors linked to promiscuity was able to explain up to 82.9% of approved cases of drug repurposing listed by repoDB (Brown & Patel, 2017), excluding trivial cases. The authors concluded that therapeutic categories seem to display characteristic (and mechanism-independent!) molecular patterns, which could be used to unveil drug repurposing opportunities across specific therapeutic categories.

3 BIOINFORMATICS AND NTD-ORIENTED DRUG REPURPOSING One of the fundamental principles underlying computer-guided drug repositioning is that health conditions linked to similar dysfunctional proteins/pathways may be treated with the same drugs (disease-centric approach). Bioinformatic tools, from sequence alignment to domain similarity applications, may be suitable to expose unknown protein-protein similarities and, consequently, repurposing opportunities. The reader should bear in mind that experts in a given disease are obviously familiarized with target proteins linked to their specific subject, but they may ignore that other diseases are linked to closely related targets, therefore missing repurposing opportunities. What is more, as previously discussed when presenting the SEA approach (Section 2.2), proteins with similar sequences often have completely different ligand sets, while scarcely related proteins may have similar or shared ligands.

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FIG. 2 33 Promiscuous chemotypes identified by Hu and Bajorath. These authors have shown that certain scaffolds tend to interact with particular sets of targets, which make their research particularly interesting to conceive or detect polypharmacology cases. (The figure has been generated by the authors of this chapter, after the results presented in Hu, Y., & Bajorath, J. (2010). Polypharmacology directed compound data mining: identification of promiscuous chemotypes with different activity profiles and comparison to approved drugs. Journal of Chemical Information and Modeling, 50, 2112–2118.)

Interrogations connected to distant similarities between proteins with no obvious evolutionary relationships or not even a similar fold or function are more challenging, but also present less obvious repositioning opportunities. In that regard, the identification of binding site similarities has lately produced much interest as a mean to spot repositioning prospects (Ehrt et al., 2016; Haupt et al., 2013;

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Haupt & Schroeder, 2011; Salentin et al., 2014). It is interesting to note that different studies indicate that the binding of similar ligands cannot be deduced from fold but from local similarities (Barelier et al., 2015; Haupt et al., 2013). Haupt et al. identified 164 promiscuous drugs, each binding to three or more nonredundant targets. The heatmaps in Fig. 3 display the sequence similarity, structure similarity, and binding site similarity between pairs of targets of three representative highly promiscuous drugs (acarbose, methotrexate, and quercetin). Each drug has over 15 distinct targets with weak average pairwise sequence similarities (10%–18%). Note that the similarity across the targets is best reflected by binding site similarities and, in some cases, by tertiary structure. The primary structure does not predict practically any relationship between the targets, while the structural similarity highlights clusters of similar targets but misses interconnections among the clusters. One should have in mind that while similar binding sites often bind the same or similar ligands, the converse does not hold: a single ligand can bind to very different binding sites (Ehrt et al., 2016). Therefore, binding site comparison only covers a part of the possible drug repositioning prospects and it is advised to use bioinformatic and cheminformatic approximations complementarily. The TDR Targets Database (http://tdrtargets.org) can be mentioned as a particularly valuable online resource to facilitate the rapid identification and prioritization of molecular targets for drug development, focusing on pathogens responsible for neglected human diseases (Magarin˜os et al., 2012). The database integrates pathogen-specific genomic information and functional data (e.g., phylogeny, essentiality) for genes collected from various sources, including literature curation, thus making it a priceless database to guide both de novo drug discovery and drug repurposing initiatives. Other public resources with a focus on more specific taxonomic ranks are also available, such as WormBase (which delivers knowledge on Caenorhabditis elegans and other nematodes and parasitic flatworms, see Lee et al., 2018) or TriTrypDB (which focuses on trypanosomatids, see Aslett et al., 2010). The current examples of drug repurposing guided by purely bioinformatic tools are still meager within the realm of NTDs. An interesting example can be found in the study by Neves et al. (2015) to repurpose drugs against schistosomiasis. These authors compiled 2076 genes from Schistosoma mansoni that are differentially expressed among the 24-hour schistosomula vs. adult life stages, plus 38 genes from the TDR Targets database. The resulting list of S. mansoni putative targets was used to interrogate the public online resources Therapeutic Target Database (Chen et al., 2002), DrugBank, and STITCH (Kuhn et al., 2008) for targets of known drugs. The search strategy for DrugBank and TTD was based on the principle of homology, selecting those matches with E-values 1020. For its part, STITCH integrates data from the literature and various databases of biological pathways, drug-target relationships, and binding affinities, and matches are ranked through a score ranging from 0 to 1. In this case, a cutoff score value of 0.8 was selected. The authors filtered out those S. mansoni proteins with less than 80% overlap with the corresponding drug target. Subsequently, they compared the functional regions among the approved drug targets and the S. mansoni targets to estimate the conservation of active sites between the proteins and the preservation of affinity for the predicted schistosomicidal drugs. The degree of conservation of the amino acids within the active site of each approved drug target was estimated using 150 homologues from other organisms. A multiple sequence alignment was constructed, and position-specific conservation scores were computed using a

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FIG. 3 Haupt and collaborators have explored different approaches to assess target similarity (sequence similarity, structure similarity, binding site similarity) and reveal possible repurposing opportunities. Binding site similarity is more likely to predict distant (off-target) repurposing prospects. The heatmaps display the results for three promiscuous drugs: (A) methotrexate; (B) acarbose; (C) quercetin. (Reproduced from Haupt, V. J., Daminelli, S., & Schroeder, M. (2013). Drug promiscuity in PDB: protein binding site similarity is key. PLoS One, 8, e65894, under Creative Commons license. # Haupt et al. 2013.)

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Bayesian method. The results were classified as functional residues with high (80%) or moderate conservation (60%–79%). When the conservation between functional residues was less than 59%, the predicted targets were excluded from further analyses. Finally, the resulting hits were submitted to principal components analysis to map them in the chemical space, and 115 predicted hits were retained. A flowchart summarizing the whole study is presented in Fig. 4. A conceptually similar (though simpler) study to repurpose drugs for Chagas disease was executed by Rodrigues et al., who explored TriTrypDB to list 65 protein sequences related to cellular transport; each of those sequences was used as query to find homolog proteins with known ligands in DrugBank and the Therapeutic Target database, leading to three hit targets (Rodrigues et al., 2015). Unfortunately, while several potential repurposing opportunities were found in the two later studies, in no cases, to our best knowledge, were experimental validations of the predictions performed.

FIG. 4 Flowchart illustrating the in silico study by Neves et al. (2015), to identify repurposing opportunities against schistosomiasis. (Reproduced from Neves, B.J., Braga, R.C., Bezerra, J.C., Cravo, P.V., & Andrade, C.H. (2015). In silico repositioning-chemogenomics strategy identifies new drugs with potential activity against multiple life stages of Schistosoma mansoni. PLoS Neglected Tropical Diseases, 9, e3435, under Creative Commons license. # Neves et al. 2015.)

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4 HIGH-THROUGHPUT LITERATURE ANALYSIS The fast growth of biomedical knowledge implies the existence of hidden meaningful associations between biomedical concepts; the probability that such associations stay ignored is substantially enhanced by the increasingly fragmented nature of knowledge that results from specialization. Even for a specialist, it is difficult to stay up to date with all the pertinent literature on a defined subject (Jensen, Saric, & Bork, 2006). The challenge intensifies under the perspective of drug repurposing, which intrinsically requires from the researcher to stretch out to other fields of knowledge (e.g., the one related to the original therapeutic indication of the repurposed drug). Therefore, automated text mining methods are more and more needed to screen large volumes of information to find implicit associations. Co-occurrence methods are the simplest approaches to bridge biomedical terms of interest. Tacit connections between terms that do not co-occur are established by finding one or more linking terms that occur independently with each of them. This basic idea has later been expanded and refined. For instance, predication-based semantic indexing has been used to find sequences of relationships termed discovery patterns, e.g., “drug x INHIBITS substance y, substance y CAUSES disease z”. Such predications are extracted from the biomedical literature by applying natural language processing. Although we have failed to find examples of drug repurposing based on text mining in the field of NTDs, we have been able to find an interesting one from the close field of rare diseases (which are also characterized by a relative lack of investment, but for different reasons than NTDs). Gramatica et al. resorted to literature mining to repurpose active peptides and small molecules against sarcoidosis and Creutzfeldt-Jakob disease (Gramatica et al., 2014). They used a double-layer methodology combining computational linguistics and graph theory. More than 3 million abstracts from PubMed-published biomedical papers were screened and relevant concepts were detected by dictionary-defined expressions (each term was enriched by synonyms and acronyms and subjected to disambiguation). The concepts were later organized as nodes of a graph (see Fig. 5 for an outline of the process), whose links are defined by co-occurrence. Specifically, the pathophysiological connections between peptides and diseases have been detected to provide inferences for biomedical rationales for drug repurposing. A measure for the likeliness of the paths between the nodes of the resulting graph was proposed based on a stochastic process. Such measure depends on the abundance of indirect paths between a drug (e.g., a peptide) and a disease, rather than solely on the strength of the shortest path connecting them, an idea inherited from Swamson’s ABC model (Swanson, 1988). The biochemical entities in the graph are connected along paths that mimic a chain of reasoning and lead to prospect inferences about the mechanism of action of a chemical substance in the pathophysiology of a disease. For instance, a number of connections between some bioactive peptides (vasoactive intestinal peptide—VIP, α-melanocyte stimulating hormone—α-MSH, and C-type natriuretic peptide—CNP) and sarcoidosis were obtained through rationales of the form:

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FIG. 5 Graph-building process used by Gramatica and colleagues to build a network of biomedical concepts oriented to drug repurposing. (A) The screened abstracts are split into their constituent sentences and each of them is scanned to identify expressions registered on a dictionary of relevant terms. (B) The concepts co-occurring in a sentence are linked pairwise. The weight of an edge is increased if more instances of the same co-occurrence are registered. (C) The sentence graphs are merged in such a way that each concept appears only once in the graph, as a unique node. (D) The result of the merging is a new graph where the weight of the link is associated with the frequency of the same co-occurrence. (Reproduced from Gramatica, R., Di Matteo, T., Giorgetti, S., Barbiani, M., Bevec, D., & Aste, T. (2014) Graph theory enables drug repurposing—how a mathematical model can drive the discovery of hidden mechanisms of action. PLoS One, 9, e84912, under Creative Commons license. # Gramatica et al. 2015.)

VIP—VIPR1—INFLAMMATION—SARCOIDOSIS α-MSH—HGFR—INFECTION—SARCOIDOSIS CNP—NPRB—GUANYLIN_CYCLASE—INFLAMMATION—SARCOIDOSIS

The results obtained in this manner served to confirm previous clinical trials and they also set the basis to start a new clinical ex-vivo trial. The authors also identified the potential application of imatinib as a treatment option for Creutzfeldt-Jakob disease, showing that their system can provide rationales for the repurposing of small molecules. 3. EXAMPLES AND CASE STUDIES

6 CONCLUSION

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5 NETWORK ANALYSIS In the omics era, integration of large amounts and different types of data is the key concepts underlying network analysis. Networks deal with complexity by relying on simplified representations (graphs) like the ones employed by Gramatica et al. in the example from Section 4. In a graph, entities are represented as nodes while relationships between nodes are shown as edges (Vidal et al., 2011). Such simplification allows visualizing the system with a holistic perspective, extracting useful associations from the topology of the network (e.g., modularity, “party” and “date” hubs, etc.). Current networks are mostly multimodal ones, i.e., they include different types of concepts or elements (for example, proteins, genes, drugs, diseases). What is more, the edges can be established using experimental data (e.g., the value of an affinity constant could be used to link a drug to a protein) and/or predicted data (e.g., an association between two drugs might result from a molecular similarity measure, a link between a protein and a drug could be inferred from text mining). Edges may be weighted to reflect different strengths or degrees of reliability of the correspondent associations; eventually, they could even encode some sort of dynamic link (e.g., semantic edges) (Chen et al., 2012). Basically, all the approaches to drug repurposing described in Sections 2–4, in combination with experimental data, can be used to build networks. At present, many public resources provide valuable data to develop protein-protein, drugprotein, drug-disease, and drug-protein-disease networks. Among them: the Therapeutic Target Database, STICH, DisGeNET (Pin˜ero et al., 2017), BindingDB (Gilson et al., 2016), and STRING (Szklarczyk et al., 2015), just to mention a few examples. Recently, Berenstein et al. used data from extensively studied organisms (Homo sapiens, Escherichia coli, and others) to produce a multilayer network comprising proteins and bioactive compounds (Berenstein et al., 2016). The network edges reflected chemical similarities (quantified by Tanimoto similarity) between 170,000 compounds and functional relationships (orthology, shared protein domains, shared participation in biochemical pathways) among 167,000 proteins. The authors applied the network to prioritize targets in kinetoplastids; most of the proteins obtained using this prioritization method were protein kinases with homologs in humans. They also applied such procedure to propose putative targets for target-orphan compounds active against Plasmodium, which had previously been found through highthroughput screening.

6 CONCLUSION Drug repurposing represents a cost-efficient strategy to develop therapeutic solutions for NTDs. Computer-aided drug repurposing poses an even more efficient approach, since repurposing candidates are prioritized using in silico methods, thus optimizing the use of expensive experimental procedures. It is to be noted that, due to the availability of freely available computational resources, the advent of cloud computing and low-cost parallel computing, in silico techniques are probably today the one field within drug discovery associated with the smallest technological gap. It is also interesting to underline that even within those conditions grouped by the “neglected” status, some disorders are more neglected than others when one reviews 3. EXAMPLES AND CASE STUDIES

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the applications of modern drug discovery approximations among NTDs. This may well depend on the R&D funding available for each condition, the public awareness on some diseases (which often correlates with investment in a direct manner), and the local political and economic scenarios in those countries and regions where the different NTDs are endemic. Among the variety of in silico techniques that may be used to guide drug repurposing, VS is today the one that has been more extensively applied, but other promising approaches have begun to be contemplated by ongoing investigations.

Acknowledgments The authors would like to thank CONICET, University of La Plata (UNLP) and ANPCyT (PICT 2013-0520).

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Salentin, S., Haupt, V. J., Daminelli, S., & Schroeder, M. (2014). Polypharmacology rescored: protein-ligand interaction profiles for remote binding site similarity assessment. Progress in Biophysics and Molecular Biology, 116, 174–186. Sbaraglini, M. L., Bellera, C. L., Fraccaroli, L., Larocca, L., Carrillo, C., Talevi, A., & Alba Soto, C. D. (2016). Novel cruzipain inhibitors for the chemotherapy of chronic Chagas disease. International Journal of Antimicrobial Agents, 48, 91–95. Sbaraglini, M. L., Vanrell, M. C., Bellera, C. L., Benaim, G., Carrillo, C., Talevi, A., & Romano, P. S. (2016). Neglected tropical protozoan diseases: drug repositioning as a rational option. Current Topics in Medicinal Chemistry, 16, 2201–2222. Shineman, D. W., Alam, J., Anderson, M., Black, S. E., Carman, A. J., Cummings, J. L., … Fillit, H. M. (2014). Overcoming obstacles to repurposing for neurodegenerative disease. Annals of Clinical Translational Neurology, 1, 512–518. Soares, M. B. P., Silva, C. V., Bastos, T. M., Guimara˜es, E. T., Figueira, C. P., Smirlis, D., & Azevedo, W. F., Jr. (2012). Anti-Trypanosoma cruzi activity of nicotinamide. Acta Tropica, 122, 224–229. Sterling, T., & Irwin, J. J. (2015). ZINC 15—ligand discovery for everyone. Journal of Chemical Information and Modeling, 55, 2324–2337. Stone, H., Sacks, L., Tieman, R., Duggal, M., Sheils, T., & Southall, N. (2017). Collaborative Use Repurposing Engine (CURE): FDA-NCATS/NIH effort to capture the global clinical experience of drug repurposing to facilitate development of new treatments for neglected and emerging infectious diseases. Open Forum Infectious Diseases, 4, S12. Sturm, N., Desaphy, J., Quinn, R. J., Rognan, D., & Kallenberger, E. (2012). Structural insights into the molecular basis of the ligand promiscuity. Journal of Chemical Information and Modeling, 52, 2410–2421. Swanson, D. R. (1988). Migraine and magnesium: eleven neglected connections. Perspectives in Biology and Medicine, 31, 526–557. Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., … von Mering, C. (2015). STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Research, 43, D447–D452. Talevi, A. (2018a). Drug repositioning: current approaches and their implications in the precision medicine era. Expert Review of Precision Medicine and Drug Development, 3, 49–61. Talevi, A. (2018b). Computer-aided drug design: an overview. Methods in Molecular Biology, 1762, 1–19. Vidal, M., Cusick, M. E., & Baraba´si, A. L. (2011). Interactome network and human disease. Cell, 144, 987–998. Wallace, H. M., & Fraser, A. V. (2004). Inhibitors of polyamine transport: review article. Amino Acids, 26, 353–365. Wang, J. C., Chu, P. Y., Chen, C. M., & Lin, J. H. (2012). idTarget: a web server for identifying protein targets of small chemical molecules with robust scoring functions and a divide-and-conquer docking approach. Nucleic Acids Research, 40, W393–W399. Wang, L., & Xie, X. Q. (2014). Computational target fishing: What should chemogenomics researchers expect for the future of in silico drug design and discovery? Future Medicinal Chemistry, 6, 247–249. https://dx.doi.org/10.4155/ fmc.14.5. Wang, Z., Liang, L., Yin, Z., & Lin, J. (2016). Improving chemical similarity ensemble approach in target prediction. Journal of Cheminformatics, 8, 20. Wenderski, T. A., Stratton, C. F., Bauer, R. A., Kopp, F., & Tan, D. S. (2015). Principal component analysis as a tool for library design: a case study investigating natural products, brand-name drugs, natural product-like libraries, and drug-like libraries. Methods in Molecular Biology, 1263, 225–242. Wo´jcikowski, M., Ballester, P. J., & Siedlecki, P. (2017). Performance of machine-learning scoring functions in structure-based virtual screening. Scientific Reports, 7, 46710. World Health Organization (2017). Neglected tropical diseases. Available from: http://www.who.int/neglected_diseases/ diseases/en/. Accessed 29 March 2018. Wu, L., Ai, N., Liu, Y., Wang, Y., & Fan, X. (2013). Relating anatomical therapeutic indications by the ensemble similarity of drug sets. Journal of Chemical Information and Modeling, 53, 2154–2160. Yang, S. Y. (2010). Pharmacophore modeling and applications in drug discovery: challenges and recent advances. Drug Discovery Today, 11-12, 444–450. Yang, Y., Chen, H., Nilsson, I., Muresan, S., & Engkvist, O. (2010). Investigation of the relationship between topology and selectivity for druglike molecules. Journal of Medicinal Chemistry, 53, 7709–7714. Zheng, C., Guo, Z., Huang, C., Wu, Z., Li, Y., Chen, X., … Wang, Y. (2015). Large-scale direct targeting for drug repositioning and discovery. Scientific Reports, 5, 11970.

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22 Ascorbic Acid Is a Potential Inhibitor of Collagenases—In Silico and In Vitro Biological Studies Vijaya Lakshmi Bodiga*, Sreedhar Bodiga† *

Department of Molecular Biology, Institute of Genetics & Hospital for Genetic Diseases, Begumpet, Osmania University, Hyderabad, India †Department of Biochemistry, Kakatiya University, Warangal, India

1 INTRODUCTION 1.1 Drug Repositioning The advent of informatics has overhauled the drug discovery process and enhanced our understanding of the pathophysiology and the underlying mechanisms. This has required identification and analysis of relevant literature and associated data. This daunting job craves the adoption of bioinformatic and computational tools to retrieve relevant data. It also requires examination of enormous databases to generate meaningful information (data mining), making the best use of the knowledge (knowledge management) in a logical manner and scrutinizing the data to devise a verifiable hypothesis with clinical relevance. These approaches, along with prediction algorithms, confirm the alignment of a particular disease to developmental or marketed drugs and natural compounds with definite structural features. Thus, drug repositioning opportunities require enormous informatics effort and appraisal of the biological and commercial viability of the repurposed drug. The informatics effort involves a computerized approach, with knowledge management assisted by the subject specialists. Initially the disease(s) is assessed and ranked in order of importance. Then a study to fulfill the experimental objectives and test the hypothesis is designed and executed. Knowledge integration requires comparison and compilation of employable enzymatic targets in a specific disease and the list of probable drug candidates available to hit the target (Harland & Gaulton, 2009). This drug-centric informatics approach requires knowledge of

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the drug’s mechanisms of action (MOAs) that can be obtained from various databases. Competitive intelligence databases (Prous Integrity, TrialScale, and BioPharm Insight) provide the most up-to-date indications for each drug based on the mechanism of action. This informatics strategy is target-centric and can be hooked to high-throughput data mining approaches for incorporation of known disease relevant data (Loging, Harland, & Williams-Jones, 2007). Therefore, computational challenge is about identification, retrieval, and analysis of quality and reliable databases. For additional resources that can aid in providing relevant information, please refer to Loging et al. (2007) For any drug molecule to enter into the market, clinical trial data indicating the efficacy of the drug candidates against a particular target are essential. Genetic studies with nucleotide polymorphism data and their functional implications or their association with upregulation/downregulation of a particular gene of interest would add substantial weight to the identified target. Generation of specific gene knockouts and silencing of mRNA can further confirm the role of the target in a particular disease. Identification of new targets can also be hypothetically achieved by an extensive literature search and data integration using MEDLINE (Notter, 1972). The obvious mention of a disease and its target together in a title are scored higher. A novel visualization tool termed the “target opportunity universe” is capable of analyzing the best fit of a particular drug against a target in a specific disease (Campbell et al., 2010).

1.2 Molecular Modeling in Drug Discovery A therapeutic target molecule in a disease can be any biological macromolecule with known relevance to the disease. Gene coding for a defective enzyme or protein, membrane-bound receptor molecule, or intracellular enzyme are the widely targeted biomolecules to achieve the therapeutic effect. The interaction between the drug and biomolecules most often involves noncovalent interactions, but in some cases a covalent interaction cannot be excluded. Regardless of the type of interactions, the net effect of the drug on the target can classify it as an agonist or antagonist. Agonists mimic the endogenous ligands in structure and function and produce a similar effect or better at lower concentrations. Antagonists, although structurally similar to endogenous ligands, are most likely inhibitory in nature due to altered structural elements. A higher degree of complementarity in terms of shape and structure between the drug and target results in tight binding. The geometrical complementarity can be assessed by visualizing the molecular surface of the target where the drug is supposed to bind. Polar groups of the drug tend to bind to hydrophilic groups on the target, while nonpolar elements attach to the hydrophobic pockets. The binding may sometimes involve structural changes in the target and mere binding may not elicit the desired effect. The drug has to reach its target inside the cell before the therapeutic effect is seen. The drug needs to have lipophilicity to cross the biological membranes. The knowledge of half-life of the drug and its metabolic fate will determine the efficacy. New drug discovery and proving its medicinal value require lot of time and effort. The drug is expected to exhibit the therapeutic effect with no undesired properties, and should score better than the current drugs. Two terms commonly referred to during the drug discovery are identification of hit molecules (hits) and lead series (leads). Hit refers to a molecule with consistent activity in a screening assay, while lead refers to a group of structurally

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similar molecules with varied activities due to minor structural variations. Fine tuning of the structure of a lead series can result in a potent drug with the desired effect and selectivity. Finding a novel lead series can be a difficult problem. High-throughput screening (HTS) enables large numbers of compounds to be screened using highly automated, robotic techniques. Although HTS desires to test every molecule in the lead series in a biological screen, this is not always practical and feasible, due to practical considerations of cost involved. Unit costs associated with testing and the sheer number of samples add to the overall expenses. Some biological screens are not amenable to a high-throughput mode and the conventional assay consumes time. For these reasons, it becomes mandatory to limit the lead series to a reasonable number based on computational approach. Some methods rely on “2D” properties, as distinct from “3D” methods, which take into account the three-dimensional structure of a molecule. A basic approach of identifying the molecules with a defined substructure would reduce the number of molecules to be tested. For example, we might wish to identify all compounds containing a sulfonamide group. More complex queries are also possible in most systems; these would, for example, permit a query atom to match groups of atoms or features such as ring bonds or to specify stereochemistry. Sophisticated algorithms help identify the molecules containing the defined substructure but are time-consuming. To eliminate the molecules not possessing a given substructure, a bitstring binary screen is commonly employed. There are two types of binary screens in common use. Presence or absence of a particular substructure is assigned bit values of 1 or 0, respectively. The structural keys with 0-bit values are eliminated, saving time. Bitstring operated structural keys are commonly used in MACCS and Isis systems from Molecular Design.

1.3 Molecular Docking Molecular docking is a structural prediction tool that comprehensively analyzes the best fit between the drug and the target molecules. Docking is an algorithm-based tool that can produce all varied possible structures for the given drug and target molecule and assign scores to each structure. It can help assess the binding modes of drugs with biomolecules (Blaney & Dixon, 1993). However, the docking is constrained by the structural complexity and degrees of freedom of rotation and conformation of molecules. Interactive computer graphics afford manual handling of the docking problem, if the expected binding mode is discerned from the binding of the related ligand. It should be cautioned that very closely related structures may adopt different binding modes. Automated docking algorithms without manual intervention are more probabilistic and less biased. Multiple algorithms that negate the number of degrees of freedom are in place to tackle the docking problem. The earliest simple algorithm, DOCK, considers the two molecules as rigid bodies without a conformational degree of freedom (Kuntz, Blaney, Oatley, Langridge, & Ferrin, 1982). DOCK screens for molecules with a high degree of shape complementarity to the target site. A negative image of the target site from the molecular surface is produced. The image is constructed with molecular spheres of different radii, touching the surface at two points. The atomic elements in the ligand are then aligned to spheres to sort matching sets (cliques) with some degree of tolerance. The ligand is then positioned by accomplishing a least-squares fit of the atoms to the spherical centers. The alignment is examined to confirm unfavorable

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steric interactions. Once the confirmation of the intermolecular complex is established, then binding energies are calculated and the binding modes are assigned scores. New assortments can be produced through matching different sets of atoms and sphere centers. The conformations with high scores are chosen for further analysis. To achieve a conformationally flexible docking, the conformational degrees of freedom have to be considered. It is important to contemplate the conformational degree of freedom of both the ligand and the macromolecule in question. Commonly used docking algorithms typically incorporate conformational freedom into the algorithm. Monte Carlo simulation is used for the molecular docking interactions, with simulated annealing (Goodsell & Olson, 1990). Multiple iterations with different rotational and conformational degrees of freedom are generated in the Monte Carlo procedure. Free energy of binding either accepts or rejects the docking and complex formation. An interesting variant on the basic Monte Carlo approach is the tabu search (Baxter, Murray, Clark, Westhead, & Eldridge, 1998). This maintains a record of those regions of the search space that have already been visited, thus ensuring that the method is encouraged to explore more of the binding site. Distance geometry can also be employed for molecular docking. However, this procedure requires producing multiple conformations of the drug within the target site. This can be achieved by assigning penalty scores for drug conformations that are pinned down to the binding site. A strategy that is frequently employed utilizes cumulative structural arrangement of the ligand (Leach & Kuntz, 1992; Rarey, Kramer, Lengauer, & Klebe, 1996; Welch, Ruppert, & Jain, 1996). This algorithm initially identifies the rational “base fragments” that are often part of a rigid component of the molecule. The base fragments are logically pinned down to the binding site and may then be bundled to get rid of identical arrangements. The base fragments are chosen as the starting points for further conformational analysis of the other part of the ligand in each intermolecular complex. Although this strategy appears time consuming, it might yield information about worthy constraints that might otherwise reduce the search and accelerate the process. An optimal docking should allow both the ligand and macromolecule to be flexible. Molecular dynamics (MD) simulation of the ligand-macromolecule complex would allow the flexibility of both molecules. These calculations, being onerous, are only used for fine tuning the docking results obtained with other methods. MD simulations limit the range of binding modes, except for small ligands, and surmounting the energy barriers while transitioning from one binding mode to the other complicates the matter. These hurdles can be overcome to some extent by limiting to side chain flexibility rather than molecular flexibility (Leach, 1994). Most docking algorithms are capable of generating an overwhelming number of fixes. Many of these fixes can be denied automatically due to energy constraints. The remaining fixes can be graded based on the chosen scoring function. When we are only interested in how a single ligand binds to the protein, then the scoring function only reads the arrangement that matches with the original structure of the complex. However, when docking a database of molecules, then the scoring should determine the appropriate binding mode for a given ligand and also grade the ligands for their affinity to the binding site. A rapid scoring function such as the binding free energy for the ligand is required, considering the overwhelming number of arrangements that could be generated during the docking. GOLD uses a genetic algorithm ( Jones, Willett, Glen, Leach, & Taylor, 1997), whereas FlexX uses an incremental construction method (Kramer, Rarey, & Lengauer, 1999). 3. EXAMPLES AND CASE STUDIES

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1.4 Evaluation of Docking Results Regardless of the docking tools employed, all docking data have to be evaluated for chemical complementarity between the two molecules. Evaluation includes confirming if all the purported hydrogen bonds are involving right donors and acceptors in the ligand. One also needs to confirm if the salt linkages only involve the oppositely charged residues at an appropriate distance and not the hydrophobic entities. The docking data can be further judged by the consistency and reproducibility of the binding modes and free energy calculations derived from it. The docking would be considered a success if comparison of the all-atom root ˚ between the docked position and the X-ray mean square deviation (RMSD) falls within 2 A crystallography image. Multiple runs of the docking with different search parameters are advised when employing stochastic methods for docking. Predicted binding modes can be analyzed by compiling a matrix of pair-wise RMSD values and bundling the docked ˚ . If the predicted binding modes were similar, conformations using an RMSD threshold of 2 A the dockings would converge into one family, implying the reliability of the initial conditions and docking procedure. Absence of clusters would require repetition of the procedure with multiple iterations and increased sample size or population size. Assuming a perfect scoring function, the most stable docked conformation would be the one and should match with the crystallographically observed binding mode. Sometimes, a different binding mode from the one yielding the lowest energy is observed.

1.5 Matrix Metalloproteinases Matrix metalloproteinases (MMPs) are proteases containing zinc in the catalytic site and that target the extracellular matrix and basement membrane. Various essential physiological processes, including growth, wound healing, and tissue reorganization, involve the activity of MMPs (Massova, Kotra, Fridman, & Mobashery, 1998; Matrisian, 1990; Nagase & Woessner Jr., 1999; Shapiro, 1998). MMP activity is tightly controlled due to their synthesis as precursor zymogens and transcriptional regulation. Endogenous regulation by specific molecules, called tissue inhibitors of metalloproteinases (TIMPs), also controls the MMP activity (Nagase & Woessner Jr., 1999; Sternlicht & Werb, 2001). Dysregulation of MMP expression and activity are consistently observed in various pathophysiological conditions such as arthritis, cancer, atherosclerosis, aneurysms, nephritis, tissue ulcers, and fibrosis (Woessner, 1991). The majority of the MMPs need to be proteolytically activated in the extracellular space and highly regulated tissue-specific expression of MMPs is noted. MMPs are classified into five groups based on the substrates for proteolysis (Table 1): Collagenases, gelatinases, stomelysins, membraneassociated, and unclassified. Collagenases either occur as fibroblast type (MMP-1, collagenase 1) (Goldberg et al., 1986), the neutrophil type (MMP-8, collagenase 2) (Hasty, Jeffrey, Hibbs, & Welgus, 1987), or collagenase-3 (MMP-13) (Freije et al., 1994). MMP-1, an interstitial collagenase that targets type III collagen, is found to be expressed in human fibroblasts, keratinocytes, endothelial cells, monocytes, and macrophages. Neutrophil collagenase expression is not just confined to the polymorphonuclear neutrophils, but also is found in fibroblast-like cells in the rheumatoid synovial membrane as well as in cultured rheumatoid synovial fibroblasts and human endothelial cells (Hanemaaijer et al., 1997). MMP-8 expression in arthritic lesions indicates that chondrocytes, and synovial fibroblasts are capable of expressing the neutrophils collagenase (Shlopov et al., 2005; Tetlow, Adlam, & Woolley, 2001). Native type I and II collagens 3. EXAMPLES AND CASE STUDIES

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TABLE 1 Classification, Common Names, and Substrates of the Matrix Metalloproteinases (MMP) Family Enzyme

Common Name

Substrate

MMP-1

Collagenase-1; interstitial collagenase

Collagen type 1–3, 7, 8, 10; aggrecan; gelatin;

MMP-2

Gelatinase A; 72 kDa gelatinase; MMP-5

Collagen type 1–5, 7, 10, 11, 14; aggrecan; elastin; fibronectin; gelatin; laminin; MMP-9, -13

MMP-3

Stromelysin-A; Procollagenase; transin-1

Collagen type 2–4, 9–11; aggrecan; elastin; fibronectin; gelatin; laminin; MMP-1, -7, -8, -9, and -13

MMP-7

Matrilysin-1; PUMP-1

Collagen type 1–3, 5, 7, 8, and 10; aggrecan; elastin; fibronectin; gelatin; laminin; MMP-1, -2, and -9

MMP-8

Collagenase-2; neutrophils collagenase

Collagen type 4 and 10; aggrecan; elastin; fibronectin; gelatin; laminin

MMP-9

Gelatinase B; 92 kDa gelatinase

Collagen type 4, 5, 7, 10, 14; aggrecan; elastin; fibronectin; gelatin

MMP-10

Streomelysin-2; transin-2

Collagen type 3–4; aggrecan; elastin; fibronectin; gelatin; laminin; MMP-1 and 8

MMP-11

Streomelysin-3

Aggrecan; fibronectin; laminin; α-1 antitrypsin

MMP-12

Macrophage metalloelastase

Collagen type 4; elastin; fibronectin; gelatin; laminin; vitronectin

MMP-13

Collagenase-3

Collagen type 4; elastin; fibronectin; gelatin; laminin; vitronectin

MMP-14

MT-1 MMP (Membrane Type-1 MMP)

Collagen type 1–3; aggrecan; elastin; fibronectin; gelatin; laminin; MMP-2, -13

MMP-15

MT-2 MMP

Fibronectin; gelatin; laminin; MMP-2

MMP-16

MT3-MMP Ovary metalloproteinase

MMP-2

MMP-17

MT-4 MMP

Fibrin; fibrinogen; TNF precursor

MMP-18

Xenopus MMP

Unknown

MMP-19

RASI-1; RASI-6

Collagen type 4; aggrecan; COMP; gelatin; laminin; large tenas; nidogen firbillin

MMP-20

Enamelysin

Ameloginin; aggrecan; COMP

MMP-21

Xenopus MMP

Unknown

MMP-22

Gallus domesticus MMP

Casein; gelatin

MMP-23

CA-MMP

Unknown

MMP-24

MT5-MMP

MMP-2

MMP-25

MT6-MMP; leukolysin

Gelatin

MMP-26

Matrilysin-2; endometase

Collagen type 4; α-PI; fibronectin; fibrinogen; gelatin; Pro-MMP-9

MMP-28

Epilysin

Casein

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are more efficiently hydrolyzed by MMP-8 when compared to MMP-1. On the other hand, a more stringent expression of MMP-13 confined to the connective tissue is observed (Vincenti et al., 1998), in addition to malignant breast tumors showing upregulation of MMP-13 (Freije et al., 1994). MMPs share basic structural features despite substrate variability. They are synthesized as Pre-Pro-MMPs. They contain an N-terminal signal predomain region that directs the protein for secretion. The propeptide domain of about 80 amino acids contains a conserved cysteine in the sequence -PRCGXPD-. Coordination of the cysteinyl sulfur atom to the active site zinc(II) ion ensures that the enzyme activity is suppressed in the proforma. Cleavage of the propeptide domain by other MMPs or proteases such as plasmid by a “cysteine switch” mechanism activates the MMPs (Nagase & Woessner Jr., 1999; Shapiro, 1998; Whittaker, Floyd, Brown, & Gearing, 1999; Woessner & Nagase, 2000). The general domain structure of an MMP is shown in Fig. 1. The catalytic domain of 170 amino acids shown in Fig. 2, is organized into a five-stranded β-sheet, three α-helices, and bridging loop structures (Nagase & Woessner Jr., 1999). One of the zinc(II) ions in this domain is buried in the protein and coordinated to one aspartate and three histidine residues in a tetrahedral geometry, playing a structural role. The other zinc(II) is required for catalytic action, i.e., peptide hydrolysis, and coordinated by three histidine nitrogen atoms in a conserved sequence -VAAHEXXGHXXXGXXH- (Babine & Bender, 1997). The rest of the coordination sphere is filled with water molecules, which are essential for the

FIG. 1 Domain structure of MMPs.

FIG. 2 Secondary structure of the catalytic domain of MMPs.

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catalytic activity (Bertini et al., 2003). Upon binding of acarbonyl oxygen of a peptide to the zinc(II) ion, the amide bond is attacked by a zinc-bound water molecule that is hydrogen bonded to an adjacent glutamate residue. Proton transfer from the water molecule to the glutamate residue and then to the amide nitrogen completes the cleavage of the peptide (Babine & Bender, 1997; Whittaker et al., 1999). All MMPs (excluding MMP-7 and MMP23) possess a hemopexin-like domain consisting of
210 amino acids, important for substrate binding.

1.6 Inhibitors of MMPs The inhibition of MMPs has assumed great importance, due to the variety of diseases the MMPs are involved in. An important field of chemotherapeutics directed at suppressing MMP activity has grown. Most inhibitors (MPIs) use the same basic design (Fig. 3): a peptidomimetic backbone, coupled with metal-chelating moiety (zinc-binding group, ZBG) (Skiles, Gonnella, & Jeng, 2004; Whittaker et al., 1999). Following a substrate-based approach, early MPIs were designed to mimic natural substrates of MMPs by using a short peptide derivative attached to a ZBG (Whittaker et al., 1999). Availability of NMR and X-ray crystallographic structures of the MMPs further paved the way for this structure-based MPI design (Skiles et al., 2004), which is dictated by the shape of the active site subsites. The active site subsites and a variety of residues lining the subsites determine the substrate selectivity of various MMPs (Whittaker et al., 1999). The subsites depicted in Fig. 4 located to the left of the zinc(II) ion are termed unprimed (S1, S2, S3), while the ones located to the right of the zinc(II) ion are denoted primed subsites (S10 , S20 , S30 ). The functional groups of MPIs interacting specifically with the subsites are termed P or P0 groups (Whittaker et al., 1999), i.e., a P1 group is expected to interact with the S1 subsite. Most inhibitors of MMPs were directed to the primed subsites (right-handed inhibitors), particularly the S10 pocket. The S10 subsite is a deep, hydrophobic pocket common to all MMPs, with the exception of MMP-1 and MMP-7 (Bode et al., 1999; Lovejoy et al., 1999). The depth of the pocket varies between MMPs, and the residues in this pocket lack any significant sequence homology. The differences in the S10 pocket determine the substrate specificity and therefore it is called the specificity pocket (Bode et al., 1999; Lovejoy et al., 1999) and several MPIs have been designed to exploit this feature (Hajduk et al., 1997; Olejniczak et al., 1997), as in the case of WAY-170523, a hydroxamate-based MPI (Chen et al., 2000). This inhibitor (Fig. 5), designed by using NMR structural data of the active site, contains a large P10 group that fits tightly into the S10 pocket of MMP-13, but is too bulky for accommodation by the S10 pocket of MMP-1. Consequently, WAY-170523 displays nearly 6000-fold more

FIG. 3

Generalized structure of MPIs. The ZBG binds to the catalytic zinc(II) ion. The P substituents occupy various subsite pockets in the MMP active site.

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FIG. 4

657

Molecular surface diagram of the active site of MMP-3 (top). The catalytic zinc(II) ion is shown in black; the enzyme is in gray. Location of the subsites are labeled. Schematic figure of the MMP active site before and after inhibition (bottom).

FIG. 5 Chemical structure of a hydroxamate-based MPI, WAY-170523.

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potency for MMP-13 over MMP-1. However, in some inhibitor-protein complexes with MMP1, the S10 pocket expands, allowing for an MPI with a large P10 substituent to occupy this cavity (Lovejoy et al., 1999). The S20 pocket that is exposed to the solvent is directly positioned above the S10 pocket opening. The S20 cavity is hydrophobic in nature in most of the MMPs, except MMP-1 and MMP-7 where it contains Ser and Thr residues, respectively. The S20 subsite, therefore, offers less selectivity compared to the S10 pocket. Molecules with bulky hydrophobic P20 substituents are selective for MMP-2, MMP-3, MMP-8, MMP-9, and MMP-13 over MMP-1 and MMP-7, due to differences in S20 subsites. Other MPIs have been designed with large P20 substituents to better the pharmacokinetic properties of MPIs by preventing the amide bond hydrolysis of the inhibitor (Hirayama et al., 1997; Skiles et al., 2004; Skiles, Gonnella, & Jeng, 2001; Whittaker et al., 1999). The S30 subsite lies on the outer rim of the S10 pocket entry site and therefore is exposed to the solvent. This subsite has not been seriously considered for designing MPIs. But a recent study observed that some P30 substituents can afford specificity. Substituted benzylic groups at the 30 position lead to 1000-fold less potent inhibition of MMP-2, without altering the MMP-3 activity (Fray, Burslem, & Dickinson, 2001). The unprimed subsites are relatively less characterized and not studied for their role in selective inhibition. The unprimed region is a closely arranged group of subsites that are more exposed to the solvent than the primed sites. The S2 subsite is positioned next to the catalytic center, but the other unprimed subsites are remotely away from the catalytic center. The role of unprimed subsites has been gaining importance recently. The positioning of analogous residues in the S2 subsite of MMP-2 and MMP-9 offered substrate selectivity between the gelatinases. Substrate specificity was found to be due to the ability of Glu to form a hydrogen bond with specific substrates, while Asp cannot, due to shorter side chain length (Chen, Li, Godzik, Howard, & Smith, 2003). Few studies could still demonstrate that left-handed inhibitors can offer selectivity (Finzel et al., 1998). The S1 and S3 subsite-based inhibitors were proven to be successful in selectively targeting MMPs over collagenase (MMP-1) (Finzel et al., 1998). Thiadiazole inhibitors that bind to the unprimed subsites showed selective inhibition of MMP-3 (Ki ¼ 0.018 μM) over MMP-1 (Finzel et al., 1998). MPIs utilizing both primed and unprimed subsites have also been designed. Potent and selective inhibition of MMP-13 could be achieved using this strategy. Targeting the deep S10 pocket along with the S2 subsite of MMP-13 yielded 100-fold more selective inhibitors for MMP-13 over MMP-1 (Reiter et al., 2003). Thus, both unprimed and primed subsites have offered avenues for designing selective inhibitors.

1.7 Zinc-Binding Groups of MMP Inhibitors Zinc-binding groups (ZBGs) have also played an important role in developing a successful MPI. According to the nature of the group in the inhibitor interacting with the metal ion, MPIs are broadly categorized into six groups: hydroxamates, carboxylic acids, thiols, phosphorusbased, other ligands, and natural products. The hydroxamic acid group is the most widely used and most effective ZBG in inhibitor design. Hydroxamates interact with the catalytic zinc(II) in a bidentate manner, disallowing the substrate entry into the active site and incapacitating the catalytic zinc(II) of peptide hydrolysis. The binding of hydroxamate-based 3. EXAMPLES AND CASE STUDIES

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MPIs has been confirmed by X-ray crystallography, which unambiguously displays bidentate ˚. coordination with average ZndO bond lengths of
2.0 A Carboxylate ZBGs have gained momentum next to the hydroxamate-based MPIs (Whittaker et al., 1999), apparently due to their precursor character. The carboxylate ZBG has been proposed to be a bidentate ligand (Esser et al., 1997; Natchus et al., 2001); however, detailed examination suggests that the carboxylate ZBGs prefer monodentate interaction with the catalytic zinc(II) ion. Among eight X-ray structures of MMP-inhibitor complexes with carboxylate-based compounds, the average ZndO bond lengths were found to be 1.9 and ˚ for the two oxygen atoms (Becker et al., 1995; Browner, Smith, & Castelhano, 1995; Esser 2.7 A et al., 1997; Matter et al., 1999; Natchus et al., 2001; Pavlovsky et al., 1999). The sum of the ˚ , which is significantly shorter than the covalent radii for oxygen and zinc is only
2.1 A ˚ reported 2.7 A bond. In addition, a survey of the Cambridge Structural Database (http:// www.ccdc.cam.ac.uk/) identified 16 small molecule structures of zinc coordinated to three ˚ are reported, nitrogen atoms and a carboxylate; no ZndO bond distances larger than 2.5 A suggesting that longer ZndO bonds are not true coordinate bonds, or at best are very weak interactions. In the structures that were described as truly five-coordinate, the carboxylate oxygen atoms are bound nearly equidistantly, contrary to the asymmetric binding found in the MPI complexes (Darensbourg, Wildeson, & Yarbrough, 2002; Kremer-Aach et al., 1997). In addition, examination of the geometry at the metal centers in the MMP carboxylate MPI structures is most appropriately described as distorted tetrahedral. This supports the contention that the coordination sphere of the zinc(II) ion is essentially unaffected by the more distant oxygen atom, as true bidentate coordination should tend to induce a more standard five-coordinate geometry, such as trigonal bipyramidal or square pyramidal, similar to that found with the hydroxamic acid MPIs discussed previously (O’Brien et al., 2000). Finally, hydrogen bonding is frequently found between the distant carbonyl oxygen atom of the carboxylate ZBG and the protein sidechains (Becker et al., 1995; Browner et al., 1995; Natchus et al., 2001; Pavlovsky et al., 1999), which further reduces the ability of this atom to be strongly coordinated to the zinc(II) ion. As monodentate ligands, carboxylates should be more weakly bound to the zinc center, due to the loss of a ZndO bond (relative to hydroxamate-based compounds) and a loss of the chelate effect (see the following). Consistent with this description of carboxylic acid ZBGs being only monodentate ligands, most carboxylate-based MPIs are inferior to the bidentate hydroxamates (O’Brien et al., 2000). MPIs with thiol-based ZBGs were found to be effective inhibitors at subnanomolar concentrations (Baxter et al., 1997; Campbell et al., 1998; Freskos et al., 1999; Hughes, Harper, Karran, Markwell, & Miles-Williams, 1995). The thiophilic nature of zinc(II) in proteins has prompted the development of thiol-based MPIs. The “cysteine switch” self-inhibitory mechanism of MMPs (see previous discussion) has sparked a greater interest in thiol-ZBGs (Morgunova et al., 1999). Thiol-containing MPIs use a sulfhydryl group as the lone donating atom or in combination with other donor atoms, as in the case of mercaptoketones, mercaptoalcohols, and mercaptoamides. Very few X-ray structures are available for MPI-MMP complexes of thiol-based inhibitors. One of the available structures reveals that the MPI uses the thiol ˚ , resulting ZBG to bind the zinc(II) ion in a monodentate fashion with a ZndS distance of 2.2 A in a tetrahedral geometry around the metal center (Grams et al., 1995). Studies of inhibition of thermolysin (zinc-dependent protease) by a phosphorus-based compound have directed the efforts towards synthesizing and testing phosphorous-based MPIs (Komiyama, Aoyagi, Takeuchi, & Umezawa, 1975). Phosphonic or phosphinic acid 3. EXAMPLES AND CASE STUDIES

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groups are responsible for chelation of active site Zn (II). Phosphorous-based MPIs have not shown inhibitory activity comparable to that of hydroxamates. Examination of the X-ray crystal structure of a phosphonic acid-based MPI bound to MMP-8 reveals the binding mode of the inhibitor (Gavuzzo et al., 2000). The ZBG is described as binding in a bidentate manner through two of the three oxygen atoms (Gavuzzo et al., 2000). The ZndO distances are 1.9 and ˚ . Again, based on the long ZndO bond length, monodentate coordination appears to be 2.7 A a more apt description of the binding. Additionally, the coordination geometry of the zinc center in this structure is clearly a distorted tetrahedron and shows little influence from the more distant oxygen atom. The structure of a phosphinic acid MPI has also been obtained (Gall et al., 2001). The structure of this MPI bound to MMP-11 displays a tetrahedral geometry at the metal center. One phosphinic oxygen atom is bound to the zinc(II) ion at a distance of ˚ . The second phosphinic oxygen is located 2.9 A ˚ from the metal center and is in hydrogen 2.4 A bonding distance to Glu220. A number of other MPIs have been synthesized that employ ZBGs that do not fall into any of the previously described categories. Among the many different ZBGs, two unconventional MPIs that utilize thiadiazole and 2,4,6-pyrimidine trione (barbituric acid) ZBGs have been structurally characterized bound to their MMP targets. The thiadiazole-derived MPIs PNU-142372 and PNU-141803 bind to the active site zinc(II) ion in a monodentate fashion ˚ . The crystal through the sulfur atom of these ZBGs with a ZndS bond distance of 2.3–2.4 A structures of two barbituric acid-based MPIs bound to MMP-3 and MMP-8 reveal their unusual mode of binding where the active site zinc atom is coordinated through the N3 nitrogen ˚ (Dunten et al., 2001). Of the two reported barbiatom with a ZndN bond distance of
2.1 A turate structures, one suggests that the ZBG is bidentate (Brandstetter et al., 2001) bound through the N3 nitrogen atom and one of the oxygen atoms of the ZBG, while the other reference describes the inhibitor as bound in a monodentate fashion. Inspection of the zinc centers in each of these structures indicates that the coordination geometry is best described ˚ away as distorted tetrahedral. The oxygen atoms of the ZBG are located approximately 3.0 A from the zinc(II) ion, suggesting they do not coordinate to the metal center. Based on the long ZndO distance and the tetrahedral coordination geometry, these compounds appear to be properly described as monodentate ligands, similar to carboxylate-based ZBGs. Beyond the many types of synthetic inhibitors that have been explored, various natural products have also been shown to inhibit MMPs. Indeed, the natural product group of inhibitors represents the only clinically approved MPI. The tetracycline antibiotic Periostat (doxycycline hyclate) is clinically used as an MPI against periodontal disease. Although tetracyclines were only modestly effective against MMPs, they showed tremendous anticancer properties. The antitumor activity of tetracyclines cannot be attributed solely to the MMP inhibition property. Exact knowledge of the interactions between tetracycline and the catalytic zinc is lacking, as there are no crystal structures of a tetracycline bound to an MMP. Binding of tetracyclines to divalent metal ions indicates possibly bidentate coordination through adjacent keto/hydroxyl oxygen atoms. Other natural products found to inhibit MMPs include derivatives of the compound futoenone. Futoenone is found in the Chinese herbal plant Piper futokadsura, which is used to treat inflammatory disease. Futoenone and its derivatives have been studied as MPIs, and they demonstrate modest inhibitory activity. The mode of binding of these compounds to MMPs is unknown; however, it is proposed that the 2-methoxyphenol moiety in these molecules is responsible for zinc chelation. 3. EXAMPLES AND CASE STUDIES

1 INTRODUCTION

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Although thousands of MPIs have been synthesized, only Periostat has been approved for therapeutic use and it does not contain a hydroxamate ZBG. One main criticism of the numerous failed MPIs is a lack of specificity and potency resulting in low in vivo activity and unwanted side effects, such as musculoskeletal syndrome (Coussens, Fingleton, & Matrisian, 2002; Hutchinson, Tierney, Parsons, & Davis, 1998). It has been proposed that inhibition of collagenase (MMP-1) is partially responsible for the development of musculoskeletal pain. However, this pain is observed in patients after treatment with prinomastat, an MPI that does not inhibit MMP-1 (Coussens et al., 2002). This finding suggests that the current method of improving the selectivity of inhibitors for specific MMPs may not completely eliminate the problem of unwanted side effects. In addition, a more alarming study has discovered that the MPI batimastat promotes the metastasis of human breast carcinoma cells in nude mice (Kruger et al., 2001). It has been suggested that less specific inhibitors may be acceptable when treating cancer if they exhibit potency, but in the remedy of more benign diseases such as arthritis, specific inhibitors with minimal side effects are preferred. Ideally, the development of potent MPIs that are more selective for MMPs over other metalloproteins could reduce the required dosages and minimize the potential side effects. Attempts to improve the potency and selectivity of MPIs are evidenced by the vast diversity of inhibitor backbones. Efforts made to develop superior ZBGs are miniscule in comparison. The consensus in the field appears to be that hydroxamic acids are satisfactory metal chelators and that MPI design should focus on the development of more effective backbones. However, an examination of MPI activity with identical backbones but different ZBGs indicates that both the ZBG and the backbone are essential for obtaining an effective inhibitor, and therefore adequate efforts should be made in the development of improved ZBGs. Despite the popularity of hydroxamic acids as a ZBG, there are significant limitations associated with its use in MPIs. Hydroxamic acids are vulnerable to rapid excretion and in vivo hydrolysis; in a study of 5-lipoxygenase inhibitors, hydroxamic acid-based inhibitors were found to rapidly hydrolyze to the corresponding carboxylic acid, which is a significantly weaker ZBG (see the preceding discussion) (Singh et al., 1995; Summers et al., 1987). The metal binding selectivity that can be obtained through the ZBGs is a largely overlooked area in MPI development, which is surprising considering that hydroxamic acids have poor selectivity for zinc(II) ions over other divalent first row transition metals. Hydroxamic acids bind tightly to a variety of metal ions in several oxidation states, as evidenced by their widespread use in bacterial siderophores where they act as strong iron(III) chelators (Farkas, Enyedy, & Cso´ka, 1999). Indeed, the only medically approved chelator for iron overload is the tris(hydroxamate) siderophore desferrioxamine (DFO) (Farkas, Enyedy, Z
eka´ny, & Dea´k, 2001). In terms of hard/soft acid-base chemistry, the zinc(II) ion is often classified as intermediate and is generally regarded as softer than metals such as iron(III) or manganese(II) (Bertini, Gray, Lippard, & Valentine, 1994; Sigel & McCormick, 1970). In addition to the low selectivity of hydroxamates for various metal ions, the carbon-nitrogen bond in these compounds can undergo a cis to trans conformational change, which reduces its affinity for binding to all metal ions, relative to more rigid ligands. The different forms of collagenases are upregulated and activated by cytokines and growth factors. Use of MMP inhibitors is a major strategy in pathological conditions with aberrant expression and activation of MMPs. Development of inhibitors of MMPs for the last three decades had little success. Activation of latent forms of MMPs is associated with altered coordination of the zinc with the dSH group of a Cys residue, for water. Close contact between 3. EXAMPLES AND CASE STUDIES

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Cys and zinc can activate the MMPs, and molecules that can prevent this contact through chelation of the active zinc can serve as potential MMP inhibitors. Despite the massive efforts towards developing MMP inhibitors and repeated failures during clinical trials, it is widely believed that repurposed drugs and natural compounds can emerge as therapeutic clinical entities. Thus, in the current study, we have tested the ability of ascorbic acid (vitamin C), as a potential inhibitor of MMP-8. Among the 28 types of collagenenases found so far, MMP-8 belongs to the class of secreted or membrane-associated collagenases, which target type I, II, and III collagens (Freije et al., 1994; Hasty et al., 1987). MMP-8 possesses an essential catalytic zinc-binding domain, a propeptide domain hinge region, and a C-terminal hemopexin-like domain, which is conserved in other MMPs, along with substructural zinc and two to three calcium ions that are required for stability and the collagenase activity (Visse & Nagase, 2003). The catalytic domain of MMPs is well conserved, except for the specificity pocket (S10 ) region, which is lined by Tyr 219-Leu 229 in the case of MMP-8 and is rather deep. The size of the S10 pocket strongly influences the substrate specificity (Kalva, Vadivelan, Sanam, Jagarlapudi, & Saleena, 2012). Sequence and structural alignment data reveal that the S10 loop in MMP-8 differs from other MMPs with two residues Arg 222 and Tyr 227 (Aureli et al., 2008). Because of their unique localization to the S10 pocket in MMP-8, any inhibitor molecule that binds to these residues may offer selectivity. Ascorbic acid ((5R)-[(1S)-1,2-dihydroxyethyl]-3,4-dihydroxyfuran-2(5H)-one), an essential vitamin and more than just a micronutrient, is identified for its role in antiscurvy treatment. Vitamin C–dependent enzymes prolyl and lysyl hydroxylase are required for the hydroxylation of collagen. In addition to this cofactor role, ascorbate was shown to influence cancer cell growth and expression of MMPs and TGF-β (Ma et al., 2014; Philips, Dulaj, & Upadhya, 2009). These differential dose-dependent effects suggest a rather interesting dual function of ascorbic acid in ECM remodeling. We therefore hypothesize that ascorbate may partly regulate the collagen turnover by influencing the MMP activity. It is also known that ascorbic acid forms zinc chelates at alkaline pH (Kleszczewska & Misiuk, 2000). We therefore probed the interactions of ascorbic acid with MMP-8 by molecular docking, molecular dynamic simulations, and validated these results using in vitro studies.

2 EXPERIMENTAL PROCEDURES 2.1 Molecular Docking ˚ were retrieved The crystal structures of matrix metalloproteinase with a resolution of 2.0 A from protein data bank (PDB ID: 2TCL, 1ZP5, 3ZXH and 3DNG) (Campestre et al., 2006). The retrieved protein was subjected to primary preparation, using the Protein preparation wizard, predefining major properties like specified bond orders to hydrogens, zero order bonds created to metal atoms; capping the termini and desolation was done by removing crystal˚ (Sastry, Adzhigirey, Day, Annabhimoju, & Sherman, lized free water molecules beyond 5 A 2013). Following this, the hydrogen bonds in the protein were optimized and minimized in the presence of force field Optimized Potential for Liquid Simulations (OPLS) 2005 (Shivakumar et al., 2010). Ascorbic acid was docked in the active site of collagenases using 3. EXAMPLES AND CASE STUDIES

2 EXPERIMENTAL PROCEDURES

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Glide extra-precision (XP), version 5.5. Ligplot was chosen for analyzing the protein-ligand complexes (Wallace, Laskowski, & Thornton, 1995). The in silico work was executed using the Schr€ odinger suite 2014-3 on the HP Z600 workstation.

2.2 Ligand Preparation Primary information regarding GM 6001(2R)-N0 -hydroxy-N-[(2S)-3-(1H-indol-3-yl)-1(methylamino)-1-oxopropan-2-yl]-2-(2-methylpropyl)butanediamide) ascorbic acid structural coordinates was acquired from Chemspider. The retrieved compounds’ geometries were optimized through OPLS 2005 force field, employing the LigPrep module.

2.3 Ligand Docking The Glide module in the Maestro suite was applied for ligand docking. The module uses a grid-based ligand docking method with energetics that reflect the favorable interactions between the molecule and a protein as output. Prior to docking, a Grid box was generated at the centroid of the ligand with the assistance of receptor grid generation protocol. Following this, the prepared molecules were docked with the extra precision (XP) docking protocol (Friesner et al., 2006).

2.4 Molecular Dynamic Simulations The Desmond module was used to perform the molecular dynamic simulations of all the interactions between ligands and protein under the force field OPLS 2005 (Guo et al., 2010). In the present study, protein ligand complexes were subjected to a TIP3P water model in an orthorhombic periodic boundary under solvated condition using the system builder. In order to neutralize the system, Na+ ions or Cl ions were added with respect to the net charge of the system and a salt concentration of 0.15 M was also included. This prepared model system using the system builder was minimized up to a maximum of 5000 iterations and the total number of atoms present in the built system were calculated using the minimization step. Further molecular dynamic simulation studies were carried out with a periodic boundary condition in an NPT ensemble, temperature at 300 K, 1 atmospheric pressure, and finally relaxed using the default relaxation protocol integrated in Desmond. The simulation job was carried out for a time period of 10 ns with 5 ps intervals and a time step of 5 ps. This procedure was carried out for all protein ligand complexes for a time period of 10 ns. RMSD), root mean square fluctuation (RMSF), H-bond and total energy of all the complexes were studied.

2.5 Rheumatoid Synovial Fibroblast Culture for MMP-8 Conditioned Medium Freshly dispersed synovium obtained from rheumatoid arthritis subjects was used for preparation of synovial fibroblasts (Unemori, Hibbs, & Amento, 1991). Local ethics committee guidelines were strictly followed. Briefly, the outer superficial layer of synovium was removed, cut into pieces, and treated with 4 mg/mL clostridial collagenase (Worthington Biochemical, St. Louis, MO) and 0.1% DNase I (Sigma Chemicals, St. Louis, MO) in DMEM at 37°C for 60–90 min. The tissue was further treated with 0.25% trypsin for the next 30 min. 3. EXAMPLES AND CASE STUDIES

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Suspension of synovial cells was washed 2 in 50% PBS-50% DMEM + 15% FBS at 37°C and seeded at 106–107 cells/100 mm tissue culture plate in DMEM-FBS. After allowing the cells to adhere, the nonadherent cells were eliminated by washing. Conditioned media were collected after treatment of the cells for 24 h with phorbol 12-myristate 13-acetate (PMA, 10 nM). A 50-kDa immunoreactive band corresponding to MMP-8 was observed.

2.6 Fluorogenic MMP Activity Measurements MMP-8 activity in culture media was measured by a SensoLyte Plus 520 MMP assay kit (AnaSpec, San Jose, CA), after centrifugation of the medium at 13,000  g for 4 min; 1 mM APMA (4-aminophenylmercuric acetate) was used for activation of the Pro-MMP to MMP. Ascorbate at the indicated concentrations was added to the harvested media aliquots and incubated for 24 h, to determine their inhibitory action against MMPs.

2.7 MMP-8 Enzyme Assay The full-length MMP-8 enzyme activity was tested against type II collagen. The enzyme was activated with 2 μg trypsin and incubated with canine type II collagen (1–12 μg) at 30°C in 0.05M Tris, 0.005M CaCl2 for 30 min. Digestion was stopped by boiling the samples for 3 min in sodium dodecyl sulfate–polyacrylamide gel electrophoresis loading buffer. The samples were then subjected to electrophoresis on 7.5% polyacrylamide gels and stained with Coomassie blue. The gels were analyzed by densitometry and digestion of collagen was quantified by measuring the amount of TCA present in each sample (Mitchell et al., 1996).

2.8 Collagen Zymography Zymography analysis also used similar aliquots that were utilized for fluorogenic enzyme activity measurements. Conditioned media appropriately incubated with APMA, GM 6001, and ascorbic acid was mixed with 2  SDS sample buffer, omitting β-mercaptoethanol and used for zymography. Briefly, aliquots of the conditioned media were electrophoresed on a 10% SDS-polyacrylamide gel containing 0.5 mg/mL collagen (Collagen Type I and Collagen Type IV, Sigma-Aldrich). The zymographic activities were revealed by staining with 1% Coomassie blue and, subsequently, destaining of the gel.

2.9 Statistical Analyses Data were analyzed by one-way analysis of variance using SigmaStat 3.5. Values are expressed as mean  standard error (SE).

3 RESULTS MMPs, either secreted or membrane bound, possess a general structure consisting of a prodomain, a catalytic domain, a hinge (linker) region, and a hemopexin domain (Nagase, Visse, & Murphy, 2006). The catalytic domain exhibits similar structure in all MMPs and is 3. EXAMPLES AND CASE STUDIES

3 RESULTS

665

composed of two zinc(II) and two or three calcium(II) ions. One of the two zinc ions is catalytic and the other plays a structural role. The inhibitor-protein interactions in the MMP active site are determined by the type of the catalytic zinc-coordination group, the presence of hydrogen bonds, and the hydrophobic interactions between the inhibitor and the S10 pocket residues (Nagase et al., 2006).

3.1 Docking Studies Molecular docking of GM 6001 ascorbic acid ligands against the 3DNG S11 active pocket of the protein revealed that the two molecules were seated inside the active pocket and produced hydrogen bond interactions with the important amino acids of the pocket. The compound GM 6001 produced hydrogen bond interactions with Pro 211, Gly 212, Ala 213, Asn 218, Arg 222 residues. Arg 222, the key amino acid that differentiates MMP-8 from other MMPs, formed two backbone hydrogen bonds with the GM 6001 via NH groups of the res˚ (Fig. 6A). The overall proteinidue and ¼O of the ligand with a distance of 1.81 and 2.77 A ligand complex was maintained with the help of six hydrogen bonds and produced a G-score of 7.99 and 40 Kcal/mol free energy of binding. The binding mode of the ascorbic acid– MMP-8 complex was maintained by three hydrogen bonds, two with Arg 222 and one with Ser 228. Arg 222, the key amino acid residue, formed two H bonds with two OH groups in the ligand. However, the two H bonds shared by Arg 222 with ascorbic acid were reported to ˚ respectively as depicted in Fig. 6B. The overall comexhibit bond lengths of 2.31 and 2.48 A plex maintained a G-score of 4.55 and 23 Kcal/mol of free energy of binding. Free energy of binding revealed by molecular docking was found to be lower for ascorbic acid when compared to GM6001, indicating a decrease in binding affinity for ascorbic acid. Arg 222 is observed to form two important H-bonds in the binding pockets in both the interactions (Fig. 7). The active site is mainly a hydrophobic groove with a much less positively charged region. Arg 222, the positively charged residue, is found on this small region of positive charge, making it exclusive for these interactions. The variation of the fitting in the binding pockets may be attributed to the difference in the size of the molecules. Ascorbic acid, being a smaller molecule, fit comfortably into shallow portions of the pocket, whereas the relatively bigger molecule GM 6001 does not seem to fit in the shallow groove, as depicted in Fig. 7B. Comparison of the G-scores indicates that the ascorbic acid molecule exhibits a low score when compared to standard GM 6001, again owing to the size and the difference in the number of functional groups. After the docking studies, these two complexes were further subjected to dynamic studies to observe the deviations, fluctuations, and consistency in maintaining the hydrogen bond with the important amino acid, Arg 222.

3.2 Molecular Dynamic Simulations The stability of the two docked complexes, namely 3DNG with GM6001 or ascorbic acid, was further analyzed with 20 ns molecular dynamic studies. In the simulation studies, both the ligand and protein were allowed complete flexibility, unlike in the docking protocol, where the protein was rigid and ligand was maintained in the flexible mode. Such a study involving different flexibilities was employed to understand the binding modes between 3. EXAMPLES AND CASE STUDIES

666 22. ASCORBIC ACID AS A NOVEL INHIBITOR OF COLLAGENASES

FIG. 6

Binding modes of the molecules with MMP-8 protein; (A) 3d and 2d plots of molecule GM 6001; and (B) 3d and 2d plots of ascorbic acid.

3 RESULTS

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FIG. 7 (A) Interactions of GM-6001 and ascorbic acid with Arg 222; (B) Occupancy of the GM 6001 and ascorbic acid molecules inside pocket (black surface indicates the binding surface of the MMP-8).

the protein-ligand complex and also their consistency in maintaining the interactions observed during the docking studies.

3.3 Simulation Analysis of 3DNG Protein With GM6001 ˚ . During the initial simThe Cα of the protein RMSD was recorded between 1.0 and 2.25 A ˚ and graphed to ulation time scale, the deviations in the protein were found starting at 1.0 A ˚ ˚. 1.75 A by the end of 1.5 ns. From there up to 5.5 ns, the deviations depreciated to around 1.5 A From there, a trend of elevations and depressions in the deviations was observed until 18 ns had elapsed. Finally, the deviations acquired stability from 18 to 20 ns, recording an RMSD of ˚ . A maximum deviation, i.e., 2.25 A ˚ , was observed around the 13th nanosecond of around 1.6 A the trajectory run. The ligand fit over the protein RMSD displayed initial deviations from ˚ by the end of 3 ns. Until 12 ns the deviations were around 3.2 A ˚ and then spiked 1.6 to 3.2 A ˚ ˚ by the drastically to 5.4 A. As the simulation continued, the deviations were decreased to 3.2 A ˚ end of 15.5 ns. Between 16 and 17 ns, a sudden rise to 6.4 A was observed in the deviations, ˚ by the end of the simulation time. which settled to 4.0 A The ligand fit the exhibited deviations in the initial time period up to 5 ns, and then ˚ till the end of simulations. The deviations in the proteinexhibited deviations around 1.8 A 3. EXAMPLES AND CASE STUDIES

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ligand RMSD is explained by the presence of more loop regions and the active pocket being a part of the loop region in the protein. The fluctuations in the protein were mainly reported in a ˚ , meaning that all the residues were within the appropriate range. Constable range, below 3 A tacts between amino acids and the ligand were analyzed broadly to check the H-bond formation with the important amino acid Arg 222. During the simulations run, the ligand was observed to form two hydrogen bonds with an interaction percentage of 34 and 72, respectively, unlike docking studies where it was a single hydrogen bond. The number of hydrogen bonds formed through the 20-ns trajectory run ranged between 6600 and 7000 widely held. The energy required by the protein-ligand complex was observed between 230 and 260 Kcal/mol. A stable energy consumption was observed at 230–240 Kcal/mol after 15 ns simulation, till which time a fluctuating trend of energy was observed. The RMSD, RMSF, H-bonds, energy, and percentage of interaction are illustrated in Fig. 8.

3.4 Simulation Analysis of 3DNG Protein With Vitamin C Extra docking exhibited two H-bond interactions by ascorbic acid with the protein active site residue Arg 222. Molecular dynamic simulations also turned in two H-bond interactions with Arg 222, strengthening the importance of Arg 222 in the interaction validation. The protein RMSD in this complex reported continuous deviations throughout the trajectory between ˚. 0.9 and 1.8 A The ligand fit RMSD also showed deviations up to 10 ns and from where persistent devi˚ . The ligand fit protein deviations were very high ations were observed between 1 and 2 A compared to the protein and ligand RMSD. Initially the deviations were reported around ˚ towards 10 ns; after that the RMSD was on continuous deviations progressing to 7.5 A ˚ 4A ˚ , was noticed at one instance, i.e., by the end of simulations. Maximum RMSD, above 8 A around 13 ns. This is due mainly to the size of the molecule and pocket in the protein. The protein pocket was large but shallow, whereas the molecule, ascorbic acid, was very small, due to which the deviations were continuous, in order to stay intact as a complex. The RMSF ˚ , maintaining an acceptable of the protein showed fluctuations in all the residues below 2.6 A range. The hydrogen bonds were observed to form throughout the simulations. The RMSD, RMSF, H-bonds, energy, and percentage of interaction are illustrated in Fig. 9.

3.5 Inhibition of MMP-8 Activity Against Collagen by Ascorbic Acid The activity of MMP-8 was tested against type II collagen. The full-length MMP-8 cleaved this substrate, yielding the expected TCA and TCB fragments (Fig. 10A). When the enzyme was tested in the presence of 5 and 10 μM ascorbic acid, the activity of MMP-8 was inhibited by 64% and 87%, respectively. We used a conditioned medium of synovial fibroblasts from rheumatoid arthroid patients treated with 10 nM PMA as a source of MMP-8. Rheumatoid fibroblast cells in culture showed a very faint band corresponding to a 50-kDa pro-MMP-8 on the zymogram. Treatment of the cells with 10 nM PMA for 24 h significantly increased the pro-MMP-8 form released into the culture medium, as shown earlier (Hanemaaijer et al., 1997). A very faint band corresponding to a 40-kDa protein also was observed in the conditioned medium concentrate, indicating it is an active form of MMP-8. To increase the level of active MMP-8, we incubated the conditioned 3. EXAMPLES AND CASE STUDIES

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FIG. 8 Molecular dynamics profiles of MMP-8-GM6001 complex. (A) Protein-ligand-RMSD with respect to time in; (B) Protein RMSF plot with respect to residues; (C) H-bond plot along the trajectory; (D) Total energy variations in Kcal/mole along the trajectory; and (E) Lig-Plot of MMP-8 and GM6001 showing percentage of interaction with key residues.

670 22. ASCORBIC ACID AS A NOVEL INHIBITOR OF COLLAGENASES

FIG. 9 Molecular dynamics profiles of MMP-8-ascorbic acid complex; (A) Protein-ligand-RMSD with respect to time in; (B) Protein RMSF plot with respect to residues; (C) H-bond plot along the trajectory; (D) Total energy variations in Kcal/mole along the trajectory; and (E) Lig-Plot of MMP-8 and ascorbic acid showing percentage of interaction with key residues Arg 222 and Ser 228.

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FIG. 10

(A) Collagen degradation by MMP-8 and inhibition by ascorbic acid. MMP-8 was activated with trypsin and incubated at room temperature overnight with type II collagen in the presence or absence of ascorbic acid. Each assay mixture contained 6 μg canine type II collagen in 50 μL assay buffer. The assay mixtures contained 2 pmoles of human recombinant MMP-8. In each pair of lanes, the sample on the left contained no ascorbic acid while that on the right contained 5 and 10 μM ascorbic acid. The MMP-8 cleaved the collagen to yield TCA and TCB fragments, which were quantified by densitometry. (B) Collagen zymography of MMP-8 conditioned medium from human rheumatoid synovial fibroblasts, with unstimulated conditioned medium (Lane 1), PMA-treated cells without APMA (Lane 2), and with 1 mM APMA (Lane 3); conditioned medium as in Lane 3, in the presence of 1 mM GM 6001 (Lane 4, ascorbic acid) (1 μM, Lane 5), ascorbic acid (5 μM, Lane 6) and ascorbic acid (10 μM, Lane 7). Markings depict bands that were intensified by APMA and are consistent with Pro-MMP-8 (upper arrow) and active MMP-8 (lower arrow) activity.

medium with 1 mM APMA for 24 h, which increased the active/inactive MMP-8 ratio. In parallel aliquots, 1 mM GM 6001 significantly attenuated APMA-induced formation of active MMP-8. Co-incubation of conditioned medium with APMA and increasing concentrations of ascorbic acid showed a clear reduction in the amount of the active form of MMP-8, as shown in the zymogram (Fig. 10B).

4 DISCUSSION Various physiological processes including embryogenesis, tissue repair and remodeling, and organ morphogenesis involve collagenolysis (Sternlicht & Werb, 2001). Zn2+ ion in the catalytic domain of MMPs is coordinated to a tris (histidine) motif and is required for both substrate binding and cleavage. MMP expression and activity are deregulated in multiple pathological conditions. Either attenuating the expression of endogenous TIMPs or design and development of molecules with potential for specific and selective inhibition of the MMPs is warranted. MMP inhibitor design (MMPi) requires two parts: a ZBG and a peptidomimetic backbone. The catalytic Zn2+ in the active site is surrounded by subsite pockets designated as S1, S2, S3, S10 , S20 , and S30 (Cuniasse et al., 2005). Of the different subsite pockets, targeting of the S10 pocket has provided the basis of selectivity for many MMPi (Rao, 2005).

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Ascorbate (vitamin C) has the potential to influence the extracellular matrix and tumor state. At low concentrations, ascorbate stimulates MMP-1, MMP-2, and TGF-β, with the end result being the elimination of cancer cells with damage to the ECM. At high concentrations, ascorbate inhibits MMP and TGF-β expression, implicating growth and ECM advantage (Philips, Keller, & Holmes, 2007). Ascorbate inhibited digestion of type II collagen by MMP-8, but the concentration necessary for 50% inhibition was lower than the concentration of ascorbate likely to be present in human plasma (22–85 μmol/L
0.4–1.7 mg/dL) (Halliwell, Wasil, & Grootveld, 1987). Based on the simulation results it can be inferred that both molecules fit into the inhibitory pocket of the MMP-8, producing antagonistic interactions. Interaction with Arg 222, with two H-bonds, in both the molecules is a striking similarity in binding mode. However, the percentage of interaction with that amino acid varied in both complexes. The percentage interactions with H-bonds between ascorbic acid and Arg 222 are 100% and 30%, respectively, and higher than that of standard GM6001. From these results, it is evident that, even though smaller in size, the interaction of ascorbic acid with MMP-8 was strong enough and showed interactions similar to those exhibited by GM6001. A comparison between the uninhibited MMP-8 and the ascorbic acid–bound complexes reported here reveals important structural differences regarding essentially the S10 specificity loop. The first part of the loop, containing the third coordinated H207 and two consecutive β-turns (210–216) with the strictly invariant M215, remains practically unaltered. Large conformational changes are induced by both inhibitors on the segment 219–229, separating the tube-like crevice from the bulk of water. The largest Cα displacements induced by both inhibitors occur for the sequence R222-N226. As a consequence, the Y227 side-chain of both complexes has been pushed from the position occupied in the uninhibited enzyme. MMP-8 is routinely considered as a target for cancer, but could also be a hit for treatment of acute liver failure, as MMP-8 deficient mice were found to be resistant to hepatitis induced by TNF-α (Van Lint et al., 2005). MMP-8 inhibitors could also work in alleviating inflammation and tumor progression (Van Lint & Libert, 2006). Therefore, the current study paves the way for testing of vitamin C as a potential inhibitor for specific inhibition of MMP-8, which could be useful in various pathophysiological conditions. This work provides compelling evidence for the concept of repurposing currently available drugs/natural compounds in therapeutic interventions to treat aberrant MMP expression. Considering the repeated clinical trial failures for various MMP inhibitors in the past, the repurposing may open new vistas in drug research and combinatorial chemistry can push this further to avoid nonspecific actions, while providing more potency. Novelty in drug discovery does not have to depend on new chemical structures but on finding new targets for a known chemical with biological potency. This approach is likely to provide new avenues to fight a disease with the small effort of in silico simulations, rather than wasting time and effort on finding new chemicals.

Acknowledgments The authors thank Dr. Talluri Venkateswara Rao for providing access to the Schrodinger suite at KL University. This work was supported by a UGC-Startup Grant for faculty selected under the Faculty Recharge Programme (F.4-5(23-FRP)/2013(BSR)) sanctioned to SB.

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C H A P T E R

23 Bioinformatic Approaches for Repurposing and Repositioning Antibiotics, Antiprotozoals, and Antivirals Xuhua Xia*,† *

Department of Biology, University of Ottawa, Ottawa, ON, Canada †Ottawa Institute of Systems Biology, Ottawa, ON, Canada

1 INTRODUCTION Drug development is a time-consuming and expensive process (David, Tramontin, & Zemmel, 2009; DiMasi, 2018; DiMasi, Grabowski, & Hansen, 2016; Drews & Ryser, 1997). This problem is particularly pronounced in vaccine and antibiotics development, because frequent occurrence of drug resistance (Davies & Davies, 2010) often renders a costly developed drug useless. Drug repurposing and repositioning (DRR) offer a cost-effective alternative in drug development (Das, Dasgupta, & Chopra, 2016; Ding, Takigawa, Mamitsuka, & Zhu, 2014; Karuppasamy, Verma, Sequeira, Basavanna, & Veerappapillai, 2017; Luo et al., 2018; Riedel et al., 2018; Xu et al., 2016) because (1) pharmacodynamics (what a drug does to the body or to the pathogen in the body) and pharmacokinetics (what the body or pathogen in the body does to the drug) of the drug typically are already known, (2) the potential side effects have already been thoroughly tested for getting the drug through the regulatory authority, and (3) the problem of synthesis and mass production of the drug has already been solved. While DRR are often used synonymously in the literature, a fine difference has been noted (Abbruzzese et al., 2017). Drug repositioning typically refers to the process of finding a new use for an approved drug. For example, honeysuckle extract has been used in Chinese medicine for thousands of years. The extract was found to have a strong antibacterial effect resulting in enhanced bacterial clearance in mice (Kim et al., 2014). Further experiments

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revealed an antiviral effect of the extract against influenza A virus (Zhou et al., 2015), so the extract could be “repositioned” as an antiviral agent against influenza A viruses. Drug repurposing refers to the process of reviving a drug that is not used either because of too strong a side effect, low efficacy, high toxicity, no known target, or other reasons. For example, AZT was first synthesized against retroviruses in 1964 (Horwitz, Chua, & Noel, 1964). However, there were no human diseases known to be caused by retroviruses at that time, so the drug was shelved until after the discovery of retrovirus HIV, which resulted in repurposing of the drug against retroviruses, specifically as an inhibitor of HIV reverse transcriptase (Mitsuya et al., 1985). Similarly, chloroquine, originally developed against malaria, was found too toxic and shelved until the war effort in malarial regions revived (repurposed) the drug (Krafts, Hempelmann, & Skorska-Stania, 2012; Schlitzer, 2007). The honeysuckle extract serves as another example of drug repurposing. While the extract works against influenza virus in some cases, the efficacy is not consistent. Experimental studies show that the extract inhibits the proliferation of influenza A subtypes H1N1, H5N1, and H7N9, but not all different influenza subtypes (Zhou et al., 2015). Thus, the extract can potentially be “repurposed” from a general antiviral agent against influenza A viruses to a specific antiviral agent against the three specific subtypes of influenza A viruses. However, it is often not possible to distinguish between drug repositioning and drug repurposing, and in most cases these will be referred to simply as DRR in short. Perhaps the best-known case of successful DRR is sildenafil (Viagra), which was originally intended “for the treatment of various cardiovascular disorders such as angina, hypertension, heart failure and atherosclerosis” (Bell, Brown, & Terrett, 1993). Because sildenafil was known to be a potent and selective inhibitor of cyclic guanosine 30 ,50 -monophosphate phosphodiesterase 5 (cGMP PDE5) that modulates vasodilation, the finding of cGMP and PDE5 in the penis suggests (and validated) that sildenafil has an effect on penile engorgement and erection (Boolell, Allen, et al., 1996; Boolell, Gepi-Attee, et al., 1996). This resulted in the reposition of sildenafil from cardiovascular disorders to male erectile dysfunction. Other examples of DRR include minoxidil, which is an antihypertensive vasodilator medication repositioned to treat hair loss (Zappacosta, 1980), and thalidomide, originally intended for morning sickness but repositioned for leprosy and multiple myeloma (Singhal et al., 1999). Both the honeysuckle extract and sildenafil help to highlight the potential contribution bioinformatics could have made in DRR. In the case of honeysuckle extract, it was found that the active antiviral agent is an atypical miRNA (MIR2911) that significantly inhibits H1N1encoded PB2 and NS1 protein expression (Zhou et al., 2015). One can immediately use bioinformatics tools to scan human mRNAs to see if some human mRNA species might also be inhibited by MIR2911, leading to undesirable side effects, and whether one can redesign a new miRNA that can specifically target mRNAs of influenza A viruses but not human mRNAs. One can also scan MIR2911 against mRNAs from essential genes of pathogens. If an essential gene of a new pathogen happens to overexpress an mRNA that is targeted/ inhibited by MIR2911, then honeysuckle extract (or MIR2911 specifically) could be repositioned as a medicine against this pathogen. In the case of sildenafil, bioinformatics tools can be used to search human transcriptomic and proteomic data to see which tissue/organ may also express PDE5 because such tissue/organ would also be affected by sildenafil, causing undesirable side effects. Here, the bioinformatics approaches relevant to DRR are first reviewed, taking advantage of genomic and transcriptomic data. A cautionary note is included in using transcriptomic 3.. EXAMPLES AND CASE STUDIES

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data in drug discovery because such data, even analyzed by highly cited bioinformatics tools published in high-impact journals, may generate contradictory and inexplicable results (Xia, 2017a, 2018, Chapter 5). This is followed by an illustration for the need of big data and deep learning (LeCun, Bengio, & Hinton, 2015) in DRR.

2 DRR FRAMEWORK Efficient DRR requires relationships between four entities: the drug, the drug target, the pathogen, and the host, which is human in most cases (Fig. 1). Ideally, we should know which drug kills which pathogen by targeting which drug target. For example, Drug3 in Fig. 1 kills Pathogen3 by targeting Target1. There is no drug against a new pathogen labeled Pathogen4 in Fig. 1. However, if we know that Pathogen4 contains Target1, then Drug3 can be repurposed against Pathogen4. Another new pathogen (Pathogen5 in Fig. 1) also does not have a drug against it, but it contains two potential drug targets (Target2 and Target3, Fig. 1). Target2 is also present in human, which implies that a drug targeting Target2 would cause side effects in humans. So Target3, which is absent in human, is more promising than Target2. A database containing the relationships illustrated in Fig. 1 would be tremendously useful in guiding DRR. While such a database is still not available, progress has been made. The Open Target platform (Koscielny et al., 2017) contains an extensive list of disease-target pairs, and the database infrastructure could be used to store pathogen-target relationships. The Drug Repurposing Hub (Corsello et al., 2017) includes an almost exhaustive list of drugs with extensive annotations. An ideal database of this kind should allow one to query either with a pathogen, a drug, or a drug target. Inputting a pathogen should retrieve all drugs and associated drug targets together with information on experimental evidence substantiating the relationships. Similarly, entering a drug should retrieve all drug targets and pathogen information with associated empirical substantiation. What is needed is to identify and validate the relationships among drugs, drug targets, pathogens, and hosts, and then integrate all these relationships into the database. The bioinformatics approaches that help to identify and validate such relationships will be focused here. Drug1

Drug3 Drug3

Pathogen1 Pathogen2 Pathogen3

Target1

FIG. 1 Relationships among drugs, drug targets, pathogens, and host (human). Drug3 can kill Pathogen3 by targeting Target1. Pathogen4 is a new pathogen with no drug developed against it, but it contains Target 1 and therefore Drug3 can be repurposed against Pathogen4. A homolog of Target1 is present in Pathogen5 but may be evolutionarily so divergent from Target1 (indicated by a dashed line) that it no longer responds to Drug3. Two drugs (Drug1 and Drug2) are known to kill Pathogen1 and Pathogen2, respectively, but with no known drug targets.

Target1A Pathogen4 Target2 Pathogen5

Target3

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3 GENOMICS AND DRR 3.1 Identify Essential Genes/Metabolic Pathways in Pathogens for DRR Genomic analysis can help identify essential genes or metabolic pathways as potential drug targets in pathogenic bacteria, protozoans, and viruses. Essential genes are often highly conserved and can be revealed by genomic comparisons between pathogens and their phylogenetic relatives. Sometimes they may also be inferred from experimental data from model organisms such as Escherichia coli, Bacillus subtilis, or Saccharomyces cerevisiae whose genes have been systematically and individually knocked out. Genes essential for the two microbial species are likely to be essential in another microbial species. Once such an essential gene or metabolic pathway has been identified, then existing drugs can be screened and repurposed. This typically involves inhibiting the essential proteins or modifying the substrate of the essential protein to disrupt the essential metabolic pathway. Nucleotide synthesis is essential for any free-living organisms and is highly conserved. For example, the bifunctional inosine monophosphate synthase, which catalyzes the last step in the de novo synthesis of IMP, has conserved homologs in both human (ATIC) and E. coli (purH) (Fig. 2). It is in the context of essential genes that one can sometimes make a claim 10 20 30 40 50 60 ----|----|----|----|----|----|----|----|----|----|----|----|-Ecoli_purH MQQRRPVRRALLSVSDKAGIVEFAQALSARGVELLSTGGTARLLAEKGLPVTEVSDYTGFPE Human_ATIC MA---PGQLALFSVSDKTGLVEFARNLTALGLNLVASGGTAKALRDAGLAVRDVSELTGFPE * * ** ***** * **** * * * * **** * ** * ** ***** 70 80 90 100 110 120 --|----|----|----|----|----|----|----|----|----|----|----|---Ecoli_purH MMDGRVKTLHPKVHGGILGRR-GQDDAIMEEHQIQPIDMVVVNLYPFAQTVAREGCSLEDAV Human_ATIC MLGGRVKTLHPAVHAGILARNIPEDNADMARLDFNLIRVVACNLYPFVKTVASPGVTVEEAV * ******** ** *** * * * * * * ***** *** * * ** 130 140 150 160 170 180 |----|----|----|----|----|----|----|----|----|----|----|----|Ecoli_purH ENIDIGGPTMVRSAAKNHKDVAIVVKSSDYDAIIKEMDDNEGSLT-LATRFDLAIKAFEHTA Human_ATIC EQIDIGGVTLLRAAAKNHARVTVVCEPEDYVVVSTEMQSSESKDTSLETRRQLALKAFTHTA * ***** * * ***** * * ** ** * * * ** ** *** *** 380 390 400 410 420 430 --|----|----|----|----|----|----|----|----|----|----|----|---Ecoli_purH SASEEALKITAAKQNVRVLTCGQWGERVPGLDFKRVNGGL-LVQDRDLGMVGAEEL-RVVTK Human_ATIC GYEEEALTILSKKKNGNYCVLQMDQSYKPDENEVRTLFGLHLSQKRNNGVVDKSLFSNVVTK **** * * * * * ** * * * * * **** 440 450 460 470 480 490 |----|----|----|----|----|----|----|----|----|----|----|----|Ecoli_purH RQP-SEQELRDALFCWKVAKFVKSNAIVYAKNNMTIGIGAGQMSRVYSAKIAGIKA-----Human_ATIC NKDLPESALRDLIVATIAVKYTQSNSVCYAKNGQVIGIGAGQQSRIHCTRLAGDKANYWWLR * *** * ** **** ******* ** ** ** 560 570 580 590 600 610 620 -|----|----|----|----|----|----|----|----|----|----|----|----| Ecoli_purH VKGSSMASDAFFPFRDGIDAAAAAGVTCVIQPGGSIRDDEVIAAADEHGIAMLFTDMRHFRH Human_ATIC LTEVSISSDAFFPFRDNVDRAKRSGVAYIAAPSGSAADKVVIEACDELGIILAHTNLRLFHH * ********* * * ** * ** * ** * ** ** * * * *

FIG. 2 Alignment of protein sequences of Inosine monophosphate synthase from E. coli (purH) and human (ATIC). A middle segment is removed to save space.

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that what is true in E. coli is also true in the elephant. However, the human pathogen Trypanosoma brucei has no homolog in its genome. It also lacks other enzymes involved in de novo purine synthesis. Thus, the pathogen depends on its salvage pathway to obtain purine from its host (Hassan & Coombs, 1988). Gene loss occurs frequently in pathogens, especially intracellular ones, which can obtain nutrients and building blocks for biosynthesis from the host cell. Interestingly, the parasite does have CTP synthetase for de novo CTP synthesis, which makes sense because the CTP concentration is typically low in vertebrates (Colby & Edlin, 1970; Xia et al., 2006). For example, in the exponentially proliferating chick embryo fibroblasts in culture, the concentrations of adenosine, cytidine, guanosine, and uridine triphosphates, in the unit of (moles  1012 per 106 cells), are 1890, 53, 190, and 130, respectively, in 2-h culture, and 2390, 73, 220, and 180, respectively, in 12-h culture (Colby & Edlin, 1970). This suggests that CTP synthetase may be an essential enzyme in T. brucei (Hofer, Steverding, Chabes, Brun, & Thelander, 2001) and repurposing known CTP synthetase inhibitors may lead to efficacious new drugs against T. brucei. Indeed two CTP synthetase inhibitors were found to be effective in stopping T. brucei growth in vitro and in T. brucei-infected mice (Hofer et al., 2001). This would lead to the next step in drug repurposing, i.e., whether the CTP synthetase inhibitors would also inhibit human enzymes involved in nucleotide synthesis. If they do, then inhibiting such essential genes in the pathogen may have an adverse effect on the function of the host homologs, and one consequently would need to refine the drug to minimize its effect on the host. In this particular case of targeting CTP synthetase in T. brucei, the two inhibitors, 6-diazo-5-oxo-L-norleucine (DON) and acivicin, unfortunately are both toxic to humans. This is illustrated by Target2 (Fig. 1) shared by both the pathogen and human. Such a target should generally be avoided. Instead, one should develop a drug target that is specific in pathogen (such as Target3 in Fig. 1) but not shared by the host. Unfortunately, protozoans are more similar to human than bacteria. Consequently, it is more difficult to develop drugs specifically against protozoans with few side effects in humans. Another example of this difficulty involves the human parasite Giardia intestinalis. The phosphoinositide-3 kinase (PI3K) signaling pathways are essential in G. intestinalis and could serve as a drug target. However, the PI3K pathway is also essential in many eukaryotes including human. One way out of this difficulty is through sequence analysis to identify what is unique in the gene/protein of the pathogen. There are two PI3K homologs (Gipi3k1 and Gipi3k2) in G. intestinalis. Sequence comparisons between human and G. intestinalis homologs revealed a unique insertion only in the parasite that can serve as a pathogen-specific drug target (Cox, van der Giezen, Tarr, Crompton, & Tovar, 2006). The same approach has also been used in targeting Pseudomonas aeruginosa (Fernandez-Pinar et al., 2015). DRR involving antivirals against HIV-1 represents an example of targeting essential proteins specific in the pathogen but not in the host. For example, HIV-1 reverse transcriptase activity was detected since its first discovery (Barre-Sinoussi et al., 1983) and is essential for completing the viral life cycle. This pointed to repurposing existing drugs against retroviral reverse transcriptase. AZT, first synthesized against retroviruses in 1964 (Horwitz et al., 1964), then became one of the first efficacious drugs against HIV-1 (Mitsuya et al., 1985). Similarly, HIV-1 protease is essential for recognizing cleavage sites on the HIV-1 polyprotein and cutting the polyprotein into multiple functional proteins. A variety of protease inhibitors have subsequently developed (Flexner, 2007; Merry, Barry, Mulcahy, Halifax, & Back, 1997;

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Zeldin & Petruschke, 2004). An alternative approach is to modify the cleavage sites of the polyprotein so that HIV-1 protease cannot cut the polyprotein properly into functional units (Li et al., 2003). This later approach is promising because HIV-1 polyprotein represents a unique target with no human homolog. Today’s AIDS “cocktail” includes drugs (inhibitors) against both essential HIV-1 enzymes, reverse-transcriptase and protease. Bioinformatic analysis revealed a glutamate transport system that is present in the pathogen Clostridium perfringens but absent in mammals and birds (Bhatia, Ponia, Solanki, Dixit, & Garg, 2014). Drugs developed against such a transport system to protect humans can be repurposed to protect domesticated mammals and fowls. Another example of a pathogenspecific target is UDP-galactopyranose mutase (UGM). UGM is required to synthesize galactofuranose (Galf ), which is an important constituent on the cell surface of a variety of bacterial pathogens (Gruber, Borrok, Westler, Forest, & Kiessling, 2009; Kincaid et al., 2015). As Galf is absent in human (Bhatia et al., 2014), UGM has been used as a desirable drug target (Pedersen & Turco, 2003). Can we repurpose drugs against bacterial UGM encoded by gene GLF to fight against eukaryotic pathogens? Several eukaryotic unicellular pathogens (Beverley et al., 2005) as well as in nematodes (Wesener, May, Huffman, & Kiessling, 2013). This seems to be akin to the scenario involving Pathogen3 and Pathogen 4 in Fig. 1 that share Target1 (UGM in this case). So we can repurpose Drug3 originally intended for Pathogen3 to fight against Pathogen4. Unfortunately, bacterial GLF (encoding UGM) and eukaryotic GLM have diverged substantially. They are related targets but not the same target. The relationship between the bacterial UGM and eukaryotic UGM is symbolized by Target1 and Target1A linked not by a solid line (representing equivalent drug targets) but by a dashed line (representing homologous but nonequivalent drug targets). How would we know if two related drug targets are equivalent (i.e., responding to the same drug in the same way)? Computationally, one can obtain a similarity index by assessing sequence, transcriptomic, and structural similarity between the two targets. Obviously, if Target1 and Target1A are proteins with identical sequences, then the drug effect against Target1 is expected to be similar, if not identical, to that against Target1A. This would facilitate DRR. We will discuss transcriptomic and structural similarities later. Here we examine sequence similarity among prokaryotic and eukaryotic homologs. Alignment of amino acid sequences and phylogenetic analysis reveal that while GLF is similar among prokaryotes, there are large differences between prokaryotic and eukaryotic homologs (Fig. 3). For example, the branch length linking the human pathogen Leishmania major to any one of the prokaryotic species is almost two. This means that an amino acid site has undergone almost two amino acid FIG. 3

Phylogenetic tree built from aligned GLF amino acid sequences from four prokaryotic species and two eukaryotic species. Aligned by MAFFT with all optimized options. Phylogenetic tree reconstructed with DAMBE (Xia, 2017c) and the FastME method.

1 Bacteroides_caccae Campylobacter_ jejuni Escherichia_coli Mycobacterium_tuberculosis Leishmania_major Caenorhabditis_elegans

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replacements between a prokaryotic and the L. major GLF. Thus, eukaryotic UGMs is quite different from prokaryotic UGMs, suggesting difficulty in drug repurposing from bacterial pathogens to eukaryotic pathogens. The previous example shows that a target seemingly shared by two pathogens may not necessarily lead to efficient DRR. Pathogen3 and Pathogen4 have equivalent drug targets (Target1 in Fig. 1), and drugs against one pathogen can be repurposed against the other. In contrast, Pathogen3 and Pathogen5 have related but nonequivalent drug targets (Fig. 1), and drugs developed against one pathogen may not be readily repurposed against the other. Sequence similarity is easy to measure and can help prioritize the repurposing of different drugs. For example, let us consider three essential genes (designated A, B, and C) present in a new pathogen which are also present (designated a, b, and c) in a known pathogen with three effective antibiotics (designated D1, D2, and D3) targeting a, b, and c, respectively. If gene pair A and a are nearly identical in sequence, but the other two pairs of homologous genes (B and b, C and c) are highly divergent, then we should consider repurposing drug D1 first because genes A and a are more likely to respond equivalently to D1 than other gene pairs. In addition to targeting essential genes, one could also target essential cellular components. For example, the malaria parasites, including the most lethal Plasmodium falciparum, share a prokaryotic organelle, the apicoplast, derived from a secondary endosymbiotic event. This plastid-derived organelle is essential for parasite survival, and one could potentially repurpose herbicides targeting plant plastids against malaria parasites (Pradel & Schlitzer, 2010). Because animals do not have plastid-specific genes, such an antiplastid drug should be safe in human.

3.2 Identify Alternative Pathways to Achieve an Essential Function for Effective DRR Essential genes or essential cellular processes are often functionally redundant. For example, USP4 and USP15 in mice and human are partially functionally redundant. Mice can survive and reproduce with either USP4 or USP15 knocked out, but not when both are knocked out (Vlasschaert, Cook, Xia, & Gray, 2017; Vlasschaert, Xia, Coulombe, & Gray, 2015). Understanding such functional redundancy is crucial for effective DRR because knocking out one of several redundant genes or pathways will not achieve the desired efficacy. One example of redundant pathways in bacteria involves the sequestration of iron by pathogenic bacteria. Iron is essential for almost all forms of life, but iron in host cells is typically wrapped in iron-binding proteins so that free iron concentration is as low as 10–24 mol/L (Raymond, Dertz, & Kim, 2003) so pathogenic bacteria would need to evolve special means to wrestle iron from host cells (Fischbach, Lin, Liu, & Walsh, 2006; Raines et al., 2016; Raymond et al., 2003). Different pathogenic bacteria often produce different kinds of siderophores (specific, often nonribosomal peptides with strong iron affinity) to gain iron from host cells. For example, Bacillus anthracis produces both bacillibactin and petrobactin for iron sequestration. To repurpose drugs against nonribosomal peptide synthetases that synthesize such siderophores, one needs to identify all nonribosomal peptide synthetases instead of just one and repurpose drugs against all of them. This also represents an interesting evolutionary problem. As pathogens always need iron sequestration, the host has a tendency

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to evolve an immune response against bacterial siderophores. This would force the pathogen to evolve new siderophores not targeted against by the host immune system. Such an arms race would result in pathogens evolving a series of new genes for new nonribosomal peptide synthetases. Another example of redundant pathways involves arabinofuranosyltransferases (Mt-EmbA, Mt-EmbB, and mtAftA) in Mycobacterium tuberculosis (Alderwick, Seidel, Sahm, Besra, & Eggeling, 2006). The enzymes contribute to the synthesis of cell wall mycolylarabinogalactan-peptidoglycan complex and are targeted by the drug ethambutol, which inhibits both Mt-EmbA and Mt-EmbB but does not inhibit Mt-AftA (Alderwick et al., 2006). Without knowing these alternative pathways to accomplish an essential function, one is prone to repurposing drugs against one or two enzymes and missing the third, and then concluding that the drugs are useless. Activating alternative biological pathways to satisfy the need of growth and survival has been known in bacterial species since the discovery of the lac operon and the glucose/lactose genetic switch (Jacob & Monod, 1961), and a drug cannot be effective against a pathogen or a cancer cell unless we know how cells do things with alternative pathways that can be activated in response to the drug.

3.3 Genomic Analysis to Reduce Drug Resistance and Environmental Impact Drug resistance (Davies & Davies, 2010) has contributed significantly to the high cost of drug development (David et al., 2009; Drews & Ryser, 1997) because it often renders a costly developed drug useless. Drug resistance is associated with the large-scale application of the first effective antibiotic penicillin (Abraham et al., 1941; Abraham & Chain, 1940), and is known not only in bacterial pathogens but also in eukaryotic ones, e.g., in the malaria parasite P. falciparum against the currently most effective antimalaria drug artemisinin, soon after the large-scale application of artemisinin in Asian countries (Noedl et al., 2008, 2010; Noedl, Socheat, & Satimai, 2009). The rapid development of drug resistance in HIV-1 (Smyth et al., 2014; Smyth, Davenport, & Mak, 2012) highlights the importance of understanding drug resistance in developing antibiotics and antivirals. Obviously, one should be cautious in repurposing a drug that is known to induce drug resistance. There are two different approaches to reduce the development of drug resistance in microbial pathogens. The first is to attack multiple targets to minimize the number of pathogens that can survive the drug and develop drug resistance. Anti-HIV-1 drugs minimally target protease and reverse transcriptase simultaneously (Flexner, 2007). The other approach aims to develop drugs with high pathogen-specificity. If only one bacterial strain is pathogenic out of 10 strains, then an ideal drug should target this specific pathogenic strain instead of targeting all 10 strains, as the latter would increase the number of bacterial cells exposed to the drug. Drug resistance could evolve in those nine nonpathogenic strains and then be horizontally transferred back into the pathogenic strain. Genomic analysis has contributed to drug specificity by identifying pathogenic islands (distinct and typically horizontally transferred DNA segment conferring virulence) present in the genome of the pathogenic strain but not in the nonpathogenic strain, and drugs targeting genes in such pathogenic islands would be specific to the pathogenic strain. For this reason, genes in pathogenic islands have become preferred sources of drug targets (Gal-Mor & Finlay, 2006; Hacker, Blum-Oehler, Muhldorfer, & Tschape, 1997; Hacker & Kaper, 2000). Bioinformaticians 3.. EXAMPLES AND CASE STUDIES

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have created databases (Pundhir, Vijayvargiya, & Kumar, 2008; Yoon, Park, & Kim, 2015) to facilitate the identification pathogenicity islands as drug targets. Genomics sequence analysis and phylogenetics have been frequently used to identify a conserved sequence or structure that can guide the development of vaccine or drug targets (Anisimova, 2015; Manocheewa, Mittler, Samudrala, & Mullins, 2015; O’Connell, Kim, & Excler, 2014). A recent paper on fighting against drug resistance in tumor development (Wanget al., 2018) sheds light on countering drug resistance in viruses and bacteria. Suppose that cancer evolution is a diversifying process starting from a few cancer cells leading to many different cancer cell lineages, all with different mechanisms for rapid cell proliferation, and all those slowly replicating lineages eliminated during cancer evolution. These diversifying cancer cell lineages are equivalent to moving (and unpredictable) targets that are difficult to aim at. However, the application of an anticancer drug, which represents a specific selection force, may drive different cancer cell lineages to evolve through a specific and predictable pathway of drug resistance. Thus, in the presence of the anticancer drug, all different cancer cell lineages may share this hopefully unique and identifiable drug-resistance mechanism, which can then be targeted. This seems to be an excellent idea that may help in the fight against bacterial and viral drug resistance. One problem with this approach is that drug resistance may arise from diverse mechanisms, e.g., there are many different HIV-1 variants that are resistant to HIV-1 protease inhibitor. Modern bioinformatic analysis coupled with innovative experiments has substantially enhanced our understanding of mechanisms leading to the evolution of drug resistance. In one experiment (Belanger, Lai, Brackman, & LeBlanc, 2002), error-prone PCR was used to introduce random mutations in Streptococcus pneumoniae genes. These mutated amplicons were then used to transform S. pneumoniae with some resulting colonies exhibiting resistance against antibiotic fusidic acid. DNA sequence analysis revealed a single mutation in the fusA gene accounting for the drug resistance. Many cases have been documented in HIV-1 protease in which a single mutation can significantly change the susceptibility of the protease to its inhibitors (Rhee et al., 2010; Young et al., 2010). Such studies allow us to estimate the proportion of mutations that confer drug resistance among all random mutations. As has been pointed out previously (Xia, 2017b), the likelihood of pathogens developing drug resistance depends mainly on mutation rate, parasite population size and genetic diversity. Lack of genetic diversity implies that drug resistance needs to arise de novo, in which case the mutation rate becomes a major limiting factor in pathogens evolving drug resistance. Population size, genetic diversity, and mutation rate can be measured by representative sampling of pathogens and genomic analysis. Spontaneous mutation rate traditionally was measured in mutation accumulation experiments, which are tedious and, for practical reasons, have been done mainly on viruses and a few rapidly replicating bacterial species (Drake, 1964, 1966). Dating the origin of pseudogenes and then comparing their divergence against their functional counterparts (Chang & Li, 1995; Li, Gojobori, & Nei, 1981; Li, Wu, & Luo, 1984; Wu et al., 1986) allow for an estimation of spontaneous mutation rate (approximated by the neutral substitution rate) and mutation spectrum. High mutation rate and large population size increase the chance of parasites developing drug resistance. Unfortunately, drug approval has almost never taken into consideration the pathogen population size, diversity, and mutation rate.

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4 TRANSCRIPTOMICS AND DRR: A WORD OF CAUTION Transcriptomic data have been used extensively to identify and validate relationships illustrated in Fig. 1 to guide DRR. Conventionally, if two pathogens respond equally in growth and reproduction (or lack thereof ) to a panel of drugs, then they have drug-pathogen equivalence. A drug developed for one pathogen can therefore be repurposed against the other. Similarly, if a drug inhibits the function of reverse transcriptase of HIV-1 and also achieves the same in HTLV-1, then we have drug-target equivalence. However, in many cases, drug targets are not known, and our previous method of using sequence similarity to assess drug target equivalence is consequently inapplicable. One may use similarity in transcriptomic profiles (Shoemaker, 2006) or metabolomic profiles (Wishart, 2016a, 2016b; Xia, Psychogios, Young, & Wishart, 2009) to approximate drug-pathogen equivalence. That is, if we expose two pathogens to the same panel of drugs, and if changes in gene expression before and after the drug application are very similar between the two pathogens, then they are approximately drug-pathogen equivalent. This would also allow us to identify a small set of candidate genes that always increase or decrease with the drug application. Such a set of candidate genes helps us to identify drug targets and eventually establish drug-target equivalence. That is, transcriptomic analysis will allow us to add putative links between drugs and drug targets as in Fig. 1. Instead of illustrating the application of transcriptomic data in DRR, a cautionary note is offered here because there are too many uninformative data generated and erroneous results reported. The success of bioinformatic tools depends on a large amount of high-quality data and results, in particular the high-throughput sequencing data that now permeate almost all aspects of drug discovery. Ribosomal profiling, traditionally done through microarray (Arava et al., 2003; MacKay et al., 2004), is now almost exclusively done with deep sequencing of ribosome-protected segments of messages (Ingolia, 2010, 2014, 2016; Ingolia, Ghaemmaghami, Newman, & Weissman, 2009). Alternative splicing, which used to be detected by EST-based (Rogers, Thomas, Reddy, & Ben-Hur, 2012) and microarray-based (Pleiss, Whitworth, Bergkessel, & Guthrie, 2007), is now detected by RNA-Seq (Kawashima, Douglass, Gabunilas, Pellegrini, & Chanfreau, 2014), especially by lariat sequencing (Awan, Manfredo, & Pleiss, 2013; Stepankiw, Raghavan, Fogarty, Grimson, & Pleiss, 2015). High-throughput sequencing is also used for characterizing epigenetic patterns, in ChIP-Seq for characterizing protein binding sites on the genome ( Johnson, Mortazavi, Myers, & Wold, 2007) and Hi-C for 3-D DNA architecture (Lieberman-Aiden et al., 2009). Anyone can take such data, pump them through a pipeline, and generate beautiful graphs for publication. Whether the results are correct or not has little relevance. What is key to publication is to have data so big that reviewers are overwhelmed and do not have time and resources to check if the results are correct. This point has been highlighted with a real-data analysis (Xia, 2017a). The most frequently used software for gene expression is Cufflinks (Trapnell et al., 2012), which was cited nearly 5000 times by May 16, 2018. A detailed comparison of gene expression results between Cufflinks and the more recent ARSDA (Xia, 2017a) will be made here. The same data set is used, i.e., gene expression from an E. coli wild type characterized by RNA-Seq data (Pobre & Arraiano, 2015), with original read data archived in NCBI’s SRA database as

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(A)

689

(B)

FIG. 4

Contrast in gene expression (RPKM) between ARSDA and Cufflinks output for the same transcriptomic data in file SRR1536586.sra for E. coli wild type, with normal (A) and logarithmic scale (B). Four genes exhibiting strong discrepancies between ARSDA and Cufflinks are labeled.

SRR1536586.sra, and the gene expression data in RPKM computed with Cufflinks is in file GSM1465035_WT.txt.gz from NCBI Geo DataSets GSM1465035 (Pobre & Arraiano, 2015). We compare these Cufflinks-determined RPKM values with the equivalent from ARSDA. Gene expression from ARSDA (Xia, 2017a) and Cufflinks are mostly concordant (Fig. 4A), but four points (labeled in Fig. 4A) stand out as outliers. There are more outliers if we plot the points on logarithmic scale (Fig. 4B). RPKM values from a third program, Rockhopper (Tjaden, 2015), are similar to those from ARSDA. We will first focus on the four labeled outlying points in Fig. 4A). Either ARSDA severely overestimated or Cufflinks severely underestimated the expression of these four genes. If one researcher characterized the gene expression of the same wild type with ARSDA and deposited the RPKM values in NCBI Geo DataSets, then subsequent researchers, analyzing these two sets of RPKM values, would find these four genes highly differentially expressed. Thus, it is important to know where the discrepancies occur between different bioinformatics tools (Cufflinks and ARSDA in this case). One might mistake the outlying rpmD and rmpJ (Fig. 4A) for paralogous genes given the similarity in their name and attribute the discrepancy between Cufflinks and ARSDA to differences in assigning reads to closely related homologous genes, which is often difficult (Xia, 2017a). There are rpmA, rpmB, …, rpmJ genes in E. coli, but they are not paralogous. There are 6426 reads that can be mapped unambiguously to rpmJ (which is a single-copy ribosomal protein gene). One particular read alone occurs 2684 times in SRR1536586.sra, matching perfectly to the 36 nt at the 30 end of rpmJ and 14 nt immediately downstream (Fig. 5A). We therefore checked if these reads might have overlapped a downstream gene. However, the downstream gene (rpsM) is 146 nt away, so there is no ambiguity to assign these

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(A)

(B)

(C)

(D)

FIG. 5 Distribution of number of matched reads in the four genes highlighted in Fig. 4 ((A) rpmJ, (B) rplV, (C) rpmD, and (D) cspA of sequence lengths of 117, 333, 180, and 213, respectively). Each read is of length 50, and “Nucleotide site” is the site of the gene sequence where the first nucleotide of the matched read lands.

reads to rpmJ only. Cufflinks output reported a count of only 2114 reads for rmpJ instead of 6426 (and consequently the much-reduced RPKM value in Fig. 4A). Similarly, rpmD and rplV (Fig. 4A) have 14,468 and 22,747 unambiguous read matches, respectively, with the distribution of the number of matched reads shown in Fig. 5B and C, respectively. The corresponding counts in Cufflinks output are only 8108 and 11,801, respectively. Note that rpmD and rplV are also single-copy genes with no ambiguous read matches. As mentioned before, E. coli genes rpmA to rpmJ are not paralogous to rpmD, and neither are rplA to rplY paralogous to prlV. The last outlying gene (cpsA in Fig. 4A) does involve a paralogous gene family, with the phylogenetic relationship of the paralogous genes shown in Fig. 4A. Except for cspF and cspH, the rest of the paralogous genes are sufficiently distinct that no read matched equally well to two or more paralogous genes. cspA has 19,776 unambiguous read matches, but Cufflinks output has only 10,957, leading to a much lower RPKM than that from ARSDA (Fig. 4A). These discrepancies have been reported before (Xia, 2017a), although not as detailed. We hope that these discrepancies, presented in such detail, will help bioinformaticians analyzing transcriptomic data to appreciate the problem of erroneous results. Many claimed differential gene expressions may simply be due to incorrect data analysis. If these problems are not corrected, then we will encounter many false positives, i.e., false claims of identified relationships among drugs, drug targets, pathogen, and human.

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5 STRUCTURAL BIOLOGY AND DRR Structural biology in DRR aims to identify and validate three kinds of relationships: (1) the structural similarity among drugs, (2) the structural similarity among drug targets, and (3) the docking/binding propensity of drugs onto proteins and nucleic acids in the cell. All these tasks require that the structure of the drugs and drug targets be already known, e.g., already deposited in Protein Data Bank or PDB (Berman et al., 2000; Berman, Henrick, & Nakamura, 2003). If a drug target is a protein with unknown structure but a close homolog of the drug target has a known structure, then the structure of the drug target can be inferred by homology modeling tools such as SWISS-MODEL (Waterhouse et al., 2018). Software platforms such as SwissSimilarity (Zoete, Daina, Bovigny, & Michielin, 2016) characterizes structural similarities among drugs (or small molecules). One can input or construct a chemical and screen it against thousands of drugs from a variety of libraries, including ligand databases such as ChEMBL(Gaulton et al., 2012). Many drug-related databases and software tools for structural biology are available at http://click2drug.org/ maintained by the Swiss Institute of Bioinformatics. For drug targets that are proteins, the simplest structural comparison is between two drug targets that are homologous proteins because there are standard statistical tools such as generalized Procrustes analysis or GPA (Goodall, 1991; Gower, 1975; Ten Berge, 1977), which has been used not only for structural comparisons but also for data normalization (Xiong, Zhang, Martyniuk, Trudeau, & Xia, 2008). All structures deposited in PDB are characterized by a three-dimensional matrix with each row containing the X, Y, and Z coordinates of an atom in the protein. For two protein sequences that can be well aligned, each row in the 3-D matrix for one protein corresponds to a row in the 3-D matrix for the other protein. GPA takes two such 3-D matrices (representing two 3-D objects) and match one to the other by P translation, rotation, and scaling. The best match is measured by the least-squares criterion d2i , where di is often referred to as the squared deviation and is the distance between two corresponding points in the two 3-D matrices, and i is the ith atom with X, Y, and Z coordinates (i ¼ 1, 2, ..., L where LPis the protein sequence length). RMSD (root mean squared deviation) is the square root of d2i /L and has been used as an index of structural similarity between two homologous proteins, or between any proteins from which one can identify a set of landmark points present in both proteins. The docking/binding propensities between drugs and drug targets can be studied with docking software such as SwissDock (Grosdidier, Zoete, & Michielin, 2011). SwissBioisostere (Wirth, Zoete, Michielin, & Sauer, 2013) can be used to design and refine ligands. Such structural studies have contributed to the design and refinement of drugs against HIV-1 protease (Heal, Jimenez-Roldan, Wells, Freedman, & Romer, 2012), leading to effective inhibitors that can squeeze their way between the two monomers (each with 99 amino acids) to disrupt the protease function (Broglia, Levy, & Tiana, 2008; Wlodawer & Erickson, 1993; Wlodawer & Vondrasek, 1998). Given one well-documented protein-ligand interaction, it is natural to infer that other proteins with similar sequence or structure may also bind to the ligand. In other words, if the ligand is a drug targeting one protein, then it could be repurposed to target other proteins with similar structures. Such a similarity-based approach (Ding et al., 2014; Ekins et al., 2015) is the conceptual foundation for the software SwissTargetPrediction (Gfeller et al., 2014).

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A ligand and protein may adapt to each other and interact in a dynamic fashion. Such dynamic interactions are not captured by X-ray crystallography or by NMR, which represents only a snapshot of structural dynamics. The software CHARMM (Brooks et al., 2009) and its derivatives aim to characterize such dynamic interactions of proteins with their binding partners.

6 PHENOTYPIC SCREENING, BIG DATA, AND DEEP LEARNING 6.1 Conventional Phenotypic Screening in DRR What constitutes phenotypic screening remains controversial but the common consensus (Eder, Sedrani, & Wiesmann, 2014; Moffat, Rudolph, & Bailey, 2014; Xia, 2017b) includes: (1) the screening involves a large number of compounds (drug candidates) ideally chosen systematically, (2) phenotypic changes in response to each compound are monitored, (3) a criterion of desirability is formulated and used in ranking the compounds, (4) those compounds generating desirable biological effects (phenotypes) are kept as drug candidates for further testing and validation, and (5) the mechanism of action is unknown and is not the focus of the screening. Phenotypic screening protocols differ with diseases. In screening for cancer drugs, one can use the 60 tumor cell lines maintained by the National Cancer Institute to screen anticancer drugs that may kill or suppress tumor growth (Shoemaker, 2006). Phenotypic screening against human cancer cell lines has led to not only successful repurposing of anticancer drugs such as rapamycin as early as the early 1980s (Douros & Suffness, 1981), but also better understanding of the important mTOR pathway. With transcriptomic data showing differences between tumor cells and matched normal cells, one may also check if a drug can restore tumor cells to normal gene expression or induces gene expression patterns similar to those of the apoptosis pathway. In contrast, phenotypic screening for antibiotics and antivirals for DRR often take advantage of the Prestwick Chemical Library of approved drugs (Nylen et al., 2014; Torres et al., 2016;Ulferts et al., 2016 ; Zuo et al., 2012). Such screening has yielded several antivirals against enteroviruses that were not considered as antivirals (Ulferts et al., 2016; Zuo et al., 2012). For example, fluoxetine, which is a selective serotonin reuptake inhibitor (Zuo et al., 2012) with effect on the reproductive axis of female goldfish (Mennigen et al., 2008), was found to be effective against coxsackievirus. Similarly, several antimicrobial agents, which were not considered as antimicrobials (such as niclosamide, carmofur, and auranofin), were identified as effective against Staphylococcus aureus biofilms (Torres et al., 2016). The screening also led to the discovery of several drugs that enhance innate immunity by inducing the production of antimicrobial peptides such as LL-37 in the host (Nylen et al., 2014). Phenotypic screening from thousands of Chinese traditional medicines is responsible for the successful discovery of artemisinin, which is the most effective drug against the malaria parasite P. falciparum (Miller & Su, 2011). Phenotypic screening of FDA-approved drugs for drug repurposing is cost-effective because these drugs have already gone through the difficult path of regulatory authorities and the development of manufacturing protocols.

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6.2 Problems With Phenotypic Screening and Its Deep Learning Solution Phenotypic screening, by definition, does not reveal the drug target or the mechanism of drug action. This can cause misinterpretations that are perhaps best illustrated through Simpson’s Paradox with real data involving “phenotypically screening” two surgical operations on their efficacy for kidney stones (Charig, Webb, Payne, & Wickham, 1986). The example is equally applicable to phenotypic screening of drugs if we simply replace the two surgical operations by two drugs. There are actually several surgical procedures for treating kidney stones, but I include here only two: (1) all open procedure (AOS) and (2) percutaneous nephrolithotomy (PN). A part of the results is shown in Table 1 from which Charig et al. (1986) derived their concluding statement that “Success was achieved in 273 (78%) patients after open surgery, 289 (83%) after percutaneous nephrolithotomy”. Thus, the conclusion is that AOS is worse (78% success rate) than PN (83% success rate) in treating kidney stones. Our previous conclusion would be fundamentally altered if we include an additional factor: kidney size (Table 2). Now it is quite obvious that AOS is consistently better than TABLE 1 Data for Evaluating Two Surgical Procedures in Treating Kidney Stones AOS

PN

Success

273

289

Failure

77

61

Total

350

350

%Success

78%

83%

AOS, all open procedure; PN, percutaneous nephrolithotomy. Source: Derived from Table 2 in Charig, C. R., Webb, D. R., Payne, S. R., & Wickham, J. E. (1986). Comparison of treatment of renal calculi by open surgery, percutaneous nephrolithotomy, and extracorporeal shockwave lithotripsy. British Medical Journal (Clinical Research Edition), 292(6524), 879–882.

TABLE 2 Success Rate of Two Surgical Treatments for Removing Kidney Stone Small

Large

AOS

PN

AOS

PN

Success

81

234

192

55

Failure

6

36

71

25

Total

87

270

263

80

%Success

93%

87%

73%

69%

AOS, all open procedure; PN, percutaneous nephrolithotomy. Source: Data taken from Table 2 of Charig, C. R., Webb, D. R., Payne, S. R., & Wickham, J. E. (1986). Comparison of treatment of renal calculi by open surgery, percutaneous nephrolithotomy, and extracorporeal shockwave lithotripsy. British Medical Journal (Clinical Research Edition), 292(6524), 879–882.

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PN for both small stones (93% vs. 87%) and large stones (73% vs. 69%). We also note from Table 2 that (1) both AOS and PN have much higher success rate for small stones than for large stones and (2) patients treated with PN had mostly small stones and patients treated with AOS had mostly large stones. Thus, the high success rate in Table 1 for PN is not because PN is better than AOS, but because PN was applied to a large number of patients with small stones. It is this association between PN and small stones that leads to the previous misleading conclusion based on the data in Table 1 that disregard kidney stone size. In short, the “big data” in Table 2 and our “deep learning” of the big data reduce the chance of reaching a misleading conclusion based on the limited data in Table 1. One might further argue that the success rate may depend not only on surgical procedures and stone size, but also on gender, age, ethnicity, etc., of patients. Thus, one should go many more levels deeper, leading to much bigger data and much deeper learning. This means that (1) a much larger number of patients are needed because the number of patients in each subcategory will decrease rapidly as the number of factors included in the model and (2) the human mind may not be sufficient to comprehend multifactorial interactions. A computer can do this much more reliably. For this reason, a comprehensive collection of data together with large-scale computational data analysis are essential to derive reliable patterns from the data. This will drive the deposition and compilation of big data and development of artificial neural networks for deep learning (LeCun et al., 2015). The previous example also highlights the point that bioinformatic analysis, when carried out by researchers with little biological background, runs a high risk of generating meaningless or wrong conclusions without incorporating essential biological factors. If the data analyst does not suspect the effect of kidney stone size, then the analysis may not include stone size as a factor and we will arrive at a wrong conclusion that PN is better than AOS. Lack of biological insights is mostly responsible for the many bioinformatics analyses contributing nonsense to the drug discovery literature. In summary, many databases and bioinformatic tools have been created and their application will contribute to more linked relationships as schematically shown in Fig. 1. These relationships should lead to much more efficient and much less costly DRR. However, much more research is needed to take up the challenge of explosively increasing high-throughput sequencing data.

Acknowledgments This study is supported by a Discovery Grant from Natural Science and Engineering Research Council (NSERC, RGPIN-2018-03878) of Canada. The author thanks K. Roy for the invitation to contribute to the book and members in XiaLab for their discussion and comments.

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C H A P T E R

24 In Silico Databases and Tools for Drug Repurposing Onur Serc¸inog˘ lu, Pemra Ozbek Sarica

Department of Bioengineering, Faculty of Engineering, Marmara University, I˙stanbul, Turkey

1 INTRODUCTION Drug repurposing is based on the idea that drugs that have been approved for the treatment of a particular disease or those that passed the initial stages of evaluation but failed in later stages of development may be used to treat disease conditions other than the original purpose of a treatment (Hodos, Kidd, Shameer, Readhead, & Dudley, 2016). Repurposing of drugs can be accomplished either experimentally (activity-based) or computationally. One disadvantage of the experimental approach is the need to develop a screening test and collect a large number of drug molecules for testing, leading to major time and labor costs. Computational drug repurposing, on the other hand, is considered a faster approach since it utilizes bioinformatics tools and databases containing drug-related data to identify candidate drugs for repurposing (Shim & Liu, 2014). A computational drug repurposing approach should facilitate finding new routes between a known drug molecule and a disease. Hence, from this perspective, the data to be used should allow the making of connections between multiple drugs or diseases and should even clarify them. These connections are largely chosen depending on the method(s) or strategies used in a drug repurposing project. For example, if the researcher chooses to utilize genomewide gene expression profiles, then drug- or disease-induced gene expression profiles (GEPs) would be of primary interest and importance. In this case, such data would represent the connection between drugs/diseases. A GEP is not the only type of connection that can be utilized, as the effect of a drug can be described and measured on many other levels such as molecular interactions (drug targets), side effects, phenotypic changes, or disease indications. Indeed, an increasingly higher number of studies continues to demonstrate the integration of different types of data for repurposing of drugs (Hodos et al., 2016; Vanhaelen et al., 2017).

In Silico Drug Design. https://doi.org/10.1016/B978-0-12-816125-8.00024-9

703 # 2019 Elsevier Inc. All rights reserved.

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FIG. 1 A representation of the connections between data entities in a drug repurposing context.

Fig. 1 demonstrates how new connections between a drug and a disease can be made using drug-related data in the context of drug repurposing in a simplified manner. Imagine that a drug (Drug A) is used successfully for the treatment of a disease. Upon treatment, Drug A interacts with a “target,” resulting in further “perturbations” on other levels (changing GEPs, pathways, etc.), finally leading to an observable improvement in the disease condition, possibly with side effects. These drug-induced responses are recorded and deposited at relevant databases with appropriate links between each entity. Now let’s imagine that another drug, Drug B, is investigated for its potential to treat the same disease (i.e., repurposing). A widely used method is to use similarities between disease-associated drugs, GEPs, pathways, and targets. For example, if two drugs are structurally similar to each other, then one may hypothesize that they bind to the same target (guilt-by-association). The similarity can be in terms of GEPs or targets as well. Alternatively, novel connections between Drug B and disease-associated targets or genes may be found. Hence, data describing connections on different levels between drugs, genes, target proteins, pathways, diseases, and side effects may be exploited for drug repurposing. Owing to years of pharmacological research, there is a vast amount of freely available drug-related data deposited in a number of online resources and databases. This chapter aims

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to present an overview of different data sources that may be used in customized computational drug-repurposing workflows for bioinformatics researchers as well as ready-made web-based or stand-alone tools that allow an end-user to test drug repurposing hypotheses or form new ones in a straightforward manner.

2 DATABASES FOR DRUG REPURPOSING This section includes summaries of selected databases containing different data types that can be used for drug repurposing in the order presented in Table 1. The content of the databases covers different types of drug-related information, including interactions involving drugs, genes, targets, pathways and diseases; rare diseases and orphan drugs; clinical trials; side effects and toxicity profiles; repurposed drugs; and 3D structures of drug molecules. While compiling the list of databases, we paid particular attention to the accessibility and up-to-dateness of each source. Data sources that were not accessible or have not been updated for long periods of time (i.e., more than a few years) were excluded. We should also note that the database classification we use here may be subjective, as the boundary between data types is not very clear and many databases include multiple types of data.

2.1 ADReCS-Target Adverse Drug Reaction Classification System-Target Profile (ADReCS-Target) (Huang et al., 2018) provides information on drug interactions with protein, gene, and genetic variation and their related adverse reactions and toxicity details. The current version of ADReCSTarget is an update of previous DITOP (Zhang et al., 2007) and DART databases. Containing over 66,000 relations, the protein-ADR data is retrieved by manual curation of literature while gene-ADR data is obtained from the ADRAlert project.

2.2 BindingDB BindingDB (Gilson et al., 2016) is a database including 1,439,799 experimental binding affinity values of proteins and drug-like small molecules. It provides simple and advanced search options for the users. The latter option combines multiple criteria (sequence, MW, SMILES) and data source restriction options such as BindingDB, ChEMBL, and PubChem that can be identified by the user.

2.3 BioGRID Established in 2003 (Breitkreutz, Stark, & Tyers, 2003), BioGRID (The Biological General Repository for Interaction Datasets) is one of the oldest data sources for interactions between genes, proteins, or other biomolecules. In April 2018, the database contained more than 1 million interactions between genes, proteins, and chemicals in 66 different species.

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706

TABLE 1 Data Resources for Drug Repurposing Reference

A database including toxicity/ adverse event information of drugs/chemicals as well as druggene-protein interactions.

2017

http://bioinf.xmu.edu. cn/ADReCS-Target

(Huang et al., 2018)

Interactions involving drugs, genes, targets and proteins

A database of measured binding affinities for small, drug-like molecules.

2016

https://www. bindingdb.org/bind/ index.jsp

(Gilson et al., 2016)

BioGRID

Interactions involving drugs, targets and proteins

A curated web resource for interactions between drugs, chemicals, genes and proteins.

2018

http://wiki.thebiogrid. org

(Chatr-Aryamontri et al., 2017)

ChEMBL

General drug information, drug-target interactions, disease indications, mechanisms of action

A manually curated chemical database of bioactive molecules with drug-like properties

2017

https://www.ebi.ac. uk/chembl/ws

(Davies et al., 2015)

ChemProt-3.0

Interactions involving drugs, genes, targets, proteins, side effects, diseases

A database focusing on chemical- 2018 protein interactions

http://potentia.cbs. dtu.dk/ChemProt/

(Kringelum et al., 2016)

Comparative Toxicogenomics Database (CTD)

Interactions involving drugs, genes, targets, proteins and diseases

A database including geneprotein-disease interactions and toxicity information

2018

http://ctdbase.org

(Davis et al., 2017)

CTD2 Dashboard

Interactions involving drugs, genes, targets and proteins

A database including observations from CTD2 member research centers related to cancer

2017

https://ctd2(Aksoy et al., 2017) dashboard.nci.nih.gov/

DGIdb 3.0

Interactions involving drugs and genes

A database including information 2018 on drug-gene interactions and the druggable genome

www.dgidb.org

(Cotto et al., 2018)

DrugBank 5.0

Interactions involving drugs, genes, targets and proteins, side effects, disease indications, mechanisms of action

A database containing comprehensive molecular information about drugs, their mechanisms, their interactions and their targets.

www.drugbank.ca

(Wishart et al., 2018)

Data Type(s)

Short Description

ADReCS-Target

Interactions involving drugs, genes and proteins, adverse events

BindingDB

2018

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4. TOOLS AND DATABASES

Last Update URL

Database

A web portal including general drug information, drug-target interactions, pharmacological action, bioactivity, approvals, pharmaceutical products

2018

http://drugcentral.org

(Ursu et al., 2017)

ECOdrug

Evolutionary data, drug-target interactions

A database connecting drugs and conservation of their targets across species

2018

http://www.ecodrug. org

(Verbruggen et al., 2018)

IntAct

Interactions involving drugs, targets and proteins

A molecular interaction database 2018 curated from literature and direct data uploads.

http://www.ebi.ac.uk/ (Orchard et al., 2014) intact

KEGG Databases

Interactions involving drugs, genes, targets, proteins and pathways

A suite of databases categorized into systems, genomic, chemical and health information

2018

http://www.genome. jp/kegg

(Kanehisa et al., 2016)

Pharos

Interactions involving drugs, genes, targets, proteins and pathways

A resource connecting drugs, targets and diseases

2018

https://pharos.nih. gov/idg/index

(Nguyen et al., 2017)

PDBBind

Interactions involving drugs, targets and proteins

A database providing 2017 experimental binding affinity data for biomolecular complexes present in the PDB

http://www.pdbbind. org.cn/

(Liu et al., 2017)

PDID

Interactions involving drugs and proteins

A database include protein-drug interaction data

2015

http://biomine.cs.vcu. edu/servers/PDID/ index.php

(Wang et al., 2016)

PharmGKB

Interactions involving drugs, genes, pathways and diseases

An expert-curated web resource including information about drugs, drug labels, dosing guidelines and drug-gene interactions

2018

https://www. pharmgkb.org/

(Whirl-Carrillo et al., 2012)

STITCH

Interactions involving drugs, targets and proteins

A database including compoundprotein interactions

2018

http://stitch.embl.de

(Szklarczyk et al., 2016)

SuperDRUG2

An online resource for approved/ 2018 Interactions involving drugs, genes, targets and proteins, side marketed drugs effects

http://cheminfo. charite.de/superdrug2

(Siramshetty et al., 2018)

SuperTarget

Interactions involving drugs, targets and proteins

http://insilico.charite. de/supertarget

(Gunther et al., 2007)

A database including information 2012 related to drug-target interactions, pathways and ontologies

Continued

707

Interactions involving drugs, genes, targets and proteins, side effects, disease indications, mechanisms of action

2 DATABASES FOR DRUG REPURPOSING

4. TOOLS AND DATABASES

DrugCentral

TABLE 1

Data Resources for Drug Repurposing—cont’d Short Description

IUPHAR/BPS Guide to Pharmacology (GtoPdb)

Interactions involving drugs and targets

A database including information 2016 on biological targets of licensed drugs and other small molecules

http://www. guidetopharmacology. org/

(Southan et al., 2016)

Therapeutic Interactions involving drugs Target Database and targets (TTD)

Database of therapeutic protein 2018 and nucleic acid targets, pathway information and corresponding drugs and ligands directed at each of these targets

http://bidd.nus.edu. sg/group/ttd/ttd.asp

(Li et al., 2018)

ChemSpider

3D structures of chemicals/ drugs

A database mainly including chemical structures

2015

http://www. chemspider.com/

(Pence & Williams, 2010)

ZINC 15

3D structures of chemicals/ drugs

A database including drug-like compound 3D structures

2018

https://zinc15.docking. (Sterling & Irwin, 2015) org/

SWEETLEAD

3D structures of chemicals/ drugs, general drug information

An in silico database of approved 2013 drugs, regulated chemicals, and herbal isolates for computer-aided drug discovery

https://simtk.org/ home/sweetlead

Side Effect Resource (SIDER)

Adverse events, side effects

A resource including information on drugs and their side effects

2015

http://sideeffects.embl. (Kuhn et al., 2016) de/

ClinicalTrials. gov

Clinical trials

A web resource for accessing latest clinical trials information from around the world.

2018

https://clinicaltrials. gov/

DrugPath: A Database of Drug-Induced Pathways

Drug-induced pathways

A database that mainly includes drug-induced pathways

2014

http://www.cuilab.cn/ (Zeng et al., 2015) drugpath

Connectivity Map (CMap)

Gene expression profiles

A genome-scale library of cellular 2018 signatures.

http://clue.io/cmap

(Lamb, 2007; Lamb et al., 2006; Subramanian et al., 2017)

ArrayExpress

Gene expression profiles

A resource including data from high-throughput functional genomics experiments, including responses to drug treatments.

https://www.ebi.ac. uk/arrayexpress/

(Kolesnikov et al., 2015)

2018

Reference

(Novick et al., 2013)

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Data Type(s)

708

Last Update URL

Database

A major genomics data repository 2018

https://www.ncbi.nlm. (Barrett et al., 2013) nih.gov/geo/

PubChem

General drug information

A database including information 2018 on chemical structures, identifiers, chemical and physical properties, biological activities, patents, health, safety, toxicity data, and many others

https://pubchem.ncbi. nlm.nih.gov/

DailyMed

General drug information, drug FDA drug labels and indications labels and indications

2018

http://dailymed.nlm. nih.gov

eRAM

rare diseases

2018

http://www.unimd. org/eram/

Orphanet

Rare diseases and orphan drugs A resource for rare diseases and orphan drugs

2018

http://www.orpha.net

repoDB

Repurposed drugs

Contains information about drug repositioning successes and failures

2017

http://apps. chiragjpgroup.org/ repoDB/

Repurposed drug database

Repurposed drugs

Data source related to old and new uses of drugs

2018

http:// (Shameer et al., 2017) drugrepurposingportal. com/repurposed-drugdatabase.php

FAERS

Side effects

A database containing adverse event reports, medication error reports and product quality complaints.

2017

https://www.fda.gov/ (Fang et al., 2014) Drugs/Guidance ComplianceRegulatory Information/ Surveillance/ AdverseDrugEffects/

Offsides

Side effects

A resource including information on drugs and their side effects

2012

http://tatonettilab. org/resources/ tatonetti-stm.html

(Tatonetti et al., 2012)

ACTOR

Toxicity

A database including toxicity information on chemicals

2015

http://actor.epa.gov

( Judson et al., 2008)

WITHDRAWN

Withdrawn and orphan drugs

A database of withdrawn and discontinued drugs

2015

http://cheminfo. charite.de/withdrawn

(Siramshetty et al., 2016)

encyclopedia of rare disease annotations for precision medicine

(Kim et al., 2016)

( Jia et al., 2018)

(Brown & Patel, 2017)

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Gene Expression Gene expression profiles Omnibus (GEO)

709

710

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The information contained in BioGRID is based on manual curation of scientific literature for reported biological interactions. Text-mining methods are used to accelerate and facilitate this process. Addition of interactions of chemicals such as drugs to the database as well as a visualization interface for the resulting interaction networks are relatively new features (Chatr-Aryamontri et al., 2017).

2.4 ChEMBL ChEMBL is the small molecule database of the European Molecular Biology Laboratory (EMBL) (Davies et al., 2015). As of April 2018, the database included more than 10,000 targets and 2 million compound records. In addition to general molecular information such as the structure, mechanisms of action, and disease indications (if the molecule is a drug), the database offers target information as well.

2.5 ChemProt 3.0 ChemProt 3.0 is a data resource of chemical-protein-disease interactions (Kringelum et al., 2016). As of April 2018, the database included more than 1.7 million compounds and 20,000 proteins. ChemProt aggregates data from external open-access databases including ChEMBL (Davies et al., 2015), BindingDB (Gilson et al., 2016), PDSP Ki, DrugBank (Wishart et al., 2018), PharmGKB (Whirl-Carrillo et al., 2012), IUPHAR-DB (Southan et al., 2016), and STITCH (Szklarczyk et al., 2016). The users can query the database starting from drug/chemical names, diseases, targets, or side effects. The results are returned in the form of a heatmap (global pharmacological heatmap), which allows inspection of chemical-protein interactions as well as other similar chemicals/proteins to the interacting chemical-protein pair.

2.6 Comparative Toxicogenomics Database The Comparative Toxicogenomics Database (CTD) is a database containing a large number of manually curated chemical-gene-disease relationships from scientific literature (Davis et al., 2017; Grondin, Davis, Wiegers, Wiegers, & Mattingly, 2018). The latest update (April 2018) contains more than 37 toxicogenomics interactions among chemicals, genes, and diseases. The user is able to search and query the database by entering chemical, disease, gene names, or gene ontology (GO) terms. The CTD website offers several tools to extract and visualize connections between all these chemicals and genes or diseases. A relatively new update to the database includes the connections between genes and GO biological process terms and diseases as inferred from the data already included in CTD. Such “inference networks” may be used to find commonalities between seemingly disconnected biological processes and diseases. For example, if there is a connection between a gene and a GO biological process as well as a disease, then that GO biological process can be inferred to be connected to the disease. This inference may be utilized by further integrating chemical-gene associations in a drug repurposing setting.

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2.7 CTD2 Dashboard The CTD2 Dashboard is a database mainly containing cancer-related experimental findings of CTD2 (Cancer Target Discovery and Development) network member groups (Aksoy et al., 2017). A major feature of the database is that the reported data is organized in the form of a gene, protein, or a compound (subject) and observations related to it. The authors indicate that this was done to enable a unified presentation of the very different types of data produced by the members of the network. As such, the data may come from different types of experiments such as small molecule (e.g., drugs), genetic perturbation, or molecular interaction assays. Such an integrated representation of different types of data may be quite useful for hypothesis generation in a drug repurposing project. However, being a recently introduced database, the data size currently is rather limited (139 submissions from 13 centers) and only a few drug molecules are included for the time being.

2.8 DGIdb 3.0 DGIdb (Drug-gene Interactions database) is a database containing drug-gene interactions from 30 different resources (Cotto et al., 2017). The latest update contains more than 6000 druggable genes and 20,000 drug-gene interactions covering more than 40,000 genes and 9000 drugs, respectively. The website offers two options to query the database for (1) drug-gene interactions or (2) druggable genes. Drug-gene interactions are reported after the user enters a list of genes or drugs containing drug/gene names, type of interaction, data source, and PMIDs, including cross-links to data resources.

2.9 DrugBank 5.0 DrugBank is a major resource containing comprehensive drug-related information (Wishart et al., 2018). As of April 2018, the database contained more than 10,000 drugs. The data is organized in the form of DrugCards, each containing drug-related and targetrelated information including general drug information, indications, mechanisms of action, interactions with other drugs, and clinical trial information. External links to other drug, pathway, and target databases are provided. Target information includes literature references and the action of the drug on the target (i.e., agonist/antagonist or substrate/inducer, etc., for enzyme targets).

2.10 DrugCentral DrugCentral is a major web portal for accessing drug-related information including general drug information, disease indications, mechanisms of action, drug targets, clinical trials, regulatory information, side effects, active pharmaceutical ingredients, marketing status, etc. (Ursu et al., 2017). The database is continuously updated by aggregating related information from external public databases. Drug labels and literature references are manually curated. The web interface allows the users to query the database starting from drugs, targets, pharmacological action, or diseases. As of April 2018, the database contained information about 4509 drug molecules.

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2.11 ECOdrug ECOdrug (Verbruggen et al., 2018) is a database aiming to connect drugs and targets across species. Information regarding the conservation of drug targets in nonhuman species is used to provide data regarding similar pharmacological effects that can be observed. The database involves 640 eukaryotic species. Three ortholog prediction methods are available: Ensembl (Herrero et al., 2016), EggNOG (Huerta-Cepas et al., 2016), and InParanoid € (Sonnhammer & Ostlund, 2015). For the chosen drug targets, species group, Latin name, common name, and taxonomy group are listed along with the confidence of predictions retrieved from the databases individually.

2.12 IntAct IntAct is a molecular interaction database (Orchard et al., 2014) curated from literature and direct data uploads. The developers are aiming to provide the researchers with the most up-to-date and extensive amount of data. IntAct serves as a common curation environment of 11 molecular interaction databases. It includes data from 20,206 publications, 851,299 interactions, and 107,104 interactors. The input query can be given in terms of gene, protein, RNA or Chemical name, UniProtKB ID, RNACentral ID, RNACentral ID, PMID, and IMEx ID. As an output, the interaction detection method, references, and source databases are tabulated with links to the original data sources.

2.13 KEGG Databases KEGG (Kyoto Encyclopedia of Genes and Genomes) (Kanehisa, Goto, Furumichi, Tanabe, & Hirakawa, 2010) is a collection of databases containing a variety of biological information such as disease, pathways, and drug-related information. KEGG DRUG is the main resource contained within KEGG for drug-centric data. KEGG Drug contains general drug information, structure, targets, pathways, and drug-drug interactions.

2.14 Pharos Pharos is the web interface for collected and deposited data in the Target Central Resource Database (TCRD). The initial aim of constructing this database was to collect data on four protein families with known drug targets: ion channels, nuclear receptors, GPCRs, and kinases. With the expansion in the size of proteins that are considered druggable (Makley & Gestwicki, 2013), the database focuses now on all available human proteins. The data is comprised of text mining–based associations from literature and patents, bioactivities, drugtarget interactions, gene and protein expression, and other datasets from Harmonizome (Rouillard et al., 2016), which is another database including aggregated data from 66 different databases.

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2.15 PDBBind PDBBind database includes data regarding the experimental binding affinities of biomolecular complexes present in the Protein Data Bank (PDB) curated from 32,000 original references (Liu et al., 2017). The current 2017 version contains information on 17,900 complexes. Protein small-molecule ligand, nucleic acid and small-molecule ligand, protein and protein, and protein and nucleic acid complexes are included in the database along with their experimental affinity data that is reported in terms of dissociation constant (Kd), inhibition constant (Ki), or concentration at 50% inhibition (IC50). Upon query, the output displays the complex structures that can be viewed in various formats, binding data, hyperlinks to external databases such as PDB (Berman et al., 2000) and PDBsum (de Beer, Berka, Thornton, & Laskowski, 2014), as well as similar proteins with 90% sequence similarity and similar complexes with the same small molecule ligand. Searches can be conducted using data-based (PDB ID, name, resolution, year, etc.), ligand-based, and sequence-based options. High-quality test sets are also present that can aid developing docking/scoring methods. The user needs to log in to use the web site and the services.

2.16 PDID Protein-Drug Interaction Database in the structural human proteome (PDID) intends to include protein-drug interaction data for the entire human proteome (Wang et al., 2016). The ˚ are retrieved from PDB and the drug molecules are structures having a resolution of >3 A retrieved from PDBSum (de Beer et al., 2014). Protein-drug interactions include the known and the predicted interactions. The former data is collected from DrugBank (Wishart et al., 2018), BindingDB (Gilson et al., 2016), and PDB (Berman et al., 2000). For the latter, the predictions are done based on binding pocket similarity, profile alignments, and threading techniques using eFindSite (Brylinski & Feinstein, 2013), SMAP (Ren, Xie, Li, & Bourne, 2010), and ILbind (Hu et al., 2012).

2.17 PharmGKB PharmGKB (Pharmacogenomics Knowledgebase) is an expert-curated web resource aiming to clarify the impact of human genetic variation on drug responses (Whirl-Carrillo et al., 2012). The data in PharmGKB include general information about drugs, drug labels, dosing guidelines, as well as gene-drug associations, drug-centered pathways, and literature references. The database is continuously updated—as of April 2018 it contained 641 drugs, 130 pathways, 100 dosing guidelines, and 498 drug labels. The web interface offers an intuitive and easy-to-use browsing feature via molecule names, genes, variants, pathway names, disease names, etc. For drugs, general information including alternate names, structure, metabolites, molecular properties (such as the SMILES string), drug labels, clinical annotations, literature information, and pathways on which the drug is known to act are available in the web service.

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2.18 STITCH STITCH (Search Tool for Interacting Chemicals) is a database mainly including interactions of chemicals (such as drugs) with proteins (Szklarczyk et al., 2016). As of April 2018, STITCH included more than 40,000 chemicals, 9 million proteins, and 1.5 billion interactions. Chemical-protein interactions are extracted from a variety of resources including DrugBank (Wishart et al., 2018), MATADOR (Gunther et al., 2007), Therapeutic Target Database (TTD) (Li et al., 2018), CTD (Davis et al., 2017), ChEMBL (Davies et al., 2015), PDSP Ki and PDB (Berman et al., 2000). STITCH allows the users to browse the database using a network view. The user can start the search by entering the name of a drug/protein. The interactions of the queued item are represented via edges to other items in the bases (represented as nodes of the network). Detailed information regarding each chemical/protein in this network as well as interactions, such as the confidence, evidence level, binding affinity, or mode of actions can be inspected by clicking on the respective edges/nodes.

2.19 SuperDRUG2 SuperDRUG2 is a database containing comprehensive information about approved and marketed drugs (Siramshetty et al., 2018). The database was developed considering the difficulty of manual monitoring of drug approval data sources and finding detailed information regarding these molecules for computational drug discovery studies. It is built upon the previous version of the same database, SuperDrug, which includes mainly the 3D structures of drug molecules (Goede, Dunkel, Mester, Frommel, & Preissner, 2005). As of April 2018, information about more than 4000 drugs is included. Data from several regulatory agencies are aggregated along with drug targets extracted from DrugBank (Wishart et al., 2018), TTD (Qin et al., 2014) and ChEMBL (Davies et al., 2015). Unknown targets for known drugs are predicted using the SuperPred method (Nickel et al., 2014). The database also includes physiochemical properties and side-effect information (from SIDER [Kuhn, Letunic, Jensen, & Bork, 2016]). SuperDRUG2 provides additional features for 3D superposition to evaluate structural similarity of drug molecules as well as identification of problematic interactions.

2.20 SuperTarget SuperTarget is a database that includes information related to drug-target relations, pathways, and ontologies (Hecker et al., 2012). Drug-protein, protein-protein, and drug-side effect information is included. Proteins are retrieved from Uniprot (The UniProt Consortium, 2017), 3D structures from PDB (Berman et al., 2000), drugs from SuperDrug (Goede et al., 2005), binding information from BindingDB (Gilson et al., 2016), and side-effect data is from SIDER (Kuhn et al., 2016). Information from DrugBank (Wishart et al., 2018), BindingDB (Gilson et al., 2016), and SuperCyp are combined together to form the most comprehensive dataset available in addition to PubMed literature curation. Protein interaction data is gathered from ConsensusPathDB (Kamburov et al., 2011). The search within the drug database can be conducted via drug name, PubChemID, ATC-Code, or side effect. The search for a target can be conducted via target name, EC-Name, Uniprot ID, accession number, PDB ID, or KEGG target ID. The results are displayed, and more detailed information can be accessed

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upon clicking on the interesting result. KEGG pathway maps can be used to visualize the drug target relations. The results can be “added to basket” for further comparisons within the basket.

2.21 IUPHAR/BPS Guide to Pharmacology The IUPHAR/BPS Guide to Pharmacology (GtoPdb), also encompassing the Guide to IMMUNOPHARMACOLOGY (Southan et al., 2016), aims to provide high-quality data for immunology and pharmacology studies containing human targets and quantitative ligand relationships. Targets, ligands, processes, cell types, and diseases can be selected to search within. For the searched item, a summary section involving classification and various database IDs, a biological activity section involving targets of interaction and affinities, and a reference section involving structure download options in a variety of 2D formats are available.

2.22 Therapeutic Target Database The Therapeutic Target Database (TTD) is a database focusing on disease-related drug targets and related information (Li et al., 2018). TTD contains drugs, targets, mutations conferring drug resistance, differential expression profiles of disease-related drug targets, and drugs binding to these targets. The website allows queries by target names, drug names, or diseases.

2.23 ChemSpider The ChemSpider database offers users the ability to search within 245 data sources and 64 million chemical structures (Pence & Williams, 2010). The search can be done by using various keywords, such as systematic name, synonym, trade name, registry number, SMILES, InChI, CSID, or by drawing the chemical structure. The output data contains literature references, chemical formula, physical properties, chemical supplier, and other related sources if available (patents, etc.).

2.24 ZINC 15 The ZINC 15 (Sterling & Irwin, 2015) database consists of commercially available compounds that can be used for virtual screening, ligand docking, pharmacophore screening, and benchmarking. Over 230 million drug-like compounds are present in 3D forms that can be purchased. Additionally, 750 million compounds are also available to be searched for analogs. Searches can be conducted using ZINC ID, SMILES, SMARTS, or InChI, as well as drawings. This is a tool that brings chemistry and biological information together for druglike small compounds. Ligands and ligand-related data on their activities, chemical properties, commercial availability, purchasability, target, and biology association information are available.

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2.25 SWEETLEAD The SWEETLEAD database (Novick, Ortiz, Poelman, Abdulhay, & Pande, 2013) contains highly accurate chemical structures of approved drugs, illegal drugs, and chemical isolates that can be used in in silico screening studies. The dataset is curated from other publicly available sources such as ChemSpider (Pence & Williams, 2010), DrugBank (Wishart et al., 2018), KEGG (Kanehisa, Sato, Kawashima, Furumichi, & Tanabe, 2016), PharmGKB (Whirl-Carrillo et al., 2012), etc. Each piece of information is cross-checked among these sources and the data with the highest consensus is reported. For each drug, a molecular structure, collection of known synonyms, and relevant database IDs are provided.

2.26 Side Effect Resource (SIDER) SIDER is an information source including the adverse drug reactions of the marketed medicines, side-effect frequencies, drug and side-effect classifications, and drug target relations (Kuhn et al., 2016). The updated version in 2015 contains information on 1430 drugs, 5880 adverse drug reactions (ADR), and 140,064 drug-ADR pairs. The user can either process a search based on the drug name and get a list of side effects and the frequencies of occurrence, or alternatively can search for a side effect and get a list of drugs that can cause this effect. The data is also downloadable for the interested users.

2.27 ClinicalTrials.gov ClinicalTrials.gov is a web resource for ongoing and completed clinical trials on approved drug candidates, maintained by the U.S. Library of Medicine. As of April 2018, ClinicalTrials. gov included more than 250,000 studies from 173 countries. The user has the option to query the database by disease, country, or drug name. The returned results list includes individual studies, conditions, the drug name, and the location of trial.

2.28 DrugPath DrugPath is a database containing drug-induced pathways (Zeng, Qiu, & Cui, 2015). Unlike other databases containing similar pathway information, where pathways are constructed based on drug-target interactions, DrugPath utilizes the vast amount of druginduced genomic expression patterns (i.e., up- and downregulated genes) located in the CMap database (Lamb, 2007; Lamb et al., 2006; Subramanian et al., 2017) to infer pathways that are affected/regulated as a result of drug treatment. Pathways are constructed by performing enrichment analysis on KEGG pathways (Kanehisa et al., 2014). DrugPath offers a quite simple interface to query the database. The user simply gives the drug name as input. A list of pathways affected by the input drug is returned, with columns including KEGG Drug Pathway ID, Pathway Name, number of genes in the pathway, overlap amount, whether the genes are up- or downregulated by the drug, the P-value of the drug enrichment analysis, and the corrected P-value by the false discovery rate (FDR).

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2.29 Connectivity Map The Connectivity Map (CMap) is a resource containing both a library of genome-wide gene expression (transcriptome) signatures of culture human cells and a tool for finding “connections” between drugs (Lamb, 2007; Lamb et al., 2006). The database was lately extended to include gene expression profiles from the L1000 method (Subramanian et al., 2017). Given a list of up- and downregulated genes that are specific to a phenotypic condition (such as one that is specific to a disease), the “Query” interface can be used to query against the catalogue of perturbagen (drug)-induced expression signatures to find similar profiles (e.g., those that occur as a result of treatment with a specific drug). The website then returns a list as well as a heatmap of connectivity scores between the input expression signatures and those that are included in the database, allowing the user to see drugs that result in similar gene expression responses to their target condition.

2.30 ArrayExpress ArrayExpress (Kolesnikov et al., 2015) is a major functional genomics database maintained by the European Bioinformatics Database (EMBL-EBI), including next-generation sequencing (NGS) and microarray-based biological data. Continuously increasing in size, it contains more than 2.2 million arrays from more than 70,000 experiments. Users can browse the database and perform a text search to identify data sets associated with a target drug or disease.

2.31 Gene Expression Omnibus The Gene Expression Omnibus (GEO) hosted by the NCBI is a major resource of genomics data (Barrett et al., 2013). The latest update (April 2018) included more than 2.4 million samples, including results from mainly microarray and NGS experiments under different conditions (e.g., drug treatment). All the data in the GEO meet the requirements of MIAME (minimum information about a microarray experiment) and MIASE (minimum information about a high-throughput sequencing experiment). The database is organized in two subsections of GEO Datasets and GEO Profiles. GEO Datasets provide study-level information where each entry describes an individual study performed by (a) researcher(s). GEO Profiles provide gene-level information; the data in GEO Profiles is derived from GEO Datasets. NCBI GEO further offers a web tool (GEO2R) for analysis and comparison of two samples using established differential gene expression tools in R.

2.32 PubChem PubChem provides information on chemical substances (Kim et al., 2016). The data at PubChem is organized into three subdatabases: Substance, Compound, and Bioassay. Substance mainly includes chemical information whereas Compound provides structural data for chemicals extracted from Substance. Bioassay includes experimental bioactivity data.

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2.33 DailyMed DailyMed is a web site provided by the National Library of Medicine (NLM) including information on the marketed drugs in the United States. The medication content and labeling data can be downloaded and searched for each drug. The data is collected from the FDA, which collects information from the pharmaceutical industry.

2.34 Encyclopedia of Rare Disease Annotation for Precision Medicine The Encyclopedia of Rare Disease Annotation for Precision Medicine (eRAM) holds valuable information on rare diseases and enables clinicians to make diagnostic and therapeutic decisions regarding these diseases ( Jia et al., 2018). Formed by text mining of published medical literature and clinical data, it includes information on 15,942 rare diseases. On the website, the query can be conducted in terms of either a disease or a genotype. For an exact disease search, disease name, OMIM, UMLS, or OrphaNet IDs can be used, or alternatively a fuzzy disease search can also be conducted if the exact name of the disease is not known. The search returns general knowledge on the disease, phenotype information, and symptom and genotype information. Being a source of rare diseases, eRAM could be useful for the development of new drugs for them.

2.35 Orphanet Orphanet is a resource focusing on rare diseases and orphan (i.e., commercially undeveloped) drugs. The database includes more than 5000 such diseases and allows queries by disease name, gene symbol, or drug name. Rare diseases represent major drug repurposing potential since developing drug treatments for rare diseases is generally considered unprofitable due to their rare occurrence in the population. Moreover, orphan drugs usually have some clinical trial history, so they may also represent potential candidates for repurposing applications.

2.36 repoDB repoDB (Brown & Patel, 2017) is a database enabling drug-centric and disease-centric search options for the drug repurposing successes and failures extracted from DrugCentral and ClinicalTrials.gov. The user can alternatively download the full database as well. Drugs, indications, trial status, and further details regarding the status can be accessed. It contains information about 2051 diseases and 1571 drugs and aims to serve as a benchmark for the computational repurposing methods.

2.37 Repurposed Drug Database The Repurposed Drug Database (Shameer et al., 2017) is a source for drug repurposing including the latest news, methods, funding, and collaborations taking place in this research field. It also contains information on more than 300 repurposed drugs with their names

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and original and new indications. The users can upload their own data to improve the content of the database as well.

2.38 FAERS FAERS (Fang et al., 2014) is a database containing adverse event reports, medication error reports, and product quality complaints resulting in adverse events that were submitted to FDA by healthcare professionals, consumers, and manufacturers. The data files include patient outcomes for the event, report sources of the event, drug therapy start and end dates for the reported drugs, and indications for use of the drug. The observed clinical side effects might be useful in repurposing drugs.

2.39 Offsides The Offsides database (Tatonetti, Ye, Daneshjou, & Altman, 2012) was developed with an aim to identify common effects of drug interactions and characterizing the overall side-effect profile of drugs by mining the FAERS system. It can be used to find novel associations for the drugs in the clinical trial period before the drug approval stage. It contains 438,801 off-label side effects for 1332 drugs and 10,097 adverse events.

2.40 ACToR The Aggregated Computational Toxicology Online Resource (ACToR) ( Judson et al., 2008) serves as a data source for toxicity, including over 500,000 chemicals. It includes several dashboards with varying scopes. The chemistry dashboard provides information such as intrinsic properties, structural identifiers, and linked substances on the searched item. The toxicity forecaster dashboard integrates data from various sources and serves to provide information on toxicity. The Endocrine Disruption Screening Program in the 21st Century dashboard evaluates chemicals for potential endocrine disruption. The chemical product category categorizes more than 43,000 chemicals in terms of usage or functions. Lastly, Downloadable Computational Toxicology data enables users to download and search through highthroughput chemical screening data.

2.41 WITHDRAWN WITHDRAWN is a database of both withdrawn and discontinued drugs that were pulled out of the market in at least one country due to safety reasons, such as severe side effects as well as reported deaths (Siramshetty et al., 2016). For the 578 drugs contained in the database, information on physicochemical properties, therapeutic (or primary) targets, off-targets, toxicities, and biological pathways is included in addition to drug-target interactions and the genetic variations of these protein targets. The data on chemical information as well as the reason for withdrawal were collected from various sources such as the FDA, European Medicines Agency (EDA), literature sources, and DrugBank (Wishart et al., 2018). A multiple search option is present enabling users to systematically analyze drugs of interest in terms

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of drug search, target search, pathway search, and toxicity type search. For the drug search, the user has the option to choose from existing drugs as well as to upload another drug of interest.

3 WEB-BASED AND STAND-ALONE TOOLS FOR DRUG REPURPOSING This section describes selected ready-to-use web-based servers or stand-alone programs and packages that can be used as tools specifically for drug repurposing, in the order presented in Table 2. A computational drug-repurposing tool can be defined as “a set of packages/ programs or an online interface to a computational drug repurposing workflow/algorithm that accepts user-supplied data as input.” Here we extend this definition and include practical interfaces for querying the precomputed results of a drug repurposing study as well, since these can also be considered as tools facilitating the formation of drug repurposing hypotheses. Rather than categorizing each tool based on the underlying methodologies, we focus on the input data types and list what type of input is requested by each tool in order to perform a particular computational workflow.

3.1 Cogena Cogena is a Bioconductor package for performing differential coexpressed gene analysis (hence the name “cogena”) ( Jia et al., 2016). The main motivation for the development of this tool was the difficulty in translating new indications of drugs (e.g., drug repurposing candidates) into Mode of Action, for further study and validation in experiments. It is known from previous studies that coexpressed genes are usually prominent in the same pathways. Hence, identification of differentially coexpressed genes may be useful for disease prognosis (Yang et al., 2014). Hence, analysis of differential gene expression data together with coexpressed gene sets may provide valuable information regarding the biological processes involved. Cogena offers a computational pipeline for this purpose. First, given gene expression data, coexpressed gene groups are found using clustering methods. Then, a hypergeometric test is applied for enrichment of gene sets in each cluster for pathways (gene sets are extracted from the Molecular Signatures Database (MSigDB) (Liberzon et al., 2011), the GO (Ashburner et al., 2000; The Gene Ontology Consortium, 2017) biological process, and KEGG gene sets). For drug repurposing, the same test is then applied for each cluster, this time using gene sets from the CMap database (Lamb et al., 2006; Subramanian et al., 2017). Finally, the results are given in heatmap form containing the FDRs of the enrichment analyses. This way, starting from an input of differentially expressed genes, the user can inspect which gene clusters are enriched for which drugs as well as pathways.

3.2 DIGEP-Pred DIGEP-Pred (drug-induced gene expression profile) is an online tool for prediction of gene expression profiles, with solely the molecule structural formula given as input (Lagunin, Ivanov,

4. TOOLS AND DATABASES

TABLE 2

Web-Based and Stand-Alone Tools for Drug Repurposing Tool Type

Input Data

Last Update

Cogena

Gene-expression based drug repurposing

Gene expression profiles

DIGEP-Pred

Gene-expression based drug repurposing

Drug versus Disease (DvD)

4. TOOLS AND DATABASES

Reference

2016

http://bioconductor.org/ packages/release/bioc/html/ cogena.html

( Jia et al., 2016)

3D structure of a drug molecule

2015

http://www.way2drug.com/GE

(Lagunin et al., 2013)

Gene-expression based drug repurposing

Gene expression profiles

2016

https://www.ebi.ac.uk/ saezrodriguez/DVD/

(Pacini et al., 2013)

Galahad

Gene-expression based drug repurposing

Gene-expression profiles

2015

https://galahad.esat.kuleuven. be/

(Laenen et al., 2015)

gene2drug

Gene-expression based drug repurposing

A list of pathways

2017

http://gene2drug.tigem.it/

(Napolitano et al., 2017)

ksRepo

Gene-expression based drug repurposing

Gene expression profiles

2016

https://github.com/adam-sambrown/ksRepo

(Brown et al., 2016)

MANTRA

Gene-expression based drug repurposing

Gene expression profiles

2014

http://mantra.tigem.it/

(Carrella et al., 2014)

NFFinder

Gene-expression based drug repurposing

Gene expression profiles (upand downregulated genes)

2015

http://nffinder.cnb.csic.es/

(Setoain et al., 2015)

geneXpharma

Gene-expression based drug repurposing

Keywords (e.g., drug or disease names)

2017

http://genexpharma.org

(Turanli, Gulfidan, & Arga, 2017)

DRRS

Network-based drug repurposing

Drug-drug, disease-disease similarity matrices; known drug-disease associations

2018

http://bioinformatics.csu.edu. cn/resources/softs/ DrugRepositioning/DRRS/ index.html

(Luo et al., 2018)

DrugNet

Network-based drug repurposing

Drug or disease names

2015

http://genome.ugr.es:9000/ drugnet

(Martı´nez et al., 2015)

GeneDisease Repositioning

Network-based drug repurposing

2016

https://bitbucket.org/nclintbio/genediseaserepositioning

(Mullen, Cockell, Woollard, & Wipat, 2016)

Hetionet and Project Rephetio

Network-based drug repurposing

2017

http://het.io/repurpose/

(Himmelstein et al., 2017) Continued

721

URL

3 WEB-BASED AND STAND-ALONE TOOLS FOR DRUG REPURPOSING

Tool Name

TABLE 2 Web-Based and Stand-Alone Tools for Drug Repurposing—cont’d Tool Type

Input Data

PDOD

Network-based drug repurposing

A list of Entrez Ids and altered states of genes

PROMISCUOUS

Network-based drug repurposing

ChemMine Tools

Predicting and visualization of drugdrug similarities

C-SPADE

Last Update

Reference

2016

http://gto.kaist.ac.kr/pdod/ index.php/main

(Yu et al., 2016)

2011

http://bioinformatics.charite. de/promiscuous/

(Von Eichborn et al., 2011)

A list of compounds with SMILES strings and names, or SDF files or PubChem IDs

2011

http://chemmine.ucr.edu/

(Backman et al., 2011)

Prediction and visualization of drugdrug similarities

A list of compounds with bioactivity measurements

2018

http://cspade.fimm.fi/

(Ravikumar et al., 2017)

ChemTreeMap

Prediction and visualization of drugdrug similarities

A list of compounds, optionally with extra attributes/properties

2016

http://chemtreemap. readthedocs.io/en/latest/

(Lu & Carlson, 2016)

DeSigN

Prediction of drug efficacy against cancer cell lines

Gene-expression profiles

2018

http://design.cancerresearch. my/

(Lee et al., 2017)

SuperPred

Prediction of drug-drug similarities and drugtarget interactions

PubChem IDs, SMILES strings, molecular structure or drawing

2014

http://prediction.charite.de

(Nickel et al., 2014)

BalestraWeb

Prediction of drugtarget interactions

Drug or target IDs

2014

http://balestra.csb.pitt.edu/

(Cobanoglu et al., 2015)

DASPFind

Prediction of drugtarget interactions

Known drug-target interactions, drug-drug and target-target similarities

2016

http://www.cbrc.kaust.edu.sa/ daspfind/

(Ba-alawi et al., 2016)

DDI-CPI

Prediction of drugtarget interactions

3D structure of a drug molecule

2016

http://cpi.bio-x.cn/ddi/

(Luo et al., 2014)

DINIES

Prediction of drugtarget interactions

Drug/target names or any drug-target interaction data

2014

http://www.genome.jp/tools/ dinies/

(Yamanishi et al., 2014)

DPDR-CPI

Prediction of drugtarget interactions

3D structure of a drug molecule

2016

https://cpi.bio-x.cn/dpdr/

(Luo et al., 2016)

DRAR-CPI

Prediction of drugtarget interactions

3D structure of a drug molecule

2011

https://cpi.bio-x.cn/drar/

(Luo et al., 2011)

24. IN SILICO DATABASES AND TOOLS

4. TOOLS AND DATABASES

URL

722

Tool Name

Known drug-target interactions, drug-drug and target-target similarities

2015

https://alpha.dmi.unict.it/ dtweb/ https://alpha.dmi.unict.it/ dtweb/dthybrid.php

(Alaimo et al., 2015)

HitPick

Prediction of drugtarget interactions

Bioactivity screening results

2013

http://mips.helmholtzmuenchen.de/hitpick/

(Liu et al., 2013)

iDrugTarget

Prediction of drugtarget interactions

FASTA sequence of the protein or drug ID

2014

http://www.jci-bioinfo.cn/ iDrug-Target/

(Xiao et al., 2015)

Polypharmacology browser (PPB)

Prediction of drugtarget interactions

Keywords (e.g., drug names)

2017

http://gdbtools.unibe.ch:8080/ PPB/

(Awale & Reymond, 2017)

SEA

Prediction of drugtarget interactions

SMILES string

2007

http://sea.bkslab.org/

(Keiser et al., 2007)

SwissTargetPrediction

Prediction of drugtarget interactions

SMILES string or drawing

2014

http://www. swisstargetprediction.ch/

(Gfeller et al., 2014)

DR PRODIS

Structural-interaction based drug repurposing

Drug or target keywords

2015

http://cssb.biology.gatech.edu/ skolnick/webservice/ repurpose/index_search.html

(Zhou et al., 2015)

DrugQuest

Text-mining based drug repurposing

Keywords (e.g., drug or disease names)

2016

http://bioinformatics.med.uoc. gr/cgi-bin/drugquest/ drugQuest.cgi

(Papanikolaou et al., 2016)

MeSHDD

Text-mining based drug repurposing

Drug or disease names

2016

http://apps.chiragjpgroup.org/ MeSHDD/, https://github.com/ adam-sam-brown/MeSHDD

(Brown & Patel, 2016)

PolySearch2

Text-mining based drug repurposing

Keywords (e.g., drug or disease names)

2015

http://polysearch.ca

(Liu et al., 2015)

LimTox

Text-mining based toxicity prediction

Keywords (e.g., drug names)

2017

http://limtox.bioinfo.cnio.es/

(Can˜ada et al., 2017)

Drug Repurposing Hub

Other

Keywords (e.g., drug or disease names)

2017

www.broadinstitute.org/ repurposing

(Corsello et al., 2017)

RE:fine Drugs

Other

Keywords (e.g., drug or disease names)

2017

http://drug-repurposing. nationwidechildrens.org/search

(Moosavinasab et al., 2016)

723

Prediction of drugtarget interactions

3 WEB-BASED AND STAND-ALONE TOOLS FOR DRUG REPURPOSING

4. TOOLS AND DATABASES

DT-Web and DT-Hybrid

724

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Rudik, Filimonov, & Poroikov, 2013). It uses the method of PASS (Prediction of Activity Spectra for Substances), which predicts different types of biological activities for organic compounds given a molecular structure. The underlying prediction model for DIGEP-Pred was trained using existing gene and protein expression profiles at the CTD (Davis et al., 2017). Being a prediction tool, the tool does not suffer from the limitations of other gene expression–based tools where only drugs included in the gene expression databases can be studied.

3.3 Drug vs. Disease Drug vs. Disease (DvD) is both an R package and a Cytoscape app developed for drug repurposing (Pacini et al., 2013). DvD requires either a drug or a disease gene expression profile. The method is based on comparing gene expression profiles of drugs and diseases from Array Express (Kolesnikov et al., 2015), Gene Expression Omnibus (GEO) (Barrett et al., 2013), and CMap (Lamb, 2007) databases. The comparison is made via correlation analysis using Gene Set Enrichment Analysis (GSEA) (Subramanian et al., 2005). Negatively correlated drugs and diseases may represent drug repurposing opportunities under the hypothesis that the action of the drug is likely to revert the expression profile under the disease conditions back to a healthy one. The ability to dynamically query the relevant databases is a particularly useful feature of DvD, since it allows the user to utilize the most up-to-date data in the repurposing workflow.

3.4 Galahad Galahad is a web portal offering various tools for investigating the effect of drugs using genome expression profiles (Laenen, Ardeshirdavani, Moreau, & Thorrez, 2015). Starting from user-supplied control and treatment Affymetrix microarray data, Galahad implements a data processing and analysis pipeline including quality control and differential expression analysis as well as functional enrichment of differentially expressed genes and drug-target prioritization. Differential expression analysis is done via the Bioconductor limma package (Ritchie et al., 2015). A useful feature of the Galahad server for drug repurposing is the drug-target prioritization. In order to achieve this, the authors integrate the user-supplied expression profiles into a protein-protein interaction network using a method they developed previously (Laenen et al., 2015). In this method, the network-based gene prioritization method by Nitsch, Gonc¸alves, Ojeda, de Moor, and Moreau (2010) is extended and applied in a genome-wide context. In essence, differential gene expression profiles are integrated into a protein-protein functional association network from STRING (Szklarczyk et al., 2015) and genes are ranked according to whether they are surrounded by differentially expressed genes. This is based on the assumption that differentially expressed genes are functionally associated with the drug’s target, since they are often present in the same pathway.

3.5 Gene2drug Gene2drug (Napolitano et al., 2017) is an online tool for drug repurposing based on druginduced genome expression data from the CMap database (Lamb, 2007; Lamb et al., 2006;

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Subramanian et al., 2017). The tool is based on a method that the authors developed previously, called Drug Set Enrichment Analysis (DSEA) (Napolitano, Sirci, Carrella, & Di Bernardo, 2015). DSEA uses the GSEA method (Subramanian et al., 2005) with one particular feature that is distinct from GSEA: instead of a given set of genes, a set of drugs is provided by the user to find pathways that are most up- or downregulated by the action of most of the drugs in this set. In DSEA, the aim is to find a common mechanism of action (MoA) of a given set of drugs based on the drug-induced expression data in the CMap database. In Gene2drug, the authors modify the DSEA method to rank drugs based on their potential to target a given input pathway. The Gene2drug website requires a set of pathways as an input. It also allows the user to find relevant pathways for a therapeutic target gene by simply querying the gene name against a set of pathway databases. Once given a list of pathways, the tool returns ranked lists of compounds with Enrichment Scores that represent the tendency of up- or downregulation by the respective drug for each pathway database included. This way, drugs with a repurposing potential for a target therapeutic application can be discovered.

3.6 ksRepo ksRepo is a tool developed for allowing flexibility in the usage of experimental datasets and databases for computational drug repurposing (Brown, Kong, Kohane, & Patel, 2016). The idea behind ksRepo is to allow the user to utilize any control/case study expression profiles with any drug-gene interaction database. The tool implements an expression-based approach analogous to that of CMap (Lamb et al., 2006). A common identifier that allows conversion between gene expression and disease datasets is required. The usage for drug repurposing is demonstrated by using five prostate cancer datasets from GEO and the CTD as the gene-drug interaction database in (Brown et al., 2016). The tool is offered as an R package and example scripts for application are provided by the authors.

3.7 MANTRA MANTRA 2.0 (Mode of Action by Network Analysis) is an online tool for drug repurposing (Carrella et al., 2014) that utilizes the drug-induced genome expression profile data included in the CMap database (Lamb, 2007; Lamb et al., 2006; Subramanian et al., 2017), to describe similarities between multiple drugs using the GSEA method. Similar to other tools utilizing gene expression profiles, the underlying idea is that if two drugs share similar profiles, then their MoA will be similar as well. In MANTRA, a GSEA-based distance is used to represent drug-drug similarities. These are then integrated into a massive drug-drug network. Analysis of this network to find communities yields groups of similar drugs with repurposing potential. Another feature of MANTRA 2.0 is that the users can upload their own drug-induced gene expression profile from a microarray experiment. This way, novel compounds can also be assessed in terms of their gene expression–based similarity to other drug molecules.

4. TOOLS AND DATABASES

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24. IN SILICO DATABASES AND TOOLS

3.8 NFFinder NFFinder is a web-based tool that can be used for drug repurposing applications (Setoain et al., 2015). Given an input list of up- and downregulated genes from a microarray experiment, the tool performs a comparison of this set to large databases of microarray experiments using the MARQ method (Vazquez et al., 2010). The NFFinder database includes data from GEO (Barrett et al., 2013), CMap (Lamb, 2007; Lamb et al., 2006) and DrugMatrix (Ganter, Snyder, Halbert, & Lee, 2006), enriched with terms related to drugs, diseases, and expert researchers. The output of NFFinder lists the most similar/dissimilar expression profiles that correspond to different drugs. Entering a list of up- and downregulated genes that correspond to a disease condition to NFFinder, for example, can aid in the discovery of novel uses for existing drugs based on gene expression profile similarity.

3.9 Drug Repurposing Recommendation System Luo et al. (2018) recently developed the Drug Repurposing Recommendation System (DRRS) method, based on matrix completion, for drug repurposing. The idea behind DRRS is to use a recommendation system in the context of drug repurposing. In a similar way to other applications of recommendation systems, such as offering goods to purchase based on user preferences, diseases are “recommended” to a given list of drugs based on known drug-drug and disease-disease similarities and drug-disease connections. First, a heterogeneous network is constructed using drug-drug, disease-disease, and drug-disease similarity networks. Then, the adjacency matrix of the constructed network is completed using a singular value thresholding algorithm (Cai, Cande`s, & Shen, 2010). Thus, previously unknown connections between drugs and diseases are predicted. The whole workflow can be implemented by running the stand-alone package with drug-drug, disease-disease similarities and known drug-disease associations given as input.

3.10 DrugNet DrugNet is an online tool developed specifically for drug repurposing using a networkbased approach (Martı´nez, Navarro, Cano, Fajardo, & Blanco, 2015). In DrugNet, a global network, including subnetworks describing connections/similarities between drugs, diseases, and proteins, is formed using data from several external resources. For the drug-drug network, a score is calculated for describing similarities between drugs (as found in DrugBank 3.0 entries) using the Anatomical Therapeutic Chemical Codes (ATC) classification system for drugs. For the disease-disease network, Disease Ontology (DOA) (Schriml et al., 2012) is used to describe connections between diseases. The protein-protein network is taken from BioGRID (Chatr-aryamontri et al., 2015). The connections between drugs and diseases are then introduced by adding a connection if a disease is mentioned in the indications field of a drug in the DrugBank database. Drug-protein interactions are obtained similarly by querying for the existence of a protein in the drug’s targets list in the same database. Diseaseprotein connections are taken from disease and gene annotation (DGA) (Peng et al., 2012). Once the network is constructed, a network prioritization method developed by the same

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authors, ProphNet (Martı´nez, Cano, & Blanco, 2014), is used to yield a prioritized list of diseases given a query drug or vice versa. This way, similarities between drugs, diseases and protein may be utilized to find novel connections between drugs and diseases, aiding the user in forming novel drug repurposing hypotheses.

3.11 GeneDiseaseRepositioning The GeneDiseaseRepositioning dataset was developed for drug repurposing using an integrated network analysis of drug-gene-protein-disease associations (Mullen, Cockell, Tipney, Woollard, & Wipat, 2016). The authors used 11 different resources including CTD (Davis et al., 2017), UniProtKB (The UniProt Consortium, 2017), and OMIM (Hamosh, Scott, Amberger, Bocchini, & McKusick, 2005) to collect a list of gene-disease associations. These are then scored and ranked using a Bayesian statistical method developed by Lee, Date, Adai, and Marcotte (2004), yielding log-likelihood scores (LLS). Next, an integrated semantic network was constructed using these scored associations with data from complementary data resources describing gene-protein, drug-protein, drug-gene, and drug-side effect interactions. In this semantic network, different types of nodes interact via different types of edges. For example, if a drug is known to bind a protein, then node types of drug and protein are connected via a “binds_to” edge. Finally, this network is mined for “routes” from drugs to diseases using the mining algorithm developed by the same authors (Mullen et al., 2016). The final dataset, created in the Neo4j Graph Platform, is available for download.

3.12 Hetionet and Project Rephetio Hetionet is a network constructed by Himmelstein et al. (2017) mainly for drug repurposing, as a part of Project Rephetio. The network is a heterogeneous one in which multiple types of nodes interact with each other via multiple types of edges. There are 11 types of nodes (such as compounds, genes, diseases, pathways, side effects, symptoms, etc.) and 24 types of edges (such as Disease-resembles-Disease, Compound-binds-Compound, Geneparticipates-Biological Process, etc.). The edges are added using information from 29 different public-domain data resources, representing a vast amount of information from years of biomedical research. Using this network, Himmelstein et al. also performed an analysis to identify specific drugdisease connections for repurposing existing drugs with known disease relationships. For this purpose, they utilized a machine learning approach by training a logical regression model using 755 known drug-disease treatments and 29,044 nontreatments. They used this model to predict the probability of treatment between each compound and disease included in the network. The final list includes 209,186 compound-disease treatment predictions and can be accessed at http://het.io/repurpose. Detailed information regarding how specifically (i.e., via which type of edges) each drug and a possible treatment is connected can be obtained via cross-links to a Neo4j browser.

4. TOOLS AND DATABASES

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24. IN SILICO DATABASES AND TOOLS

3.13 PDOD PDOD (Yu et al., 2016) can be used to identify drugs having opposite effects on altered disease genes. A scoring function is used to discover drugs that can restore altered states of disease genes. This scoring is obtained using information from drug-drug target interactions from DrugBank (Wishart et al., 2018), directed molecular pathways from KEGG (Kanehisa et al., 2016), and disease-related genes from CTD (Davis et al., 2017). A benchmark set of 898 drugs and nine diseases were used and the results showed that PDOD outperformed other approaches. The users can submit altered genes as input, for which those drugs that might have an opposite effect on these genes will be predicted as an output.

3.14 PROMISCUOUS PROMISCUOUS (Von Eichborn et al., 2011) is a database including three different types of entities: drug-protein, protein-protein, and drug-side effect relations. The entities are connected in a network where the nodes are comprised of drug-side effect and drug-target, while the edges are defined as drug-side effect, drug-target, protein-protein, and drug-drug interactions. The database and the network were constructed using data from other public sources (SuperDrug (Goede et al., 2005), UniProt (The UniProt Consortium, 2017), PDB (Berman et al., 2000), and SIDER (Kuhn et al., 2016)). The user can query the database using a drug’s name, PubChem ID, ATC-code, or by their side effects. The results are visualized in the form of a network, showing the connections of a given drug with other drugs, targets, or side effects. For repurposing a drug, the user can, for example, find drugs that are similar to the input drug in the context of this network by utilizing the relevant options on the userinterface.

3.15 ChemMine Tools ChemMine is a tool that can be used to analyze small molecules by either importing or by drawing structures using the online molecular editor (Backman, Cao, & Girke, 2011). It offers various applications in the form of toolboxes, such as visualization of the compound via compound workbench, structural similarity quantification via structure similarity toolbox, retrieving similar compounds from PubChem (Kim et al., 2016) using a search toolbox, clustering, and data visualization via a clustering toolbox and predicting physicochemical properties of compounds via the physicochemical properties toolbox. For the given molecule(s), similar structures can be searched within specified cut-offs using the selected algorithm and database and giving the desired number of outputs. Structural similarities among the compounds can be quantified. The clustering toolbox enables systematic structure and activity–based analyses of custom compound sets. By using the physicochemical property tool, 38 physicochemical property values such as H-bonding metrics (acceptors and donors), molecular weight, number of acidic groups, and number of OH groups can be retrieved. An R package version of the program is also available.

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3 WEB-BASED AND STAND-ALONE TOOLS FOR DRUG REPURPOSING

729

3.16 C-SPADE C-SPADE (Compound SPecific bioActivity DEndrogram) is a web-based tool for inspecting drug screening experiments alongside structural similarity of drugs (Ravikumar, Alam, Peddinti, & Aittokallio, 2017). Given a list of compounds with measured bioactivity values (e.g., IC50, EC50, Ki, or Drug Sensitivity Scores), the tool measures molecular fingerprint– based similarity and performs a hierarchical clustering accordingly. The results are presented in the form of a dendogram where structurally similar compounds are clustered together. Measured bioactivity values are shown as bubbles to represent the magnitude next to each compound in this dendogram. If annotations for compounds are provided, such as class of the compound or other properties, these are represented via different colors as well. Such a drug-drug similarity–based analysis augmented with screening results may aid in the formation of drug repurposing hypotheses.

3.17 ChemTreeMap ChemTreeMap is another tool developed for exploring similarity between chemical compounds with other properties such as bioactivity (Lu & Carlson, 2016). Molecules are described by extended connectivity fingerprints (Carhart, Smith, & Venkataraghavan, 1985; Rogers & Hahn, 2010). Similarity between molecules is calculated using a Tanimoto coefficient (Levandowsky & Winter, 1971). The results are presented in the form of a tree, much like a phylogenetic tree, where the length of each branch in the tree corresponds to the similarity between molecules (i.e., shorter branch lengths meaning more similar molecules, and vice versa). The user can also provide additional features for each compound in the input list, such as different molecular properties, bioactivity values, etc. When this is done, ChemTreeMap allows mapping of these extra properties/data on the tree by adjusting the color and size of the nodes in a flexible manner.

3.18 DeSigN DeSigN (Differentially Expressed Gene Signatures—Inhibitors) (Lee et al., 2017) is a tool for the prediction of drug efficacy against cancer cell lines. It links gene expression patterns from experimental studies with the drug response in a cancer cell line database. Given an input gene signature, the candidate drugs can be identified. Hence, the unknown drugs with unanticipated efficacy can be predicted. The algorithm depends on a reference database containing a set of predefined gene expression data associated with 140 drugs, DEG signatures supplied as an input, and a pattern-matching algorithm to match the similarities among the given query and the reference dataset. The reference dataset is constructed from the data retrieved from Genomics of Drug Sensitivity in Cancer Project (Yang et al., 2012). Given the upregulated and downregulated genes as an input, the output lists the drug name, target, connectivity score, and a P-value for the prediction.

4. TOOLS AND DATABASES

730

24. IN SILICO DATABASES AND TOOLS

3.19 SuperPred SuperPred tool (Nickel et al., 2014) is comprised of two methods: drug classification and target prediction. For the former, 2D- and fragment-similarity and a 3D superposition method are used. The query molecules are screened against 2600 known compounds. For the latter, a 2D similarity–based approach is used. The ligand-target information is gathered from SuperTarget (Hecker et al., 2012), ChEMBL (Davies et al., 2015), and BindingDB (Gilson et al., 2016) databases in order to predict targets. The query molecule is screened against 341,000 compounds, 1800 targets and 665,000 compound-target interactions. The queries can be given in terms of PubChem-Name, SMILES, drawing, or by loading a molecule.

3.20 BalestraWeb The BalestraWeb (Cobanoglu, Oltvai, Taylor, & Bahar, 2015) tool can be used to predict the potential occurrence of interaction between drug-target pairs and the possible interaction partners of the drugs/targets in the DrugBank (Wishart et al., 2018), using a probabilistic matrix factorization method. Additionally, similar drugs and similar targets can be identified based on their interaction partners. It contains drug-target, drug-drug, and target-target options. Similarities between two drugs are predicted to be high in the case of having similar target profiles. By typing the name of the drug, similar drugs are displayed. Additionally, similarity between two drugs can be measured as well. The target-target option can be used to find similarity between the targets. Targets with similar interaction profiles are given high similarity scores.

3.21 DASPFind The DASPFind tool (Ba-alawi, Soufan, Essack, Kalnis, & Bajic, 2016) can be used to predict drug-target interactions based on known drug-target interactions, drug similarities, and target similarities using a graph interaction model. The individual similarity data is retrieved from BRENDA (Placzek et al., 2017), SuperTarget (Hecker et al., 2012), DrugBank (Wishart et al., 2018), and KEGG (Kanehisa et al., 2016). The subnetworks containing similarity information of drug-drug, target-target, and drug-target are combined with the topology of the heterogeneous network in order to predict new interactions. As an input, the user needs to supply drug-target interaction dataset, drug similarity dataset, target similarity dataset, and drug and target similarity thresholds.

3.22 DDI-CPI The DDI-CPI (Drug-drug interaction chemical-protein interactome) tool (Luo et al., 2014) aims to predict unexpected drug-protein interactions based on CPI. It contains information about 2000 + FDA-approved drugs and 600 + ligand-bindable proteins. For the submitted molecules, potential interaction targets are suggested by the server. Furthermore, probabilities of drug-drug interactions between the submitted molecule and the library of drugs are shown based on the CPI profiles.

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3.23 DINIES DINIES (Drug-target Interaction Network Inference Engine based on Supervised Analysis) is a tool for predicting drug-target interactions (Yamanishi et al., 2014). Given the name of a drug/target, MOL text, or SMILES string as an input, the tool can list precalculated drug-target interactions based on data (such as chemical structure, side effects, sequence and protein domains) from KEGG or other databases (DINIES Search interface). In another interface, DINIES Predict, the users can submit their own data about drugs and targets in a tab-delimited format. DINIES uses a supervised network inference algorithm to make predictions.

3.24 DPDR-CPI and DRAR-CPI DPDR-CPI (Luo et al., 2016) is an updated version of DRAR-CPI (Luo et al., 2011) webserver, utilizing molecular docking simulations to construct a chemical-protein interactome for a user-supplied drug molecule and report putative drug targets and possible indications using machine-learning models. Unlike DRAR-CPI, this server can accept a variety of input formats including MOL, MOL2, PDF, SDF, and SMILES strings. The chemical-protein interactome is constructed via molecular docking simulations using Autodock Vina (Trott & Olson, 2010).

3.25 DT-Web and DT-Hybrid DT-Web (Alaimo et al., 2015) is the web interface for the DT-Hybrid tool (Alaimo, Pulvirenti, Giugno, & Ferro, 2013), which is offered as a stand-alone R package as well. DT-Hybrid is a network-based recommendation tool for drug-target interaction (DTI) prediction. The user is required to provide an initial list (matrix) of known drug-target interactions. This information can be obtained from other resources, such as DrugBank (Wishart et al., 2018). In addition, the user may choose to upload two similarity matrices for drug-drug and target-target similarities as well. In the original DT-Hybrid paper, the authors use SIMCOMP (Hattori, Okuno, Goto, & Kanehisa, 2003) to get similarity scores between drugs and either BLAST hit scores or Smith-Water Local Alignment scores for target-target similarities. DT-Hybrid then constructs a drug-target interaction network using the three similarity matrices. In the web interface, the server returns the list of drugs including predicted targets for each drug with their corresponding correlation and P-values. This way, direct or indirect targets for drugs that are included in the given matrices can be predicted, allowing the user to explore additional uses for a drug of interest.

3.26 HitPick HitPick (Liu, Vogt, Haque, & Campillos, 2013) provides hit identification and target prediction based on chemical screening data using the B-score method. For target prediction, 2D molecular fingerprints are used based on the combination of 1NN similarity (Schuffenhauer, Floersheim, Acklin, & Jacoby, 2003) searching and Laplacian-modified naı¨ve Bayesian target (Nidhi, Davies, & Jenkins, 2006) models. For the input query, the most similar compound

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from the database of known ligand-target interactions is identified using Tanimoto (Willett, 2006) coefficient calculations. Then a score is generated by Laplacian-modified naı¨ve Bayesian target models for all known targets, enabling ranking of the target predictions. The validation set of the server is composed of 22,868 positive and 20,779,507 negative compoundtarget relationships. The user can choose the option of hit identification or target prediction from the top menu. The latter is listed with precision values.

3.27 iDrugTarget iDrugTarget (Xiao et al., 2015) offers four types of web services for the prediction of interactions between drugs and GPCRs (iDrug-GPCR), enzymes (iDrug-Ezy), ion channels (iDrug-Chl), and nuclear receptors (iDrug-NR). The benchmark datasets are available for download in each of these cases. As an input query, the protein sequence in FASTA format and the drug code is entered by the user. The result will indicate the existence or nonexistence of the interaction in a yes/no format for each query.

3.28 Polypharmacology Browser The Polypharmacology Browser (PPB) (Awale & Reymond, 2017) performs target predictions based on the CheMBL 21 database and using 10 different fingerprints as a similarity metric. The fingerprints are composed of various metrics such as molecular shape, molecular quantum numbers, polarity, pharmacophores, and fusions of these metrics in different combinations. When a query is placed, the search is conducted among 2.7 M ligand-target interactions, 4613 groups of at least 10 bioactive molecules for which the activities against a biological target are available. The output panel displays the target molecules and their related CheMBL links. The performance of the tool was also validated on 670 approved drug-target pairs.

3.29 SEA Using a 2D similarity approach, SEA (Similarity Ensemble Approach) (Keiser et al., 2007) uses chemical similarity of the ligands to relate and cluster the proteins. Their technique relates ligand sets and their corresponding protein targets in minimal spanning trees. Although based on chemical similarity, biological relations were observed in the obtained clusters. The origins, relationship, side effects, and polypharmacology of individual chemicals are all taken into account. For a given query, the output includes target key, target name, description, and P-values. Library searches in reference targets and query targets can also be conducted to search one or more queries to compare by SEA. The proposed drugs for repurposing were shown to be validated by experimental methods in their paper as well.

3.30 SwissTargetPrediction The SwissTargetPrediction tool (Gfeller et al., 2014) allows the users to obtain target predictions for small molecules. It does comparisons based on a library of five different

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organisms (Homo sapiens, Mus musculus, Rattus norvegicus, Bos taurus, Equus caballus), 2000 targets, and 280,000 compounds. For a chosen organism, a small molecule can either be drawn or SMILES can be pasted to the search box. Targets are predicted using ChEMBL (Davies et al., 2015) as a data source and displayed. Predicted targets are categorized according to their classes such as an ion channel, a membrane receptor, etc. In the output table, the name of the targets, common names, Uniprot IDs and links, ChEMBL IDs and links, probability metrics, number of available 3D and 2D compounds, and target classes are detailed.

3.31 DR. PRODIS The DR. PRODIS webserver provides an interface for predicted drug-target interactions by Zhou, Gao, and Skolnick (2015). The methodology presented in this study is distinct from other similarity-based approaches to drug repurposing: the predictions were made for the whole human proteome and not just for the portion of drugs and targets with known drug-target interaction information. The authors utilized a ligand homology method FINDSITEcomb (Zhou & Skolnick, 2013) on the whole human proteome (more than 20,000 proteins) using all the drugs present in the DrugBank (Wishart et al., 2018) and obtained an unprecedentedly high number of drug-protein interactions. The users can query the prediction results by drug or target names.

3.32 DrugQuest In essence, DrugQuest (Papanikolaou, Pavlopoulos, Theodosiou, Vizirianakis, & Iliopoulos, 2016) is an online tool for exploring similarities between DrugBank (Wishart et al., 2018) records. The tool uses a tagged copy of the DrugBank database to perform text mining on Description, Indication, Pharmacodynamics, and Mechanism of Action fields of each record. Then, binary vectors for each record, indicating whether given input term(s) occur in these fields, are constructed. These are then clustered using various similarity measures and clustering methods. The results are returned to the user in the form of “term clouds” occurring in different clusters, indicating similarities between drugs and/or diseases.

3.33 MeSHDD MeSHDD is an online drug repurposing tool based on drug-drug similarities from the literature, as represented by MeSH terms (Brown & Patel, 2016). In order to compute a similarity measure between drug pairs, first all MeSH Terms from the MEDLINE Base Repository, Drug Mentions from MEDLINE Chemical Items and Approved Drugs list from DrugBank were downloaded. Then, Drug Terms were enriched using a hypergeometric test. Then, based on the cooccurrence of drugs in this data, a drug-drug distance metric, serving as a measure of drug-drug similarity, was computed. Finally, drugs were clustered based on their distances to other drugs. Results are represented as a cluster dendogram with similar drugs in the same cluster. While the tool can be used by selecting a drug as an input, it can also

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be used in a disease-centric mode, where the user selects a disease and the drugs that are enriched for the treatment of the input disease are returned.

3.34 PolySearch2 Introduced in 2008, PolySearch has been a major tool for text mining–based extraction of relationships between various types of biological entities such as diseases, drugs, chemicals, genes, SNPs, tissues, organs, etc. (Cheng et al., 2008). The latest update (PolySearch2) incorporates a higher number of data sources (14 databases with text-rich content including UniProt, DrugBank, and Human Metabolome Database) and a much faster search and association extraction (Liu, Liang, & Wishart, 2015). PolySearch2 is very intuitive and easy to use for the end user as well. Given a biological entity (such as a drug name), PolySearch2 can find all associated entities (such as a disease). It is also possible to find associations of a drug with multiple diseases or other entities such as drugs as well. As such, the tool can be used in a very straightforward manner for drug repurposing.

3.35 LimTox LimTox is a recently developed tool for extraction of toxicity data for chemical compounds/drugs using a combination of machine-learning, term lookup, rule-based and pattern-based text mining strategies on scientific abstracts, papers, and assessment reports (Can˜ada et al., 2017). Given a drug name, chemical identifier, SMILES, or an InChI string, the tool performs preprocessing of literature text, compound recognition, text classification, and compound text ranking steps to return a list of matches to the user. The results list includes links to the publication and the sentence in which the drug molecule is mentioned. Several scores here (SVM, SVM confidence, Pattern, Term, and Rule) indicate the relevance/nonrelevance of the entry for adverse hepatobiliary effect.

3.36 Drug Repurposing Hub The Drug Repurposing Hub is a manually curated library of drugs and drug-related information developed by the Broad Institute for the specific aim of drug repurposing (Corsello et al., 2017). More than 3000 drugs are included in the database. The content has been compiled from various open access and proprietary vendor catalogs. A “repurposing app” allows the user to query the dataset by drug name, SMILES string, target, disease indication, disease area, and MoA. As a result, a list of drugs with detailed information is produced that shares the specific search criteria.

3.37 RE:fine Drugs RE:fine Drugs is a database created specifically for drug repurposing (Moosavinasab et al., 2016; Regan, Moosavinasab, Payne, & Lin, 2016). The idea behind RE:fine Drugs is that if a drug is known to be associated with a gene, then the association between that gene and a disease may indicate a repurposing potential for that drug molecule. With this motivation, the

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authors used the gene-drug associations at DrugBank and integrated this information with a GWAS and a PheWAS source, describing the genome-disease and phenome-disease associations. The final database includes 52,966 unique drug-disease relationships that can be exploited for drug repurposing. The user is able to query the database by entering a drug name, disease name, or a gene name. In return, a list of entries, including known and new indications, genes, the type of study (PheWAS or GWAS), P-values and Odds Ratios, number of Medline Abstracts and Clinical Trial Registers, and “Potential” for drug repurposing, are displayed. The “Potential” here describes one of the four categories: “Known/rediscovered,” “Strongly supported,” “Likely,” and “Novel.” “Known/rediscovered” indicates that a drugdisease relationship has already been reported in DrugBank (Wishart et al., 2018). “Strongly supported” indicates that the relationship is reported at the NIH clinical registry and the drug/disease terms cooccur in biomedical literature, whereas “Likely” indicates only one is true. “Novel,” on the other hand, indicates that neither a connection with an NIH trials registry nor a cooccurrence in the literature is reported for the given drug-disease pair. This may indicate that either there is no association between the drug-disease pair or it can be a novel finding.

4 CONCLUSION This chapter provides summaries of databases and web-based/stand-alone tools that can be used for drug repurposing study. Bioinformatics experts may explore the existing databases to integrate their content into a customized repurposing workflow, whereas the experimentalists can benefit from the tools listed while forming new drug repurposing hypotheses.

Acknowledgments Icons in Fig. 1 made by Smashicons and Freepik from www.flaticon.com.

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25 An Overview of Computational Methods, Tools, Servers, and Databases for Drug Repurposing Sailu Sarvagalla, Safiulla Basha Syed, Mohane Selvaraj Coumar Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Puducherry, India

1 DRUG REPURPOSING Drug discovery is a multistep, arduous process that requires a lot of time, money, and effort and has a low success rate. During the drug development phases, medicinal chemists often face challenges pertaining to the optimization of the compounds. However, the cuttingedge technologies such as microfluidics assisted chemical synthesis, microfluidics based biological activity screening, and artificial intelligence systems somehow accelerate the optimization process and allow the chemists to explore a broad area of chemical space compared with the manual synthesis and testing of compounds. Nevertheless, these systems are quite expensive; moreover, the lead compounds still need to pass through preclinical and clinical trials, which again require a lot of time, effort, and expense before they are approved (Schneider, 2018). To overcome this hurdle, over the past decades drug repurposing has gained more importance in the drug discovery process (Doan, Pollastri, Walters, & Georg, 2011). Drug repurposing or drug repositioning is a method used to find new therapeutic use of drugs which have already been approved for other indications. Drug repurposing apparently makes the drug discovery process much easier, because it not only speeds up the process but also circumvents the preclinical and clinical studies related to the ADMET properties, drug optimization issues, and enhances the drug development success rate (Fig. 1) (Novac, 2013). Moreover, recent data also suggest the high success rate of the drug repurposing strategy (Brown & Patel, 2017; Shameer et al., 2017). During the past decade, there has been an advancement in the acquisition of biological and chemical data pertaining to the alteration of pathways in diseases, alteration in protein

In Silico Drug Design. https://doi.org/10.1016/B978-0-12-816125-8.00025-0

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25. AN OVERVIEW OF COMPUTATIONAL METHODS

Schematic representation of the steps involved in conventional new drug discovery and drug repurposing

strategy.

structures related to the disease, drug targets, mechanism of drug actions, disease biomarkers, and genomics, which altogether offer biologists an opportunity to understand the complexity of diseases, drugs, and drug-target interactions and eventually to help in the drug repurposing process (Li & Jones, 2012). Consequently, there has been an increase in the number of tools available in different areas, such as chemoinformatics ( Jonsdottir, Jorgensen, & Brunak, 2005), bioinformatics (Issa, Kruger, Byers, & Dakshanamurthy, 2013), systems biology (Zou, Zheng, Li, & Su, 2013), and biological networks (Lotfi Shahreza, Ghadiri, Mousavi, Varshosaz, & Green, 2017). Application of these computational methods in drug repurposing is cost and time effective. Depending upon the knowledge and data available about the disease, drugs and drug-target interactions, and treatment outcomes, two types of strategies could be adopted by the scientific community for drug repurposing: phenotype-based screening, and knowledge and database-based methods (Fig. 2).

1.1 Phenotype-Based (Blinded) Screening Phenotype-based screening is helpful when there is little or no information about the target. Basically, in this approach large compound libraries are randomly screened (Sardana et al., 2011). However, in this approach the molecular mechanism of drug action and target

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FIG. 2 Different approaches for drug repurposing.

may remain unknown. The compounds identified through this approach can target more than one protein and even different pathways (Zheng, Thorne, & McKew, 2013). There are plenty of examples of drugs that were identified by this route, and the best example is aspirin, whose mechanism of action was revealed almost 100 years after its approval (Vane & Botting, 2003). The major advantage of this approach is that it can be applied to numerous diseases without the prerequisite knowledge about the targets involved in them.

1.2 Knowledge and Database Methods Knowledge and database-oriented drug repurposing requires specific knowledge and data about the disease, targets or biomarkers, drug target interactions, drug mechanism of action, altered biological pathways, and genomics. This strategy comprises target-based, signature-based, and pathway or network-based methods ( Jin & Wong, 2014). 1.2.1 Target-Based Methods The target-based approach requires prerequisite knowledge about the targets related to the disease. This approach starts with the identification of a target related to the disease, and the DrugBank database can be used for in vitro and in vivo high-throughput screening of drugs (Swamidass, 2011). Computational and bioinformatics tools are handy in this approach, as all drugs available in the DrugBank can be completely screened by in silico docking studies (Sawada, Iwata, Mizutani, & Yamanishi, 2015), and later they can be validated by in vitro and in vivo studies. The major advantage of this approach is that the most known targets are directly related to the disease; this approach improves the drug discovery process and guarantees the likelihood of finding better drugs, unlike the phenotype-based screening where the information about the drug target may remain unknown.

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1.2.2 Signature-Based Methods In this approach, knowledge of the large transcriptomic data is utilized to reveal the difference in gene expression between the disease and healthy controls (Sithara, Crowley, Walder, & Aston-Mourney, 2017). This comparison would help to uncover the unknown disease mechanisms. Similarly, the gene-expression signature data generated before and after treatment of a disease can help to discover a drug-induced differential gene-expression signature and to know whether the drug reverts the drug-disease associated differential geneexpression signature back to the healthy conditions (Iorio, Rittman, Ge, Menden, & SaezRodriguez, 2013). The advantage of this approach is that it discloses the new mechanism of action of drugs, gene connections, and relation of the same pathways related to different diseases. The best example is the Connectivity Map (CMap), a database that contains the gene-expression analysis of five human cancer cell lines treated with more than 1000 FDAapproved drugs (Lamb et al., 2006). 1.2.3 Pathway or Network-Based Methods In this method, data of signaling pathways and relationships among different biological molecules are used to reorganize the pathways in the form of networks to find key targets for drug repurposing (Lotfi Shahreza et al., 2017; March-Vila et al., 2017). The NCBI Gene Expression Omnibus (GEO) dataset has constructed a large network that includes 645 diseasedisease, 5008 disease-drug, and 164,374 drug-drug relationships (Barrett, 2013). This large network data set offers researchers an opportunity to find a new drug target/pathway for a disease and for effective drug repurposing. Depending upon the objectives and the availability of information, any of these methods or a combination of these methods can be applied by scientists in their research for efficient drug repurposing. This chapter focuses on the methods, tools, databases, and servers that are used for target-based drug repurposing. With this in mind, Section 3 discusses computer-aided drug design (CADD) techniques in general for drug discovery. Ways we can use CADD for target-based drug repurposing, along with the tools, databases and servers available freely over the Internet for this purpose (i.e., modeling the target and screening the drugs), are listed and discussed in Section 4. Section 5 briefly describes the databases/tools available for gene signature and pathway-based drug repurposing. Furthermore, the utility of the target-based method for drug repurposing using the freely available online resources is illustrated with a case study using Aurora kinase C as target protein and screening the DrugBank database compounds in Section 6. Since the availability of open access tools and databases for drug design is exploding exponentially, it is difficult to list all of them in this chapter. Hence, interested readers may also refer to the following web resources and publications for more information: 1. Open source molecular modeling (Pirhadi, Sunseri, & Koes, 2016) (https:// opensourcemolecularmodeling.github.io/). 2. VLS3D (Villoutreix, Lagorce, Labbe, Sperandio, & Miteva, 2013) (http://www.vls3d.com/ links/51-shortlist). 3. Off-targets, repurposing, repositioning hypotheses (http://www.vls3d.com/index.php/ links/chemoinformatics/off-targets-repurposing).

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2 COMPUTER-AIDED DRUG DESIGN Drug design is a critical step in the drug discovery process. It is an iterative process of finding a new drug molecule to a particular drug target (e.g., enzymes, receptor proteins, ion channels, etc.), which can alter its functional activity (Kindt, Morse, Gotschlich, & Lyons, 1991; Prachayasittikul et al., 2015; Takenaka, 2001). The designed drug molecule could be complementary in shape and charge to the target protein and strongly bind to the target and inhibit or activate its function, which in turn provides therapeutic benefit to the patient. However, the design and development of new drug molecules using traditional drug discovery techniques that depend on trial-and-error methods (random) involving the testing of large numbers of chemical substances on cultured cells/biochemical assays (in vitro testing) or on animals (in vivo testing) are very time consuming and expensive. However, with the recent advancements in computational methodologies, a more rational approach to the design and development of drug molecules is possible. The process of drug design using computational (in silico) tools is referred to as CADD (Tang, Zhu, Chen, & Jiang, 2006; Zhang, 2011). Once the molecules are designed in silico, the most promising ones can be synthesized/purchased and tested in vitro/in vivo, thereby effectively decreasing the number of molecules tested in vitro/in vivo. Hence, CADD acts like a “virtual shortcut” to identify small molecules for in vitro testing, predict the effectiveness and plausible side effects, and help in the improvement of bioavailability of the molecules. Hence, CADD is more cost-effective and it reduces the time required to discover a drug. Moreover, the field of CADD has recently witnessed rapid growth and advancement in the technologies, both at the hardware and software levels, and has immense potential in the drug discovery process. A number of drugs, including aliskiren, saquinavir, oseltamivir, dorzolamide, boceprevir, zanamivir, rupintrivir, and nolatrexed were discovered and optimized using CADD techniques (Sliwoski, Kothiwale, Meiler, & Lowe Jr., 2014; Talele, Khedkar, & Rigby, 2010). CADD can be classified into two types, namely structure-based drug design (SBDD) and ligand-based drug design (LBDD) (Sliwoski et al., 2014). LBDD is carried out when the target protein 3D structure is not available. It mainly depends on the knowledge of other molecules that bind to the target protein/enzyme of interest. The ligand-based pharmacophore screening, molecule similarity search, and quantitative structure-activity relationship (QSAR) are a few of the standard LBDD methods that are widely employed in CADD to shortlist the molecules for testing. The basic assumption of LBDD is that similar molecules will have similar activities. In the ligand-based pharmacophore screening, the ligands that bind to the target protein are used to develop a common pharmacophore hypothesis to screen the small molecule libraries. In the molecular similarity search, the known ligand’s molecular fingerprint/ substructure can be used to identify new molecules with similar fingerprint/substructure by screening the small molecule libraries. QSAR is a regression or classification-based method that models the relationship between the structural features of a set of known ligands that bind to the target protein and their corresponding biological activity. Thus, the generated QSAR models can be used to screen small molecule libraries to find new molecules with improved biological activity (Verma, Khedkar, & Coutinho, 2010). On the other hand, SBDD depends on the knowledge of the 3D structure of the target protein and it has rapidly grown because of the advancements in proteomics and genomics,

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25. AN OVERVIEW OF COMPUTATIONAL METHODS

along with concomitant development in structural biology and computational chemistry. Moreover, SBDD has helped in the discovery of many successful drugs such as saquinavir, oseltamivir, and zanamivir. Once the drug target’s 3D structure is resolved using either experimental (X-ray or NMR) or computational methods (homology modeling and threading), the drug target’s structure can then be used to virtually screen small molecule libraries and prioritize molecules for biological testing. Alternatively, the 3D structure of the target protein can be used to design molecules by assembling molecular fragments in the active site of the target, which is referred to as de novo drug design (Hartenfeller & Schneider, 2011). In addition, SBDD is extensively used in the lead optimization process, to improve the binding affinity of a molecule with the target protein by analyzing the protein-ligand interaction and contemplating the necessary structural modifications in the molecules to improve the interaction with the target (Sliwoski et al., 2014).

2.1 CADD Techniques 2.1.1 Virtual Screening Virtual screening (VS) is a computational method used to search small molecule libraries in order to identify and narrow down potential molecules that are likely to bind/interact with the drug target (Lavecchia & Di Giovanni, 2013; Rester, 2008). The potential molecules identified in VS can be tested in an in vitro assay (e.g., biochemical assay, cell culture, etc.) to confirm the activity of the molecule. Molecules with confirmed activity are referred to as hits or leads. The VS process decreases the time and money required to test a small molecule library, as it acts as a filter to select only a few potential molecules for testing in an in vitro assay. Moreover, VS is very fast and widely employed in the drug discovery process to identify small molecule inhibitors. The VS methods used to screen the small molecule libraries are generally classified as either ligand-based screening methods (e.g., similarity search and pharmaophore) or structure-based screening methods (e.g., docking and structure-based pharmacophore (SBP)). 2.1.1.1 DOCKING-BASED VIRTUAL SCREENING

Molecular docking is a computational technique used to predict the binding orientation of a ligand with a biomolecule (target) (Bartuzi, Kaczor, Targowska-Duda, & Matosiuk, 2017; Lengauer & Rarey, 1996), including protein-small molecule, protein-protein, protein-DNA, and protein-RNA interactions. Docking involves two processes, a searching and a scoring function (Kitchen, Decornez, Furr, & Bajorath, 2004). The searching algorithms (e.g., systematic, molecular dynamics (MD) simulation, and genetic algorithm) employ several degrees of translational, rotational, and conformational freedom to find an appropriate binding orientation of the ligand in the target protein. The knowledge derived from this binding orientation could be used to predict the binding affinity or activity of the ligand using the scoring functions (e.g., force field, empirical, machine-learning, and knowledge-based). Hence, docking has become a powerful computational tool to predict the various bimolecular interactions. Some of the well-known docking programs commonly used by scientific communities are AutoDock, GOLD, DOCK, Glide-XP, LibDock CDOCKER, FlexX, and LigandFit. All these programs use different searching and scoring algorithms with different parameters, and these

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could be classified broadly as shape-based (e.g., LibDock) and energy-based (e.g., Glide XP) methods. Using the docking programs, compound libraries can be virtually screened for identifying hits. 2.1.1.2 STRUCTURE-BASED PHARMACOPHORE SCREENING

Virtual screening using SBP is an important and an alternative method to docking-based VS (Falchi, Caporuscio, & Recanatini, 2014; Pirhadi, Shiri, & Ghasemi, 2013). In SBP, a set of pharmacophore features (hydrophobic, aromatic, hydrogen bond acceptor, hydrogen bond donor, positive and negative ionizable groups) of the ligands are identified. These features are essential for optimal intermolecular interactions of the ligands with the biological target. In the absence of a protein 3D structure or when the drug target is unknown, ligand-based pharmacophore hypotheses are generated to screen the small molecule databases. However, recent advancements both in structural biology and computational chemistry have greatly helped to derive pharmacophore hypotheses from the 3D structures of protein and/or protein-ligand complexes. Several tools such as E-pharmacophore (Schr€ odinger Suite 9.2 software package), Catalyst (Discovery Studio 3.1), Ligand and Structure-Based Query Editor (MOE), ZINCPharmer, and Ligand Scout can be employed for this purpose. The derived pharmacophore hypotheses can be used for virtual screening of compound libraries to identify hits. 2.1.2 Molecular Dynamics Simulation The hits/leads identified from virtual screening could be further evaluated using molecular dynamics (MD) simulations. MD simulation (Ciccotti, Ferrario, & Schuette, 2014; Foroutan, Fatemi, & Esmaeilian, 2017) is a method used to measure the dynamic behavior of atoms and molecules by solving Newton’s equations of motion, starting from a defined conformational state. In this system, the atoms and molecules are allowed to interact over a period of time to evolve the system, and the generated interatomic forces and potential energies are calculated using molecular mechanics force fields. By using MD simulation, the nature and stability of the target protein-ligand interaction in the presence of solvent (water) molecules can be studied. Based on favorable results from the computational investigations, the identified hits/leads could be further tested in vitro and then subjected to chemical optimization to improve the biological activity.

3 TARGET-BASED DRUG REPURPOSING USING CADD TECHNIQUES The advancements in biological sciences such as genomics, proteomics, and molecular and structural biology have greatly assisted in identifying and understanding various pathological conditions and have also provided a number of opportunities to explore and identify novel drug targets for disease intervention and drug discovery processes (Hood et al., 2012; Ma & Zhao, 2013). At the same time, the number of approved and investigational drugs for various disease conditions has increased in recent years (Wishart et al., 2018). Since the design and development of new drug molecules for particular disease conditions are very time consuming and expensive processes, utilizing the available knowledge of drugs and

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FIG. 3 Target-based drug repurposing using CADD techniques.

drug target information for drug repositioning is an effective and beneficial way to identify novel therapeutic molecules for new disease indications (Dudley, Deshpande, & Butte, 2011; Jin & Wong, 2014; Wang, Chen, & Deng, 2013). One of the common approaches used by the scientific community for drug repurposing is target-based methods ( Jin & Wong, 2014; Li et al., 2016). In this approach, we can virtually screen the approved DrugBank compounds (Wishart et al., 2018) against different target proteins using CADD techniques. In this section, we briefly described the methods to carry out drug repurposing using CADD, along with the available drug and drug target databases, tools, servers, and software that are useful for predicting the target structure and ligand binding sites and carrying out VS. The overall framework of target-based drug repurposing using CADD techniques is shown in Fig. 3.

3.1 Drug/Small Molecule Databases The DrugBank is a database that combines bioinformatics and cheminformatics resources for establishment of an extensive chemical, pharmacological, and pharmaceutical (i.e., drugs) 4. TOOLS AND DATABASES

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3 TARGET-BASED DRUG REPURPOSING USING CADD TECHNIQUES

database in combination with drug target (i.e., sequence, structure, and pathway) information (Wishart et al., 2018). Currently, DrugBank contains over 11,027 drug entries, which includes 2517 FDA-approved small molecule drugs, 948 FDA-approved biotech (protein/peptide) drugs, and 109 nutraceuticals. In addition to this, it also contains over 5114 experimental drugs and extensive SNP (single nucleotide polymorphism) drug data that can be used for pharmacogenomics studies. Along with drug data, this database also contains 4911 nonredundant drug targets (i.e., protein/enzyme/transporter/carrier) sequences that are linked to drug entries. In addition to the DrugBank database, there are a number of other databases for drugs, such as SuperDRUG2, DrugCentral, and WITHDRAWN, along with small molecule library databases, e.g., BindingDB, ChEMBL, KEGG Drug, ZINC, etc. (Table 1). These databases are freely accessible for retrieving information and compounds, which can be virtually screened against different drug targets for the identification of novel therapeutic molecules.

TABLE 1

List of Available Drug/Small Molecule Databases

Sl. No.

Database

Description and Web-Links

References

1

DrugBank

Freely available curated database containing approved, investigational and nutraceuticals drugs and drug target information (https://www.drugbank.ca/)

(Wishart et al., 2018)

2

SuperDRUG2

A free one-stop resource for approved drugs that is searchable by text and 2D chemical structure (http:// cheminfo.charite.de/superdrug2/)

(Siramshetty et al., 2018)

3

DrugCentral

The web server provides information on active chemical entities, mode of action, and indications for the drugs. The database is searchable by drugs, targets, and disease (http://drugcentral.org/)

(Ursu et al., 2017)

4

WITHDRAWN

Is a database of withdrawn and discontinued drugs due to safety concerns, grouped according to their toxicity. The database is searchable by text and 2D chemical structure (http://cheminfo.charite.de/withdrawn/ index.html)

(Siramshetty et al., 2016)

5

Drug Repurposing Hub

Provides biological screening information about 5000 drugs that are either approved or have reached clinical trials. Data such as chemical structure, status of clinical development, supplier information, mode of action, drug targets, and approved indications are available (https://clue.io/repurposing)

(Corsello et al., 2017)

6

BindingDB

BindingDB is a publicly accessible web database of measured binding affinities of proteins considered to be drug targets with small, drug-like molecules. BindingDB contains 1,439,799 binding data, for 7042 protein targets and 644,978 small molecules (https:// www.bindingdb.org/bind/index.jsp)

(T. Liu, Lin, Wen, Jorissen, & Gilson, 2007)

Continued

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TABLE 1 List of Available Drug/Small Molecule Databases—cont’d Sl. No.

Database

Description and Web-Links

References

7

ChEMBL

ChEMBLdb is a manually curated chemical database of bioactive molecules that are drug-like (https://www. ebi.ac.uk/chembl/)

(Gaulton et al., 2012)

8

KEGG Drug

KEGG Drug has comprehensive drug information of approved drugs in United States, Europe, and Japan. Chemical structures are associated with therapeutic target, metabolizing enzyme, and other molecular interaction network information (http://www.genome.jp/kegg/drug/)

(Kanehisa, Goto, Furumichi, Tanabe, & Hirakawa, 2010)

9

ZINC database

ZINC database contains over 35 million commercially purchasable compounds. These compounds are stored in ready-to-dock 3D formats for virtual screening and are freely accessible (http://zinc.docking.org/)

(Irwin & Shoichet, 2005)

10

Therapeutic Target DB

The database currently contains 2025 targets, 17,816 drugs, and 3681 multitarget agents (http://bidd.nus. edu.sg/group/cjttd/)

(Y. H. Li et al., 2018)

3.2 Drug Target Databases In the drug discovery pipeline, the first step is to understand the pathophysiology of a particular disease condition, followed by identification of drug targets (i.e., protein/nucleic acid) and then lead discovery for the target (Lindsay, 2003). The advancements and technology in various fields of science have greatly helped to understand disease mechanisms as well as to identify drug targets using different approaches, including genomics, proteomics, transcriptomics, epigenetics, phenotypic screening, and biomarker studies (Schenone, Dancik, Wagner, & Clemons, 2013). The resulting drug target sequence, structure, and functional information from such studies is already deposited in various freely available databases for data retrieval, analysis, and understanding of disease mechanisms. This information can be used for drug repositioning using CADD techniques. The target-based drug repurposing methods invariably depend on the three-dimensional (3D) structure of drug targets (protein/DNA/RNA); these methods are powerful and many success stories have been reported in the literature (Shim & Liu, 2014; Vasaikar, Bhatia, Bhatia, & Chu Yaiw, 2016). The 3D structures of the drug targets (protein/DNA/RNA) can be resolved using experimental techniques, including X-ray, NMR, and cryo-electron microscopy (cryo-EM) methods, and the derived structural knowledge is deposited in the Protein Data Bank (PDB) archive (Berman, Henrick, Nakamura, & Markley, 2007). PDB serves as a single repository for the structures of proteins, nucleic acids, and their complexes. Presently, the Worldwide Protein Data Bank (wwPDB) organization manages the PDB archive and assures that it is freely available to the global research community. Moreover, this organization distributes the deposited structural information to the scientific community through its group members, including Research Collaborator for Structural Bioinformatics Protein Data Bank

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(RCSB PDB) (Rose et al., 2011), Biological Magnetic Resonance Data Bank (BNMR) (Markley et al., 2008), Protein Data Bank Japan (PDBj) (Kinjo et al., 2012) and Protein Data Bank in Europe (PDBe) (Gutmanas et al., 2014). From these databases, we can download the highresolution 3D structure of proteins or protein-ligand complexes by simple query search (or) advanced search options. Moreover, these databases also offer various structure analysis and visualization tools, and using these tools we can analyze and understand the interaction mechanisms that exist between the biomolecules, including protein-ligand, protein-protein, etc. Understanding the structural interaction mechanism is essential for drug design and drug repurposing using SBDD methods. Even though wwPDB serves as a repository for biomolecular structural information, there is a huge gap between resolved crystal structures and the reported drug targets that are identified using various experimental methods. The reason is that resolving the 3D structures of these drug targets using experimental methods (such as NMR, X-ray, and cryo-EM) is difficult and challenging. Undoubtedly this huge gap can be filled by computational methods including homology modeling and protein-protein docking. Like the biomolecular structural databases, there are also a number of databases for protein/nucleic acid sequence deposition and retrieval. The Universal Protein Resource (UniProt) (UniProtConsortium, 2018) is a freely accessible resource for annotated protein information such as sequence, structural, and functional information. Moreover, this database integrates the knowledge derived from three other databases, including UniProt Knowledgebase (UniProtKB), UniProt Archive (UniParc), and UniProt Reference Clusters (UniRef ). Hence, it can be considered as a one-stop source for superior annotated protein sequence and functional information. The National Center for Biotechnology Information (NCBI) provides comprehensive resources for nucleic acid and protein sequence information. The NCBI RefSeq (Pruitt, Tatusova, & Maglott, 2007) database provides information about the genomic DNA and transcript sequences of various organisms. In addition, the Protein database (Pruitt et al., 2007) of NCBI contains a collection of protein sequence information from several sources, including translations from annotated coding regions in GenBank, RefSeq, and TPA, as well as records from SwissProt, PIR, PRF, and PDB. Hence, in the absence of the 3D structure of a drug target, we can search and retrieve the target sequence information from these databases. Further, this knowledge can be used to predict 3D structures of the drug targets using various computational methods. A few of the available drug target sequence and structural databases are listed in Table 2.

3.3 Prediction of 3D Structure of Drug Targets In the absence of experimental (X-ray and NMR) 3D structures, in silico methods can be used to predict the 3D structure of drug targets. Three methods are commonly used to build the target model: (1) homology modeling (Vyas, Ukawala, Ghate, & Chintha, 2012), (2) threading and fold recognition (Rost, Schneider, & Sander, 1997), and (3) ab initio modeling (Liwo, Lee, Ripoll, Pillardy, & Scheraga, 1999; Wu, Skolnick, & Zhang, 2007). Homology modeling is also called comparative modeling, and it predicts the 3D structure of the target protein based on its sequence homology with a protein (called a template) whose 3D structural information is solved experimentally. Homology modeling comprises the following steps: (i) identification of template structures (homolog/related sequence structures), (ii) alignment of target sequence to template, (iii) model building (copying template

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TABLE 2 List of Available Protein Sequence and Structural Databases Database/ Sl. No. Server Description and Web-Links

References

1

UniProt

UniProt is a freely accessible resource that provides comprehensive high-quality protein sequence and functional information (http:// www.uniprot.org/)

(UniProtConsortium, 2018)

2

RefSeq

A freely accessible database of genomic DNA, transcripts sequence, and (Pruitt et al., 2007) protein sequence information (https://www.ncbi.nlm.nih.gov/refseq/)

3

Protein

The Protein database contains a collection of sequence information from (Pruitt et al., 2007) several sources, including translations from annotated coding regions in GenBank, RefSeq, and TPA, as well as records from SwissProt, PIR, PRF, and PDB (https://www.ncbi.nlm.nih.gov/protein/)

4

wwPDB

The single largest repository of information for 3D structures of biomolecules and is freely accessible (http://www.wwpdb.org/)

(Berman et al., 2007)

5

RCSB PDB

A database repository of 3D structures of proteins, nucleic acids, and complex assemblies (https://www.rcsb.org/)

(Rose et al., 2011)

6

PDBe

PDBe is the European resource for biological macromolecular structures (Gutmanas et al., (http://www.ebi.ac.uk/pdbe/) 2014)

7

PDBj

PDBj is the Japanese resource for biological macromolecular structures (Kinjo et al., 2012) and also provides integrated tools for analysis (https://pdbj.org/)

8

BMRB

A repository for NMR Spectroscopy data from proteins, peptides, nucleic acids, and other biomolecules (http://www.bmrb.wisc.edu/)

(Markley et al., 2008)

coordinates to target), (iv) loop modeling and side chain refinements, and (v) model optimization and validation. Threading and fold recognition predicts the unknown sequence structure based on recognizing probable folds in the structural databases and then selecting the best-fitting fold for further model building and refinement. The rule of thumb of this method is that the structures are more conserved than the protein sequences. There are fewer protein folds available as compared to the reported protein sequences in the SCOP (Structural Classification Of Proteins) database (Andreeva et al., 2008). Hence, this method could identify structurally comparable proteins even without noticeable sequence similarity (distantly related sequence). On the other hand, ab initio methods attempt to predict the full-length structure of the protein based on the sequence information alone without depending on the template or other structural information. These methods are empirical in nature and are not accurate. Based on the energy minimization principles, these methods find the possible conformation with the lowest global minima energy. There are several in silico tools, web servers, and databases available for modeling the protein structures using the three methods of comparative modeling, threading, and ab initio methods and they are listed in Table 3.

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TABLE 3

List of Available Protein 3D Structure Prediction Tools/Servers

Tools/ Sl. No. Servers

Description and Web-Links

References

HOMOLOGY BASED OR COMPARATIVE METHODS 1

Modeller

MODELLER is a downloadable program that can run on most Unix/ (Webb & Sali, 2016) Linux, Windows, and Mac systems. It is used for homology modeling of protein 3D structures. It can also perform de novo modeling of loops, optimization of modeled protein structures, multiple alignment of protein sequences and/or structures (http:// salilab.org/modeller/)

2

Modeweb

A web server for protein modeling (https://modbase.compbio.ucsf. (Pieper et al., 2014) edu/modweb/)

3

Modebase

Modebase (http://salilab.org/modbase) is a database of modeled protein structures by the modeling pipeline ModPipe (https:// salilab.org/modpipe/)

(Pieper et al., 2014)

4

SWISSMODEL

The web server performs homology modeling in an automated manner and can be accessed through the ExPASy web server (http://swissmodel.expasy.org/)

(Biasini et al., 2014)

5

PyMod 2.0 A downloadable program for sequence similarity searches, multiple ( Janson, Zhang, Prado, & Paiardini, 2017) sequence alignments, and homology modeling within PyMOL environment (http://schubert.bio.uniroma1.it/pymod/index. html)

THREADING AND FOLD RECOGNITION-BASED METHODS 6

I-TASSER

Iterative Threading ASSEmbly Refinement (I-TASSER) uses a (Yang et al., 2015) hierarchical approach to protein structure and function prediction. It is available for download (http://zhanglab.ccmb.med.umich.edu/ I-TASSER/download/) and as a web server (http://zhanglab.ccmb. med.umich.edu/I-TASSER/)

7

RaptorX

Predicts secondary and tertiary structures and assigns confidence (Kallberg et al., 2012) scores to the prediction results. It is available for download as well as a web server (http://raptorx.uchicago.edu/)

8

MUSTER

The web server MUlti-Sources ThreadER (MUSTER) identifies multiple template structures from the PDB library and builds the model (http://zhanglab.ccmb.med.umich.edu/MUSTER/)

(Wu & Zhang, 2008)

AB INITIO AND COMBINATION OF THREADING AND AB INITIO METHODS 9

ROBETTA

The web server uses Rosetta software for ab initio and comparative (Kim, Chivian, & Baker, modeling of proteins (http://www.robetta.org/) 2004)

10

Bhageerath An energy-based protein structure prediction server that is ( Jayaram et al., 2006) validated using 80 small globular proteins, and it predicts five candidate structures for input amino acid sequence (http://www. scfbio-iitd.res.in/bhageerath/index.jsp) Continued

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TABLE 3 List of Available Protein 3D Structure Prediction Tools/Servers—cont’d Tools/ Sl. No. Servers

Description and Web-Links

11

QUARK

12

PEP-FOLD A web server that predicts peptide structure from amino acid sequences using a de novo approach (http://bioserv.rpbs.univparis-diderot.fr/services/PEP-FOLD/)

References

A web server for ab initio protein structure prediction and protein (Xu & Zhang, 2013) peptide folding that aims to construct the correct protein 3D model from amino acid sequence (http://zhanglab.ccmb.med.umich.edu/ QUARK/) (Thevenet et al., 2012)

LOOP MODELING 13

FALCLoop

Fragment Assembly and Loop Closure (FALC) is a downloadable tool for modeling missing regions in a protein (http://galaxy. seoklab.org/softwares/falc.html)

(Ko et al., 2011)

14

FREAD

The web server can be used to fill the gaps in the 3D protein model (Choi & Deane, 2010) (http://opig.stats.ox.ac.uk/webapps/fread/php/)

15

RCD+

RCD+ server is a fast loop-closure modeling tool based on an (Chys & Chacon, 2013) improved version of the RCD method (http://rcd.chaconlab.org/)

16

ModLoop

A web server for automated modeling of loops in protein structures (Fiser & Sali, 2003) (https://modbase.compbio.ucsf.edu/modloop/)

17

LoopIng

Uses Random Forest automatic learning technique to select structural templates for protein loops from a set of candidates (http://circe.med.uniroma1.it/looping/)

(Messih, Lepore, & Tramontano, 2015)

18

Sphinx

The protein loop modeling algorithm generates high-accuracy predictions and decoy sets enriched with near-native loop conformations (http://opig.stats.ox.ac.uk/webapps/sabdabsabpred/Sphinx.php)

(Marks et al., 2017)

3.3.1 Homology Modeling/Comparative Modeling 3.3.1.1 IDENTIFICATION OF TEMPLATE STRUCTURE

The first step in homology modeling is to identify a suitable template structure for the given query sequence. This can be carried out using a sequence similarity search program called protein-BLAST (https://blast.ncbi.nlm.nih.gov/Blast.cgi, part of the Protein database of NCBI; Fig. 4), which identifies several homolog protein structures in the PDB database, and the identified structures are displayed in order based on their E value, sequence identity score, and maximum query coverage area, etc. When selecting the template, we have to choose the structure with high resolution, lowest E value, maximum query coverage area, and identity score. Generally, more than 30% sequence similarity in structure would be considered appropriate for homology modeling. In the absence of full-length sequence structure, multiple template structures can also be considered for improving the model quality.

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FIG. 4 Identification of template structures by querying the Protein-BLAST server of NCBI.

3.3.1.2 ALIGNMENT OF TARGET SEQUENCE TO TEMPLATE

After selecting the template structure, the next step is to align the target protein sequence to the template sequence using highly sensitive multiple sequence alignment algorithms (e.g., Clustal Omega (Sievers & Higgins, 2018), Praline (Simossis & Heringa, 2005), T-Coffee (Di Tommaso et al., 2011), etc.) to obtain optimum alignment. This is a very critical step in homology modeling, as an improper alignment could lead to incorrect disposition of homolog residues, which affect the quality of the model. Hence, we must ensure that the aligned sequence is correct by visual inspection. 3.3.1.3 MODEL BUILDING

After achieving the optimum alignment, the model can be built by copying the template residue coordinates to the target protein. If the template and target sequence are identical,

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25. AN OVERVIEW OF COMPUTATIONAL METHODS

both backbone and side chain atom coordinates are copied to the target protein; otherwise only backbone atoms are copied, and the side chain atoms are fixed in subsequent steps. While performing sequence alignment, frequently some regions of the target sequence cannot be aligned to the template sequence due to sequence insertions and deletions that produce gaps, and these gaps cannot be modeled directly by creating holes in the protein structure. Hence, closing the gap is a very essential step in protein modeling and this can be done by loop modeling methods. Currently there are two loop modeling methods available: the first one is a database search and the second one is an ab initio method. The database search method finds the “spare parts” from the known structures in the PDB database, and then selects the best segment/spare parts with minimal steric clash and maximal sequence similarity. Finally, this segment/spare part is used to fill the loops by fitting onto the two stem regions of the target protein. On the other hand, the ab initio method generates many random loops and then selects the best loop that does not have any steric clash with nearby side chains and also maintains a reasonably low free energy. There are several software tools/modules developed to model the loops using both database search (e.g., FREAD, etc.) and ab initio methods (e.g., RCD +, etc.) and they are listed in Table 3. In order to build a reasonably good model, along with loops and missing side chain atoms, modeling and refinement need to be done using an amino acids rotamer library. This library contains favorable side chain torsion angles of amino acid side chains, derived from known protein crystal structures. Using this library, finally the modeled loops and main and side chain atoms are refined to relieve the steric clashes. Most of the software modules/tools/ servers used for model building and optimization, such as Modeller (Webb & Sali, 2016), Swiss-PdbViewer (Guex & Peitsch, 1997), and UCSC Chimera (Pettersen et al., 2004), incorporate the side chain refinement functions along with model building. 3.3.1.4 MODEL OPTIMIZATION AND VALIDATION

Model optimization/refinement can be carried out using energy minimization or molecular dynamics simulation methods. The main aim of the model optimization/refinement is to correct the irregularities in the protein structure by relieving steric clashes and strains that exist among the atoms. The energy minimization methods move the atoms locally in order to get a suboptimal structure, whereas the molecular dynamics simulation methods move the atoms in uphill and downhill directions in a rough energy landscape. Thus, it overcomes the local energy barriers and gets the global minima structure. A number of software tools are available to refine the models. The UCSC Chimera (Pettersen et al., 2004), SWISS-PdbViewer (Guex & Peitsch, 1997), etc., can be used to minimize the structures using various algorithms. Moreover, the freely available molecular dynamic-based GROMACS (Hess, Kutzner, van der Spoel, & Lindahl, 2008) software can also be used to minimize and achieve the global minima structure. Once the model is optimized, it has then to be validated to make sure that the model geometry features are reliable within the stipulated physiochemical rules. This process involves checking for anomalies in bonds, bond lengths, bond angles (phi-psi angles), and close contacts. This could be carried out using statistical models, where the constructed model parameters are compared to the existing standard models. The obtained results show which region of the sequence is folded properly and which region has errors. If there are any errors and irregularities in the model, further refinement needs to be done. Rampage, ProSA-web

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(Wiederstein & Sippl, 2007), Verify3D (Eisenberg, Luthy, & Bowie, 1997), etc. are some of the tools/modules/servers that are widely used to check the structural geometries of the models. The available open source model optimization and validation tools/servers are listed in Table 4.

TABLE 4

List of Open Source Model Optimization and Validation Tools and Servers

Sl. No. Tools/Servers

Description and Web-Links

References

MODEL OPTIMIZATION 1

UCSC Chimera A downloadable interactive visualization and analysis (Pettersen et al., 2004) tool for molecular structures, sequence alignments, and docking results. It can provide high-quality images and animations for publication and presentation purposes (https://www.cgl.ucsf.edu/chimera/)

2

SWISS PDBViewer

The downloadable program has an intuitive graphic and (Guex & Peitsch, 1997) menu interface that allows the analysis of several proteins to carry out structural alignments and compare their active sites (https://spdbv.vital-it.ch/)

3

YASARA

A web server that performs energy minimization of 3D (Krieger et al., 2009) structures of proteins using the YASARA force field (http://www.yasara.org/minimizationserver.htm)

4

3Drefine

Web server for protein structure refinement (http:// sysbio.rnet.missouri.edu/3Drefine/)

5

Gromacs

A downloadable package to perform molecular (Hess et al., 2008) dynamics simulation of proteins, lipids and nucleic acids (http://www.gromacs.org/)

(Bhattacharya, Nowotny, Cao, & Cheng, 2016)

MODEL VALIDATION 6

Ramachandran plot

Web server that generates Ramachandran plot for 3D (Gopalakrishnan, Sowmiya, structure of a protein, which will help in analyzing the Sheik, & Sekar, 2007) quality of the structure (http://dicsoft1.physics.iisc. ernet.in/rp/index.html)

7

RAMPAGE

Web server for Ramachandran plot analysis of 3D – structure of a protein (http://mordred.bioc.cam.ac.uk/ rapper/rampage.php)

8

ProSA-web

Downloadable and also web server to detect errors in 3D (Wiederstein & Sippl, 2007) structure of a protein (https://prosa.services.came.sbg. ac.at/prosa.php)

9

VERIFY3D

The web server determines the compatibility of the (Eisenberg et al., 1997) atomic model (3D) with its own amino acid sequence (1D) by assigning a structural class based on its location and environment (alpha, beta, loop, polar, nonpolar, etc.) and comparing the results to good structures (http://services.mbi.ucla.edu/Verify_3D/) Continued

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25. AN OVERVIEW OF COMPUTATIONAL METHODS

TABLE 4 List of Open Source Model Optimization and Validation Tools and Servers—cont’d Sl. No. Tools/Servers

Description and Web-Links

References

10

PROCHECK

A downloadable program that checks the residue-by(Laskowski, Rullmannn, residue geometry of a protein structure and also MacArthur, Kaptein, & includes PROCHECK-NMR for checking the structures Thornton, 1996) solved by NMR (https://www.ebi.ac.uk/thornton-srv/ software/PROCHECK/)

11

MolProbity

Web server for validation of macromoleular structure (http://molprobity.biochem.duke.edu/)

12

SAVES

The Structure Analysis and Verification Server (SAVES) – is a metaserver that runs six programs (PROCHECK, WHAT_CHECK, ERRAT, VERIFY_3D, PROVE, CRYST1 record matches) for checking and validating protein structures during and after model refinement (https://services.mbi.ucla.edu/SAVES/)

(Chen et al., 2010)

3.4 Binding Site Identification The binding site is the cavity or pocket on the surface of the target protein/nucleic acid to which the ligand/drug molecule binds through intermolecular forces (e.g., hydrogen bonds, electrostatic, ionic bonds, Van der Waals forces, etc.), resulting in conformational change of the target protein and leading to its functional activation/inhibition. Identification of the ligand binding site on a given target protein is the first prerequisite step in the design of a drug that can interact with the target (Guo et al., 2015; Perot, Sperandio, Miteva, Camproux, & Villoutreix, 2010; Sliwoski et al., 2014). Generally, the ligand binding site information can be identified from the cocrystal structures of reported drug targets (or) closely related protein structures that are in complex with natural/nonnatural ligands/peptide molecules. In the absence of ligand binding site information, in silico methods/tools can be used to identify the potential druggable cavities/pockets on the surface of the target protein. Usually, these in silico methods/tools are categorized as protein geometry-based methods (e.g., CASTp, PocketDepth, etc.), energy-based methods (e.g., PocketFinder, Q-SiteFinder, FTMap, etc.), evolutionary-based machine learning methods (e.g., eFindSite, ATPbind, etc.) and combinations of these methods. These different methods can be used to identify and characterize the potential druggable binding sites and the obtained binding site knowledge can then be used for drug repurposing. Some of the freely available binding site prediction software/tools/web servers are listed in Table 5.

3.5 Virtual Screening for Drug Repurposing Once the 3D structure of the drug target is ready, the next step is to discover suitable drug molecules that can bind to the target protein and alter its function. As discussed in Section 3, VS of database compounds can help in identifying suitable ones for testing. VS methods are broadly classified as docking-based screening and pharmacophore-based screening, and both the methods could be employed for drug repurposing for new indications.

4. TOOLS AND DATABASES

3 TARGET-BASED DRUG REPURPOSING USING CADD TECHNIQUES

TABLE 5

761

List of Freely Available Binding Site/Pocket Prediction Tools/Servers

Tools/ Sl. No. Servers

Description and Web-Links

References

1

eFindSite

A ligand binding site prediction and virtual screening web (Brylinski & Feinstein, 2013) server that detects common ligand binding sites in a set of evolutionarily related proteins identified by 10 threading/ fold recognition methods (http://brylinski.cct.lsu.edu/ efindsite)

2

COACH

Uses metaserver approach for protein-ligand binding site (Yang, Roy, & Zhang, 2013) prediction (https://zhanglab.ccmb.med.umich.edu/ COACH/)

3

ATPbind

ATPbind is a metaserver that predicts the protein ATP (Hu, Li, Zhang, & Yu, 2018) binding site using support vector machine (SVM) method (https://zhanglab.ccmb.med.umich.edu/ATPbind/)

4

CASTp

Computed Atlas of Surface Topography of proteins (Binkowski, Naghibzadeh, & (CASTp) is an online resource for locating, delineating, and Liang, 2003) measuring concave surface regions on 3D structure of proteins (http://sts.bioe.uic.edu/castp/index.html?2cpk)

5

fpocket

fpocket is a Voronoi tessellation-based open source protein (Le Guilloux, Schmidtke, & pocket (cavity) detection algorithm (http://fpocket. Tuffery, 2009) sourceforge.net/)

6

3DLigandSite 3DLigandSite is an automated method for the prediction of (Wass, Kelley, & Sternberg, ligand binding sites in 3D protein structures (http://www. 2010) sbg.bio.ic.ac.uk/3dligandsite/)

7

Pocketome

An encyclopedia of conformational ensembles of druggable binding sites that can be identified experimentally from the cocrystal structures available in the Protein Data Bank (http://www.pocketome.org/)

8

FINDSITE

FINDSITE is a threading-based binding site prediction and (Brylinski & Skolnick, 2008) ligand screening algorithm that detects common ligand binding sites in a set of evolutionarily related proteins (http://cssb.biology.gatech.edu/findsite)

9

PocketDepth

A geometry-based pocket prediction method that uses a depth-based clustering (http://proline.physics.iisc.ernet.in/pocketdepth/)

(Kalidas & Chandra, 2008)

10

DeepSite

DeepSite uses deep neural networks to predict protein binding pockets (http://www.playmolecule.org/ deepsite/)

( Jimenez, Doerr, MartinezRosell, Rose, & De Fabritiis, 2017)

(Kufareva, Ilatovskiy, & Abagyan, 2012)

There are a number of docking tools/servers (e.g., Autodock, Dock, eFindSite, etc.) available and they are widely applied in drug design. These docking methods can also be used for drug repurposing, where the approved DrugBank compounds can be used to evaluate their binding affinity towards a novel drug target and, based on the predicted binding affinity, the compounds can be shortlisted for testing using various in vitro and in vivo methods.

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25. AN OVERVIEW OF COMPUTATIONAL METHODS

AutoDock Vina is an open-source standalone docking-based VS tool used to screen a large number of chemical compounds from different databases. It is a grid-based docking method and accurately predicts the binding mode of the compounds at the defined binding site of the target protein. AutoDock Vina can be employed to screen DrugBank compounds to assess their binding affinity towards other novel drug targets. MTiOpenScreen is a web server that provides docking-based VS facility based on the AutoDock Vina methods. Using this server, diverse compounds from different databases as well as the custom library compounds can be screened against a particular drug target. EFindSite is another web server that automatically predicts the ligand binding site on a given target protein using a metathreading approach, and then uses this information to screen a large number of compounds from different databases, including BindingDB, ChEMBL, DrugBank, KEEG compounds, RCSB PDB, etc. Then the compounds are ranked based on their Z-scores, and these can be tested using in vitro methods. In addition to these, a number of other servers, databases, and tools are available for docking-based screening, listed in Table 6.

TABLE 6 List of Available Docking-based Screening Tools/Web Servers Sl. No. Tools/Servers

Description and Web-Links

1

Autodock 4

Downloadable Lamarckian genetic algorithm-based (Morris et al., 2009) docking software that consists of two main programs: (1) Autodock, which performs the docking of the ligand to a set of grids describing the target protein and (2) Autogrid, which precalculates these grids (http://autodock.scripps.edu/)

2

AutoDock Vina

An open-source downloadable program for (Trott & Olson, 2010) molecular docking that supports Windows and Linux systems. The accuracy of binding mode predictions by AutoDock Vina is better, as compared to that of AutoDock 4 (http://vina.scripps.edu/)

3

DOCK

Downloadable and the latest DOCK 6 version incorporates receptor flexibility (http://dock. compbio.ucsf.edu/)

(Allen et al., 2015)

4

iGEMDOCK

iGEMDOCK is a downloadable graphical environment for virtual screening that uses an evolutionary technique for docking (http:// gemdock.life.nctu.edu.tw/dock/igemdock.php)

(Yang & Chen, 2004)

5

Rosetta

A multipurpose application for structure prediction, (Kaufmann, Lemmon, design, and remodeling of proteins and nucleic Deluca, Sheehan, & Meiler, acids. It uses Monte Carlo minimization-based 2010) docking algorithm (https://www.rosettacommons. org/software)

6

GalaxyDock

Downloadable and works based on the protein side (Baek, Shin, Chung, & chain conformational space annealing method. Runs Seok, 2017) on Linux and MAC systems (http://galaxy.seoklab. org/softwares/galaxydock.html)

4. TOOLS AND DATABASES

References

3 TARGET-BASED DRUG REPURPOSING USING CADD TECHNIQUES

TABLE 6

763

List of Available Docking-based Screening Tools/Web Servers—cont’d

Sl. No. Tools/Servers

Description and Web-Links

References

7

MTiOpenScreen

AutoDock Vina pipeline-based web server for virtual screening (http://mobyle.rpbs.univ-parisdiderot.fr/cgi-bin/portal.py#forms:: MTiOpenScreen)

(Labbe et al., 2015)

8

Computer-Aided DrugDesign Platform using PyMOL

A downloadable PyMOL plugin that runs on Linux (Lill & Danielson, 2011) OS, that requires preinstallation of PyMOL for the analysis, computations, and simulations of proteinligand complexes (http://people.pharmacy. purdue.edu/mlill/software/pymol_plugins/ install.shtml)

9

GriDock

(Vistoli, Pedretti, Downloadable virtual screening front-end for AutoDock 4, designed for dockings of ligands stored Mazzolari, & Testa, 2010) in a single database (SDF format) (http://159.149.85. 2/cms/index.php?Software_projects:GriDock)

10

SwissDock

A web service that predicts the molecular (Grosdidier, Zoete, & interactions between a target protein and a small Michielin, 2011) molecule. It works based on docking EADock_DSS software (http://www.swissdock.ch/)

11

PatchDock

The web server uses shape complementarity principles for the docking of proteins, DNA, peptides, and drugs (http://bioinfo3d.cs.tau.ac.il/ PatchDock/patchdock.html)

(Schneidman-Duhovny, Inbar, Nussinov, & Wolfson, 2005)

12

iScreen

The world’s first cloud-computing web server for virtual screening and de novo drug design based on TCM database@Taiwan (http://iscreen.cmu.edu.tw/)

(Tsai, Chang, & Chen, 2011)

13

idock

Structure-based virtual screening web server and also downloadable (http://istar.cse.cuhk.edu.hk/ idock/)

(H. Li, Leung, & Wong, 2012)

As discussed in Section 3, SBP screening is a complementary and alternative method to docking for screening large chemical library compounds against a given drug target. The pharmacophore features derived from the protein-ligand complex uses the knowledge of the potential interactions that exist between the protein and the ligand, whereas the features that are derived from the protein alone use active site/hot spot residue information. The derived pharmacophore features can be used to screen DrugBank compounds for new indications. There are a few tools/software (e.g., AnchoreQuery, ZINCPharmer, etc.) available for SBP screening and they are listed in Table 7.

3.6 Drug Repurposing for Protein-Protein Interactions The previous sections discuss the application of CADD techniques and tools for drug repurposing using target-based approaches. These described methods, tools, servers, and

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25. AN OVERVIEW OF COMPUTATIONAL METHODS

TABLE 7 List of Available Pharmacophore-based Screening Tools/Web Servers Sl. No. Tools/Servers Description and Web-Links

References

1

PharmDock

A pharmacophore-based docking program that combines pose (Hu & Lill, 2014) sampling and ranking based on optimized protein-based pharmacophore models with local optimization using an empirical scoring function. The program comes with an easy-to-use GUI within PyMOL and is downloadable (http://people.pharmacy.purdue. edu/mlill/software/pharmdock/)

2

ZINCPharmer An open source pharmacophore search web server that can identify (Koes & Camacho, pharmacophore features directly from structure or use MOE and 2012b) LigandScout pharmacophore definitions for searching chemical structures from ZINC or Molprot databases (http://zincpharmer. csb.pitt.edu/)

3

AnchorQuery Web server specialized in pharmacophore search for targeting protein-protein interactions (http://anchorquery.ccbb.pitt.edu/)

4

Pharmit

(Koes, Domling, & Camacho, 2018)

A pharmacophore-based virtual screening web server, which can (Sunseri & Koes, generate pharmacophore features from input ligand or from protein- 2016) ligand complex (http://pharmit.csb.pitt.edu/)

databases focus on targeting the classical drug targets for drug discovery, i.e., enzymes, receptors, ion channels, and transporters. However, growing evidence supports the fact that altered signaling pathways leading to pathological states could also be due to aberrations in the protein-protein interactions (PPIs). Such PPIs are emerging as possible drug targets for modulation by small molecule inhibitors. One of the main advantages of PPI inhibitors are that they are very specific to a particular PPI, which will offer more selectivity for the drug with lower toxicity. The availability of computational methods and tools helps in faster identification of PPI inhibitors (Choi & Choi, 2017; Cierpicki & Grembecka, 2015; Villoutreix et al., 2014). The availability of the PPI partners’ 3D structure can be a limiting factor for identifying a drug or repurposing a drug to disrupt their interaction. Specialized servers, tools, and databases are available to gather information on PPIs related to disease states, and to model and predict these PPIs. Moreover, tools are available to detect the hot spot regions of the PPIs. Using these tools, one can predict the complex structure of PPIs and identify the hot spot regions/residues of the complex. Hot spot residues in a PPI are the most important, contributing to the binding of two proteins in the complex (Kuttner & Engel, 2012; Rosell & Fernandez-Recio, 2018). Typically, hot spot region information is used to design ligands that will bind strongly to these residues and interfere in the PPI and that could alleviate the pathophysiological conditions. Interested readers may refer to recent reviews detailing how computational approaches can be utilized for the design of PPI inhibitors (Gromiha, Yugandhar, & Jemimah, 2017; Johnson & Karanicolas, 2017; Peng, Wang, Peng, Wu, & Pan, 2017; Sarvagalla & Coumar, 2016). Tables 8–10 provide a list of databases, tools, and servers freely available to model PPIs and use them for drug repurposing.

4. TOOLS AND DATABASES

TABLE 8

List of Curated PPI Modulator Databases

Name of the Sl. No. Database Description and Web-Links

References

1

2P2IDB

A hand-curated structural database dedicated to protein-protein interactions with known orthosteric modulators (http://2p2idb. cnrs-mrs.fr)

(Basse et al., 2013)

2

iPPI-DB

A manually curated and interactive database of small nonpeptide inhibitors of protein-protein interactions (http://www.ippidb. cdithem.fr/)

(Labbe et al., 2016)

3

TIMBAL

A database holding molecules of molecular weight