A Handbook of Artificial Intelligence in Drug Delivery 9780323899253

Table of contents :
Cover
A Handbook of Artificial Intelligence in Drug Delivery
Copyright
Contributors
An overview of artificial intelligence in drug development
Introduction
Impact of AI on drug development
AI in drug repurposing
AI in developing improved policies
Conclusion
References
General considerations on artificial intelligence
The introduction of AI and its importance in pharmaceutical operations
Role of ML in drug design and drug delivery
Artificial intelligence in drug design
Databases for virtual screening
Gold-standard datasets
Applications of DNNs in VS
Feed-forward DNNs
Recurrent neural networks
Application of AI in biomedical and tissue engineering
Wearables in clinical trials
AI in semantic annotation of healthcare data
AI in tissue engineering
Challenges in applications of ML to healthcare data
Artificial intelligence integration with nanotechnology
References
Role of artificial intelligence in quality profiling and optimization of drug products
The concept of quality in drug formulation development
Quality concept and definitions
Quality and quality control
QbD concept and benefits
QbD approach in biotechnology products
Risk management, risk assessment, and optimization
Risk management
Risk assessment
Analytical quality by design
Role of AI and ML models on determining of quality profile and optimization studies
AI in pharmaceutical manufacturing
AI in advancing pharmaceutical product development
AI in the lifecycle of pharmaceutical products
Machine learning algorithms
K nearest neighbor
Support vector regression
Classification and RT
Bagging trees
Random forest
Gradient boosting machine
Extreme gradient boosting
Artificial neural network
Comparison of Java and Python programming languages
Key differences between Python and Java
Conclusion
References
AI applications for multivariate control in drug manufacturing
Artificial intelligence
AI in pharmaceutical manufacturing
Process validation and multivariate control
Continued process verification and drug manufacturing control
Artificial intelligence considered from a multivariate perspective
Data and multivariate analysis
First requirement for MVA in biopharma: Data quality
Data governance
Data transformation as a required step under data governance
Use cases of AI application in drug manufacturing
AI and regulations
AI resources
Conclusions
References
AI approaches for the development of drug delivery systems
Introduction
Artificial neural networks
Applications of ANNs in drug delivery system design
AI approach to predicting drug release profiles
AI methods for optimization of drug delivery systems
Microspheres and microparticles
Implants and transdermal products
Optimization of inhalers
Nanomedicines
Conclusions
References
Artificial neural network (ANN) in drug delivery
Introduction
Modeling the drug efficiency
ANN in the prediction of drug properties
Prediction of drug properties
Prediction of toxicity
Prediction of adverse drug reactions
ANN in drug formulation
Effectiveness of drug dosing
Stability of active pharmaceutical ingredients
ANN in drug administration
Efficacy of the loading
Membrane interaction and cellular uptake prediction
Drug-target interactions
ANN in targeted drug delivery
Modeling of drug delivery using carriers
Control of micro-/nanorobots
Control of nanomaterials
ANN in the prediction and monitoring of drug release profile
ANN in personalized medicine delivery
Prospective of AI application in future drug delivery systems
Conclusion
References
Relevance of AI in microbased drug delivery system
Introduction
General considerations on the broad utility of AI for designing novel microsystems for drug delivery systems
Types of formulation using ANN
Microspheres
Lipid-based carriers
Lipid microparticles
Liposome
Microemulsion
Liquid crystals
Solid dispersion
Prospects and challenges
References
Further reading
Application of artificial intelligence driving nano-based drug delivery system
Introduction
Artificial intelligence description
Nanoscience and nanotechnology description
General aspects on the broad utility of AI for designing novel nano systems for drug delivery
Types of formulations
Vesicular nanosystems (liposomes, niosomes, and transfersome)
Nanoemulsion, nanosuspension, and nanogels
Polymeric and lipid nanoparticles
Inorganic, metallic, and magnetic nanoparticles
Polymeric micelles and dendrimers
Nanosized biomaterials
Nanopowder, nanocrystals, electrospun-medicated nanofibers, and quantum dots
Artificial DNA nanostructures and protein nanoparticles
Conclusions
References
AI in microfabrication technology
Introduction to MEMS/NEMS devices
AI in fabrication of MEMS devices
Implantable microchips for programmed delivery of drugs
AI in drug delivery application to the artificial pancreas
Controlled drug release
Microfabricated external drug reservoirs for continuous and pulsatile drug delivery
Oral tablets
Contact lenses
Nanoemulsions
Vaginal delivery
AI in drug reservoirs devices
AI-integrated smart biosensors in targeted delivery
Microneedles
Responsive polymers
Conducting polymers
AI in fabrication of smart biosensors
Nanobots
Microfluidic platforms for drug delivery
Microfabrication of the particles
Microfluidic platforms and smart drug delivery
Janus particles
Microneedles
Lab-on-a-chip
Organ-on-a-chip technologies in drug delivery
AI in fabrication of OOC devices
Drug delivery via OOC
AI in drug delivery via OOC
References
Tracing the nose-to-brain nanoparticulate drug delivery using bio/chemoinformatics tools
Rationale of nose-to-brain delivery and targeting
Advantages of nose-to-brain delivery
Challenges of nose-to-brain drug delivery
Nanomedicine in nose-to-brain delivery
Computer-assisted drug formulation design (pharmaceutics informatics=bioinformatics+chemoinformatics)
Applications of bio/chemoinformatics tools in tracing and comparing the nose-to-brain delivery of different drug mo ...
Bio/chemoinformatics tracing the nose-to-brain delivery of curcuminoids in the treatment of Alzheimer's disease
Bio/chemoinformatics tracing the nose-to-brain delivery of cefotaxime and ceftriaxone in the treatment of meningitis
The use of bio/chemoinformatics in evaluating the efficiency of targeting moieties in nose-to-brain delivery
Conclusion
References
Applicability of machine learning in three-dimensionally (3D) printed dosage forms
Introduction
Classification of printers by printing mechanism
Material deposition based 3D printing
Fused deposition modeling/fused filament fabrication (temperature-based deposition)
Semisolid extrusion 3D printing (temperature and pressure-based deposition)
3D bioprinting (pressure mediated deposition of shear-thinning inks)
Material bed based 3D printing
Binder jetting/material jetting (liquid binder-based building of 3D structures)
Selective laser sintering/Selective laser melting (UV/N-IR laser-based building of 3D structures)
Light-based photochemical cross-linking (laser/light crosslinking based building of 3D structures)
Laser-based bioprinting
Materials in pharmaceutical 3D printing
Materials in 3D printing of small molecules
Extrusion based (FDM, PAM)
Powder-based (binder jetting, SLS)
Materials in 3D printing of large molecules
Materials used in bioprinting
Bio printability
Shape fidelity
References
Further reading
Role of AI in ADME/Tox toward formulation optimization and delivery
Introduction to the history of AI in ADME/Tox
An in vitro-in vivo correlation
Levels and the classification system of IVIVC for drugs
In silico ADME/Tox profiling
In silico modeling of ADME/Tox properties with descriptors
Absorption modeling and physicochemical properties and descriptors
Permeability
Solubility
Caco-2 cells
Lipophilicity (Log Po/w)
Intestinal absorption in humans
Protein and tissue binding
Distribution modeling
Volume of distribution
P-glycoprotein (P-gp) substrate
BBB permeability
Fraction unbound
Metabolism modeling
Excretion modeling and descriptors
Total clearance (CLt)
Renal clearance (CLr)
Hepatic clearance (CLh)
Fraction of drug excreted unchanged in urine (fe)
In silico methods for predicting drug toxicity
Acute toxicity
Genotoxicity
Systems toxicology
hERG inhibition
Ames toxicity
Hepatotoxicity
Drug toxicity
Drug toxicity classifications
Acute toxicity
Chronic toxicity
Drug-related death
Drug toxicity and poisoning
Pharmacological toxicity
Pathological toxicity
Genetic toxicity
Drug toxicity mechanisms
Specific toxicity or sublethal toxicity
Nonspecific toxicity or lethal toxicity potential
Types of therapeutic drug toxicities
Dose-dependent reactions
Allergic reactions
Idiosyncratic reactions
Drug-drug interactions
Toxicokinetics
Clinical pharmacology
Pharmacokinetics
Target discovery
Drug development
Drug evaluation
Pharmacodynamics
Artificial intelligence tools and software in ADME/Tox
Machine learning algorithms in ADME/Tox
Random forest
Support vector machine
Neural network
K-nearest neighbors
Naïve Bayes
Deep learning
References
Recent advances in self-regulated drug delivery devices
Introduction
Strategies
Nanodevices
Microdevices
AI-based devices
Applications
Diabetes
Infections
Anesthesia and pain relief
Nervous system diseases
Prospects and challenges
Conclusions
References
Design and control of nanorobots and nanomachines in drug delivery and diagnosis
Introduction
Drug delivery
Nanotechnology for drug delivery
Nanorobots and nanomachines
Design and fabrication of nanorobots and nanomachines
Actuation mechanisms of nanorobots and nanomachines
Nanotechnology-enabled artificial blood: Respirocytes, clottocytes, and microbivores
Nanorobots and nanomachines as smart biosensors
Applications of nanorobots and nanomachines in targeted drug delivery
In vitro applications
In vivo applications
Self-driven and bioinspired nanorobots and nanomachines for drug delivery
Autonomous nanorobots and nanomachines as drug delivery vehicles
Biologically inspired nanorobots as drug delivery vehicles
Challenges and future outlook
Conclusions
References
Artificial intelligence (AI) in drug product designing, development, and manufacturing
Introduction
Impact and risk assessment of material attributes and process parameters
Risk assessment of the material attributes of APIs
Risk assessment of the material attributes of excipients
Risk assessment of the processing parameters of manufacturing processes
Design space development for CMAs and CPPs with DoE
Selection of factors (CMAs/CPPs) and types of experimental designs
Screening of independent factors
Setting factor ranges: Levels
Identifying dependent response variables
Selection of a mathematical model
Types of experiment designs
Randomization of runs, replication, blocking, and measurement of responses (CQAs)
Randomization of runs
Replication
Blocking
Measurement and analysis of responses
Numerical and graphical analyses of a mathematical regression model
Numerical analysis of a mathematical regression model
Graphical analysis of a mathematical regression model
Development and verification of the ideal region of a design space
Control space implementation for CMAs and CPPs
Continuous inline/online analysis and controlling with PAT
Classification of inline/online process sensors
PAT tools
Designing of sampling strategies and location of sensors
Analyzing and controlling CMAs, CPPs, and CQAs using PAT and data science
Digital twins, Internet of Things, and outlook
Simulation tools in drug product designing and manufacturing process development
Introduction and need for simulation tools
Data-driven modeling: AI/ML/DL for product and process development
Flowsheet modeling for product and process development
Limitations of simulation tools in product designing and process development
Data science, machine learning, and outlook
Artificial intelligence in drug product commercial manufacturing and analysis
AI in the drug product batch manufacturing process
AI in the drug product continuous manufacturing process
AI in drug delivery and drug product research and development
AI in drug delivery
AI in product research and development
Challenges in the implementation of AI
High AI implementation cost
Time constraints in AI development
Inadequate expertise of pharmaceutical IT teams
Inadequate clarity on the correct use and implementation of AI in the existing process
Proper feed of well-organized data
Compliance with regulatory requirements
Legal challenges associated with process
Harmonization of requirements for AI implementation
Future scope for the implementation of AI
For compliance with regulatory requirements and quality assurance
For predictive maintenance of the manufacturing line
Planning of production activities
Conclusions
References
Further reading
Impact of AI on drug delivery and pharmacokinetics: The present scenario and future prospects
Introduction
Applications of artificial intelligence in drug delivery
Significance of AI in drug delivery
Artificial intelligence in the development of a drug delivery system: A research outlook
Applications of artificial intelligence in pharmacokinetics
Computational pharmacokinetic modeling
In silico physicochemical property prediction
Hydrogen bonding
Lipophilicity
Permeability
Solubility
Molecular modeling
QSAR modeling
ADME modeling
Molecular and pharmacophore modeling
Mathematical modeling
Process simulation in pharmacokinetics
The current status of AI in pharmacokinetics
Future prospective
References
Artificial intelligence in vaccine development: Significance and challenges ahead
Machine learning approaches in vaccine development
Supervised classification in bioinformatics
Proteomics
Genomics
Pattern recognition
Employing proteomics for gonorrhea antigen mining
The basic workflow of a machine learning algorithm for classification
K-means clustering algorithms
Requirements of clustering
Logistic regression
Regression approaches to the assessment of influenza vaccine effectiveness
Prediction of vaccination outcomes by neural networks and logistic regression
Naïve Bayesian classification
Applications of Naïve Bayes algorithms
Implementation of a vaccine development model
Neural networks
Graph convolutional neural networks
Epidemic graph convolutional networks
Recurrent neural networks
Long short-term memory networks
Deep convolutional neural networks
Computational protein design using deep neural networks
Design of epitope-based vaccines using deep learning
Reverse vaccinology
Reverse vaccinology prediction using VAXIGEN-ML
Random forest analysis
Support vector machine-based prediction of binding peptides
Recursive feature elimination
AI in the vaccine adverse event reporting system
An AI-powered vaccine safety data Bank: The key to vaccine development
mRNA- and protein-based vaccines in collaboration with the AI ecosystem
Advanced deep Q learning network with fragment-based drug design
Challenges of implementing an AI-based vaccine development model
Machine learning platforms for vaccine development
SIMON: Sequential iterative modeling OverNight
MIT's OptiVax
References
AI-enabled quadrupole stimuli-responsive targeted polymeric nanodrug delivery for cancer therapy
Introduction
Existing pharmaceuticals
Nanomedicines
Properties of cancer cells
The Nano4XX (XX=Dox, Cis, etc.) platform
Cell intercalation
Cytotoxicity
Biodistribution of FA-targeted nanocontainers in HeLa tumor-bearing mice
Switching effect
Conclusions
References
Convergence of artificial intelligence and nanotechnology in the development of novel formulations for cancer ...
Introduction
Areas where AI is potentially implied in drug discovery and development
Target identification and validation
Prediction of the target protein structure
Predicting drug-protein interactions
Hit discovery
Hit-to-lead optimization and lead optimization
Lead optimization and preclinical testing
Prediction of bioactivity
Prediction of toxicity
Clinical trials
Statistical and ML methods for modeling cancer risk
Common features and differences in statistical and ML models
Commonly used models
Statistical models
ML models
Supervised learning
Unsupervised ML
Regularized logistic regression
Support vector machine
Artificial neural network
Preparation and optimization of nanomedicines
Nanoparticles
Targeted drug delivery
ML and AI in formulation designing
ML in nanoformulations
AI in nanoformulations
ML in cancer nanomedicines
Application of AI and related technologies in cancer treatment using nanomedicines
Big data libraries for nanomedicines
AI and ML for determining the in vivo fate of nanomedicines
Prediction of the in vivo behavior of nanomaterials
Nanomaterials and biotoxicity prediction
Data on the interplay between nanotechnology and biology
ML promotes bioapplications of NMs
Cancer nanomedicine: The future
Conclusions and future outlook
References
Artificial intelligence in precision medicine
Artificial intelligence and precision medicine
Precision medicine
AI in cancer classification and subtype determination
Major AI-based methodologies
Importance of structural variant detection in cancer
Detection of somatic SVs in short-read WGS data
Combinatorial algorithms integrate multiple read alignment patterns
New PCWAG and TCGA approach to classify structural variants
Histology and imaging in cancer diagnosis
Solid tumors and radiographic images
AI in the characterization of biomarkers
Motor proteins
Motor proteins and neurofilaments in neural diseases
Engineered motor proteins
Artificial intelligence in the structure-function analysis of kinesins
AI and precision medicine in a clinical setting
Commercial companies focused on AI and precision medicine
Molecular structure prediction: AlphaFold
Emergence of AlphaFold: Description of Google's algorithm
AlphaFold: Challenges to tackle
AI and precision medicine in COVID-19 research
New-generation sequencing and rapid identification of SARS-COV2 variants
Epidemiological models and COVID-19 severity
AI in innovative diagnosis approach and analysis of cough pattern
Challenges in AI
Future prospects and issues in artificial intelligence and precision medicine
References
Artificial intelligence and machine learning in clinical trial design and application
Introduction: Clinical trials in this new world
History of clinical trials and innovation
Gaps in clinical trials today
Limited applicability
Time
Cost
Next generation of clinical trials powered by deep technology and AI
External control arm
Risk models to optimize cohorts
Cohort selection and optimization
Patient recruitment/site recruitment
Discussion
Prospective ECA and retrospective comparative arms
Cohort optimization
Decentralized RCTs to improve health equity and representation
References
Artificial intelligence from a regulatory perspective: Drug delivery and devices
Introduction
Regulatory agencies and regulatory pathways
USFDA regulatory regime [1]
European regulatory regime [2]
The regulatory alliances [3,4]
Artificial intelligence and machine learning synergies with mission of regulatory agencies
Prevailing challenges for regulatory agencies
Current state of regulatory affairs and drug regulations
Artificial intelligence and machine learning in drug discovery and development-Current regulatory perspective [6-8]
Artificial intelligence and machine learning in medical devices-Current regulatory perspective [9-11]
The future regulatory perspective
Artificial intelligence and machine learning in drug discovery and development-Future perspective [14,15]
Artificial intelligence and machine learning in medical devices-Future perspective [17-19]
References
Index
A
B
C
D
E
F
G
H
I
J
K
L
M
N
O
P
Q
R
S
T
U
V
W
X
Z

Citation preview

A HANDBOOK OF ARTIFICIAL INTELLIGENCE IN DRUG DELIVERY

A HANDBOOK OF ARTIFICIAL INTELLIGENCE IN DRUG DELIVERY Edited by

ANIL PHILIP School of Pharmacy, University of Nizwa, Nizwa, Sultanate of Oman

ALIASGAR SHAHIWALA Dubai Pharmacy College for Girls, Dubai, United Arab Emirates

MAMOON RASHID Aemers LLC, Richmond, VA, United States

MD. FAIYAZUDDIN School of Pharmacy, Al-Karim University, Katihar, Bihar, India; Nano Drug Delivery® (A product development partnership company), Raleigh-Durham, NC, United States

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 Copyright © 2023 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. ISBN 978-0-323-89925-3 For information on all Academic Press publications visit our website at https://www.elsevier.com/books-and-journals

Publisher: Stacy Masucci Acquisitions Editor: Andre G. Wolff Editorial Project Manager: Sara Pianavilla Production Project Manager: Sajana Devasi P K Cover Designer: Miles Hitchen Typeset by STRAIVE, India

Contributors

M. Yusuf Ali Department of Molecular Physiology and Biophysics, University of Vermont, Burlington, VT, United States Nurul Iman Aminudin Department of Chemistry, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, Malaysia Abdul Basit Department of Pharmaceutical Chemistry, Faculty of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur, Punjab, Pakistan Atakan Bas¸ kor Berga Consultancy, Istanbul, Turkey Robert J. Beetel HealthPals, Redwood City, CA, United States Farahnaz Behgounia Golden Gate University, Ageno School of Business, San Francisco, CA, United States N. Buket Aksu Altınbas¸ University, Faculty of Pharmacy, Pharmaceutical Technology Department, Istanbul, Turkey Gulden Camci-Unal Department of Chemical Engineering, University of Massachusetts Lowell, Lowell; Department of Surgery, University of Massachusetts Medical School, Worcester, MA, United States Subhodeep Chakraborty Zydus Lifesciences Limited, Gandhinagar, India Shivang Chaudhary QbD-Expert, Ahmedabad, Gujarat, India Ting-Jing Chen-Mayfield Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, United States Uli Chettipally InnovatorMD, San Francisco, CA, United States Nirav Chokshi Zydus Lifesciences Limited, Gandhinagar, India xiii

xiv

Contributors

Wan Hazman Danial Department of Chemistry, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, Malaysia Rajesh Dash HealthPals, Redwood City, CA, United States Abhay Dharamsi Department of Pharmaceutics, Parul Institute of Pharmacy, Parul University, Vadodara, India Venkateswaran R. Elangovan Department of Pediatrics-Endocrinology, University of Michigan, Ann Arbor, MI, United States Ibtihag Yahya Elhag Biomedical Engineering Department, Sudan University of Science and Technology, Khartoum, Sudan Md. Faiyazuddin School of Pharmacy, Al-Karim University, Katihar, Bihar, India; Nano Drug Delivery®, (A product development partnership company), Raleigh-Durham, NC, United States Jalisa Holmes Ferguson Department of Chemistry, Eckerd College, St. Petersburg, FL, United States Vinod Gaikwad Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Bihar, India Nurasyikin Hamzah Department of Chemistry, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, Malaysia Rania M. Hathout Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt Quanyin Hu Pharmaceutical Sciences Division, School of Pharmacy; Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States Mohamad Wafiuddin Ismail Department of Chemistry, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, Malaysia Sunil S. Jambhekar School of Pharmacy, LECOM Bradenton Campus, Bradenton, FL, United States Shantani Kannan Department of Electronics and Communications Engineering, Kumaraguru College of Technology, Coimbatore, India

Contributors

Beyza Karacaoglu Department of Medical Biochemistry, Institute of Health, Ege University, Izmir, Turkey George Kordas Sol-Gel Laboratory, INN, NCSR Demokritos, A. Paraskevi Attikis, Greece Vineet R. Kulkarni PharmE3D Lab, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, United States Zhaoting Li Pharmaceutical Sciences Division, School of Pharmacy; Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States Sivakumar Loganathan Department of Environmental Science, Periyar University, Salem, Tamil Nadu, India Thiagarajan Madheswaran International Medical University, Kuala Lumpur, Malaysia Mohammed Maniruzzaman PharmE3D Lab, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, United States Toni Manzano Aizon (Industrializing Artificial Intelligence for Pharma Manufacturing), Barcelona, Spain Mohd Adli Md Ali Department of Physics, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, Malaysia Burcu Mesut Istanbul University, Faculty of Pharmacy, Pharmaceutical Technology Department, Istanbul, Turkey Abdelkader A. Metwally Department of Pharmaceutics and Industrial Pharmacy, Faculty of Pharmacy, Ain Shams University, Cairo, Egypt; Department of Pharmaceutics, Faculty of Pharmacy, Health Sciences Center, Kuwait University, Kuwait City, Kuwait Yang Ming Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China; Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, State Key Lab of Innovation Drug and Efficient Energy-Saving Pharmaceutical Equipment, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China Fatemeh Mohammadipanah Pharmaceutical Biotechnology Lab, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, Iran

xv

xvi

Contributors

Prakash Muthudoss A2Z4.0 Research and Analytics Private Limited, Chennai, Tamil Nadu, India Abid Naeem Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China; Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, State Key Lab of Innovation Drug and Efficient Energy-Saving Pharmaceutical Equipment, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China Archana Mohit Navale Department of Pharmacology, Parul Institute of Pharmacy, Parul University, Vadodara, India Himanshu Paliwal Department of Pharmaceutics and Pharmaceutical Technology, Shree S.K. Patel College of Pharmaceutical Education and Research, Ganpat University, Mehsana, Gujarat, India Amrit Paudel Research Center Pharmaceutical Engineering GmbH (RCPE), Graz, Austria Anil K. Philip School of Pharmacy, University of Nizwa, Nizwa, Oman Amit Raviraj Pillai PharmE3D Lab, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, United States Firoozeh Piroozmand Pharmaceutical Biotechnology Lab, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, Iran Bhupendra G. Prajapati Department of Pharmaceutics and Pharmaceutical Technology, Shree S.K. Patel College of Pharmaceutical Education and Research, Ganpat University, Mehsana, Gujarat, India Dhvanil N. Prajapati Department of Information Technology, LDRP Institute of Technology and Research, Gandhinagar, Gujarat, India Jigna B. Prajapati Faculty of Computer Applications, Ganpat University, Mehsana, Gujarat, India Zheng Qin Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China; Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, State Key Lab of Innovation Drug and Efficient Energy-Saving Pharmaceutical Equipment, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China

Contributors

Muhammad Zahir Ramli Institute of Oceanography and Maritime Studies (INOCEM), Kullliyyah of Science; Department of Marine Science, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, Malaysia Surovi Saikia Translation Research Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, Tamil Nadu, India Hedieh Sajedi Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran € ur Seydibeyo Mehmet Ozg€ glu Department of Materials Science and Engineering, Izmir Katip Celebi University, Izmir, Turkey; Advanced Structures and Composites Center, University of Maine, Orono, ME, United States Saiful Arifin Shafiee Department of Chemistry, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, Malaysia Aliasgar Shahiwala Dubai Pharmacy College for Girls, Dubai, United Arab Emirates Sushant Shankar HealthPals, Redwood City, CA, United States Shahid S. Siddiqui McGenome LLC, Glenview; Abbott, Abbott Park; Department of Medicine, University of Chicago, Chicago, IL, United States Kannan Subbaram School of Medicine, The Maldives National University, Male, Maldives Muhammad Suhail School of Pharmacy, Kaohsiung Medical University, Kaohsiung City, Taiwan, ROC Deny Susanti Department of Chemistry, Kulliyyah of Science, International Islamic University Malaysia, Kuantan, Pahang, Malaysia Muhammad Taher Department of Pharmaceutical Technology, Kulliyyah of Pharmacy, International Islamic University Malaysia, Kuantan, Pahang, Malaysia Rishi Thakkar PharmE3D Lab, Division of Molecular Pharmaceutics and Drug Delivery, College of Pharmacy, The University of Texas at Austin, Austin, TX, United States

xvii

xviii

Contributors

Fulden Ulucan-Karnak Department of Medical Biochemistry, Institute of Health, Ege University, Izmir, Turkey Yixin Wang Pharmaceutical Sciences Division, School of Pharmacy; Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States William Whitford DPS Group, Strategic Consulting Group, Framingham, MA, United States Zhang Ming Xia Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China; Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, State Key Lab of Innovation Drug and Efficient Energy-Saving Pharmaceutical Equipment, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China Liu Yali Jiangxi University of Traditional Chinese Medicine; Nanchang Medical College, Nanchang University, Nanchang, Jiangxi, People’s Republic of China Sarah Kathryn Zingales Department of Chemistry, University of Saint Joseph, West Hartford, CT, United States Bahman Zohuri Golden Gate University, Ageno School of Business, San Francisco, CA, United States

CHAPTER 1

An overview of artificial intelligence in drug development Anil K. Philipa and Md. Faiyazuddinb,c a

School of Pharmacy, University of Nizwa, Nizwa, Oman School of Pharmacy, Al-Karim University, Katihar, Bihar, India c Nano Drug Delivery® (A product development partnership company), Raleigh-Durham, NC, United States b

1.1 Introduction Artificial Intelligence (AI) became popular in the 1940s, and it was studied to see if computers could process information and make decisions faster than humans [1]. AI is a term that was coined in 1956 [2]. One of the most exciting subfields of computer science is AI, which promises to make computers more intelligent by mimicking human behavior [3]. In the early 1970s, researchers discovered how AI systems could be applied to various areas of the life sciences [4]. AI in drug development was made possible by the large amounts of chemical and biological data accumulated over decades and technological automation was made possible by the use of high-performance processors [5,6]. There have been some breakthroughs in AI technologies. AI technologies have the potential to outperform humans in many clinical areas [7]. Drug development has long been expensive and time-consuming. However, with the help of AI, new drugs can be produced faster and more efficiently [8]. The pharmaceutical industry first used AI in drug development before other medical fields [9]. As a result, it is expected to improve the precision and efficiency of drug developers [10]. Several pharmaceutical companies have partnered with software companies specializing in AI to take advantage of AI in drug development [11]. Smalley reported that AI-based algorithms can assist in the reduction of compounds being considered for drug development, as well as the removal of drugs that may cause negative side effects [12]. AI in drug development can help speed up the drug manufacturing process. Translational research typically takes 14 years, but with the in-silico approach of AI, it has become easier to conduct tests in vitro and in vivo [13,14]. Nanobots could be used to deliver drugs to specific parts of the body in the future. Integrating AI into the nanobot system can allow more specific drugs to reach the exact areas needed for maximum effect [9]. Combination drug development could benefit from AI-based optimization techniques to explore different combinations to maximize efficacy, minimize side effects, and improve patient adherence [15].

A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00001-0

Copyright © 2023 Elsevier Inc. All rights reserved.

1

2

A handbook of artificial intelligence in drug delivery

Virtual reality (VR) technology has gained popularity. In the pharmaceutical industry, VR can replace or supplement pharmacotherapy. Pharmacist education is currently using VR to help students learn more immersive. Drug development teams are using VR to test new drugs and get feedback from users earlier in the process. VR is also being used to help discover new drugs and their side effects. Scientists are using VR to study different animals behavior and see how they respond to new drugs. Patients are being counseled about their medications through VR simulations. Interest in VR research grows despite financial constraints [16]. Drug development by using AI technology is progressing at a rapid pace. Many pharmaceutical companies are partnering with AI startups and academic institutions to launch their internal research and development projects. This shows that the industry is beginning to realize the immense potential of this technology to revolutionize drug discovery and development [17]. Zhavoronkov describes in detail the drug development process and how Deep Learning can be used in this process [18]. Ekert et al. also examined how artificial intelligence technology is used in drug discovery and development. In his opinion, machine learning, computational modeling, AI, and in-vitro modeling will proliferate in the future [19]. A report discusses how artificial intelligence can be used for rational drug design [20]. Turea reports that most healthcare decision-makers see the benefits of AI and understand its possibilities. However, they also fear that it could be responsible for a fatal error. Despite these concerns, AI has proven its value in many cases and continues to improve [21].

1.2 Impact of AI on drug development The drug development process is complex, and to find promising compounds, developers must process a large amount of information. However, AI applications have been introduced to make this process more efficient. By using these applications, developers can narrow down the search for potential drugs and save time and resources [22]. Pharmaceutical companies and startups are increasingly using AI to research and develop new drugs [23]. The next decade of drug development will have the AI approaches under intense scrutiny. These will help improve the flow of work and provide insights that researchers can observe, analyze, and understand [24]. Many companies are developing AI capabilities for applications beyond drug discovery. Schuhmacher et al. reported AI capability development was still in its early stages, which means that counting the number of AI patents filed by a company is unlikely to impact the development of new drug candidates or the sourcing of external candidates during the observation period [25]. The AI model can be updated after studying cells or organoids, to develop a molecular optimization plan. A high-throughput bioassay and AI design can be used to automate a drug development cycle based on the biological effects of the drug. This will significantly accelerate the production of new drugs [26]. A drug’s pharmacokinetic properties are

An overview of artificial intelligence in drug development

affected by physicochemical properties such as ionization, solubility, and permeability. In the development of new drugs, these factors are of critical importance. As a result, the probability of discovering a new drug is higher when using AI-based techniques [27]. The growth of data-driven and algorithm-based research and development has created the need for a new way of thinking about how data mining and AI technologies can be used to discover and develop new medicines. Some things that are important to the success of this approach include classifying diseases into endotypes and integrating artificial intelligence and machine learning into drug development [8]. There is considerable interest in developing therapeutics that alleviate the symptoms of Alzheimer’s disease without adverse side effects. This could be achieved by using AI technology to repurpose well-known drugs to treat Alzheimer’s disease [28,29]. AI models have been used to determine how the disease spreads and stop it [30]. With the help of artificial intelligence, new eye diseases may be diagnosed [31]. AI algorithms will help identify new biomarkers for diseases. This is possible because they can search for specific features themselves, rather than just recognizing clinical features [32]. AI programs are helping to shape the future of disease prediction and diagnosis. They offer innovative ways to manage diseases and make better treatment decisions [33]. AI solutions to medical problems make it possible to understand how diseases are related to different signs and symptoms. This enables doctors to create better treatment plans and diagnoses for their patients [34]. The near future will show how these AI-based digital technologies can offer new targets, improve clinical trial design, and have a broader impact on the pharmaceutical business [35]. FDA approval of AI applications paves the way for regulatory development to enable faster integration of AI-enabled technologies into healthcare [36]. The use of AI in eHealth means that mental health clinicians can engage with their patients more efficiently and effectively. This represents a shift in how mental healthcare is delivered and how patients interact with it [37,38]. GPCR pharmacology is concerned with how drugs interact with G-protein–coupled receptors (GPCRs). This has led to a high success rate in developing drugs that target these proteins, with 78% of drugs succeeding in Phase I clinical trials. The use of several AI applications in drug discovery has increased success rates further [22]. A good dataset is necessary to build a useful predictive model. Without accurate data, even a complex model will not produce valuable results [8].

1.3 AI in drug repurposing Repurposing involves using a drug already approved for a particular disease to treat another condition. Watson Drug Discovery uses AI to increase the efficiency of drug repurposing [39]. Among the best-known examples of drug repurposing are antiinflammatory drugs used as anticancer agents. Chloroquine, an antimalarial, and azithromycin, an antibacterial currently being developed as an antiviral, are two such drugs being developed to combat COVID-19 [40]. AI helps traditional technologies find and validate new drugs. AI can also

3

4

A handbook of artificial intelligence in drug delivery

aid in extracting valuable data for drug reuse faster [41]. AI can enable faster and more effective decision-making to help select and validate new targets [42]. In the drug development process, clinical trials are considered a bottleneck, and researchers believe AI technology can help conduct and design clinical trials. AI can predict whether a drug can be reclassified using transcriptomics, molecular structure data, and clinical databases [43]. The potential of using AI for repurposing drugs to treat neurodegenerative diseases is explored by Paranjpe et al. They emphasize the importance of integrating different types of data while using AI tools to avoid bias and increase accuracy. This will help ensure that the most effective drugs are found and used to treat these diseases [44]. A methodical interplay between drug discovery and drug-target interactions, which AI-based technologies can support, could help repurpose prescription drugs [45]. For machine learning and deep learning methods to be effective for specific tasks, AI must be integrated into human workflows, such as drug repurposing and clinical trials. Designing AI solutions for molecular generation is complicated, and standard practices for data exchange need to be developed and strengthened [46]. AI could help researchers connect different biological networks to find new uses for already developed drugs. Zeng et al. have developed a new AI-powered method for drug repurposing known as DeepDTnet. This method uses data from multiple biological entities to predict new drugtarget interactions more accurately than previous methods [47]. AI has been used to predict drug-target interactions. This information can be helpful to reuse old medications or avoid taking multiple medications at the same time. A drug that is repurposed automatically qualifies for phase II clinical trials. Examination of patent applications published in 2011–14 for drug repurposing has yielded surprising results. The small number of patents for parasitic or tropical diseases contrasts drastically with this area’s extensive research in peer-reviewed publications [48]. The ability to stratify complex diseases into several distinct forms based on large patient populations with well-characterized data is critical. The result is that patients can be classified into distinct subgroups based on the causes and influences of their ailments. These subgroups can be analyzed to identify new drug targets or repurposing opportunities [49]. Scientists have demonstrated that geometric deep learning could help predict and create fingerprints for molecular surface interactions [50]. Finding a drug with the opposite effect of another drug on transcriptional data can target diseases. Considering how ineffective the current approach is, new transcription-based methods such as CuGuCtD are revolutionizing our understanding and ability to determine whether a chemical can modulate gene expression in the same way that disease-modifying drugs do [51].

1.4 AI in developing improved policies AI can help with policy making in several ways, even if it is still in its early stages. For example, expert systems can help decision-makers understand complex problems, and data mining can help identify patterns in data. Adversarial search can help determine the best possible actions to take [52]. AI techniques have been shown to help reduce

An overview of artificial intelligence in drug development

the spread of disease in many different countries [53]. To build trust in AI, it is necessary to extend trust on the types of AI that ensure society benefits and reduced application risks. The development of AI faces many of the same challenges as other types of technology. Therefore, governments and AI-based companies need to develop strategies that address these challenges [54]. One way to make governance more accountable is to create well-functioning and transparent algorithms eliminating the harmful consequences of negative human decision-making. In addition, such algorithms can make the government more efficient by providing an accurate record of past decisions [55]. Many countries have been working on legislation and policies to encourage the adoption of AI technology and attract foreign investment in the technology sector. For example, the Department of Health in Abu Dhabi has developed an AI policy to regulate AI in the health-care sector [56]. The FDA’s new policy focuses on excellence for developers of AI-based medical devices rather than the approval of those devices [57]. Updating AI models will not require prior FDA review helping improve the speed and accuracy of updates [58].

1.5 Conclusion Drug repositioning is a way to develop new drugs by using old drugs that have not yet been approved. This is a very efficient, time-saving, and cost-effective way to increase the success rate of drug therapy. AI has a lot of potential to help improve many different aspects of society. However, in order for it to reach its full potential, we need to develop strategies to address the challenges that come with it. Legislation and policies have been developed by various governments in order to encourage the adoption of AI technology and attract foreign investment in the technology sector. As we continue to explore all that AI has to offer, it is important that we make sure that these technologies are used for the benefit of society as a whole.

References [1] M. Akbari, T.N.A. Do, A systematic review of machine learning in logistics and supply chain management: current trends and future directions, Benchmarking Int. J. 28 (10) (2021) 2977–3005, https:// doi.org/10.1108/bij-10-2020-0514. [2] S. Sharma, S. Ahmed, M. Naseem, W.S. Alnumay, S. Singh, G.H. Cho, A survey on applications of artificial intelligence for pre-parametric project cost and soil shear-strength estimation in construction and geotechnical engineering, Sensors 21 (2) (2021) 463, https://doi.org/10.3390/s21020463. [3] Y. Liu, S. Bi, Z. Shi, L. Hanzo, When machine learning meets big data: a wireless communication perspective, IEEE Veh. Technol. Mag. 15 (1) (2020) 63–72, https://doi.org/10.1109/ mvt.2019.2953857. [4] A. De, A. Sarda, S. Gupta, S. Das, Use of artificial intelligence in dermatology, Indian J. Dermatol. 65 (5) (2020) 352, https://doi.org/10.4103/ijd.ijd_418_20.

5

6

A handbook of artificial intelligence in drug delivery

[5] C.F. Lipinski, V.G. Maltarollo, P.R. Oliveira, A.B.F. da Silva, K.M. Honorio, Advances and perspectives in applying deep learning for drug design and discovery, Front. Robot. AI 6 (2019), https://doi. org/10.3389/frobt.2019.00108. [6] T.B. Kimber, Y. Chen, A. Volkamer, Deep learning in virtual screening: recent applications and developments, Int. J. Mol. Sci. 22 (9) (2021) 4435, https://doi.org/10.3390/ijms22094435. [7] O. Diaz, K. Kushibar, R. Osuala, A. Linardos, L. Garrucho, L. Igual, P. Radeva, F. Prior, P. Gkontra, K. Lekadir, Data preparation for artificial intelligence in medical imaging: a comprehensive guide to open-access platforms and tools, Phys. Med. 83 (2021) 25–37, https://doi.org/10.1016/j. ejmp.2021.02.007. [8] P. Schneider, W.P. Walters, A.T. Plowright, N. Sieroka, J. Listgarten, R.A. Goodnow, J. Fisher, J.M. Jansen, J.S. Duca, T.S. Rush, M. Zentgraf, J.E. Hill, E. Krutoholow, M. Kohler, J. Blaney, K. Funatsu, C. Luebkemann, G. Schneider, Rethinking drug design in the artificial intelligence era, Nat. Rev. Drug Discov. 19 (5) (2019) 353–364, https://doi.org/10.1038/s41573-019-0050-3. [9] D. Paul, G. Sanap, S. Shenoy, D. Kalyane, K. Kalia, R.K. Tekade, Artificial intelligence in drug discovery and development, Drug Discov. Today 26 (1) (2021) 80–93, https://doi.org/10.1016/j. drudis.2020.10.010. [10] A. Asai, M. Konno, M. Taniguchi, A. Vecchione, H. Ishii, Computational healthcare: present and future perspectives (review), Exp. Ther. Med. 22 (6) (2021), https://doi.org/10.3892/ etm.2021.10786. [11] D. Mhlanga, The role of artificial intelligence and machine learning amid the COVID-19 pandemic: what lessons are we learning on 4IR and the sustainable development goals, Int. J. Environ. Res. Public Health 19 (3) (2022) 1879, https://doi.org/10.3390/ijerph19031879. [12] E. Smalley, AI-powered drug discovery captures pharma interest, Nat. Biotechnol. 35 (7) (2017) 604–605, https://doi.org/10.1038/nbt0717-604. [13] A. Zhavoronkov, Q. Vanhaelen, T.I. Oprea, Will artificial intelligence for drug discovery impact clinical pharmacology? Clin. Pharmacol. Ther. 107 (4) (2020) 780–785, https://doi.org/10.1002/ cpt.1795. [14] I. Mikkili, A.P. Karlapudi, T.C. Venkateswarulu, V.P. Kodali, D.S.S. Macamdas, K. Sreerama, Potential of artificial intelligence to accelerate diagnosis and drug discovery for COVID-19, PeerJ 9 (2021), e12073, https://doi.org/10.7717/peerj.12073. [15] A. Mishra, M.P. Gowrav, V. Balamuralidhara, K.S. Reddy, Health in digital world: a regulatory overview in United States, J. Pharm. Res. Int. (2021) 438–450, https://doi.org/10.9734/jpri/2021/ v33i43b32573. [16] X. Luo, Analysis of the effect of virtual reality technology on improving drug design, in: Advances in Social Science, Education and Humanities Research, Atlantis Press, 2022, https://doi.org/10.2991/ assehr.k.220110.124. [17] Y. Peng, E. Liu, S. Peng, Q. Chen, D. Li, D. Lian, Using artificial intelligence technology to fight COVID-19: a review, Artif. Intell. Rev. (2022), https://doi.org/10.1007/s10462-021-10106-z. [18] A. Zhavoronkov, Artificial intelligence for drug discovery, biomarker development, and generation of novel chemistry, Mol. Pharm. 15 (10) (2018) 4311–4313, https://doi.org/10.1021/acs. molpharmaceut.8b00930. [19] J.E. Ekert, J. Deakyne, P. Pribul-Allen, R. Terry, C. Schofield, C.G. Jeong, J. Storey, L. Mohamet, J. Francis, A. Naidoo, A. Amador, J.-L. Klein, W. Rowan, Recommended guidelines for developing, qualifying, and implementing complex in vitro models (CIVMs) for drug discovery, SLAS Discov. 25 (10) (2020) 1174–1190, https://doi.org/10.1177/2472555220923332. [20] W. Duch, K. Swaminathan, J. Meller, Artificial intelligence approaches for rational drug design and discovery, Curr. Pharm. Des. 13 (14) (2007) 1497–1508, https://doi.org/ 10.2174/138161207780765954. [21] N.J. Ahmed, A.A. Alzahrani, R.E. Alonazi, M.A. Menshawy, The knowledge and attitudes of the public toward the clinical use of artificial intelligence, Asian J. Pharm. 15 (2021) 168–171, https://doi.org/ 10.22377/ajp.v15i1.3974. [22] N. Brown, J. Cambruzzi, P.J. Cox, M. Davies, J. Dunbar, D. Plumbley, M.A. Sellwood, A. Sim, B.I. Williams-Jones, M. Zwierzyna, D.W. Sheppard, Big data in drug discovery, Prog. Med. Chem. 57 (1) (2018) 277–356, https://doi.org/10.1016/bs.pmch.2017.12.003.

An overview of artificial intelligence in drug development

[23] S.A. Damiati, Digital pharmaceutical sciences, AAPS PharmSciTech 21 (6) (2020), https://doi.org/ 10.1208/s12249-020-01747-4. [24] Y. Luo, J. Peng, J. Ma, Next decade’s AI-based drug development features tight integration of data and computation, Health Data Sci. 2022 (2022) 1–3, https://doi.org/10.34133/2022/9816939. [25] A. Schuhmacher, A. Gatto, M. Hinder, M. Kuss, O. Gassmann, The upside of being a digital pharma player, Drug Discov. Today 25 (9) (2020) 1569–1574, https://doi.org/10.1016/j.drudis.2020.06.002. [26] G. Schneider, Automating drug discovery, Nat. Rev. Drug Discov. 17 (2) (2017) 97–113, https://doi. org/10.1038/nrd.2017.232. [27] G. Hessler, K.-H. Baringhaus, Artificial intelligence in drug design, Molecules 23 (10) (2018) 2520, https://doi.org/10.3390/molecules23102520. [28] C. Fabrizio, A. Termine, C. Caltagirone, G. Sancesario, Artificial intelligence for Alzheimer’s disease: promise or challenge? Diagnostics 11 (8) (2021) 1473, https://doi.org/10.3390/diagnostics11081473. [29] M. Zhang, G. Schmitt-Ulms, C. Sato, Z. Xi, Y. Zhang, Y. Zhou, P.S. George-Hyslop, E. Rogaeva, Drug repositioning for Alzheimer’s disease based on systematic ‘omics’ data mining, PLOS One 11 (12) (2016), e0168812, https://doi.org/10.1371/journal.pone.0168812. [30] R. Garg, A. Patel, W. Hoda, Emerging role of artificial intelligence in medical sciences we ready! J. Anaesthesiol. Clin. Pharmacol. 37 (1) (2021) 35, https://doi.org/10.4103/joacp.joacp_634_20. [31] S. Keel, P.Y. Lee, J. Scheetz, Z. Li, M.A. Kotowicz, R.J. MacIsaac, M. He, Feasibility and patient acceptability of a novel artificial intelligence-based screening model for diabetic retinopathy at endocrinology out-patient services: a pilot study, Sci. Rep. 8 (1) (2018), https://doi.org/10.1038/s41598018-22612-2. [32] A. Moraru, D. Costin, R. Moraru, D. Branisteanu, Artificial intelligence and deep learning in ophthalmology—present and future (review), Exp. Ther. Med. (2020), https://doi.org/10.3892/ etm.2020.9118. [33] A.N. Ramesh, C. Kambhampati, J.R.T. Monson, P.J. Drew, Artificial intelligence in medicine, Ann. R. Coll. Surg. Engl. 86 (5) (2004) 334–338, https://doi.org/10.1308/147870804290. [34] S. Reddy, Use of Artificial Intelligence in Healthcare Delivery, 2018, https://doi.org/10.5772/ intechopen.74714. [35] D. Hartl, V. de Luca, A. Kostikova, J. Laramie, S. Kennedy, E. Ferrero, R. Siegel, M. Fink, S. Ahmed, J. Millholland, A. Schuhmacher, M. Hinder, L. Piali, A. Roth, Translational precision medicine: an industry perspective, J. Transl. Med. 19 (1) (2021), https://doi.org/10.1186/s12967-02102910-6. [36] D. Filipiak-Strzecka, J.D. Kasprzak, E. Szymczyk, P. Wejner-Mik, P. Lipiec, Bedside screening with the use of pocket-size imaging device can be useful for ruling out carotid artery stenosis in patients scheduled for cardiac surgery, Echocardiography 34 (5) (2017) 716–722, https://doi.org/10.1111/ echo.13507. [37] D. Bakker, N. Kazantzis, D. Rickwood, N. Rickard, Mental health smartphone apps: review and evidence-based recommendations for future developments, JMIR Ment. Health 3 (1) (2016), e7, https://doi.org/10.2196/mental.4984. [38] A. Barak, L. Hen, M. Boniel-Nissim, N. Shapira, A comprehensive review and a meta-analysis of the effectiveness of internet-based psychotherapeutic interventions, J. Technol. Hum. Serv. 26 (2–4) (2008) 109–160, https://doi.org/10.1080/15228830802094429. [39] H.-Q. See, J.-N. Chan, S.-J. Ling, S.-C. Gan, C.-O. Leong, C.-W. Mai, Advancing pharmacy service using big data—are we fully utilising the big data’s potential yet? J. Pharm. Pharm. Sci. 21 (2018) 217–221, https://doi.org/10.18433/jpps29869. [40] M. Rudrapal, S.J. Khairnar, A.G. Jadhav, Drug Repurposing (DR): An Emerging Approach in Drug Discovery, 2020, https://doi.org/10.5772/intechopen.93193. [41] R. Shaw, Y.k. Kim, J. Hua, Governance, technology and citizen behavior in pandemic: lessons from COVID-19 in East Asia, Prog. Disaster Sci. 6 (2020), 100090, https://doi.org/10.1016/j. pdisas.2020.100090. [42] W.S. Son, Drug discovery enhanced by artificial intelligence, Biomed. J. Sci. Tech. Res. 12 (1) (2018), https://doi.org/10.26717/bjstr.2018.12.002189. [43] R. Mishra, B. Li, The application of artificial intelligence in the genetic study of Alzheimer’s disease, Aging Dis. 11 (6) (2020) 1567, https://doi.org/10.14336/ad.2020.0312.

7

8

A handbook of artificial intelligence in drug delivery

[44] M.D. Paranjpe, A. Taubes, M. Sirota, Insights into computational drug repurposing for neurodegenerative disease, Trends Pharmacol. Sci. 40 (8) (2019) 565–576, https://doi.org/10.1016/j. tips.2019.06.003. [45] I. Rodrı´guez-Rodrı´guez, J.-V. Rodrı´guez, N. Shirvanizadeh, A. Ortiz, D.-J. Pardo-Quiles, Applications of artificial intelligence, machine learning, big data and the internet of things to the COVID-19 pandemic: a scientometric review using text mining, Int. J. Environ. Res. Public Health 18 (16) (2021) 8578, https://doi.org/10.3390/ijerph18168578. [46] J.M. Levin, T.I. Oprea, S. Davidovich, T. Clozel, J.P. Overington, Q. Vanhaelen, C.R. Cantor, E. Bischof, A. Zhavoronkov, Artificial intelligence, drug repurposing and peer review, Nat. Biotechnol. 38 (10) (2020) 1127–1131, https://doi.org/10.1038/s41587-020-0686-x. [47] Z. Liu, X. Chen, W. Carter, A. Moruf, T.E. Komatsu, S. Pahwa, K. Chan-Tack, K. Snyder, N. Petrick, K. Cha, M. Lal-Nag, Q. Hatim, S. Thakkar, Y. Lin, R. Huang, D. Wang, T.A. Patterson, W. Tong, AI-powered drug repurposing for developing COVID-19 treatments, Ref. Mod. Biomed. Sci. (2022), https://doi.org/10.1016/b978-0-12-824010-6.00005-8. [48] H.A. Mucke, E. Mucke, Sources and targets for drug repurposing: landscaping transitions in therapeutic space, Drug Repurpos. Rescue Reposition. 1 (1) (2015) 22–27, https://doi.org/10.1089/ drrr.2015.0001. [49] S. Gardner, S. Das, K. Taylor, AI enabled precision medicine: patient stratification, drug repurposing and combination therapies, Artificial Intelligence in Oncology Drug Discovery and Development, IntechOpen, 2020, https://doi.org/10.5772/intechopen.92594. [50] R. Kumavath, S. Paul, H. Pavithran, M.K. Paul, P. Ghosh, D. Barh, V. Azevedo, Emergence of cardiac glycosides as potential drugs: current and future scope for cancer therapeutics, Biomol. Ther. 11 (9) (2021) 1275, https://doi.org/10.3390/biom11091275. [51] P. Richardson, I. Griffin, C. Tucker, D. Smith, O. Oechsle, A. Phelan, M. Rawling, E. Savory, J. Stebbing, Baricitinib as potential treatment for 2019-nCoV acute respiratory disease, Lancet 395 (10223) (2020) e30–e31, https://doi.org/10.1016/s0140-6736(20)30304-4. [52] M. Milano, B. O’Sullivan, M. Gavanelli, Sustainable policy making: a strategic challenge for artificial intelligence, AI Mag. 35 (3) (2014) 22–35, https://doi.org/10.1609/aimag.v35i3.2534. [53] A. Majeed, S.O. Hwang, Data-driven analytics leveraging artificial intelligence in the era of COVID19: an insightful review of recent developments, Symmetry 14 (1) (2021) 16, https://doi.org/10.3390/ sym14010016. [54] K. Crawford, R. Calo, There is a blind spot in AI research, Nature 538 (7625) (2016) 311–313, https:// doi.org/10.1038/538311a. [55] F. Selten, A. Meijer, Managing algorithms for public value, Int. J. Public Adm. Digit. Age 8 (1) (2021) 1–16, https://doi.org/10.4018/ijpada.20210101.oa9. [56] M.M. Hanafi, N. Kshetri, R. Sharma, N. Kshetri, Economics of artificial intelligence in the Gulf cooperation council countries, Computer 54 (12) (2021) 92–98, https://doi.org/10.1109/ mc.2021.3113094. [57] P. Shah, F. Kendall, S. Khozin, R. Goosen, J. Hu, J. Laramie, M. Ringel, N. Schork, Artificial intelligence and machine learning in clinical development: a translational perspective, npj Digit. Med. 2 (1) (2019), https://doi.org/10.1038/s41746-019-0148-3. [58] L. Nordling, A fairer way forward for AI in health care, Nature 573 (7775) (2019) 103–105, https://doi. org/10.1038/d41586-019-02872-2.

CHAPTER 2

General considerations on artificial intelligence Abhay Dharamsia, Archana Mohit Navaleb, and Sunil S. Jambhekarc a Department of Pharmaceutics, Parul Institute of Pharmacy, Parul University, Vadodara, India Department of Pharmacology, Parul Institute of Pharmacy, Parul University, Vadodara, India c School of Pharmacy, LECOM Bradenton Campus, Bradenton, FL, United States b

Machine learning is a subset of the umbrella term artificial intelligence (AI). AI has already crept into several tasks of our day-to-day life, like digital assistants, internet surfing, online shopping, etc. Machine learning (ML), as the name indicates, is a way (algorithm) of selflearning by computer. The development of ML algorithms originated from the quest of computers that learn on their own based on their experiences. The learning takes place with the help of a dataset provided to the computer as training data. It basically helps in decision making or prediction of an outcome when the situation is having manifold factors and when decision making is not straightforward as per human intelligence. Drug discovery and delivery is a complicated process requiring a lot of human aptitudes and decision-making ability. The process is characterized by abundant data handling with multiple variables, thus making it amenable to the application of ML. Opportunities for the application of ML occur at nearly all stages of drug discovery, like target identification and validation, compound screening, lead identification and optimization, preclinical development, clinical trials, and biomarker identification and analysis. However, for the effective application of ML, its basic understanding is inevitable. The knowledge and technology about ML in healthcare are advancing considerably. Various software libraries are available online that can work with a range of hardware, even simple personal computers. Proper understanding and selection of an appropriate machine learning approach may provide accurate predictions. This chapter will provide various ML approaches and their areas of applications with suitable examples. Several ambiguities in the available methods of ML are cropping up as these are being applied to actual situations in the healthcare sector. However, scientists are also coming up with new techniques better suited to the area of healthcare. Deep learning is an approach apt for complex drug discovery data. However, the level of the algorithm to be generated is more complex in this approach. Another challenge in the application of ML to drug discovery is the availability of sufficient, accurate data to be fed for training. Generation of data itself may be a costly affair in certain phases of drug development. Although, there are few bottlenecks still to be resolved before ML can be applied full-fledged in drug A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00002-2

Copyright © 2023 Elsevier Inc. All rights reserved.

9

10

A handbook of artificial intelligence in drug delivery

discovery. The lack of repeatability and interpretability of the data generated by ML is posing a challenge for its accountability and reliability in different situations like the approval processes and IPR. There are methods that are proposed by scientists to improve the fitting of ML models and improve its output data, but, a great deal of work is required to be done in this area. The purpose of this chapter is to provide a basic understanding of ML concepts. This chapter is expected to bring more clarity to the potential applications of ML in drug design and development. We anticipate providing an overall view of the ML methods, their applications, and limitations so that aspirant researchers can be benefited.

2.1 The introduction of AI and its importance in pharmaceutical operations The word intelligence means an ability to learn and solve problems. Thus, artificial intelligence (AI) is the technique where we generate machines which can learn on their own. The concept of AI is not very new. The term artificial intelligence was coined by John McCarthy in 1956, during a conference. However, till many years, there was no significant progress made in this area due to limited computational technology. In past few decades, development of cloud computing and other hardware and software advancements has resulted in considerable progress in AI. Machine learning is an area of artificial intelligence, where machines learn (predict) tasks based on previous experience (data). Basically, machine learning algorithms are developed using two types of datasets. Training dataset is used to train the model. The type of data used in this set can vary as per the type of ML approach used as describe below. After training a model, it is validated using another dataset. If the validation parameters are acceptable, it can be utilized for evaluating actual data. Machine learning approaches fall in one of the three categories: 1. Supervised learning: It is basically a classification task performed by machine. Here, the machine is trained using a labeled data. Based on such labeled training dataset, it learns the features of data. It uses this information to predict the label of data when it is provided with test data with known features. Fig. 2.1 depicts the concept with an example. Support vector machines and deep neural networks are the examples of supervised learning algorithms. 2. Unsupervised learning: In this type of ML, a machine performs a clustering task. The training data used here is not labeled. The algorithm is fed with mixed data without any labels. It predicts similar patterns in the data, and clusters data with similar features. Fig. 2.2 depicts the concept of unsupervised learning, where the algorithm is provided with images of roses and lotuses, and divides the images into two clusters with similar features. Dimension reduction method like PCA (principal component analysis) is an example of this type of ML approach.

General considerations on artificial intelligence

Rose

Rose

Rose

Lotus

Lotus

Training data set

Rose Lotus

Lotus

Supervised Learning

Lotus Apply ML

Validation/ Test Data

Rose

Fig. 2.1 Supervised learning approach in ML. The system is fed with training data with labels, rose and lotus. The algorithm learns the features necessary to identify a lotus or a rose. It uses this learning to predict the type of flower when provided with test data. The input as well as output here is labeled.

3. Reinforcement/sequential learning: Here, the agent learns to make decisions as it takes up a different action. If it achieves desired output, it gets a reward. Based on reward, it learns which action is appropriate for which type of input. Thus, here there is a reinforcement of whatever is learned by the algorithm as it works through the data. That is why it is also called reinforcement learning [1]. Multiarmed bandit (MAB) algorithms are examples of this type of learning. In MABs, a given set of actions, called arms is available. The agent interacts with the environment by selecting an arm at a time. This interaction generates some observation, which is denoted as a reward. Interaction with different arms produces different levels of reward. The agent identifies the arm which produces an optimum average reward suitable to achieve the goal (Fig. 2.3).

11

12

A handbook of artificial intelligence in drug delivery

Training data set

Unsupervised Learning

Apply ML

Validation/ Test Data

Fig. 2.2 Unsupervised learning approach in ML. The system is fed with training data without any labels. The algorithm learns the features necessary to differentiate the flowers. It uses this learning to cluster the flowers with similar features. Neither the input nor the output is labeled in this type of approach.

The progress made in AI techniques has created a lot of buzz in past decades. The concept is finding its applications in almost all fields. Sciences like engineering, space science, earth science, biotechnology, etc. are utilizing AI advances. Medical and pharmaceutical science are among the areas where AI applications can bring about revolutionary changes. AI has been applied for disease risk prediction, diagnosis and prognosis estimation [2–5]. Pharmaceutical operations like granulation, mixing, compression, etc. can be optimized

General considerations on artificial intelligence

Fig. 2.3 A Multi Armed Bandit (MAB) algorithm, an example of sequential learning. The agent selects any one arm in the environment and gets rewarded. It learns to select appropriate arm for a given environment to achieve best reward. (From C. R
eda, E. Kaufmann, A. Delahaye-Duriez, Machine learning applications in drug development, Comput. Struct. Biotechnol. 18 (2020) 241–252, https://doi. org/10.1016/j.csbj.2019.12.006.)

using AI algorithms [6]. Park et al. summarized various mechanism based modeling techniques applicable to granulation and compression processes. Various modeling principles were evaluated and their predictive values for the intermediate product quality were derived. Application of an appropriate modeling may improve ongoing manufacturing process within shorter time frame. Integration of data science with Process Analytical Technologies (PAT) can improve the outcome of continuous manufacturing line. Roggo et al. developed a DNN (deep neural network) for a continuous manufacturing line for a solid dosage form. In this study, seven critical process parameters and eight quality attributes were identified. A deep learning technique was applied to the process, where process parameters were changed and their impact on quality attributes was recorded. DNN was developed to reduce noise and improve data interpretation. A calibration error value less than 10% was achieved for Active Pharmaceutical Ingredient (API) content and two other quality parameter estimation [7]. Quality-by-Design (QbD) constitutes a well-defined roadmap of the process to ensure final product quality. The concept of artificial neural network (ANN) can be applied to QBD to achieve desired in vitro and in vivo product property. Simo˜esa et al. developed a feed-forward neural network (FFNN) for setting process parameter limits and material specifications [8]. The product was then tested for in vitro as well

13

14

A handbook of artificial intelligence in drug delivery

as in vivo drug release. It was evaluated in two clinical studies, where it was found to be bioequivalent to the Reference Listed Drug. FFNN is a basic type of DNN, where input just travels forward. There is no reverse journey of signal in the FFNN. Thus, there is no characteristic of loop formation or memory in this type of neural network. This DNN fails to process sequential data. It processes each input data as an independent query and generates output that is not affected by previous data handling. Many applications of FFNNs have been reported, e.g., for modeling drug release from pellets [9], the impact of process parameters for hot melt extrusion process on vaginal film quality [10], the estimation of polymeric nanoparticles size as a function of polymer properties [11], prediction of biophysical features of monoclonal antibodies based on amino acid composition [12], the impact of excipient selection on ejection force of tablets [13] and establishing an in vitro-in vivo correlation for a dry powder inhaler [14]. Some operations in the pharmaceutical manufacturing are based on visual inspection. Evaluation of tablet coating is one such example, where human intervention is considered indispensable. However, image analysis using various classification techniques has been applied to classify tablets with different categories of coating [15]. Mehle et al. also developed a convolutional neural network for differentiation between groups of primary particles or agglomerates in images of pellets taken during coating process [16]. Knowledge of physicochemical and biopharmaceutical properties of drug is necessary in initial stages of formulation design. Studies which are conducted to know about these properties of API are known as preformulation studies. AI can be utilized for prediction of such properties of drugs to ease formulation development. Several studies have described use of ANNs at preformulation stage. The artificial neural network developed by Ebube et al. was trained, validated, and evaluated for prediction of glass transition temperatures, water uptake, and viscosities of various amorphous polymers and their mixtures [17]. The technique was able to predict the properties with good accuracy (error 30 kPa) may damage the cells although this also depends on the formulation composition and the type of cells used, while low pressure would affect the print quality. Optimization of this parameter plays an important role in determining the print quality and functionality. For shear-thinning systems which are usually used as feedstock for pressure based extrusion printing, the applied pressure is directly proportional to the viscosity. Moreover, it has been observed that higher pressures can lead to low-resolution printing and content uniformity issues for pharmaceutical dosage forms Bigger diameter and shorter lengths of the nozzle aid to ease the extrudability. Smaller nozzles tend to clog as the particle size of insoluble components increases and

[34,35]

[34,36]

[37,38]

Applicability of machine learning in 3D printed dosage forms

Table 11.1 Processing variables in material extrusion-based 3D printing—cont’d Printing/ design parameters

Print speed (bioprinters)

Significance

Considerations

maintained above the size of a single cell to prevent cell damage while printing

therefore, the nozzle needs to be selected carefully to ensure a continuous printing process. The shorter length of the nozzle leads to faster extrusion times as the piston would have to travel a shorter distance to extrude the material The nozzle diameter is a key indicator as to the resolution of the printed structure. The larger the diameter of the nozzle, the larger the filaments being extruded which in turn reduces the resolution of the print, but it simultaneously reduces the shear stress exerted on the hydrogel Decreasing the nozzle diameter causes reduction in cell viability Highly viscous materials have often caused clogging of needles and hence increase in the nozzle diameter is an effective solution to keep the printing pressures low An increase in printing speed often results in a reduction of resolution of the print. At higher speeds, the viscosity of the extruded polymer remains constant for extrusion-based bioprinting however can also result in nonuniform extrusion In inkjet printing, an increase in the print speed can often result in nonuniform droplet sizes. Higher printing speeds will also result in higher

Although bioprinters are capable of dispensing cells at high rates, the bio-ink composition (especially cellladen) can often play a critical role in the adjustment of practical speeds that can be obtained

References

[39]

[40,41]

[39,42]

[43,44]

Continued

265

266

A handbook of artificial intelligence in drug delivery

Table 11.1 Processing variables in material extrusion-based 3D printing—cont’d Printing/ design parameters

Significance

Pressure

The pressure is a critical aspect of bioprinting as it can be a major determinant of cell viability. The pressure utilized should be high enough to allow extrusion of the material but also low enough to ensure cell viability

Print head temperature

Studies have shown that an increase in temperature leads to a reduction in the viscosity of the bio-ink. Hence for extrusion-based bioprinting, higher print head temperatures lead to higher flow rates and changes in the extrusion velocity

Considerations

extrusion speeds and this can cause a reduction in cell viability and cell division postprinting. Also, longstanding time inside a reservoir due to slow printing speeds can reduce the cell viability The printing pressure changes cause changes in the induced compressive or tensile force on the extruded polymer. A reduction in the size of the nozzle diameter causes an increase in the printing pressures. In a study conducted by Nair et al., it was seen that the effect of pressure was significantly higher than the effect of nozzle diameter as they saw an increase in the number of injured and necrotic cells. The pressure is highly dependent on the material properties and will be discussed later in this chapter In a study conducted by Gu et al., it was seen that the lowering of temperature in extrusion-based printing caused an increase in shear stress which ultimately resulted in lower cell viability. By studying the storage modulus and loss modulus of a particular bio-ink under various temperatures, the printing temperature can be restricted to a small range which would ultimately reduce temperature effects

References

[40,45]

[46]

Applicability of machine learning in 3D printed dosage forms

11.2.1.1 Fused deposition modeling/fused filament fabrication (temperature-based deposition) Fused deposition modeling (FDM) involves the additive deposition of melted feedstock (filament) extruded through a computer-controlled deposition nozzle, giving the capability of creating complex geometries as well as 3D models with controlled composition and architecture. The printer consists most commonly of a hot end which is the extruder as well as a relatively cooler end which is generally the build platform [1,4,14]. It works based on a filament entering the extruder where it is heated to its transition temperature temperatures and melted after which it is extruded out of a nozzle onto the build platform where rapid fusion and solidification takes place to form 3D printed structures while cooling to the surface temperature. This method is conducted in a layer-by-layer fashion ultimately allowing for the manufacture of complex three-dimensional constructs [47]. The technique predominantly utilizes thermoplastic polymers as the feedstock which have paved the way for processing pharmaceutical compositions using FDM as they are also predominantly composed of thermoplastic polymers or their blends as excipients [21,48]. Owing to the simplicity of the equipment coupled with the diverse choice of excipients as well as ease of producing dosage forms with complex geometries for personalized therapy, fused deposition modeling has been the most commonly evaluated 3D printing technique for the manufacturing of dosage forms (Fig. 11.1). However, there are no commercial models of FDM printers available for pharmaceutical use even though numerous adaptations of the commercial printers are required for pharmaceutical use [49]. There are two widely used methods for the manufacturing of pharmaceutically applicable filaments for FDM printing, i.e., impregnation and HME [50]. Impregnation is carried out by immersing the filament in a drug solution, where the drug is expected to either deposit on the filament or diffuse into it [51]. This technique often yields low levels of drug loading. The other technique commonly used is hot melt extrusion, where a thermoplastic polymer-based pharmaceutical composition is processed using a single screw or a twin-screw extruder under high temperature and shear conditions, and the material is collected using a die of appropriate diameter based on the model of FDM used for 3D printing. An added advantage of HME based filaments apart from the scope for high drug loads is the solubility advantage induced by the formation of amorphous solid dispersions [29,52–54]. Manufacturing techniques for filaments will not be discussed in this chapter as the process and variables are different from AM processes and have been heavily investigated previously. FDM process conditions play a crucial role in optimizing the print by improving surface roughness, dimensional accuracy, mechanical properties, material behavior, and build time [55]. A major limitation with this printing method is the exposure of the filaments to high temperatures during material extrusion which could ultimately result in degradation of the drug. In a study utilizing 4-aminosalicylic acid as the active component, thermal degradation of 50% was observed which further emphasizes the need to

267

268

A handbook of artificial intelligence in drug delivery

Filament supply Heated extrusion head

Filament supply

Extrusion zone Part

Solidifying material Solidified material Molten material

Building Platform

Fig. 11.1 Schematic of fused deposition modeling additive manufacturing [2].

reduce printing temperatures, especially for thermolabile drugs [56,57]. The manufacturing of personalized drug delivery devices using FDM often undergoes a 2-step process which includes manufacturing of the filaments using HME discussed in the last paragraph and printing of the filament into various constructs using the 3D printer. The printing step involves loading the particular filament into the desired 3D printer and a second extrusion step to allow for the deposition of the material into various three-dimensional shapes. It can be seen that both HME and FDM processes expose the drug to high levels of heat which could be detrimental to thermolabile drugs [31–33,58]. The formulation along with the manufacturing process is often challenging due to the myriad of combinations available and due to the lack of specialized software, is often empirical. This leads to heuristic trial and error methods being implemented which can be significantly time-consuming and arduous [59,60]. Design of experiments has received a lot of attention in mitigating these effects but considering that it is limited to datasets with low dimensions (i.e., number of variables), requires prior knowledge of the fabrication process, needs numerous experiments to complete the model, and is unable to learn from existing experimental data, it is often still an arduous process [60–62]. These studies are often restricted to an offline mode and are not particularly suitable for dynamic studies considering the linearity of the process. Artificial intelligence (AI) machine learning techniques provide a suitable alternative that can significantly improve the formulation development stage by using a nonlinear web-based software to increase efficiency [11,14]. With this in mind, Elbadawi et al. developed a novel machine learning approach for assessing the three-dimensional printability of medicines called M3DISEEN [11,12]. AI models are capable of comprehending both structured as

Applicability of machine learning in 3D printed dosage forms

well unstructured data and are capable of continuous learning. Considering these aspects, the user will not have to continuously train the models. This novel approach was used to predict 4 key parameters which include: extrusion temperature, filament mechanical characteristics, printing temperature, and printability to design 3D printed drug-loaded formulations. The AI model was trained and tested using various formulations. It was found that the M3DISEEN AI model was able to predict printability, filament characteristics, and FDM as well as HME temperatures with high accuracy [13,21–24,31]. Another approach using rheological properties to establish prediction mechanisms was developed for determining the rheological properties of a filament requires a lot less material than the trial-and-error approach and is capable of bolstering our understanding of the printability of a particular filament [12,63]. Recent developments in the in-line sensing rheological sensors for real-time monitoring have bolstered the ability to automate the FDM printing process. Rheological properties provide a significant amount of information that can be incredibly cumbersome to discern and will require experts to help understand it. Due to the sheer volume of observations, it can also be particularly difficult to discern patterns in the high-dimensional, nonlinear datasets. The prediction mechanism developed by Elbadawi et al. is capable of predicting the printability as well as drug dissolution of the 3D printed formulations. In the study, first, the viscosity measurements were used to establish a mathematical model and then tablets were printed using the formulations assessed. The formulation used for the study consisted of polycaprolactone (PCL), different amounts of ciprofloxacin and polyethylene glycol (PEG) as well as different molecular weights of PEG. All the printing parameters were kept constant. From the study, it was found that both binary, as well as ternary compositions, could be extruded at a temperature of 130°C, 150°C, and 170°C. These results are however in contrast to previous findings involving PCL where PCL was found to be unextrudable at 130–150°C. The drug release rates were measured over seven days. Machine learning tools were developed to predict the dissolution behavior from the viscosity measurements. Machine learning is a collection of algorithms that are capable of handling different formats of input and discerning patterns in high-dimensional linear datasets. From the study, the ideal viscosity of the filaments was found to be 102 Pa s and anything above 103 Pa s was found to be unextrudable. It was also found that the use of 15%w/w PEG 200 was unable to adhere to the build plate and hence consideration should be given to the PEG molecular weight. It was found that the nonlinear ML model was superior to both the partial least squares (PLS) method as well as the ML linear model. The similarity factor (f2) of the predicted and actual data was found to be 90.9 which shows the accuracy of the modeling tool in predicting the dissolution profiles of the 3D printed tablets [12,13,25–28,30]. The FDM printer has two major components. Print head: The print head is ultimately responsible for the heating and deposition of the material which is the whole extrusion process. It is controlled by a gantry and

269

270

A handbook of artificial intelligence in drug delivery

often moves in the x, y, and sometimes the z-direction. The direction of movement is often determined by the g code fed to the printer after slicing the CAD software [64]. Print bed: This is the surface on which the material is deposited. The print bed on certain printers controls the z-direction as well. Often these surfaces require heat control as well as material [57]. 11.2.1.2 Semisolid extrusion 3D printing (temperature and pressure-based deposition) Semisolid extrusion (SSE) also known as pressure-assisted micro syringe (PAM) 3D printing is a type of material extrusion additive manufacturing process and as the name suggests method utilizes semisolids as the starting material [65]. It may be in the form of a paste or a gel and requires a syringe-based tool for extrusion [66]. This method offers the advantage of printing at room temperature or lower temperatures as compared to processes like FDM, because of which it is preferred for thermosensitive drugs, and a similar pressure-based mechanism is utilized for bioprinting which is discussed in the next section [67,68]. A key difference in functionality from FDM is the presence of screw-based/ pneumatic/piston-driven extrusion syringes with a jacket to control the temperature [69,70]. The system can be selected based on the viscosity of the material of interest to be printed [68,71]. The properties of the materials would be discussed in the further sections. The semisolid feedstock is loaded into the syringe, which is then placed in its holder slot on the robotic printing arms of the printer. This syringe is responsible for pushing the material out from the nozzle. The robotic printing arms control the movement of the syringe and thus print a shape as per the desired 3D object. The syringe nozzle orifice controls the resolution of the printed object [40]. SSE 3DP expanded its reach into the pharmaceutical arena due to the wide variety of materials that can be used as compared to other techniques due to its ability to work without the use of any thermal aid. It has been used to print dosage forms using gels and paste. One instance was manufacturing anticancer drug-loaded calcium phosphate cement (CPC) scaffold to decrease the relapse and resurgence of bone cancer postsurgery [31–33,58]. The scaffold showed complete drug release in the first 2 h and also exhibited cell killing ability. This successfully developed scaffold can be used as a novel bone graft material while also delivering personalized medication for the treatment of bone cancers. Printlet containing carbamazepine in hydroxypropyl-β-cyclodextrin have also been successfully printed using SSE based 3D printing technology where the robustness of the method by printing printlet containing small yet accurate amounts of the drug was established. Due to the thermal independence of the process, no drug degradation and drug losses were seen [67]. Another study shows the impact of dynamic viscosities and fluid mechanics of the gel and water content in printing orodispersible films containing levocetirizine hydrochloride. The effect of excipient (HPMC) concentration was also

Applicability of machine learning in 3D printed dosage forms

Fig. 11.2 Schematic of extrusion based bioprinting (from left, pneumatic-based and right, mechanical based) [72].

explored in the same. These parameters could be explored and fed to a machine model to predict the outcomes and reduce the iterations involved in the same [66] (Fig. 11.2). 11.2.1.3 3D bioprinting (pressure mediated deposition of shear-thinning inks) Bioprinting is defined as the synchronous deposition of biomaterials and living cells in a predefined layer by layer fashion to fabricate 3D constructs [45]. This method allows for the precise deposition of living cells, proteins, DNA, drugs, growth factors, and other bioactive substances spatially and temporally to guide tissue formation and generation [35]. The material generally used for this printing technique is often referred to as bio-inks. Bio inks generally have four different types which are cell aggregates (tissue spheroids, cell pellet, and tissue strands), hydrogels, micro-carriers, and decellularized matrix components [34,35,73]. Bioprinting can also be split into two ways, which are, i.e., scaffold-based and scaffold-free bioprinting [74]. In scaffold-based bioprinting, cells are printed within exogenous biomaterial matrices such as decellularized extracellular matrix (dECM) or hydrogels and in scaffold-free bioprinting, the cell pellets or preaggregated cells are spatially arranged in a mold to allow for self-assembly [75]. There are 3 types of bioprinting mechanisms based on their method of deposition and the patterning of biological materials, i.e., extrusion-based [76], droplet-based [77], as well as laser-based bioprinting [78], the latter will be discussed in the upcoming sections as it involves the use of a material bed whereas the former two techniques work on the material deposition principle [79]. Extrusion-based bioprinting employs the extrusion of continuous filaments of bioinks which work based on either pneumatic or mechanical (piston or screw assisted) dispensing techniques. Hydrogels which are the most commonly used cell support systems

271

272

A handbook of artificial intelligence in drug delivery

with viscosities ranging from 30 mPa s to 6
107 mPa s are compatible with this system [42,80]. The ability to print high cell density material such as tissue spheroids is often hailed as one of the major advantages of this printing technique [45,46,81]. Viscous fluids however induce high pressure and shear on the cells which can lead to significant reductions in the viability of cells deposited by extrusion [82]. Whereas, droplet-based bioprinting, introduced in the 1980s inspired by the inkjet printers, is also known as “drop on demand” printing and is widely employed for printing with large molecules. The method of deposition is either thermal, piezo, or acoustic forces which help eject the droplets on the supporting substrate, this mechanism distinguished it from the binder jetting 3D printers [37,43,44,83]. These droplets further require to be crosslinked to solidify using chemical or physical crosslinking mechanisms such as crosslinking agents [84], pH, or ultraviolet (UV) radiation [85]. Some of the major advantages associated with the type of printing include low cost, high speed, high resolution, compatibility with a myriad of biomaterials, and the ability to print different concentrations of biological materials by altering the droplet density or size. Nonuniformity of droplet size and nozzle clogging due to high cell density bio-inks are however some of the major drawbacks [72]. MATLAB software and artificial neural networks (ANN) has been utilized to optimize a 3D bio-printed pseudo-bone drug delivery scaffold to mimic the morphology, matrix strength, and matrix resilience of healthy human bone. The polymers used for the preparation of this scaffold were polypropylene fumarate (PPF), free radical polymerized polyethylene glycol-polycaprolactone (PEG-PCL-PEG), and pluronic. The drug utilized in the study was simvastatin as it can be used to promote bone healing and repair. The ANN neural networks are capable of linear as well as nonlinear relationships between dependent and independent variables in the study. Ultimately, the developed optimized scaffold was capable of drug release over 20 days. The fabricated scaffold was also capable of contact adhesion with the fractured/damaged bone and promoted the formation of a pseudo-bone matrix within the defect site [37,43,44,83,86].

11.2.2 Material bed based 3D printing 11.2.2.1 Binder jetting/material jetting (liquid binder-based building of 3D structures) Binder jetting utilizes an inkjet printhead to deliver the binder liquid which helps hold the powder in place to build layer-by-layer objects from the powder bed [40]. Compared to other methods of additive methods of manufacturing, binder jetting has the potential for low cost and high-speed manufacturing. Only one 3D-printed medication is available in the US market, and it is manufactured using the drop-on-powder (DoP) deposition method which uses inkjet printing to jet a liquid binder on a powder bed to 3D print objects. This 3D printed drug Spiritam, manufactured by Aprecia Pharmaceuticals received approval from FDA in 2015 and is manufactured via the ThermiForm process [4,87]. This process is an early adaptation of the DoP process (Table 11.2).

Applicability of machine learning in 3D printed dosage forms

The inkjet printhead deposits droplets of the binder solution on the surface of the powder bed in the shape of the desired design in a two-dimensional cross-section. Following this, the build platform lowers to and a new layer of powder is then spread across the surface again and is then ready for the printing of a new two-dimensional crosssection [95]. This powder feedstock is then spread on the surface usually with the help Table 11.2 Processing variables in material bed based 3D printing. Printing/ design parameters

Powder properties

Spreading method

Significance

Considerations

References

The powder is one of the two main components that form the foundation of the binder jetting process. The interparticle forces in the case of powders vary with a change in size, shape, composition, humidity, etc., and thus require different processing approaches. The ideal powder should flow uniformly with negligible interparticle force. These forces tend to increase as the particle size starts going below the 50 μm size mark. This leads to a build-up of charges which leads to the formation of agglomerations and irregularities on the bed

One of the major properties of interest is the flow property of the powder (angle of repose, Hausner’s ratio, powder internal cohesion). These influence the spreading of the feed on the surface of the print platform which in turn affects the structural properties. However, smaller particles have the advantage of imparting higher resolution and thinner layers Glidants can be used to improve the flow properties of the material as poor flow may lead to content uniformity issues The material used for constructing the spreading apparatus can also be selected in a manner that does not induce static to the material as there is a possibility that the material or parts of the composition might adhere to the metallic material For pharmaceutical blends and dosage forms, the drug content uniformity is extremely important and a regulatory criterion. This is impacted by the difference in particle size, shape, density, solid-state, and the presence of charge.

[88,89]

In most cases, the feedstock is placed near the print bed or the build platform and is pushed over the build platform using a roller or spreader after the completion of every layer. Some instruments also employ a hopper feeder to dispense the

[90]

[91]

[92,93]

Continued

273

274

A handbook of artificial intelligence in drug delivery

Table 11.2 Processing variables in material bed based 3D printing—cont’d Printing/ design parameters

Binder properties

Significance

Considerations

feed which is then spread evenly using a spreading tool. Close packing of the powder on the bed can be achieved by using mechanical vibration compaction techniques or by applying acoustic energy. This helps to settle the powder in place for a better quality of the final product or part

Uniformity becomes more important in potent dosage forms with powder size, it takes place in four stages as follows, i.e., Droplet formation, Droplet coalescence, Binder migration into powder, Binder dispersion by mixing. In the case where droplet size < powder size, it takes place as, Coating of powder particles by a liquid, Imbibition of the binder by capillary action [105]. However, in a binder jetting process, we observe a combination of both these processes taking place together due to the range of size of particles [104]. There are 3 commonly used printing strategies [91,95,106,107]: Vector scanning: A stream of droplets is generated along the outer section of the design and starts moving inward slowly. This is the most accurate process; however, it is the most time consuming as well as it is limited to a single nozzle printing per part. Raster scanning: The printing head moves in the X-direction while moving along the Y-axis in between the steps and then starts on the new path. A stair-stepping effect is seen in this case when the build starts moving along the Z-axis. It can incorporate multiple nozzles in comparison to vector scanning and has higher efficiency as well. Vector trace with raster fill: It combines the former 2 approaches. It has a faster speed and has a better layer definition which in turn improves the build quality and accuracy. 11.2.2.2 Selective laser sintering/Selective laser melting (UV/N-IR laser-based building of 3D structures) Laser sintering (LS) involves using high-energy laser to scan the surface of a powder bed to melt and solidify the powder to form the bulk part. Selective laser sintering (SLS) is a powder bed fusion-based, a 3D printing technique that was developed in the late ‘80s for rapid prototyping and uses powder mixtures as starting materials. This 3DP technology is commonly used to print metal objects [107]. An SLS machine includes a Printing bed/ build platform on which the 3D object is printed, a Laser source responsible for the sintering process, Galvano mirrors which direct the laser beam to the designated positions on the plat, a powder dispenser to hold the feedstock, and Roller/powder spreader to evenly spread the powder feed on the printing bed [59,99]. The machine evenly spreads a layer of powder on the surface of the printing bed which is then hit with a laser in areas as per the desired design through the help of Galvano mirrors that deflect the laser beam. How the powder is spread on the surface depends on the type of equipment in use. Generally, it is a slot feeder and roller/scraper blade to even the surface. When this laser hits the surface of the powder, it transfers thermal energy to the powder particles which then get liquified and fuse. This creates the first layer of the multilayered structure of interest. Following this a new layer of powder is deposited again, followed by the action of laser and thus, forms another layer on the first fused surface. This process continues layer by layer till the entire structure of interest is printed. After the process is finished, the sintered portion

279

280

A handbook of artificial intelligence in drug delivery

forms the final object which is removed from the build plate or printing bed. The environment inside the powder bed involves a multitude of processes for heat transfer, such as powder bed radiation, convection between the external environment and the powder bed, and conduction between the powder bed and inside the powder particles. Selective laser melting (SLM) is a technology derived from SLS, which is also used for prototyping and manufacturing processes [6,101] (Fig. 11.4). The feasibility of SLS 3DP to fabricate dosage forms using pharmaceutical drugs have demonstrated using hydroxypropyl methylcellulose (HPMC E5) and Kollidon VA64. Candurin gold sheen, a photo absorber, was used to aid in the sintering process. Printlet prepared using Kollidon showed a disintegration within 4 s in a small amount of water. This study also showed that the properties of the tablet can be modified by altering the printing process parameters, which would in turn affect the sintering process [99]. Another study with SLS 3DP, to formulate paracetamol in a matrix using different pharmaceuticalgrade polymers like polyethylene oxide, Eudragit (El100-55 and RL), and ethyl cellulose was conducted. By increasing the contact surface area in the case of a gyroid lattice as compared to a cylindrical printlet, the contact surface of the printlet with the water was increased, which led to an accelerated effect in the release profile [108]. Hydrophilic polymers show faster release as compared to water-insoluble polymers. Combining the gyroid lattice with a cylindrical printlet design, a bi-layer configuration was printed, which allowed controlling the release of the drug from the dosage form. This gives way to a more patient-centric development of the dosage form for personalized treatment. This study showed that it is possible to tailor the release behavior, by appropriate selection of excipients and parameters like internal lattice geometry, dimensions, and density. Another study showed for the first time, a 21-fold improvement in the solubility of Ritonavir, a poorly water-soluble and thermosensitive drug by forming an ASD in a single step using

Fig. 11.4 Graphical representation of selective laser sintering additive manufacturing [59].

Applicability of machine learning in 3D printed dosage forms

the SLS-3DP process [90,109]. This new application of SLS-3DP to develop patient-specific, controlled release formulations in a single-step process directly from the powder blend could provide a huge leap in delivering tailored medications at the point-of-care by eliminating the extensive formulation steps involved in other processes. Parameters such as laser scanning speed and the chamber temperature can affect the tablet properties such as weight of the printlet, their disintegration time, hardness, and dissolution. Modulation of these parameters within the optimum range as determined after using DOE would help predict properties of the final formulation which would aid the formulation process and help minimize the iterations after feeding this information to an artificial intelligence model to predict outcomes [97–100,102,108,110]. Printing/powder bed temperature: It is the temperature of the surface on which the power is spread before initiating the sintering process. Preheating the powder bed would supply initial energy to the powder, which in turn get heated up before the sintering process starts and thus would reduce the energy required to be supplied using the laser beam for promoting the sintering process. Printing chamber temperature: This is the temperature of the enclosed printing chamber and is usually kept below the printing bed temperature. It helps maintain the thermal balance in the system. Laser beam: The laser can be considered to be the most important part of the equipment, in terms that it supplies the thermal energy required for sintering, to powder particles on coming in contact with them. The absorbance of energy by the particles depends on the wavelength (λ) of the laser along with other factors. The source of the laser determines the wavelength of the laser, and different materials react differently to different lasers and they need to be selected accordingly to get an optimized product. [KVR7] Crystal lasers (e.g., Nd:YAG lasers, λ ¼ 1.064 μm), gas lasers (e.g., carbon dioxide lasers, λ ¼ 10.6 μm), diode lasers (having variable wavelengths), fiber lasers (λ ¼ 1.064 μm) are a few examples of the lasers used in SLS 3DP. Higher absorption at the wavelength of the laser is important for the high manufacturing throughput of the printing process [111]. Laser scanning speed or beam speed: It is the speed at which the laser beam travels during sintering the desired 3D pattern. The laser as said earlier is responsible for imparting energy to the powder particles in contact which leads to their sintering. Now, if the beam travels faster, i.e., a high or fast laser speed, it would reduce the amount of time that the laser spends in one single spot, which means reduced contact time. This would reduce the amount of energy transferred to that area and in turn reduce the level of sintering and produce porous objects. On the other hand, when the laser speed is slow, the amount of energy transferred increases, in turn increasing the level of sintering and producing more dense objects. However, the printing time increases with a decrease in the laser speed.

281

282

A handbook of artificial intelligence in drug delivery

Scan spacing or hatch distance: This is the distance between two consecutive scans that the laser hits on the designated printing area based on the design of the object of interest. If the spacing is too small, it might end up causing thermal deformation of the exposed area due to the increase in absorbed energy by the particles present. On the other hand, too large of spacing might lead to incomplete sintering and leave un sintered areas between the two consecutive spaces. This leads to poor connections between the layers and reduces the mechanical strength of the printed object of interest. However, an optimum hatch distance would yield the most intricate designs in the perfect manner possible. Also, the time required for printing is inversely proportional to this parameter of interest. Layer thickness: The height of the individual layer printed, determines the printing resolution, and usually depends on the instrument in use. It usually ranges between 0.07 to 0.5 mm. Better the resolution of the process, smoother is the printed material. This parameter is also directly proportional to the printing time, just like the beam speed, mentioned before. The layer height can be set by setting the distance at which the platform bed would be lowered during the printing process. Another parameter that influences the sintering process is the nature of the powder feed, specifically the powder particle size and shape. The extrinsic (powder or particle shape and size) and intrinsic (optical, thermal, and rheological behavior) properties of the powder need to be considered before deciding the processing parameters for the product of interest. These properties would be discussed in detail in the materials section ahead [59,64,89,93–96] (Fig. 11.5). 11.2.2.3 Light-based photochemical cross-linking (laser/light crosslinking based building of 3D structures) Stereolithography (SLA), also known as VAT polymerization, is a rapid prototyping technique involving the use of a light source for the polymerization of liquid resins under light irradiation to form solid constructs [112]. A major advantage of SLA printing for drug delivery applications over other printing techniques such as FDA and SLS is that the objects are constructed at room temperature which helps avoid the risk of drug degradation and drugs can be incorporated into the resin before printing in either solution or suspension and hence, aqueous solubility is a not an aspect in formulation development [60–62]. The printer works based on solidifying a liquid containing a photosensitive polymer along with the drug of choice by exposing the liquid to a high energy source such as ultraviolet light. Another printer with a similar mechanism to SLA is the digital light processing printers, these printers however use digital light as the source of ultraviolet (UV) light whereas SLA printers use a laser beam as a source of ultraviolet light. The photosensitive polymer acts as a crosslinker when exposed to a high energy light source which in turn allows for solidification of the printed construct. A major drawback of this printing process is the inherent toxicity of the resins utilized [113]. Thorough

Applicability of machine learning in 3D printed dosage forms

Fig. 11.5 Illustration of different processing parameters involved in the SLS additive manufacturing [59].

washing off of the resin is essential for the safety of drug delivery devices manufactured using this process. In the pharmaceutical field, vat photopolymerization has been heavily used due to its high accuracy and superior resolution in the manufacturing of oral dosage forms [114], multilayer polypills [113], microneedles [115], implants, hearing aids [116], dental devices as well as shape-memory materials. Hearing aids, in particular, have benefitted significantly with the development of 3D printing, as more than 99% of the hearing aids that fit in the user’s ear canal are custom manufactured by 3D printing [117]. The process is based on a spatially controlled solidification of liquid resin by photopolymerization. A major limitation associated with this technique is the number of resins that are commercially available to be utilized. The resin being utilized should be capable of solidifying upon illumination with light. Polyacrylate and epoxy macromers are among the first resins developed for use in stereolithography and form glassy networks upon

283

284

A handbook of artificial intelligence in drug delivery

photo-initiated polymerization and crosslinking. A lot of the available resins used in lithography are based on low-molecular-weight, multifunctional monomers, and highly crosslinked networks [118]. These resins once crosslinked are generally glassy, rigid, and brittle. The preparation of elastomeric objects using stereolithography has only been described with the use of a few resins. These resin formulations include macromers with low glass transition temperatures and relatively high molecular weights (1–5 kg/mol), often in combination with nonreactive diluents such as N-methyl pyrrolidone (NMP) or water to reduce the viscosity of the resin [119,120]. In SLA 3D printing the resin cure depth is determined by the energy of the light source to which it is exposed [121]. The cure depth is determined based on two factors which are the power of the light source and the scanning speed or exposure time [118]. The penetration depth can be decreased by increasing the amount of photo-initiator or by incorporating a dye in the resin. The nonreactive dye competes with the photo-initiator in absorbing light. There have been reports of UV absorbers also being used for this purpose. However, it should be noted that the decrease in light penetration depth is accompanied by increased build times. Stereolithography has been used to produce a personalized antiacne drug-loaded device. In the study, 3D scanning technology was utilized to attain a 3D model of the nose of the individual. The composition used for SLA printing was a combination of PEGDa and PEG as well as antiacne drug salicylic acid. These printed constructs were compared with FDM printed masks made with polycaprolactone, FlexEco PLA (FPLA), and salicylic acid. It was seen that the SLA printed devices had higher resolution and were capable of higher drug loading as compared to the FDM counterpart. However, drug diffusion from the FDM printed devices was faster as compared to the SLA printed counterpart. This study shows the capability of SLA printers to be used to manufacture personalized drug-eluting devices [122]. Another study attempted to manufacture personalized hearing aids with two antibiotics which were ciprofloxacin and fluocinolone acetonide specifically for patients with ear infections. The two polymer resins chosen for this study were ENG hard and flexible. These devices were capable of sustained drug release for two weeks which further inhibited the formation of biofilms on the surface of the device as well as the surrounding medium. This study further emphasizes the capability of SLA to be used in the manufacture of personalized medical devices [116] (Fig. 11.6). SLA has also been used in the manufacturing of bladder devices for intravesical drug delivery. These devices can be used as an alternative oral drug delivery to attain high concentrations at the site action which in turn minimizes the degree of systemic side effects. The device was loaded with varying concentrations of lidocaine hydrochloride to attain local and prolonged drug delivery. The resin used was elastic resin which is an elastomeric material with high flexibility to improve patient comfort. Two different designs were manufactured, one being a hollow device and the other was a solid device. The solid devices were capable of releasing lidocaine for two weeks whereas the hollow device

Applicability of machine learning in 3D printed dosage forms

Fig. 11.6 Light based bioprinting illustrations. Extrusion based (top) and lithography-based (bottom) bioprinting [85].

showed complete release in 4 days. This is yet another example of SLA 3D printing being used for the manufacture of pharmaceutical devices. 11.2.2.4 Laser-based bioprinting It is also known as laser-assisted bioprinting (LAB) and laser direct-write (LDW). The method often comprises a pulsed laser beam, a focusing system, a donor slide containing two layers which include an energy-absorbing layer as well as a biological material layer and a collector substrate slide [111]. The principle of working is based on a laser being on absorbing substrates like gold or titanium to create a bubble, this subsequently generates

285

286

A handbook of artificial intelligence in drug delivery

Fig. 11.7 Illustration of laser assisted bioprinting [118].

shock waves that propel the cell containing material from the donor slide onto the collector slide. Some of the key parameters affecting the resolution are the laser energy, pulse frequency, thickness and viscosity of the biological material layer, the air gap between the donor and collector slide as well as the wettability of the substrate slide [123]. With this method, the advantages include that there is an elimination of clogging, compatibility with viscosities ranging from 1 to 300 mPa s, negligible effects on cell viability as well as function, and the capability of deposition of cells with densities of 108 cells/ml with a resolution of one cell per drop [124]. High-cost, time-consuming preparation for printing, and difficulty inaccurate targeting, as well as deposition of cells, are some of the major disadvantage [125] (Fig. 11.7).

11.3 Materials in pharmaceutical 3D printing The materials used as excipients in turn dictate the release profiles and the quality of the structure (dosage form in this case) intended to develop. The selection of these materials determines the process that we need to use for developing the dosage form. And the physiochemical properties of these materials and the physical mixture will in turn help us set the processing parameters for the process. How the properties affect the different printing processes is as follows:

11.3.1 Materials in 3D printing of small molecules 11.3.1.1 Extrusion based (FDM, PAM) Physical properties: The molecular weight, density, degree of polymerization of the polymer, and its crystallinity all affect the thermal, mechanical, and rheological properties of the polymer. The transition temperature for the amorphous class of polymers and the mechanical properties tend to be lower for molecules with low

Applicability of machine learning in 3D printed dosage forms

molecular weights while on the other hand the viscosity and mechanical properties tend to increase with an increase in the molecular weight due to an increase in the entanglement of the polymer chains. These factors need to be taken into consideration while selecting a polymer for preliminary work for the intended dosage forms [29,52,53]. Thermal properties: One of the important properties that need to be taken into consideration for FDM based 3DP is the thermal property of the raw material, which means the behavior of individual components and the physical mixture of drug and excipients as a function of temperature. This helps to determine the extrusion conditions for the polymer-drug mixture and also the printing processing parameters for the FDM process. The glass transition temperature (Tg), which is the transitional temperature at which the molecules in the polymer chain soften and show a glass-like behavior, is one such characteristic of amorphous polymers. On the other hand, for crystalline polymers, the melting temperature (Tm) or melting point, which is the temperature at which the ordered molecules in the crystalline polymer transition to a disordered state, is an important characteristic. Semicrystalline polymers on the other hand exhibit both these characteristic points due to the presence of amorphous and crystalline regions. Thermal analytical techniques such as differential scanning calorimetry (DSC), modulated temperature DSC (mDSC), and thermal gravimetric analysis (TGA) are the widely used techniques to determine these characteristic points. TGA also gives an idea about the degradation as a function of temperature and is a useful tool for preformulation studies. The Tg and Tm values were seen for a pure polymer and pure drug would appear to deviate or change when investigated for the physical mixture of the same. This is attributed to the interaction between the drug and polymers and/or other excipients. In certain cases, the drug itself may act as a plasticizer and help reduce the processing temperature for the extrusion process. The Tg and Tm values are affected by the molecular weight of the polymer and by the interactions possible between the components for the physical mixture. Ideal polymers for HME and FDM printing should be stable at high temperatures and have thermoplastic nature [63,69,70,126,127]. Miscibility/solubilization capacity: This is an important consideration when selecting polymers or excipients for processing small molecule APIs. The solubilization capacity is the extent of solubilization of API in the polymer in an aqueous solution. This is important for poorly soluble drugs, which now occupy a major section of the drugs in the market and those under development. Low molecular weight polymer or surfactants such as poloxamers can be used to improve the solubility. Factors such as the molecular weight of the drug and polymers, the branching of polymer and length of the chain, processing temperatures, affect the miscibility of the drug into the polymer mixture. The amount of shear applied by the extruder helps to aid the

287

288

A handbook of artificial intelligence in drug delivery

solubilization and needs to be considered in the preformulation stage along with other parameters [16,128]. Mechanical properties: The tensile strength of the polymer chains, the strain that it can endure before breaking during elongation, Young’s modulus, and the viscoelasticity of the polymer are some important properties that affect the selection of materials for processing a drug-using FDM or PAM printing process. These parameters also affect the rheological properties of the polymer. The molecular weight and chain arrangement of polymers affect the mechanical properties. The mechanical properties in turn affect the processing parameters, product stability, and drug release from the formulated dosage forms as well. A combination of polymers can be used to achieve the desired mechanical properties and drug release [16,128]. Material properties: For developing a good print using SSE, the material should be continuously and homogeneously extruded through the nozzle without blocking the nozzle. The viscosity of the material is one of the most important rheological properties that determine the quality of the final product. The viscosity of the semisolid dictates its flow properties and is in turn affected by the shear and temperature. Materials that exhibit shear-thinning, i.e., non-Newtonian behavior, have a fluid flow under the influence of strain and are favored in PAM applications. It helps to push the material down the nozzle without clogging the nozzle. However, the material should also be able to hold its shape after extrusion on the print bed. Not enough mechanical strength in the material would cause the upper layers to collapse or the lower layers to sag under the weight of the upper structure. Most materials used in SSE based 3D printing are associated with a swelling phenomenon, i.e., the actual dimensions of the printed dosage form or object would be slightly larger as compared to the theoretical dimensions [82]. 11.3.1.2 Powder-based (binder jetting, SLS) Effect of particle shape and size: Particle shape and size influence the flow properties of the powder, which is crucial in determining the quality of the printed design. Desirable powders would usually have spherically shaped particles with a favorable size distribution between 20 μm to 80 μm. Hausner’s ratios (HR ¼ ρTAP/ρBULK, where, ρTAP is the tapped density of the powder and ρBULK is the bulk density of the powder) and particle size distribution can be used to measure the flow properties and the size distribution of the particles, respectively. Free-flowing powders are preferred as an inferior or poor flow of the powders would lead to poor spreading of the powder on the printing bed, leading to irregularities in the final product. Also, if the particle size is distributed over a wide range, then the presence of a high number of fines would lead to an increase in the adhesive forces leading to poor flow properties, in turn preventing SLS processing [30,88,129,130].

Applicability of machine learning in 3D printed dosage forms

Effect of thermal properties: The nature of the powder particles, whether amorphous, crystalline, or semicrystalline, influences the processing parameters that need to be set to carry out the sintering process. The amount of energy supplied to the system should be enough to induce local coalescence in the polymer particles and also help them adhere to the consecutive layers to form the finished product. The characteristic melting temperature (Tm) and glass transition temperatures (T) need to be determined not just for the individual components but also for the physical mixtures to understand the interactions taking place in the mixture, which would help set the temperatures accordingly [90]. Effect of rheological properties: SLS 3DP does not provide any additional holding pressure or compaction during the printing process. As a result of which, it becomes necessary for the powder mixture to have a low melt viscosity without high shear for optimal processing through this process [131]. Effect of optical properties (For SLS 3DP): The material of interest should have a capacity to absorb enough energy at the wavelength of the laser used. This absorption of energy is what further leads to the sintering process. An increase in laser energy can help compensate in scenarios where the physical powder mixture shows poor absorption [18].

11.3.2 Materials in 3D printing of large molecules This section primarily talks about the formulation requirements for the printing of large molecules. Three factors need to be taken into consideration which is the material used for bioprinting, bio printability as well as the formation of a consolidated structure. This is a key formulation parameter as the material selected needs to have the ability to maintain cell viability as well as the integrity of the protein, peptide, or lipid structure. 11.3.2.1 Materials used in bioprinting The general method of protein delivery includes the use of water containing different concentrations of salts, surfactants, amino acids, and sugars but these formulations solely fail to meet the expectations required for bioprinting in terms of performance [132]. Biocompatible polymers are the major class of polymers currently being explored for applications in bioprinting. There are two types of biocompatible polymers and they are natural as well as synthetic biocompatible polymers. Natural polymers are the gold standard in terms of cell viability and attachment but often fail to have adequate mechanical stability. Some of the commonly used natural polymers include chitosan, alginate, gelatin, and hyaluronic acid. On the flip side, synthetic polymers (such as PCL, PLA, PEG, PEEK, Pluronic) are often mechanically stable but fail to provide a suitable environment for cell proliferation [14,133]. Poly (lactic-co-glycolic acid) (PLGA) is one such example of a synthetic polymer specifically developed to be used for drug delivery and is one of the most widely studied biocompatible and biodegradable polymers. To mitigate

289

290

A handbook of artificial intelligence in drug delivery

the problem of cell viability and proliferation, the addition of RGD peptides has been a widely studied strategy [133]. Other methods such as surface modification have also been employed to improve biocompatibility and bioactivity. Methods often employed are changing the material topology, regulating the surface hydrophobicity as well as hydrophilicity, changing the surface charge strength, and coating the surface with bioactive materials [35]. These formulation aspects are however general and widely applied and do not specifically pertain to the printing of large molecules. Hence, this topic will not be discussed in detail in this chapter. 11.3.2.2 Bio printability Bio printability refers to the ability of these formulations to be fabricated in a layer-bylayer manner to form three-dimensional constructs along with the ability to simultaneously maintain cell viability as well as protein integrity [134]. For different types of printing, different properties are required to attain the desired construct. Printability is often determined by certain factors which include: (i) rheological properties which is the ability of a material to deform, flow, and be precisely controlled to form a 3D construct; (ii) viscosity; and (iii) surface tension. In terms of extrusion-based printing, the material must demonstrate thixotropic properties which is the reduction in viscosity upon applied shear stress. The rheological properties most often analyzed using the loss modulus (G0 ), storage modulus (G00 ), and loss tangent (δ). The storage modulus defines the elastic behavior whereas the loss modulus defines the liquid character. The ratio between storage and loss modulus is known as the loss tangent (δ) and a deterministic tool for print fidelity. The most effective way to increase the storage modulus is to increase the concentration of the biocompatible polymer [135,136]. The first aspect most commonly altered although is the viscosity but an increase in viscosity often results in a reduction in the cell viability and functionality. The viscosity of bio-inks used for droplet-based bioprinting is often low and below 10 mPa s whereas for extrusion-based bioprinting the viscosities can range from 30 to 6
107 mPa s. When it comes to laserassisted printing, the viscosity is generally between 1 and 300 mPa s. Viscosity modifiers such as ethylene glycol, polyethylene glycol of different molecular weights, glycerol, poly (vinyl alcohol), and sodium carboxymethyl cellulose are widely used to improve bio printability [35,80,137]. The surface tension is another parameter particularly important in inkjet printing and surface tension of about 30 mJ m2 is often considered great for printing. Surfactants such as Triton X-100 and polysorbate 20 have also often been used [132,137]. To improve the performance of biocompatible polymers, graphene is commonly used as a nanomaterial filler in hydrogels due to its high aspect ratio as well as excellent mechanical, electrical, optical, thermal, and magnetic properties [138]. Multiwalled carbon nanotubes are another nanomaterial filler material used to improve the mechanical and electrical properties of the hydrogel [58,139,140].

Applicability of machine learning in 3D printed dosage forms

11.3.2.3 Shape fidelity The fidelity of the print often refers to the level of difference between the CAD model and the actual print. Hence, the ability of the printed construct to remain intact and mechanically stable is a critical parameter in the realm of 3D printing. Considering the inherent flow and low mechanical strength of most bio-inks, it is often challenging to attain high fidelity [134]. There are three methods often employed to improve the chances of attaining stronger mechanical support and resistance against structural deformation which include: (i) increase in polymer viscosity (ii) Blending materials to attain superior mechanical properties (iii) Crosslinking the polymer. The increase in viscosity has been covered in the previous sections and hence will not be further discussed here. Hydrogels such as alginate and synthetic polymers such as PCL are often associated with superior mechanical stability and hence blending the desired polymer with the desired polymer can often lead to better fidelity [42,134]. These support structures can often be used as a sacrificial material to attain constructs containing only the desired polymer post crosslinking. This brings us to the most commonly used technique to attain fidelity which is the use of crosslinking agents which can be classified as either being chemical or physical. Chemical crosslinking introduces covalent bonds whereas physical crosslinking is generated by electrostatic, hydrophobic, or hydrogen bonding [84,85]. Often chemical crosslinking is considered to result in stronger constructs however can often lead to cytotoxicity. Physical crosslinking can be done through light, heat as well as pH. In situ crosslinking which incorporates printing into a reservoir of crosslinker is also a technique being used to stabilize the extruded bio-inks during the printing process [123]. One such technique is the freeform reversible embedding of suspended hydrogels (FRESH) which utilizes a reservoir containing a Bingham plastic (e.g., Gelatin) combined with a crosslinker into which the bio-ink is extruded to crosslink the suspended hydrogel in situ [141,142]. In chemical crosslinking, the level of crosslinking can often be adjusted by varying the concentration of the crosslinker. Whereas for physical crosslinking, varying the exposure to the physical stimulus can help in adjusting the level of crosslinking [134].

References [1] K.V. Wong, A. Hernandez, A review of additive manufacturing, ISRN Mech. Eng. 1–10 (2012), https://doi.org/10.5402/2012/208760. [2] H. Bikas, P. Stavropoulos, G. Chryssolouris, Additive manufacturing methods and modeling approaches: a critical review, Int. J. Adv. Manuf. Technol. 83 (1–4) (2016) 389–405, https://doi. org/10.1007/s00170-015-7576-2. [3] B. Vayre, F. Vignat, F. Villeneuve, Metallic additive manufacturing: state-of-the-art review and prospects, Mech. Ind. 13 (2) (2012) 89–96, https://doi.org/10.1051/meca/2012003. [4] J. Jacob, N. Coyle, T.G. West, D.C. Monkhouse, H.L. Surprenant, N.B. Jain, Rapid disperse dosage form containing levetiracetam, 2014, WO2014144512A1. [5] M. Petrusˇevska, S. Berglez, I. Krisch, I. Legen, K. Megusˇar, L. Peternel, B. Abrahamsson, R. Cristofoletti, D.W. Groot, S. Kopp, P. Langguth, M. Mehta, J.E. Polli, V.P. Shah, J. Dressman,

291

292

A handbook of artificial intelligence in drug delivery

[6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17]

[18]

[19] [20] [21] [22]

Biowaiver monographs for immediate release solid oral dosage forms: levetiracetam, J. Pharm. Sci. 104 (9) (2015) 2676–2687, https://doi.org/10.1002/jps.24350. J.-.P. Kruth, P. Mercelis, J. Van Vaerenbergh, L. Froyen, M. Rombouts, Binding mechanisms in selective laser sintering and selective laser melting, Rapid Prototyp. J. (2005) 26–36, https://doi. org/10.1108/13552540510573365. C. Wright, J. Downing, D. Mungall, O. Khan, A. Williams, E. Fonkem, D. Garrett, J. Aceves, B. Kirmani, Clinical pharmacology and pharmacokinetics of levetiracetam, Front. Neurol. 4 (2013), https://doi.org/10.3389/fneur.2013.00192. J. Aho, J.P. Bøtker, N. Genina, M. Edinger, L. Arnfast, J. Rantanen, Roadmap to 3D-printed oral pharmaceutical dosage forms: feedstock filament properties and characterization for fused deposition modeling, J. Pharm. Sci. 108 (1) (2019) 26–35, https://doi.org/10.1016/j.xphs.2018.11.012. L. Eric, T. Savas, 3D printing for drug manufacturing: a perspective on the future of pharmaceuticals, Int. J. Bioprinting 119 (2017), https://doi.org/10.18063/ijb.v1i1.119. J. Zhang, R. Thakkar, Y. Zhang, M. Maniruzzaman, Structure-function correlation and personalized 3D printed tablets using a quality by design (QbD) approach, Int. J. Pharm. 590 (2020), https://doi. org/10.1016/j.ijpharm.2020.119945. S. Ekins, A.C. Puhl, K.M. Zorn, T.R. Lane, D.P. Russo, J.J. Klein, A.J. Hickey, A.M. Clark, Exploiting machine learning for end-to-end drug discovery and development, Nat. Mater. 18 (5) (2019) 435–441, https://doi.org/10.1038/s41563-019-0338-z. M. Elbadawi, T. Gustaffson, S. Gaisford, A.W. Basit, 3D printing tablets: predicting printability and drug dissolution from rheological data, Int. J. Pharm. 590 (2020), https://doi.org/10.1016/j. ijpharm.2020.119868. M. Elbadawi, B. Mun˜iz Castro, F.K.H. Gavins, J.J. Ong, S. Gaisford, G. P
erez, A.W. Basit, P. Cabalar, A. Goyanes, M3DISEEN: a novel machine learning approach for predicting the 3D printability of medicines, Int. J. Pharm. 590 (2020), https://doi.org/10.1016/j.ijpharm.2020.119837. F. Liu, X. Wang, Synthetic polymers for organ 3D printing, Polymers 12 (8) (2020), https://doi.org/ 10.3390/polym12081765. S. Ekins, The next era: deep learning in pharmaceutical research, Pharm. Res. 33 (11) (2016) 2594–2603, https://doi.org/10.1007/s11095-016-2029-7. M.G. Abiad, M.T. Carvajal, O.H. Campanella, A review on methods and theories to describe the glass transition phenomenon: applications in food and pharmaceutical products, Food Eng. Rev. (2009) 105–132, https://doi.org/10.1007/s12393-009-9009-1. K. Ily
es, N.K. Kova´cs, A. Balogh, E. Borba´s, B. Farkas, T. Casian, G. Marosi, I. Tomuța˘, Z.K. Nagy, The applicability of pharmaceutical polymeric blends for the fused deposition modelling (FDM) 3D technique: material considerations–printability–process modulation, with consecutive effects on in vitro release, stability and degradation, Eur. J. Pharm. Sci. 129 (2019) 110–123, https://doi. org/10.1016/j.ejps.2018.12.019. N.A. Charoo, S.F. Barakh Ali, E.M. Mohamed, M.A. Kuttolamadom, T. Ozkan, M.A. Khan, Z. Rahman, Selective laser sintering 3D printing—an overview of the technology and pharmaceutical applications, Drug Dev. Ind. Pharm. 46 (6) (2020) 869–877, https://doi.org/ 10.1080/03639045.2020.1764027. S.M. Gaytan, M.A. Cadena, H. Karim, D. Delfin, Y. Lin, D. Espalin, E. MacDonald, R.B. Wicker, Fabrication of barium titanate by binder jetting additive manufacturing technology, Ceram. Int. 41 (5) (2015) 6610–6619, https://doi.org/10.1016/j.ceramint.2015.01.108. J.D. Prince, 3D printing: an industrial revolution, J. Electron. Resour. Medical Libr. 11 (1) (2014) 39–45, https://doi.org/10.1080/15424065.2014.877247. W. Kempin, C. Franz, L.C. Koster, F. Schneider, M. Bogdahn, W. Weitschies, A. Seidlitz, Assessment of different polymers and drug loads for fused deposition modeling of drug loaded implants, Eur. J. Pharm. Biopharm. 115 (2017) 84–93, https://doi.org/10.1016/j.ejpb.2017.02.014. B. Akhoundi, M. Nabipour, F. Hajami, D. Shakoori, An experimental study of nozzle temperature and heat treatment (annealing) effects on mechanical properties of high-temperature polylactic acid in fused deposition modeling, Polym. Eng. Sci. 60 (5) (2020) 979–987, https://doi.org/10.1002/ pen.25353.

Applicability of machine learning in 3D printed dosage forms

[23] Y. Tlegenov, G.S. Hong, W.F. Lu, Nozzle condition monitoring in 3D printing, Robot. Comput. Integr. Manuf. 54 (2018) 45–55, https://doi.org/10.1016/j.rcim.2018.05.010. [24] S. Ding, B. Zou, P. Wang, H. Ding, Effects of nozzle temperature and building orientation on mechanical properties and microstructure of PEEK and PEI printed by 3D-FDM, Polym. Test. 78 (2019), https://doi.org/10.1016/j.polymertesting.2019.105948. [25] A. Elkaseer, S. Schneider, S.G. Scholz, Experiment-based process modeling and optimization for high-quality and resource-efficient FFF 3D printing, Appl. Sci. 10 (8) (2020), https://doi.org/ 10.3390/APP10082899. [26] M. P
erez, G. Medina-Sa´nchez, A. Garcı´a-Collado, M. Gupta, D. Carou, Surface quality enhancement of fused deposition modeling (FDM) printed samples based on the selection of critical printing parameters, Materials 11 (8) (2018), https://doi.org/10.3390/ma11081382. [27] P. Geng, J. Zhao, W. Wu, W. Ye, Y. Wang, S. Wang, S. Zhang, Effects of extrusion speed and printing speed on the 3D printing stability of extruded PEEK filament, J. Manuf. Process. 37 (2019) 266–273, https://doi.org/10.1016/j.jmapro.2018.11.023. [28] M. Alhijjaj, J. Nasereddin, P. Belton, S. Qi, Impact of processing parameters on the quality of pharmaceutical solid dosage forms produced by fused deposition modeling (FDM), Pharmaceutics 11 (12) (2019), https://doi.org/10.3390/pharmaceutics11120633. [29] R. Thakkar, A.R. Pillai, J. Zhang, Y. Zhang, V. Kulkarni, M. Maniruzzaman, Novel on-demand 3-dimensional (3-d) printed tablets using fill density as an effective release-controlling tool, Polymers 12 (9) (2020), https://doi.org/10.3390/POLYM12091872. [30] O. Luzanin, D. Movrin, M. Plancak, Effect of layer thickness, deposition angle, and infill on maximum flexural force in FDM-built specimens, J. Technol. Plast. 39 (2014) 49–58. [31] M.L. Dezaki, M.K.A. Mohd Ariffin, The effects of combined infill patterns on mechanical properties in fdm process, Polymers 12 (12) (2020) 1–20, https://doi.org/10.3390/polym12122792. [32] R. Srinivasan, W. Ruban, A. Deepanraj, R. Bhuvanesh, T. Bhuvanesh, Effect on infill density on mechanical properties of PETG part fabricated by fused deposition modelling, Mater. Today: Proc. 27 (2020) 1838–1842. Elsevier https://doi.org/10.1016/j.matpr.2020.03.797. [33] W. Wu, P. Geng, G. Li, D. Zhao, H. Zhang, J. Zhao, Influence of layer thickness and raster angle on the mechanical properties of 3D-printed PEEK and a comparative mechanical study between PEEK and ABS, Materials 8 (9) (2015) 5834–5846, https://doi.org/10.3390/ma8095271. [34] G. Gillispie, P. Prim, J. Copus, J. Fisher, A.G. Mikos, J.J. Yoo, A. Atala, S.J. Lee, Assessment methodologies for extrusion-based bioink printability, Biofabrication 12 (2) (2020), https://doi.org/ 10.1088/1758-5090/ab6f0d. [35] S. Ji, M. Guvendiren, Recent advances in bioink design for 3D bioprinting of tissues and organs, Front. Bioeng. Biotechnol. 5 (2017), https://doi.org/10.3389/fbioe.2017.00023. [36] K. Dubbin, A. Tabet, S.C. Heilshorn, Quantitative criteria to benchmark new and existing bio-inks for cell compatibility, Biofabrication 9 (4) (2017), https://doi.org/10.1088/1758-5090/aa869f. [37] T. Boland, T. Xu, B. Damon, X. Cui, Application of inkjet printing to tissue engineering, Biotechnol. J. 1 (9) (2006) 910–917, https://doi.org/10.1002/biot.200600081. [38] B. Webb, B.J. Doyle, Parameter optimization for 3D bioprinting of hydrogels, Bioprinting 8 (2017) 8–12, https://doi.org/10.1016/j.bprint.2017.09.001. [39] Y. He, F. Yang, H. Zhao, Q. Gao, B. Xia, J. Fu, Research on the printability of hydrogels in 3D bioprinting, Sci. Rep. 6 (2016), https://doi.org/10.1038/srep29977. [40] R. Chang, J. Nam, W. Sun, Effects of dispensing pressure and nozzle diameter on cell survival from solid freeform fabrication-based direct cell writing, Tissue Eng. Part A 14 (1) (2008) 41–48, https:// doi.org/10.1089/ten.a.2007.0004. [41] N. Paxton, W. Smolan, T. B€ ock, F. Melchels, J. Groll, T. Jungst, Proposal to assess printability of bioinks for extrusion-based bioprinting and evaluation of rheological properties governing bioprintability, Biofabrication 9 (4) (2017), https://doi.org/10.1088/1758-5090/aa8dd8. [42] C. Mandrycky, Z. Wang, K. Kim, D.H. Kim, 3D bioprinting for engineering complex tissues, Biotechnol. Adv. 34 (4) (2016) 422–434, https://doi.org/10.1016/j.biotechadv.2015.12.011. [43] B. He, S. Yang, Z. Qin, B. Wen, C. Zhang, The roles of wettability and surface tension in droplet formation during inkjet printing, Sci. Rep. 7 (1) (2017), https://doi.org/10.1038/s41598-01712189-7.

293

294

A handbook of artificial intelligence in drug delivery

[44] H. Miyanaji, N. Momenzadeh, L. Yang, Effect of printing speed on quality of printed parts in binder jetting process, Addit. Manuf. 20 (2018) 1–10, https://doi.org/10.1016/j.addma.2017.12.008. [45] L. Ning, X. Chen, A brief review of extrusion-based tissue scaffold bio-printing, Biotechnol. J. 12 (8) (2017), https://doi.org/10.1002/biot.201600671. [46] Y. Gu, B. Schwarz, A. Forget, A. Barbero, I. Martin, V.P. Shastri, Advanced bioink for 3D bioprinting of complex free-standing structures with high stiffness, Bioengineering 7 (2020) 141, https://doi. org/10.3390/bioengineering7040141. [47] A. Melocchi, M. Uboldi, M. Cerea, A. Foppoli, A. Maroni, S. Moutaharrik, L. Palugan, L. Zema, A. Gazzaniga, A graphical review on the escalation of fused deposition modeling (FDM) 3D printing in the pharmaceutical field, J. Pharm. Sci. 109 (10) (2020) 2943–2957, https://doi.org/10.1016/j. xphs.2020.07.011. [48] A. Hary
nska, I. Gubanska, J. Kucinska-Lipka, H. Janik, Fabrication and characterization of flexible medical-grade TPU filament for fused deposition modeling 3DP technology, Polymers 10 (12) (2018), https://doi.org/10.3390/polym10121304. [49] M.R.P. Arau´jo, L.L. Sa-Barreto, T. Gratieri, G.M. Gelfuso, M. Cunha-Filho, The digital pharmacies era: how 3D printing technology using fused deposition modeling can become a reality, Pharmaceutics 11 (3) (2019), https://doi.org/10.3390/pharmaceutics11030128. [50] B. Shaqour, A. Samaro, B. Verleije, K. Beyers, C. Vervaet, P. Cos, Production of drug delivery systems using fused filament fabrication: a systematic review, Pharmaceutics 12 (6) (2020) 1–16, https:// doi.org/10.3390/pharmaceutics12060517. [51] M. Ibrahim, M. Barnes, R. McMillin, D.W. Cook, S. Smith, M. Halquist, D. Wijesinghe, T.D. Roper, 3D printing of metformin HCl PVA tablets by fused deposition modeling: drug loading, tablet design, and dissolution studies, AAPS PharmSciTech 20 (5) (2019), https://doi.org/10.1208/s12249019-1400-5. [52] X. Chai, H. Chai, X. Wang, J. Yang, J. Li, Y. Zhao, W. Cai, T. Tao, X. Xiang, Fused deposition modeling (FDM) 3D printed tablets for intragastric floating delivery of domperidone, Sci. Rep. 7 (1) (2017), https://doi.org/10.1038/s41598-017-03097-x. [53] D.K. Tan, M. Maniruzzaman, A. Nokhodchi, Advanced pharmaceutical applications of hot-melt extrusion coupled with fused deposition modelling (FDM) 3D printing for personalised drug delivery, Pharmaceutics 10 (4) (2018) 203, https://doi.org/10.3390/pharmaceutics10040203. [54] R. Thakkar, R. Thakkar, A. Pillai, E.A. Ashour, M.A. Repka, Systematic screening of pharmaceutical polymers for hot melt extrusion processing: a comprehensive review, Int. J. Pharm. 576 (2020), https://doi.org/10.1016/j.ijpharm.2019.118989. [55] C. Camposeco-Negrete, Optimization of printing parameters in fused deposition modeling for improving part quality and process sustainability, Int. J. Adv. Manuf. Technol. 108 (7–8) (2020) 2131–2147, https://doi.org/10.1007/s00170-020-05555-9. [56] A. Goyanes, P. Robles Martinez, A. Buanz, A.W. Basit, S. Gaisford, Effect of geometry on drug release from 3D printed tablets, Int. J. Pharm. 494 (2) (2015) 657–663, https://doi.org/10.1016/j. ijpharm.2015.04.069. [57] Z. Liu, Y. Wang, B. Wu, C. Cui, Y. Guo, C. Yan, A critical review of fused deposition modeling 3D printing technology in manufacturing polylactic acid parts, Int. J. Adv. Manuf. Technol. 102 (9–12) (2019) 2877–2889, https://doi.org/10.1007/s00170-019-03332-x. [58] G. Kollamaram, D.M. Croker, G.M. Walker, A. Goyanes, A.W. Basit, S. Gaisford, Low temperature fused deposition modeling (FDM) 3D printing of thermolabile drugs, Int. J. Pharm. 545 (1–2) (2018) 144–152, https://doi.org/10.1016/j.ijpharm.2018.04.055. [59] A. Awad, F. Fina, A. Goyanes, S. Gaisford, A.W. Basit, 3D printing: principles and pharmaceutical applications of selective laser sintering, Int. J. Pharm. 586 (2020), https://doi.org/10.1016/j. ijpharm.2020.119594. [60] A.S. Hussain, X. Yu, R.D. Johnson, Application of neural computing in pharmaceutical product development, Pharm. Res. 8 (10) (1991) 1248–1252, https://doi.org/10.1023/A:1015843527138. [61] S.N. Politis, P. Colombo, G. Colombo, D.M. Rekkas, Design of experiments (DoE) in pharmaceutical development, Drug Dev. Ind. Pharm. 43 (6) (2017) 889–901, https://doi.org/10.1080/ 03639045.2017.1291672.

Applicability of machine learning in 3D printed dosage forms

[62] C. Wang, L. Zhang, Y. Fang, W. Sun, Design, characterization, and 3D printing of cardiovascular stents with zero Poisson’s ratio in longitudinal deformation, Engineering (2020), https://doi.org/ 10.1016/j.eng.2020.02.013. [63] T.J. Coogan, D.O. Kazmer, In-line rheological monitoring of fused deposition modeling, J. Rheol. 63 (1) (2019) 141–155, https://doi.org/10.1122/1.5054648. [64] A. Awad, S.J. Trenfield, S. Gaisford, A.W. Basit, 3D printed medicines: a new branch of digital healthcare, Int. J. Pharm. 548 (1) (2018) 586–596, https://doi.org/10.1016/j.ijpharm.2018.07.024. [65] Y. Cheng, H. Qin, N.C. Acevedo, X. Shi, Development of methylcellulose-based sustained-release dosage by semisolid extrusion additive manufacturing in drug delivery system, J. Biomed. Mater. Res. - B Appl. Biomater. 109 (2) (2021) 257–268, https://doi.org/10.1002/jbm.b.34697. [66] T.T. Yan, Z.F. Lv, P. Tian, M.M. Lin, W. Lin, S.Y. Huang, Y.Z. Chen, Semi-solid extrusion 3D printing ODFs: an individual drug delivery system for small scale pharmacy, Drug Dev. Ind. Pharm. 46 (4) (2020) 531–538, https://doi.org/10.1080/03639045.2020.1734018. [67] J. Conceic¸a˜o, X. Farto-Vaamonde, A. Goyanes, O. Adeoye, A. Concheiro, H. Cabral-Marques, J.M. Sousa Lobo, C. Alvarez-Lorenzo, Hydroxypropyl-β-cyclodextrin-based fast dissolving carbamazepine printlets prepared by semisolid extrusion 3D printing, Carbohydr. Polym. 221 (2019) 55–62, https://doi.org/10.1016/j.carbpol.2019.05.084. [68] J. Firth, A.W. Basit, S. Gaisford, The role of semi-solid extrusion printing in clinical practice, 3D Printing of Pharmaceuticals, in:, A.W. Basit, S. Gaisford (Eds.), AAPS Advances in the Pharmaceutical Sciences Series, vol. 31, Springer, Cham, 2018. [69] M. Cui, Y. Li, S. Wang, Y. Chai, J. Lou, F. Chen, Q. Li, W. Pan, P. Ding, Exploration and preparation of a dose-flexible regulation system for levetiracetam tablets via novel semi-solid extrusion three-dimensional printing, J. Pharm. Sci. 108 (2) (2019) 977–986, https://doi.org/10.1016/j. xphs.2018.10.001. [70] E. Sj€ oholm, R. Mathiyalagan, D.R. Prakash, L. Lindfors, Q. Wang, X. Wang, S. Ojala, N. Sandler, 3d-printed veterinary dosage forms—a comparative study of three semi-solid extrusion 3d printers, Pharmaceutics 12 (12) (2020) 1–26, https://doi.org/10.3390/pharmaceutics12121239. [71] M.S. Algahtani, A.A. Mohammed, J. Ahmad, Extrusion-based 3D printing for pharmaceuticals: contemporary research and applications, Curr. Pharm. Des. 24 (42) (2018) 4991–5008, https://doi.org/ 10.2174/1381612825666190110155931. ˇ .P. Kacarevi
c, P.M. Rider, S. Alkildani, S. Retnasingh, R. Smeets, O. Jung, Z. Ivanisˇevi
c, M. Bar[72] Z beck, An introduction to 3D bioprinting: possibilities, challenges and future aspects, Materials 11 (11) (2018), https://doi.org/10.3390/ma11112199. [73] J. Gopinathan, I. Noh, Recent trends in bioinks for 3D printing, Biomater. Res. 22 (1) (2018), https://doi.org/10.1186/s40824-018-0122-1. [74] D. Chimene, K.K. Lennox, R.R. Kaunas, A.K. Gaharwar, Advanced bioinks for 3D printing: a materials science perspective, Ann. Biomed. Eng. 44 (6) (2016) 2090–2102, https://doi.org/10.1007/ s10439-016-1638-y. [75] P.S. Gungor-Ozkerim, I. Inci, Y.S. Zhang, A. Khademhosseini, M.R. Dokmeci, Bioinks for 3D bioprinting: an overview, Biomater. Sci. 6 (5) (2018) 915–946, https://doi.org/10.1039/c7bm00765e. [76] M. Hospodiuk, K.K. Moncal, M. Dey, I.T. Ozbolat, Extrusion-based biofabrication in tissue engineering and regenerative medicine, in: 3D Printing and Biofabrication, 2018, pp. 255–281, https:// doi.org/10.1007/978-3-319-45444-3_10. [77] L. Xinda, C. Jianwei, L. Boxun, W. Xiong, R. Dongni, X. Tao, Inkjet Printing for Biofabrication, Springer Science and Business, Media LLC, 2018, pp. 283–301, https://doi.org/10.1007/978-3-31945444-3_26. [78] L. Koch, A. Deiwick, B. Chichkov, Laser-based 3D cell printing for tissue engineering, BioNanoMaterials 15 (2014) 71–78, https://doi.org/10.1515/bnm-2014-0005. [79] A. Panwar, L.P. Tan, Current status of bioinks for micro-extrusion-based 3D bioprinting, Molecules 21 (6) (2016), https://doi.org/10.3390/molecules21060685. [80] S.V. Murphy, A. Atala, 3D bioprinting of tissues and organs, Nat. Biotechnol. 32 (8) (2014) 773–785, https://doi.org/10.1038/nbt.2958. [81] Y. Yu, Y. Zhang, J.A. Martin, I.T. Ozbolat, Evaluation of cell viability and functionality in vessel-like bioprintable cell-laden tubular channels, J. Biomech. Eng. 135 (9) (2013), https://doi.org/ 10.1115/1.4024575.

295

296

A handbook of artificial intelligence in drug delivery

[82] C. Duty, C. Ajinjeru, V. Kishore, B. Compton, N. Hmeidat, X. Chen, P. Liu, A.A. Hassen, J. Lindahl, V. Kunc, What makes a material printable? A viscoelastic model for extrusion-based 3D printing of polymers, J. Manuf. Process. 35 (2018) 526–537, https://doi.org/10.1016/j. jmapro.2018.08.008. [83] K. Sen, A. Manchanda, T. Mehta, A.W.K. Ma, B. Chaudhuri, Formulation design for inkjet-based 3D printed tablets, Int. J. Pharm. 584 (2020), https://doi.org/10.1016/j.ijpharm.2020.119430. [84] K. Zhu, N. Chen, X. Liu, X. Mu, W. Zhang, C. Wang, Y.S. Zhang, A general strategy for extrusion bioprinting of bio-macromolecular bioinks through alginate-templated dual-stage crosslinking, Macromol. Biosci. 18 (9) (2018), https://doi.org/10.1002/mabi.201800127. [85] K.S. Lim, J.H. Galarraga, X. Cui, G.C.J. Lindberg, J.A. Burdick, T.B.F. Woodfield, Fundamentals and applications of photo-cross-linking in bioprinting, Chem. Rev. 120 (19) (2020) 10662–10694, https://doi.org/10.1021/acs.chemrev.9b00812. [86] P.J. Kondiah, P.P.D. Kondiah, Y.E. Choonara, T. Marimuthu, V. Pillay, A 3D bioprinted pseudobone drug delivery scaffold for bone tissue engineering, Pharmaceutics 12 (2) (2020), https://doi.org/ 10.3390/pharmaceutics12020166. [87] W.E. Katstra, R.D. Palazzolo, C.W. Rowe, B. Giritlioglu, P. Teung, M.J. Cima, Oral dosage forms fabricated by three dimensional printing (TM), J. Control. Release 66 (1) (2000) 1–9, https://doi.org/ 10.1016/S0168-3659(99)00225-4. [88] L.X. Liu, I. Marziano, A.C. Bentham, J.D. Litster, E.T. White, T. Howes, Effect of particle properties on the flowability of ibuprofen powders, Int. J. Pharm. 362 (1–2) (2008) 109–117, https://doi.org/ 10.1016/j.ijpharm.2008.06.023. [89] A.J. Megarry, S.M.E. Swainson, R.J. Roberts, G.K. Reynolds, A big data approach to pharmaceutical flow properties, Int. J. Pharm. 555 (2019) 337–345, https://doi.org/10.1016/j.ijpharm.2018.11.059. [90] D.A. Davis, R. Thakkar, Y. Su, R.O. Williams, M. Maniruzzaman, Selective laser sintering 3-dimensional printing as a single step process to prepare amorphous solid dispersion dosage forms for improved solubility and dissolution rate, J. Pharm. Sci. (2020), https://doi.org/10.1016/j. xphs.2020.11.012. [91] M. Ziaee, N.B. Crane, Binder jetting: a review of process, materials, and methods, Addit. Manuf. 28 (2019) 781–801, https://doi.org/10.1016/j.addma.2019.05.031. [92] S.Y. Chang, S.W. Li, K. Kowsari, A. Shetty, L. Sorrells, K. Sen, K. Nagapudi, B. Chaudhuri, A.W.K. Ma, Binder-jet 3D printing of indomethacin-laden pharmaceutical dosage forms, J. Pharm. Sci. 109 (10) (2020) 3054–3063, https://doi.org/10.1016/j.xphs.2020.06.027. [93] S. Infanger, A. Haemmerli, S. Iliev, A. Baier, E. Stoyanov, J. Quodbach, Powder bed 3D-printing of highly loaded drug delivery devices with hydroxypropyl cellulose as solid binder, Int. J. Pharm. 555 (2019) 198–206, https://doi.org/10.1016/j.ijpharm.2018.11.048. [94] N. Allahham, F. Fina, C. Marcuta, L. Kraschew, W. Mohr, S. Gaisford, A.W. Basit, A. Goyanes, Selective laser sintering 3D printing of orally disintegrating printlets containing ondansetron, Pharmaceutics 12 (2) (2020), https://doi.org/10.3390/pharmaceutics12020110. [95] F. Dini, S.A. Ghaffari, J. Jafar, R. Hamidreza, S. Marjan, A review of binder jet process parameters; powder, binder, printing and sintering condition, Met. Powder Rep. 75 (2) (2020) 95–100, https:// doi.org/10.1016/j.mprp.2019.05.001. [96] S.K. Joneja, W.W. Harcum, G.W. Skinner, P.E. Barnum, J.H. Guo, Investigating the fundamental effects of binders on pharmaceutical tablet performance, Drug Dev. Ind. Pharm. 25 (10) (1999) 1129–1135, https://doi.org/10.1081/DDC-100102279. [97] S.J. Trenfield, C.M. Madla, A.W. Basit, S. Gaisford, Binder jet printing in pharmaceutical manufacturing, in: AAPS Advances in the Pharmaceutical Sciences Series, Vol. 31, Springer Verlag, 2018, pp. 41–54, https://doi.org/10.1007/978-3-319-90755-0_3. [98] M. Lanzetta, E. Sachs, Improved surface finish in 3D printing using bimodal powder distribution, Rapid Prototyp. J. 9 (3) (2003) 157–166, https://doi.org/10.1108/13552540310477463. [99] F. Fina, A. Goyanes, S. Gaisford, A.W. Basit, Selective laser sintering (SLS) 3D printing of medicines, Int. J. Pharm. 529 (1–2) (2017) 285–293, https://doi.org/10.1016/j.ijpharm.2017.06.082. [100] G.V. Salmoria, P. Klauss, K.M. Zepon, L.A. Kanis, The effects of laser energy density and particle size in the selective laser sintering of polycaprolactone/progesterone specimens: morphology and drug

Applicability of machine learning in 3D printed dosage forms

[101] [102] [103] [104]

[105] [106] [107] [108] [109] [110]

[111]

[112] [113]

[114] [115] [116] [117]

release, Int. J. Adv. Manuf. Technol. 66 (5–8) (2013) 1113–1118, https://doi.org/10.1007/s00170012-4393-8. J. Mireles, S. Ridwan, P.A. Morton, A. Hinojos, R.B. Wicker, Analysis and correction of defects within parts fabricated using powder bed fusion technology, Surf. Topogr.: Metrol. Prop. 3 (3) (2015), https://doi.org/10.1088/2051-672X/3/3/034002. Y. Bai, C. Wall, H. Pham, A. Esker, C.B. Williams, Characterizing binder-powder interaction in binder jetting additive manufacturing via sessile drop goniometry, J. Manuf. Sci. Eng. Trans. ASME 141 (1) (2019), https://doi.org/10.1115/1.4041624. M. Ziaee, Materials and Methods to Fabricate Porous Structures Using Additive Manufacturing Techniques, University of South Florida ProQuest Dissertations Publishing, 2018, p. 10980252. A.L. Mundozah, J.J. Cartwright, C.C. Tridon, M.J. Hounslow, A.D. Salman, Hydrophobic/hydrophilic static powder beds: competing horizontal spreading and vertical imbibition mechanisms of a single droplet, Powder Technol. 330 (2018) 275–283, https://doi.org/10.1016/j. powtec.2018.02.032. V.M. Starov, S.A. Zhdanov, M.G. Velarde, Capillary imbibition of surfactant solutions in porous media and thin capillaries: partial wetting case, J. Colloid Interface Sci. 273 (2) (2004) 589–595, https://doi.org/10.1016/j.jcis.2004.02.033. E. Sachs, A. Curodeau, T. Fan, J.F. Bredt, M. Cima, D. Brancazio, Three dimensional printing methods, 2000, USOO6146567A, USPTO, https://patents.google.com/patent/US6146567. K. Subramanian, N. Vail, J. Barlow, H. Marcus, Selective laser sintering of alumina with polymer binders, Rapid Prototyp. J. 1 (2) (1995) 24–35, https://doi.org/10.1108/13552549510086844. F. Fina, A. Goyanes, C.M. Madla, A. Awad, S.J. Trenfield, J.M. Kuek, P. Patel, S. Gaisford, A.W. Basit, 3D printing of drug-loaded gyroid lattices using selective laser sintering, Int. J. Pharm. 547 (1–2) (2018) 44–52, https://doi.org/10.1016/j.ijpharm.2018.05.044. D.A. Davis, D.A. Miller, Y. Su, R.O. Williams, Thermally conductive excipient expands KinetiSol® processing capabilities, AAPS PharmSciTech 21 (8) (2020), https://doi.org/10.1208/s12249-02001817-7. S.F. Barakh Ali, E.M. Mohamed, T. Ozkan, M.A. Kuttolamadom, M.A. Khan, A. Asadi, Z. Rahman, Understanding the effects of formulation and process variables on the printlets quality manufactured by selective laser sintering 3D printing, Int. J. Pharm. 570 (2019), https://doi.org/10.1016/j. ijpharm.2019.118651. B. Guillotin, A. Souquet, S. Catros, M. Duocastella, B. Pippenger, S. Bellance, R. Bareille, M. R
emy, L. Bordenave, J. Am
ed
ee, F. Guillemot, Laser assisted bioprinting of engineered tissue with high cell density and microscale organization, Biomaterials 31 (28) (2010) 7250–7256, https://doi.org/ 10.1016/j.biomaterials.2010.05.055. P.R. Martinez, A. Goyanes, A.W. Basit, S. Gaisford, Fabrication of drug-loaded hydrogels with stereolithographic 3D printing, Int. J. Pharm. 532 (1) (2017) 313–317, https://doi.org/10.1016/j. ijpharm.2017.09.003. X. Xu, P. Robles-Martinez, C.M. Madla, F. Joubert, A. Goyanes, A.W. Basit, S. Gaisford, Stereolithography (SLA) 3D printing of an antihypertensive polyprintlet: case study of an unexpected photopolymer-drug reaction, Addit. Manuf. 33 (2020), https://doi.org/10.1016/j. addma.2020.101071. J. Wang, A. Goyanes, S. Gaisford, A.W. Basit, Stereolithographic (SLA) 3D printing of oral modifiedrelease dosage forms, Int. J. Pharm. 503 (1–2) (2016) 207–212, https://doi.org/10.1016/j. ijpharm.2016.03.016. M.A. Luzuriaga, D.R. Berry, J.C. Reagan, R.A. Smaldone, J.J. Gassensmith, Biodegradable 3D printed polymer microneedles for transdermal drug delivery, Lab Chip 18 (8) (2018) 1223–1230, https://doi.org/10.1039/c8lc00098k. M. Vivero-Lopez, X. Xu, A. Muras, A. Otero, A. Concheiro, S. Gaisford, A.W. Basit, C. AlvarezLorenzo, A. Goyanes, Anti-biofilm multi drug-loaded 3D printed hearing aids, Mater. Sci. Eng. C 119 (2021), https://doi.org/10.1016/j.msec.2020.111606. C.G. Sandstr€ om, The non-disruptive emergence of an ecosystem for 3D printing—insights from the hearing aid industry’s transition 1989-2008, Technol. Forecast. Soc. Chang. 102 (2016) 160–168, https://doi.org/10.1016/j.techfore.2015.09.006.

297

298

A handbook of artificial intelligence in drug delivery

[118] F.P.W. Melchels, J. Feijen, D.W. Grijpma, A review on stereolithography and its applications in biomedical engineering, Biomaterials 31 (24) (2010) 6121–6130, https://doi.org/10.1016/j. biomaterials.2010.04.050. [119] F.P.W. Melchels, D.W. Grijpma, J. Feijen, Properties of porous structures prepared by stereolithography using a polylactide resin, J. Control. Release (2008) e71–e73, https://doi.org/10.1016/j. jconrel.2008.09.066. [120] F.P.W. Melchels, J. Feijen, D.W. Grijpma, A poly (d,l-lactide) resin for the preparation of tissue engineering scaffolds by stereolithography, Biomaterials 30 (23–24) (2009) 3801–3809, https://doi.org/ 10.1016/j.biomaterials.2009.03.055. [121] C.C. Chen, P.A. Sullivan, Predicting total build-time and the resultant cure depth of the 3D stereolithography process, Rapid Prototyp. J. 2 (4) (1996) 27–40, https://doi.org/10.1108/13552549610153389. [122] A. Goyanes, U. Det-Amornrat, J. Wang, A.W. Basit, S. Gaisford, 3D scanning and 3D printing as innovative technologies for fabricating personalized topical drug delivery systems, J. Control. Release 234 (2016) 41–48, https://doi.org/10.1016/j.jconrel.2016.05.034. [123] S. Catros, B. Guillotin, M. Baca´kova´, J.C. Fricain, F. Guillemot, Effect of laser energy, substrate film thickness and bioink viscosity on viability of endothelial cells printed by laser-assisted bioprinting, Appl. Surf. Sci. 257 (12) (2011) 5142–5147. Elsevier B.V. https://doi.org/10.1016/j.apsusc.2010. 11.049. [124] R. Devillard, E. Page`s, M.M. Correa, V. K
eriquel, M. R
emy, J.O. Kalisky, M. Ali, B. Guillotin, F. Guillemot, Cell patterning by laser-assisted bioprinting, Methods in Cell Biol. 119 (2014) 159–174. Academic Press https://doi.org/10.1016/B978-0-12-416742-1.00009-3. [125] S. Michael, H. Sorg, C.T. Peck, L. Koch, A. Deiwick, B. Chichkov, P.M. Vogt, K. Reimers, Tissue engineered skin substitutes created by laser-assisted bioprinting form skin-like structures in the dorsal skin fold chamber in mice, PLoS One 8 (3) (2013), https://doi.org/10.1371/journal.pone.0057741. [126] M.M. Crowley, F. Zhang, M.A. Repka, S. Thumma, S.B. Upadhye, S.K. Battu, J.W. McGinity, C. Martin, Pharmaceutical applications of hot-melt extrusion: part I, Drug Dev. Ind. Pharm. 33 (9) (2007) 909–926, https://doi.org/10.1080/03639040701498759. [127] M.A. Repka, S.K. Battu, S.B. Upadhye, S. Thumma, M.M. Crowley, F. Zhang, C. Martin, J.W. McGinity, Pharmaceutical applications of hot-melt extrusion: part II, Drug Dev. Ind. Pharm. 33 (10) (2007) 1043–1057, https://doi.org/10.1080/03639040701525627. [128] K. Balani, Physical, thermal, and mechanical properties of polymers, in: Biosurfaces, John Wiley & Sons, 2014, pp. 329–344. https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118950623.app1. [129] S.E. Brika, M. Letenneur, C.A. Dion, V. Brailovski, Influence of particle morphology and size distribution on the powder flowability and laser powder bed fusion manufacturability of Ti-6Al-4V alloy, Addit. Manuf. 31 (2020), https://doi.org/10.1016/j.addma.2019.100929. [130] H. Miyanaji, K.M. Rahman, M. Da, C.B. Williams, Effect of fine powder particles on quality of binder jetting parts, Addit. Manuf. 36 (2020), https://doi.org/10.1016/j.addma.2020.101587. [131] M. Schmid, A. Amado, K. Wegener, Polymer powders for selective laser sintering (SLS), AIP Conf. Proc. 1664 (2015), https://doi.org/10.1063/1.4918516. American Institute of Physics. [132] T. Arvinte, A. Cudd, C. Palais, E. Poirier, The formulation of biological molecules, RSC Drug Discov. Ser. 2018 (64) (2018) 288–316. Royal Society of Chemistry (Chapter 11) https://doi.org/10. 1039/9781782620402-00288. [133] W.R. Gombotz, D.K. Pettit, Biodegradable polymers for protein and peptide drug delivery, Bioconjug. Chem. 6 (4) (1995) 332–351, https://doi.org/10.1021/bc00034a002. [134] A.S. Theus, L. Ning, B. Hwang, C. Gil, S. Chen, A. Wombwell, R. Mehta, V. Serpooshan, Bioprintability: physiomechanical and biological requirements of materials for 3d bioprinting processes, Polymers 12 (10) (2020) 1–19, https://doi.org/10.3390/polym12102262. [135] K. Markstedt, A. Mantas, I. Tournier, H. Martı´nez A´vila, D. H€agg, P. Gatenholm, 3D bioprinting human chondrocytes with nanocellulose-alginate bioink for cartilage tissue engineering applications, Biomacromolecules 16 (5) (2015) 1489–1496, https://doi.org/10.1021/acs.biomac.5b00188. [136] E.O. Osidak, V.I. Kozhukhov, M.S. Osidak, S.P. Domogatsky, Collagen as bioink for bioprinting: a comprehensive review, Int. J. Bioprinting 6 (3) (2020) 1–10, https://doi.org/10.18063/IJB. V6I3.270. [137] B. Derby, Bioprinting: inkjet printing proteins and hybrid cell-containing materials and structures, J. Mater. Chem. 18 (47) (2008) 5717–5721, https://doi.org/10.1039/b807560c.

Applicability of machine learning in 3D printed dosage forms

[138] S. Sayyar, E. Murray, B.C. Thompson, J. Chung, D.L. Officer, S. Gambhir, G.M. Spinks, G.G. Wallace, Processable conducting graphene/chitosan hydrogels for tissue engineering, J. Mater. Chem. B 3 (3) (2015) 481–490, https://doi.org/10.1039/c4tb01636j. [139] C. Lau, M.J. Cooney, P. Atanassov, Conductive macroporous composite chitosan-carbon nanotube scaffolds, Langmuir 24 (13) (2008) 7004–7010, https://doi.org/10.1021/la8005597. [140] Y. Wu, L. Woodbine, A.M. Carr, A.R. Pillai, A. Nokhodchi, M. Maniruzzaman, 3d printed calcium phosphate cement (CPC) scaffolds for anti-cancer drug delivery, Pharmaceutics 12 (11) (2020) 1–15, https://doi.org/10.3390/pharmaceutics12111077. [141] C.J. Boyer, D.H. Ballard, J.A. Weisman, S. Hurst, D.J. McGee, D.K. Mills, J.E. Woerner, U. Jammalamadaka, K. Tappa, J.S. Alexander, Three-dimensional printing antimicrobial and radiopaque constructs, 3D Print Addit. Manuf. 5 (1) (2018) 29–35, https://doi.org/10.1089/3dp.2017.0099. [142] T.J. Hinton, Q. Jallerat, R.N. Palchesko, J.H. Park, M.S. Grodzicki, H.J. Shue, M.H. Ramadan, A.R. Hudson, A.W. Feinberg, Three-dimensional printing of complex biological structures by freeform reversible embedding of suspended hydrogels. Science, Advances 1 (9) (2015), https://doi.org/ 10.1126/sciadv.1500758.

Further reading [143] A. Forster, J. Hempenstall, I. Tucker, T. Rades, Selection of excipients for melt extrusion with two poorly water-soluble drugs by solubility parameter calculation and thermal analysis, Int. J. Pharm. 226 (1–2) (2001) 147–161, https://doi.org/10.1016/S0378-5173(01)00801-8. [144] A. Goyanes, A.B.M. Buanz, G.B. Hatton, S. Gaisford, A.W. Basit, 3D printing of modified-release aminosalicylate (4-ASA and 5-ASA) tablets, Eur. J. Pharm. Biopharm. 89 (2015) 157–162, https:// doi.org/10.1016/j.ejpb.2014.12.003. [145] Q. Liu, H. Zhu, C. Liu, D. Jean, S.M. Huang, M.K. ElZarrad, G. Blumenthal, Y. Wang, Application of machine learning in drug development and regulation: current status and future potential, Clin. Pharmacol. Ther. 107 (4) (2020) 726–729, https://doi.org/10.1002/cpt.1771.

299

CHAPTER 12

Role of AI in ADME/Tox toward formulation optimization and delivery Ibtihag Yahya Elhag Biomedical Engineering Department, Sudan University of Science and Technology, Khartoum, Sudan

Abbreviations ADME/Tox AI BBB Caco-2 cells CLh Clint CLr CLT CNS Cp Cp0 CYP450 D Dde/dt DILI DMPK DNA ER f fe fu,p hERG HIA In silico In vitro In vivo IVIVC Log D Log Kow, log Pow Log P Log Po/w P-gp Q QIVIVE

absorption, distribution, metabolism, excretion, and toxicity artificial intelligence blood-brain barrier human colon adenocarcinoma hepatic clearance intrinsic capacity of the hepatocytes to metabolize a drug renal clearance total body clearance central nervous system plasma drug concentration concentration of drug in the plasma at time zero (milligrams/liter) cytochrome P450 amount of drug in the body (milligrams) drug elimination rate from the body hepatotoxicity or drug-induced liver injury drug metabolism and pharmacokinetics deoxyribonucleic acid renal extraction ratio Fraction of free drug (not bound) fraction of drug excreted unchanged in urine fraction unbound in the plasma human ether-a-go-go-related gene intestinal absorption in humans an experiment is performed on a computer or via computer simulation research or work is performed outside of a living organism research or work is carried out with or within an entire living organism in vitro-in vivo correlation distribution coefficient both are abbreviations that stand for the decadic logarithm of the octanol/water partition coefficient partition coefficient lipophilicity P-glycoprotein hepatic blood flow quantitative in vitro to in vivo extrapolation

A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00011-3

Copyright © 2023 Elsevier Inc. All rights reserved.

301

302

A handbook of artificial intelligence in drug delivery

QR QT interval

RNA Vd

renal plasma flow represents the time taken for ventricular depolarization and repolarization, i.e., effectively the period of ventricular systole from ventricular isovolumetric contraction to isovolumetric relaxation ribonucleic acid volume of drug distribution

12.1 Introduction to the history of AI in ADME/Tox Over the years, the process of drug discovery and development has undergone numerous challenges to ensure that the drug produced is safe, effective, and appropriate for human use with minimal side effects. Today, technology has revolutionized the world and has come to play a supportive and disparate role in our daily lives, especially with the recent renaissance of the sciences of artificial intelligence (AI) and machine learning (ML), which have helped develop all aspects of our lives, made tangible transformations in various fields and sciences, and improved the quality of life [1,2]. For this, the possibilities of research and development in the pharmaceutical industry are becoming more available as a result of the great breakthrough in technological advancements [3]. Over the past two decades, AI has achieved great progress in the medical and industrial fields [4], especially in the area of ADME/Tox development, as drug treatments aim to inhibit, treat, or regulate various conditions of diseases, and, to accomplish this purpose, sufficient amounts of the drug must reach the target tissues so that we can obtain specific therapeutic concentrations within these tissues [5]. Particularly, the movement of a drug inside the body over time can be controlled through studying the pharmacokinetic effects of medications [6]. In addition to, investigating the adverse effects produced by the drugs which related to their plasma concentrations and that they are related to the dosage, rate of absorption, besides the beginning of the drug’s effect, the effects severity, and drug duration inside the body [7]. Which can be controlled by essential properties called ADME/Toxicity prediction properties include (absorption, distribution, metabolism, excretion, and toxicity) [8,9]. However, the limitations of the predictive systems and tools available for preclinical testing have limited our ability to select appropriate treatments and predict the likelihood of either success or failure before applying them in clinical trials [10,11]. Therefore, many predictive systems have been developed in order to address this shortcoming. New technologies, such as in silico ADME/Tox prediction models and machine learning applications, have been dominating the field of pharmaceutical science for several decades and have made tremendous progress in the field of drug development, thus increasing the odds of their success [12,13]. Over the past 40 years, intense research studies have been conducted for studying the role of ADME and toxicology in drug development and for evaluating the

Role of AI in ADME/Tox toward formulation optimization

pharmacokinetics properties of a pharmaceutical compound within an organism, to predict their fate after administration until its elimination process, including absorption, distribution, metabolism, excretion properties, and toxicity, which are important factors that contribute to drug discovery and reduce the likelihood of a clinical trial failure [14,15]. In the 1980s, datasets were based on both comparative molecular field analyses and partial least squares approaches and included tens of compounds [16]. After 10 years and throughout the 1990s, datasets were increased to include more than 100 components, besides including some algorithms like decision-making and recursive partitioning [17]. In the 2000s, the number of compounds grew to 1000s due to the broad efficiency achieved by machine learning in the ADME/Tox development field [18]. In addition, model descriptors contained more prescription bases, such as the blood-brain barrier (BBB), which were collected by major pharmaceutical companies [19]. The number of compounds exceeded 100,000 datasets on the in vitro ADME properties of the pharmaceutical development phases during the 2010s, which helped expand the number of deep learning applications along with a set of ADME/Tox algorithms [14].

12.2 An in vitro-in vivo correlation An in vitro-in vivo correlation (IVIVC) is a predictive mathematical model used as a biopharmaceutical tool in the drug development process, which not only describes the relationship between an in vitro characterization of a dose form and an in vivo related response [20] but also the quality control of a product and its use as an alternative to human bioequivalence (BE) studies, to describe the relationship between the dissolution or release of a drug into a dissolution apparatus and the drug concentration in the plasma or absorption quantity that enters the bloodstream after administration [21]. This test is highly effective in long-acting oral formulations, which is when the drug has high solubility. Dissolution is the rate-limiting factor in the drug absorption process [22]. This efficiency is due to the interaction between the in vitro drug release profiles with the in vivo drug kinetic parameters. Therefore, it is used as an alternative to in vivo experiments to support product and process changes [23], thus reducing the number of human bioequivalence (BE) studies and changes both before and after approval [24]. This is achieved by developing an in vitro solubility test that predicts the in vivo performance of a drug product [25].

12.2.1 Levels and the classification system of IVIVC for drugs Nowadays, the prediction of a drug’s overall performance in vivo has come to be easy utilizing the in vitro-in vivo correlation (IVIVC), referring to the in vitro drug release profiles. Quality control and regulatory compliance are both impacted by the construction of an effective IVIVC, and this is can be done by identifying three categories of correlations, which include level A that is a point-to-point correlation, level B that utilizes all

303

304

A handbook of artificial intelligence in drug delivery

data, and level C that is a single-parameter correlation [26,27]. This classification is based on the types of data and statistics used to establish the association, which provides information on the plasma drug-level time profile for a specified dosage form [28].

12.3 In silico ADME/Tox profiling In recent decades, in silico prediction has received significant attention from all life aspects, especially pharmaceutical science, as it has become a common language used as a unique interdisciplinary interface involved in drug development and includes biologists, toxicologists, physicians, medical chemists, and others [29,30]. Moreover, the evolution of the ADME/Tox profile in silico is evident, as the importance of implementing ADME and toxicology software lies in the detection of pharmaceutical compounds that can go to the production stage by evaluating the ADME/Tox profile of pharmaceuticals and passing the test [31]. In addition, ADME descriptor information plays an important role in the production of quantitative in vitro to in vivo extrapolation (QIVIVE) results for enhanced and improved biological efficacy of drugs [32]. This is due to the accuracy in modeling the pharmacokinetic and toxicological properties of candidate drugs and predicting the safety and toxicity results of chemicals, especially in the preclinical and preindustrial stages [33]. Additionally, many sophisticated machine learning software and simulation software are used to perform complex experiments and drug tests, including software to predict the end points of ADME/Tox such as web-based tools and purpose-developed software [5]. Furthermore, reliable predictive models can replace experiments due to their advantages over conducting expensive laboratory studies, particularly as the in silico methods are accurate and have high productivity and low cost, besides reducing the duration of the pharmaceutical research and development cycle [34,35]. Thus, in silico prediction methods will contribute to facilitating and promoting drug discovery and development and make the process more streamlined from the early phases of discovering the drug and the preclinical in vivo and in vitro evaluation trial to the manufacturing stages [36]. In silico methods covering a wide range of predictive tools used in the laboratory in computer-based methods with outcomes and analyses of drug bioavailability, bioactivity, and safety tests can determine and be structurally optimized several times, thus improving and accelerating the therapy designation process and reducing the development time [37,38].

12.4 In silico modeling of ADME/Tox properties with descriptors Pharmacokinetics refers to time-related quantitative changes of both the concentration of a drug in the blood and the total amount of a drug in the body after taking the drug by its different routes [39]. This is an essential factor for promoting prescription delivery in

Role of AI in ADME/Tox toward formulation optimization

balanced and controlled doses, which is necessary to retain a sufficient amount of it in the blood to achieve a therapeutic response [40] and to prevent toxicity caused by medicines that exceed the therapeutic concentration and approximate the toxic ones [41]. Recently, it has been observed that the prediction of ADME/Tox properties such as drug molecule absorption, distribution, metabolism, excretion, and toxicity using in silico tools plays a significant role in facilitating the appropriate selection of the candidate in the drug discovery and development process from the early stages to the end of the drug discovery pipeline [31]. For instance, the preclinical testing of ADME/Tox allows evaluation of what happens to a drug in the human physiology using several computational ADME/Tox models as seen in Fig. 12.1, with a wide range of available datasets of physicochemical descriptors. These tests include primary models for ADME prediction and important descriptors such as solubility, absorption, mutagenicity, bioavailability, metabolic stability, BBB permeability, cardiac toxicity (hERG), and plasma protein binding, besides secondary models for toxicity prediction such as transport (uptake and efflux), general toxicity, hepatotoxicity, nephrotoxicity, immunogenicity, neurotoxicity, and drug-drug interactions (cytochrome P450) [40,41]. To illustrate this point, during the modeling process, the rational compounds that fit the ideal pharmacokinetic and pharmacodynamic (PK/PD) properties are extracted and those that are unlikely to be drugs are filtered out [42]. Thus, a compound can be developed into a new drug of sufficiently high quality to be designed and launched [43].

12.4.1 Absorption modeling and physicochemical properties and descriptors Absorption is the first process that occurs after drug administration, through which molecules move from the absorption and extravascular sites to reach the bloodstream and enter the systemic circulation after crossing several semipermeable membranes that serve as biological barriers composed of a two-molecule lipid matrix [44,45]. Therefore, before its design, it is essential to know any medicinal product to achieve the desired therapeutic objective, therapeutic intent, application site, type of absorption of the medicinal product, and physicochemical properties related to the drug and the absorption site’s physiology [22]. The overall bioavailability of a compound depends on the type of membrane that selectively blocks the passage of drug molecules according to the membrane permeability properties based on membrane transport, which include passive and active diffusion, facilitated passive diffusion, and pinocytosis transport [46,47]. In addition, drug dosage forms and routes of administration include inhalation through the respiratory system, ingestion through the gastrointestinal tract, and injection [48,49]. Another key point is that the condition of drug absorption must be appropriate to prevent any damage, and this depends on the location of action allocated to the drug, irrespective of whether it is for systemic or local activity [50]. Thus after taking the drug, it is important that the drug remains alive until it reaches the required place in the

305

306

A handbook of artificial intelligence in drug delivery

Fig. 12.1 Physiologically based pharmacokinetic (PBPK) modeling.

appropriate dose after it overcomes various biological barriers and without any presystemic changes or transitions, which include chemical or biochemical transportation that takes place in the stomach, e.g., hydrolysis in the gastric medium and/or first-pass metabolism [49,51]. Moreover, when developing any drug, it is important to study and test the absorption phenomenon and to determine the location of activity assigned to the

Role of AI in ADME/Tox toward formulation optimization

medicine by establishing the difference between the drug’s systemic and topical administration [52] and taking into account that any presystemic phenomenon that the drug particles are exposed to after absorption is one of the basics of the absorption process [34,35]. To demonstrate this point, in the case of systemic administration, the drug molecules will be completely and consistently dispersed and absorbed from the site of application via the circulatory system [53]. On the other hand, in the case of local administration of a drug which aims to restrict exposure to only the affected site of the body and the surrounding areas, in contrast to a systemic drug administration, in which the drug molecules are applied to the entire body [54,55]. If the drug product has multiple effects, then the drug must be slowly released along the required time by maintaining the drug at or near the site of application [56]. This is to avoid any adverse reactions to medications as a result of the drug’s interaction with different body elements, including irritation or allergic reactions [57]. Equally important, absorption in the target site condition should be minimal, such as in the case of topical administration, to avoid any possible systemic side effects due to excessive systemic exposure to drugs [58]. As for systemic administration, more negative reactions are anticipated, which would need further studies and tests to be solved and eliminated [59]. It can be modeled with different properties described hereunder. 12.4.1.1 Permeability Permeability is the ability of a drug to pass through a biological membrane and is one of the main parameters in the drug design and development process, which measures the amount of drug molecules that penetrates the membranes through different stages and concentrations [60]. In addition, it is one of the main factors that directly affect the processes of absorption and distribution. If a drug is systemically administered, it must pass several semipermeable cell membranes prior to reaching the target location and then reaching the systemic circulation. This is especially true for small particles, whose effectiveness is closely related to their size and ability to cross a membrane and thus the drug’s effectiveness [61,62]. Several factors affect the rate of diffusion across cell membranes such as the extent of the concentration gradient, mass of the diffusing molecules, temperature, and solvent density [63]. In light of permeability, there are two types of membrane transport: (a) Passive transport Passive transport, also known as passive diffusion, is a type of membrane transport that refers to the basic movement of ions, in which an ion or a molecule passes through a cell wall from a region of high concentration to that of low concentration until the concentration is equal across space [64]. Passive transport depends on the second law of thermodynamics (Fick’s law of diffusion) to drive the movement of substances across cell

307

308

A handbook of artificial intelligence in drug delivery

membranes [65]. There are four basic types of passive transmissions, including simple diffusion, facilitated diffusion, filtration, and osmosis. In contrast, transcellular transport in which materials travel across the cell membrane, through both the apical and basolateral membranes, then diffuse into the cell and travel through the corresponding membrane [66–68]. (b) Active transport Active transport is a process that takes place across the cell membrane and is an energyconsuming system. It needs cellular energy to transport molecules across the cell membrane from a region of low concentration to that of higher concentration [69]. Successful active transport absorption is mediated by the influx and uptake pathways that act as transport systems [70]. Efflux cell membrane transporters play a significant role in reducing the therapeutic or biological effect of the drug by moving the parent drug and the metabolites of such drugs outside the cell [71]. This is carried out by efflux pumps that serve as localized protein transporters present in the cytoplasmic membrane of different cell types [72]. On the other hand, drug uptake transporters are membrane proteins that transport endo- and xenobiotics, such as the hepatocyte basolateral membrane of hepatocytes, including a variety of medicines [73]. Moreover, they are important for the absorption of drugs in target tissues or organs for both metabolism and excretion processes [74]. 12.4.1.2 Solubility The solubility parameter plays a critical role as an equilibrium measure in achieving optimal concentrations and (anticipated) pharmacological responses from a drug in systemic circulation. In general, drug solubility describes the solvent dissolution phenomenon of a solid drug in liquid to create a harmonious system [75]. In addition, solubility presents a key challenge to medication formulations in which the absorption of any drug must be in the solution at form [76]. Solubilization techniques that are used to enhance poorly soluble drugs’ solubility, particle size reduction, solid dispersion, complexation, crystal engineering, use of surfactant, salt formation, and drug physical and chemical modifications are available [77]. 12.4.1.3 Caco-2 cells Caco-2 is one of the most important descriptors used in the modeling of permeability in the absorption process. It is mainly used as a model for the intestinal epithelial barrier and for expressing drug metabolism enzymes involved in the absorption of factors critical to drug metabolism [78]. It is one of the most common models of intestinal permeability. These cells effectively model human absorption properties and predict human gut permeability as well as investigate drug flow and compound

Role of AI in ADME/Tox toward formulation optimization

absorption through narrow connections between epithelial cells in the early stages of detection [79]. 12.4.1.4 Lipophilicity (Log Po/w) Prediction of the lipophilicity of active compounds in vivo can be represented by log P descriptors (also known as Kow or Pow) and represents the logarithm of the octanol-water partition coefficient and log D the distribution log, and both of these can be calculated by Eqs. (12.1) and (12.2), respectively [80], thus measuring the degree to which a compound is greasy or lipophilic and knowing its ability to penetrate the physiological membrane when a dose is taken orally [81]. Moreover, the lipophilicity property makes it easier to understand the behavior of compounds in many other areas of chemical research [82]. P ¼ Partition coefficient Concentration of neutral species dissolved in partition solvent ¼ Concentration of neutral species dissolved in water D ¼ Distribution coefficient ðConcentration of all species dissolved in partition solventÞ ¼ ðConcentration of all species dissolved in waterÞ

(12.1)

(12.2)

12.4.1.5 Intestinal absorption in humans Analyzing the effect of each drug on the human body is a complex process that needs a significant monitoring and analysis process [83]. This is why intestinal absorption in humans (HIA) is one of the most important characteristics of ADME, which must be taken into account while investigating the transition of drugs into their targets due to their direct impact on bioavailability [84,85]. In addition, different pharmaceuticals contain different ingredients that play a role in the absorption process, which is difficult to predict due to the diversity of drug absorption pathways [86]. Thus, the use of both vector-mediated transport descriptors and first-pass metabolism is necessary to build a useful predictive model of human oral bioavailability [87]. 12.4.1.6 Protein and tissue binding Both drug pharmacokinetics and pharmacodynamics are greatly influenced by the binding of drugs to plasma and tissue proteins, as they affect the therapeutic efficacy of the drug and its distribution [88] as well as the drug elimination processes because the free portion of the drug is pharmacologically active and can freely spread across membranes to various body tissues, unlike drug-bound molecules that are pharmacologically ineffective [89]. Here, the free portion of the drug is directly proportional to the rate of

309

310

A handbook of artificial intelligence in drug delivery

elimination and is inversely proportional to the rate of protein binding and the ratio of the distribution of the free drug in tissues [90]. It can be said that the pharmacodynamics of a drug is affected by the distribution of only the unbound portion of the drug to the target site and thus its interaction with the target to provide a pharmacological effect [91]. Because drugs have different bindings for different types of tissues, the binding of drugs varies with the type of tissue [92]. Given the importance of this prescription in the early stages of drug development, it allows the identification of both liquid mediums suitable for pharmacokinetic studies [93] and also the determination of the amount of medicine available for distribution in vivo, and, thus, it is easy to predict the extent to which the drug will bind to the plasma proteins in the body [94].

12.4.2 Distribution modeling The mechanism of drug distribution is a phenomenon that follows the absorption process, by which the particles of the drug are postabsorptively transferred and distributed from the blood to the various tissues present in the human body such as muscles, fat, and brain tissue [95,96]. The main goal of this process is to increase both the drug bioavailability at the target site and the in vivo circulation time to achieve optimum therapeutic possible benefits and decrease the drug degradation rate [97]. Regarding distribution, channel choices are influenced and controlled by several factors, including drug concentration, solubility, mass and types of the absorbed biological lipid membranes, regional pH, cell membrane permeability, rate of blood flow to tissues, and characteristics of the division between the blood and tissues [98,99].

12.4.2.1 Volume of distribution The volume of drug distribution is an important pharmacokinetic parameter that determines the drug concentration in the plasma according to the apparent volume of the drug that was reached after the loading dose [100]. This is because of the propensity of the individual drug to either remain in the blood plasma or to redistribute to other locations or tissue compartments [101]. As a drug with a high volume tends to achieve a greater distribution on other tissues, it leaves the plasma to enter the extravascular compartments of the body, and, here, the dose of the drug is higher than that required to achieve a certain plasma concentration [102]. On the other hand, a drug with a low drug volume tends to achieve less distribution to other tissues and to achieve a certain plasma concentration, the dose of the drug should be less than the required dose [103]. The distribution phase is the time taken for a drug to distribute from the plasma to the periphery [104]. Drug levels obtained before the end of a protracted distribution phase may not accurately reflect pharmacologically active drug levels at action sites, e.g., digoxin, lithium, etc. [105].

Role of AI in ADME/Tox toward formulation optimization

The volume of drug distribution is calculated by Eq. (12.3) as follows: Vd ¼

D Cp 0

(12.3)

where Vd is the volume of drug distribution, D is the amount of drug in the body (in milligrams), and Cp0 is the concentration of drug in the plasma at time zero (in milligrams per liter). 12.4.2.2 P-glycoprotein (P-gp) substrate P-glycoprotein is a key mediator of drug-drug interactions and one of the drug transporters that affects the absorption, efflux, distribution, and disposal of a group of pharmaceuticals [106]. In addition, it acts as a transmembrane efflux pump across the membrane, and, through this descriptor, potential drug interactions and their association with one of the vectors, which changes the drug concentration in the plasma and different tissues, and thus the final effect of the drug can all be predicted [107,108]. Moreover, the pharmacokinetics of the drug can be changed by taking it with compounds that may stimulate or inhibit the protein P-glycoprotein to interact with other drugs [109]. 12.4.2.3 BBB permeability Modeling the BBB in the absorption process plays an important role, as it represents one of the basic permeability properties [110]. The BBB is known to be a highly selective boundary consisting of semipermeable endothelial cells [3]. This barrier blocks the passage of solutes in the circulatory system into the extracellular fluid in the central nervous system, which plays an important role in the early detection of central nervous system drugs, where neurons are located [111]. 12.4.2.4 Fraction unbound The process of selecting a suitable candidate in the early stages of drug discovery and manufacture is not an easy task [112]. The fraction unbound in plasma (fu,p) parameter provides a possibility to predict drug efficacy in pharmacokinetic and pharmacodynamic studies as the unbound (free) drug can only interact with drug target proteins such as enzymes, receptors, and channels [113]. Moreover, it has the ability to spread between the plasma and tissues [114].

12.4.3 Metabolism modeling Drug metabolism is a process in which the chemical composition of a substance is changed by the biotransformation of pharmaceutical substances in the body, where drugs are enzymatically altered, and this is referred to as a drug’s metabolism or biological transformation [51]. The metabolism mechanism helps make these drugs more soluble in

311

312

A handbook of artificial intelligence in drug delivery

water (hydrophilicity), which contributes to promoting their excretion from the body as well as their elimination and removal from the body [115]. Notably, most metabolic processes involving pharmaceutical products are found in many tissues, especially within the liver and epithelial cells, where there are quantities of metabolizing enzymes that facilitate metabolic actions within the body [116]. In addition, there are extrahepatic metabolism organs, which are known as secondary organs of biotransformation as they have a lower level of metabolism enzymes, such as the kidney (proximal tubule), lungs (type IIl cells), testes (Sertoli cells), plasma membrane, nervous tissue (brain), mitochondria, and intestines [117]. The enzymes in drug metabolism can be categorized as either microsomal, which catalyze oxidatively, or nonmicrosomal, which are nonspecific enzymes that selectively catalyze oxidatively [118,119]. Markedly, most drugs become inactive when metabolized. However, the bulk of medications considered as primary drugs have pharmacologically active metabolites that are specifically responsible for the main action of the medication in the body such as oxidation, reduction, hydrolysis, hydration, conjugation, condensation, and isomerization reactions [118,120]. There are two phases of reactions that occur throughout the drug metabolism process noted as phase I and phase II. (a) Phase I In this stage, the drug undergoes biotransformation reactions in which the polarity of the compound is increased by exposing the drug to functional groups [121]. In addition to, it undergoes a set of chemical reactions, such as reduction and oxidation, which are the most frequent which lies the importance of the hepatic cytochrome P450 system which stimulates the oxidative metabolism of vital bodies, and eliminates a variety of drugs and environmental pollutants [122,123]. In addition to the hydrolysis reaction that occurs because most of the small molecular drugs are lipophilic, in the primary metabolism process, which occurs during hepatic circulation, the water solubility of these lipophilic compounds is increased, which facilitates their excretion [74,124]. It is worth noting that in microsomal preparations, most of the first-stage enzymes are enriched in the endoplasmic reticulum [125]. (b) Phase II In this stage, the process of excretion of the drug in the urine is facilitated and it passes through a stage known as “conjugation reactions” [126], whereby the drug or its metabolites of the first stage are chemically changed by appropriate enzymes into highly watersoluble compounds and the drug is linked to an internal cleft through a single or a more functional group [127]. In addition, at this stage, the metabolites formed are pharmacologically inactive, and some of the most important reactions included in this stage are glucuronide, sulfate, and acetylcholine in addition to conjugation with glutathione or amino acids [128]. Moreover, with the advancements in technology, especially in the field of drug discovery and development, the use of in silico computer models to assess drug metabolism and predict pharmacokinetic properties (DMPK) is an important step in the preclinical phases through the use of several descriptors such as inhibition of the main cytochromes and CYP450 [36,129].

Role of AI in ADME/Tox toward formulation optimization

12.4.4 Excretion modeling and descriptors In pharmacokinetics, drug elimination plays an important role by deciding the amount of time that the compound remains in the body [74], thus eliminating and excreting substances whose metabolism is not completed, according to the half-life of the drug where the plasma drug concentration declines in the blood after the drug takes its cycle in the body [130]. Thus, the residual unchanged drug molecules and their metabolites are permanently excreted from the body either by the kidneys, which is the primary way to get rid of the nonvolatile drugs and water-soluble substances, or by liver bile into the urine or feces [131]. Other excretion routes include the lungs (e.g., anesthetic gases) and the liver through biliary excretion or fecal excretion, where the normal metabolism process is adversely affected by the accumulation of foreign substances in the bloodstream [132]. One of the most important factors that must be taken into consideration when manufacturing drugs is the drug’s half-life, which refers to the time required to reduce the drug concentration in the plasma by 50% [133]. 12.4.4.1 Total clearance (CLt) Drug clearance is interpreted as the amount of plasma cleared and elimination of a drug from the body per unit time [134]. As it is important to consider the principle of drug clearance when regulating the medication dose, the standard units for drug clearance, which are volume/time as milliliters per minute (mL/min) or times per hour (liter/hour) [135], are used. Clearance can be calculated by Eq. (12.4). CLT ¼

Dde=dt Cp

(12.4)

where CLT is the total body clearance, Dde/dt is the drug elimination rate from the body, and Cp is the plasma drug concentration. 12.4.4.2 Renal clearance (CLr) The functional ability of the kidneys to filter a drug and remove it from the body separately from other pharmacokinetic processes is known as the renal drug clearance (CLR) process [21]. This prescription plays a fundamental role in modeling drug elimination, and it is defined as the volume of blood or plasma that is completely removed from the unchanged drugs per unit [136]. Renal clearance can be calculated using Eq. (12.5). CLR ¼

ðQRÞ ðERÞ

where QR is the renal plasma flow and ER is the renal extraction ratio.

(12.5)

313

314

A handbook of artificial intelligence in drug delivery

12.4.4.3 Hepatic clearance (CLh) The process losing the drug while it passes through the liver is known as hepatic clearance, which results from the phases I and II’s hepatic metabolism process [137], which can be calculated by Eq. (12.6). It should be mentioned that hepatic blood flow, hepatic clearance, and drug bioavailability are all directly proportional [138]. Q¼

ðf  Clint Þ ðQ + f  Clint Þ

(12.6)

where Q is the hepatic blood flow, f is the fraction of the free drug (not bound), and Clint is the intrinsic capacity of the hepatocytes to metabolize a drug. 12.4.4.4 Fraction of drug excreted unchanged in urine (fe) The fraction of the drug that is excreted in the urine without change is one of the important quantitative indicators that must be taken into account when developing a pharmaceutical product [139], which shows the contribution of renal excretion to the overall elimination of drugs and is known as the percentage of the active drug that is eliminated by the kidneys in an average healthy person [140].

12.5 In silico methods for predicting drug toxicity Drug toxicity is referred to as the degree of damage that a substance may inflict on an organism [92]. The toxic effects of a drug are dose-dependent and can affect an entire system such as the CNS or a specific organ such as the liver [141]. Because of this, drug treatment efficiency requires achieving optimum effectiveness without causing toxicity [142]. Toxicity modeling, used as a method of evaluating the descriptors that affect drug toxicity and its chemical compounds, is an integral part of the drug development process and is the stage that follows ADME property testing to investigate and determine the side and adverse effects of compounds on both the organism and the environment [143–145]. This is because of the restrictions imposed on conducting tests on living organisms, such as humans, animals, or plants, due to ethical considerations and high costs [146]. To this end, the trend toward using in silico methods for estimating the toxicity of chemicals would be the best option, where toxicity tests are complemented to predict toxicity, control input, and guide the toxicity test, which, in turn, will reduce late failures in drug design [147,148].

12.5.1 Acute toxicity Acute toxicity studies are one of the most important standard prescribers that provide sufficient information about the adverse health effects of a substance under testing during a given time cycle [149]. This is to determine the approximate safe and average moderate lethal dose following the administration of a large dose through the mouth, skin, and

Role of AI in ADME/Tox toward formulation optimization

inhalation routes, in addition to deciding the potential target organs for toxicity, through observing the immediate or late biochemical, physiological, and morphological changes in the organ or tissue that could lead to death [150].

12.5.2 Genotoxicity In genetics, “genotoxicity” is a term that is used to describe any factor that destroys the genetic information and genetic material of a cell (DNA, RNA), including radiation and chemical genotoxins, which has basic effects on living organisms such as mutations in various cells and body systems that may lead to cancer [151,152]. Indeed, the process of examining gene toxic chemical molecules in drugs before the manufacturing stage by examining DNA damage in cells exposed to toxic substrates is one of the most important tests that must be performed to ensure the integrity of the suspicious genetic material and test the possibility of repairing its damage in DNA to avoid potential diseases that will cause it as a side effect of the drug as part of the safety evaluation process [153,154] and to avoid the process of its transmission to future generations. Therefore, it was necessary to include this description in the toxicology testing process using in silico programs to know and understand the mechanism of genotoxicity and to evaluate the genotoxicity to predict the damage resulting from this transformation, which may have a direct effect on the DNA, as if this toxicity causes DNA damage or indirectly as induction of mutations and faulty event activation [155,156].

12.5.3 Systems toxicology Systems toxicology is concerned with changing the basis of how the adverse biological effects of biomolecules are characterized from experimental end points to toxicity pathways [157]. Hence, this science focuses on integrating in vitro and in vivo toxicity data with computational modeling and, as such, data streams are widely used [157].

12.5.4 hERG inhibition hERG prediction models are one of the most important prescriptions for toxicity tests as they are a process that facilitates the examination of compounds during early drug discovery [158]. This channel repolarizes the cardiac action potential by correcting the potassium with a delayed current, as the drug blockade of this channel leads to a slowing of the repolarization, which causes sudden death due to the prolongation of the working potential and an increase in the QT interval as measured on an ECG [159,160], thus determining whether this drug will be completely redeveloped or withdrawn.

12.5.5 Ames toxicity This is considered one of the other important prescriptions in drug toxicity tests, in which the effect of a specific chemical on a group of bacteria is tested and it is checked whether

315

316

A handbook of artificial intelligence in drug delivery

this substance can cause mutations in the DNA, thus excluding this drug from the list of safe drugs [159,161].

12.5.6 Hepatotoxicity Hepatotoxicity or drug-induced liver injury (DILI) is a critical issue and is one of the major limitations in the drug development process as the failure of many clinical trials and drugs is attributed to drug-induced liver injury, and this has led to the withdrawal of approved medicines from the market [162]. Moreover, it is considered one of the most important parameters that must be tested before drug design. In order to predict the likelihood of DILI, in silico modeling is used to predict potential infections through available algorithms designed to in vivo classify hepatotoxic compounds of a drug-using weighted molecular fingerprint as a feature to explain the presence or absence of each substructure of compounds that cause toxicity and liver injury [163,164].

12.6 Drug toxicity Drug toxicity is a major concern in the pharmaceutical industry and is a serious obstacle to the development of new drugs, as their use carries risks: their toxicity and their potential to harm the body. A toxin is any chemical that causes damage to cells, organs, or other parts of the body, with its effects ranging from mild, such as skin rashes, to potentially fatal, such as liver failure [165]. Therefore, it has become necessary to predict the toxicity of a drug before it is put on the market, as drug prescriptions now play an important role in modern medicine, given that each year, medication errors lead to about 7000 deaths in the United States, making drug poisoning one of the leading causes of death [166,167]. Thus, prescription drugs are now the fourth leading cause of death in America (after heart disease, cancer, and stroke). Despite these realistic statistics, progress has been made in the field of drug toxicity, due to the revolution in the use of artificial intelligence and machine language in drug development, which has facilitated an early understanding of the factors affecting drug toxicity [13,168]. Toxic effects of drugs can occur if the dosage exceeds the prescribed dosage or, with some drugs, drug toxicity can occur as an adverse drug reaction, which is divided into pharmacological, pathological, and genetic toxicity [169,170]. The toxic effect of a drug depends on the dose and may affect the entire system, such as the central nervous system, or a specific organ, such as the liver [171]. On the other hand, drug toxicity can often be related to the dose and duration of drug administration, which can usually be managed by stopping the drug or reducing the dose. Drugs having prolonged half-lives can build in the body and cause toxicity over time [172]. Toxicity is the degree to which a chemical is toxic or damaging, and it occurs when too much of a drug accumulates in a person’s bloodstream [173]. Many medications can cause increased toxicity when taken in excess, protein-binding sites are saturated,

Role of AI in ADME/Tox toward formulation optimization

physiological circumstances cause hypoalbuminemia, or they are displaced from plasma proteins by other drugs [174].

12.6.1 Drug toxicity classifications There are several mechanisms of drug toxicity, each of which may manifest itself as a form of drug-related death. Toxicity can be classified as acute toxicity, chronic toxicity, or drug-related death [175]. Toxic effects of drugs can be extremely serious; each week, thousands of people around the world die from toxic reactions to pharmaceuticals [176]. This is often a result of a combination of multiple factors, including individual characteristics of the user and the product itself (e.g., the dose and frequency of drug exposure, the route of administration, and the quantity and type of medication consumed) [177]. Furthermore, evidence suggests that any one individual may be at risk of experiencing a drug-related death. A large proportion of these deaths are caused by low concentrations or excess doses in the blood; such conditions may result from an accidental or intentional overdose [178]. 12.6.1.1 Acute toxicity Acute toxicity refers to the immediate effects that are experienced when taking a drug, which is the classification used to describe a drug that is capable of causing adverse effects (ranging from mild to severe) in a single exposure or administration, e.g., gentamicin toxicity [179]. These adverse side effects are often seen immediately after the intake of the drug, and this class of toxicity is the most common type of drug-related death [180]. Acute toxicity availability describes the dose administered and the likelihood of toxic effects. The absence of a toxic response following ingestion or absorption means that too low a dose or concentration was administered [181]. A subject may have sips of an alcoholic beverage, but this does not equate to intoxication; in such cases of data analysis, it is inappropriate to assign multiple acute toxicity availability-related instances because there simply have not been any exposures at all [182], as acute toxicity refers to the severity of symptoms and effects due to the exposure. This classification, categorized as an emergency, includes most cases in which the drug can cause death with accidental exposure or in a typical therapeutic use. Signs of acute toxicity include irritability, seizures, diarrhea, fever, and even death [183]. 12.6.1.2 Chronic toxicity Chronic toxicity refers to any notable health changes that occur over time after exposure to a particular drug, which is a classification used to describe a drug that can result in toxic effects over time, even with low exposure [184]. Drugs like alcohol and cigarettes are known for their frequent chronic toxicities. This class of toxicity is also associated with drug-related death. Chronic toxicity describes the symptoms and damages that occur from long-term exposure or by a high cumulative dose over time [185]. Chronic toxicity

317

318

A handbook of artificial intelligence in drug delivery

drugs can be classified as having limited toxicity if they have a median lethal dose (LD50) between 200 and 2000 mg/kg [186]. 12.6.1.3 Drug-related death Drug-related death classifies drugs that cause fatalities when taken for therapeutic purposes, such as analgesics, antibiotics, and chemotherapy agents [187]. Drugs included in this classification are usually taken on an ongoing basis and depend on their duration and mechanism of action to cause death [188]. Here, the dose must be less than 10% of the median lethal dose for acute neurotoxicity to avoid this effect. Drugs normally cause slight gastrointestinal issues and other less common symptoms [189]. However, they can be more severe if taken with other prescription medications, alcohol, or even illicit substances. This type of drug-related death can happen as soon as 1 h after ingestion [190].

12.6.2 Drug toxicity and poisoning A toxin is any substance that can harm the body, including any drug. Poisoning generally refers to the negative physiological effects that result from exposure to drugs, illegal drugs, or chemicals [191]. There are three toxicity types: pharmacological, pathological, and genetic. 12.6.2.1 Pharmacological toxicity Pharmacological toxicity is the effect of a drug on the body. Toxicity can be defined as an adverse reaction that results in impairment or death to the body. This usually occurs when the level of medication exceeds what the body can tolerate [166]. A person with certain risk factors may not have any adverse reactions to a given dose of a drug, whereas another person with other risk factors may experience more severe side effects [192]. Even if a drug has been proved safe for most people, this does not mean that it will be safe for everyone. Pharmacological toxicity refers to the adverse effects of chemicals that are ingested or used in a medical setting. It can be caused by certain medications, chemical fumes, and gases [193,194]. If one feels dizzy, nauseous, or has heart palpitations after taking a medicine, then it may be due to pharmacological toxicity. The phrase “pharmacological toxicity” is used to describe a drug-induced injury. It can be classified as one of the following: an acute toxic reaction, a chronic toxic reaction, and/or an allergic reaction to a drug [195]. The symptoms vary depending on which type of injury it stems from [196]. However, all three types of pharmacological toxicities share the main symptom in common. This symptom is typically described as “a disturbance in the body’s homeostasis, or balance” [197]. Toxicities are typically caused by the drugs themselves, secondary to an overdose or due to a drug-drug interaction. Toxicities can also be caused by other medications (e.g., acetaminophen), illnesses, or environmental toxins [198]. In most cases, a drug will have the potential for toxicity at some dose [199]. Drugs that are considered safe in therapeutic

Role of AI in ADME/Tox toward formulation optimization

doses may be toxic if taken in large amounts. There are many medications that have a high potential for toxicity. This can be because of the dosage, frequency, or duration used. It is important that a person must note any side effects that they are experiencing and immediately consult their doctor so that they can make changes or discontinue use entirely [166,200]. The toxicity of drugs and poisons is a problem for the medical community. Some drugs are toxic to humans, and some substances can be poisonous if ingested in large enough quantities [201]. When someone ingests a drug that is toxic, their cells also release chemicals to try to protect them from the toxicity. This causes more problems because they cannot remove the drug while it continues to damage their cells [202]. There are many factors that determine how toxic a substance is. It can depend on the type of chemical, how much of it was consumed, the age and sex of the person who took it, what other treatments they have recently been on, or other medical conditions that they may have [202]. Certain medicines can cause an increase in blood pressure or heart rate, leading to symptoms such as nausea, vomiting, chest pain, dizziness, or headaches [203]. 12.6.2.2 Pathological toxicity Toxicological pathology plays a significant role in the pharmaceutical industry and development to demonstrate that products are “safe” by identifying and interpreting microscopic tissue changes [204]. It depends on identifying the changes that occur in tissues, cells, or organs and thus the possibility of obtaining an accurate interpretation of the pathological changes and whether this disease is infectious, neoplastic, immunological, or toxic [205]. Thus, in this process, it is possible to gain knowledge related to the safety and efficacy of chemicals used by humans by making use of the largest possible amount of applicable information, which facilitates the process of describing the harmful effects caused by these compounds in a structural and functional manner and predicting the likelihood of these effects occurring under different conditions [206,207].

12.7 Genetic toxicity Genotoxicology is the study of genetic variants that may have a role in adverse drug responses. Toxicological genetics is one of the most important sciences in the process of drug manufacturing and development, particularly in the preclinical stage [208]. This science is concerned with the study of genetic variations, evaluation of the effects of chemical and physical factors on the genetic material (DNA) and on the genetic processes of living cells, studying the effect of genetic variation on the pharmacokinetic and pharmacodynamic properties of drugs, and analyzing the gene expression profile of several thousand genes, which contributes to the study of changes associated with drug-induced toxicities [209,210].

319

320

A handbook of artificial intelligence in drug delivery

12.8 Drug toxicity mechanisms Pharmacological doses of a drug used to treat a patient can lead to negative effects. Insect and spider venoms are potent poisons. During the early stages of drug development, known mechanisms for toxicity are taken into account [211]. The toxicity of a new compound can be assessed using in vitro studies, animal models, and in vivo models [212]. Moreover, toxic effects can be caused by broad-spectrum or specific target drugs. Toxicity can occur from multiple mechanisms at once and can be life-threatening. The mechanisms by which these effects occur in humans are not completely understood but generally fall into three categories: pharmacokinetics, pharmacodynamics, and toxicokinetics [213]. Drug toxicities are usually divided into two categories: specific toxicity or sublethal toxicity and nonspecific toxicity or lethal toxicity potential [214]. Usually, toxic effects are measured by the dose at which 50% of the subjects will experience it. Generally, the higher the dose, the more toxic it is [215].

12.8.1 Specific toxicity or sublethal toxicity A sublethal toxin is a drug that cannot kill a cell but can decrease its activity or growth to such an extent that it is unable to reproduce [216], e.g., drugs that have specific toxic effects on certain cell types, thereby causing adverse reactions like hematological toxicity, hepatic toxicity, hypersensitivity reactions, neurotoxicity, and nephrotoxicity [217]. Specific toxicity is when there is a specific target for the toxin and it causes little damage to the surrounding cells. A good example of this would be tetanus [218]. This specific toxicity occurs when a chemical has an adverse effect on a specific organ (and often not others).

12.9 Nonspecific toxicity or lethal toxicity potential Nonsubcutaneous toxicity, such as exposure to drugs of exposure, can cause death in a short time span due to its lethality [219]. Nonspecific toxicity or lethal toxicity potential can refer to both plant and animal toxins [220]. These toxins are often much more potent than specific toxins; this means that a small amount of the toxin could have lifethreatening effects on those who ingest it, as a drug has the potential to induce cellular death at any dose regardless of the mechanism involved because of some intrinsic toxicity [221,222]. The category is further subdivided into four groups: cytotoxic drugs, carcinogens, toxins, and teratogens [223]. Nonspecific toxicity is when the toxin causes tissue disturbances throughout the system and can cause death in extreme cases [224]. This type of toxicity potential means that there is no dose (e.g., no matter how small it is) at which complete recovery is likely to happen after a single exposure and occurs when a chemical has adverse effects on any or every organ in the body [225].

Role of AI in ADME/Tox toward formulation optimization

12.10 Types of therapeutic drug toxicities Drug toxicity is any adverse event that occurs when a person takes a medication, and it causes undesired reactions, effects, or harmful side effects [170]. Drugs can cause adverse reactions in patients. For a drug to be approved by the Food and Drug Administration (FDA), it must be tested on animals, humans, and ultimately on humans with special needs. Toxic reactions of a therapeutic drug are classified as either dose-dependent reactions, allergic reactions, idiosyncratic reactions, drug-drug interactions, or toxicokinetics [226]. To correctly diagnose the type of reaction, all of the following need to be considered: the type of drug, the route it takes, the time span over which it is taken, in both the past and the future, and tolerance to the drug [227]. The general types of drug toxicities depend on the type of ailment that the drug is treating:

12.10.1 Dose-dependent reactions These are defined as a toxic response to increasing doses of a drug and occur when the dose of the opioid or narcotic exceeds what the body can safely metabolize [228]. These reactions may result in predictable changes in the absorption and elimination of the drug when the dose is changed.

12.10.2 Allergic reactions Allergic reactions, also known as allergic toxicity, are immune system disorders caused by hypersensitivity to certain drugs [180]. Patients with an allergy may experience rash, diarrhea, or other symptoms that vary widely depending on whether the drug is ingested in solid form, inhaled through air, or introduced through a needle injection [229,230]. Typical culprits include antibiotics and nonsteroidal antiinflammatory drugs (NSAIDs) [231]. Moreover, people with asthma may be allergic to antibiotics like penicillin, cephalosporins, and sulfonamides, which can also cause an allergic reaction [232]. Allergy or hypersensitivity typically occurs when the body’s immune system does not recognize a component of the drug as a self but as an intruder that warrants attack [232].

12.10.3 Idiosyncratic reactions Idiosyncratic reactions may not do anything to most people but can affect certain people who have particular genes or gene mutations, which usually result from interruption of the metabolic pathways by reactive metabolites; they are unpredictable and individualized and cannot be dramatized [233,234]. This is due to the fact that it is not dose-dependent, meaning that there is no identifiable relationship between the administration of the drug and manifestations of toxicity [235].

321

322

A handbook of artificial intelligence in drug delivery

12.10.4 Drug-drug interactions Drug-drug interactions can be defined as two different medications altering each other’s effects on the body and refer to the capability of altering one or more pharmacological responses as a direct consequence of exposing one or more drugs taken together or taking place together in an organ system [236,237]. This interaction can happen when a patient taking one drug suffers from an overdose because it combines with another medication taken at the same time, which increases the potency of either or both agents (unwanted) [238]. The most likely explanation for this occurrence is the altered delivery of a medication into the body when simultaneously consumed with another medication.

12.11 Toxicokinetics Toxicokinetics refers to the complex process in which environmental factors affect the measure concentration-time curve of a particular agent, and the pharmacokinetics of a drug under conditions that cause toxicity or overexposure is called toxicokinetics [239,240]. Toxicokinetic events may include impairment due to urethral stricture, hepatic dysfunction, renal failure, pulmonary insufficiency, or recent trauma [241]. Thus, it is important to consider toxicokinetics, which refers to variations in how quickly cells take up drugs after they enter the bloodstream [242]. Some toxicity of pharmaceutical products can be predicted on the basis of a known pharmacological mechanism and symptoms such as manipulation, mental confusion, memory loss, blurry vision, dizziness, and falls [243].

12.12 Clinical pharmacology Clinical pharmacology is the study of the interaction of drugs with living organisms as a therapeutic agent [244]. It is primarily concerned with establishing the safety and efficacy of novel treatment regimens [245]. Clinical pharmacology is also sometimes referred to as clinical pharmacokinetics, which includes the study of drug action on the body, rather than solely on the mind [246]. The major goals of clinical pharmacology are to assess new drugs, determine their side effects and interactions with other drugs, establish drug dosages and dosing intervals, assess efficacy and dosage forms, compare efficacy among drugs in different indications (i.e., how one drug compares to another) [8,62], define toxicity thresholds and rates, monitor adverse reactions during clinical trials, provide information for product labels, investigate safety concerns after approval (i.e., postmarketing surveillance), and provide information for use of off-label drugs by clinicians who are not affiliated with pharmaceutical companies (aka prescribing physicians) [247].

Role of AI in ADME/Tox toward formulation optimization

12.12.1 Pharmacokinetics Pharmacokinetics is the study of the movement and fate of drugs after they are administered. This field has evolved over time to include drug discovery, development, and evaluation [248]. There are three main phases of drug evaluation: preclinical, clinical, and postmarketing surveillance [249]. Preclinical studies involve laboratory research on animals or cells in vitro (in a Petri dish) in order to identify safe doses and potential side effects before conducting trials on humans [250]. Clinical trials involve tests on healthy volunteers or people with a particular condition who are administered the drug with the goal of understanding how it affects health outcomes [251]. Postmarketing surveillance involves monitoring what happens when the drug is commercially available for use by everyone else [252]. There are three main phases of drug action: target discovery, drug development, and drug evaluation. In clinical pharmacokinetics, the study focuses on how drugs act on the body, rather than on the mind, as in pharmacogenetics [253]. 12.12.1.1 Target discovery Target discovery is the first phase of drug action. It is the process using which a new drug target is discovered. It entails screening of chemical compounds and natural products to identify those with the desired pharmacological effect [10]. The goal of this phase is to find new chemical entities with specific biological effects that can be used in drug development [254]. Important information gathered during this phase includes structureactivity relationships for novel molecules as well as their metabolic fate so that toxicity can be predicted and potentially avoided [255]. Drugs may then enter into the drug development phase where they are tested for efficacy and safety in clinical trials [256]. 12.12.1.2 Drug development Drug development describes the process of creating a new drug by synthesizing, testing, and scaling up a new chemical entity (NCE) [257]. For this phase, pharmacokinetics is used to measure the efficiency, toxicity, and predictability of a drug candidate in humans prior to its release on the market [33]. 12.12.1.3 Drug evaluation Drug evaluation involves studying how medicines might affect patients under various conditions such as age or weight and what happens when people with certain preexisting medical conditions take them [258]. In this phase, it is important to observe a person’s response to a new medication or a medication that they already take regularly (called an established medication) to see whether it has any unwanted side effects that might indicate that this particular person should not take this medicine at all or should take a smaller dose [259].

323

324

A handbook of artificial intelligence in drug delivery

12.13 Pharmacodynamics Pharmacodynamics is the scientific study of how drugs influence cellular function [260,261]. Pharmacodynamic studies offer insights into how drug absorption, distribution, and excretion affect these functions [262]. Pharmacodynamics also employs pharmacokinetics, which is the scientific study of how drugs are absorbed, distributed, metabolized, and excreted by the body [263].

12.14 Artificial intelligence tools and software in ADME/Tox Artificial intelligence was initially applied to drug discovery and development as early as the 1970s [264]. Today, AI techniques are used to predict toxicity, identify active compounds, prioritize chemical libraries, and optimize lead candidates [265]. This technology can make predictions about how a drug will behave once it reaches the body and allow to determine whether the model was accurate compared to experimental data [10]. It can be used to predict the outcome of a clinical trial, identify potential side effects, determine safety margins, and more [266]. The first step is to figure out what the goals are for the AI/ML algorithm. Once that is known, an algorithm that will best help in achieving the goal can be easily selected. In recent years, the role of artificial intelligence (AI) in health care has significantly grown. This area is one of the most promising for AI, as it has the potential to improve patient outcomes and help reduce costs, by reducing drug toxicity [4]. AI is a key technology for solving various challenges in the area of drug toxicity. The ability to identify potential toxic effects caused by new drug molecules early in the discovery process would save both time and money, optimize resources, and improve patient safety [267,268]. A number of methods are currently used to predict drug toxicity. These include in vitro assays, in vivo assays, and computational methods [269]. In vitro assays involve testing drugs in a controlled environment, whereas in vivo assays involve testing drugs in live animals [270]. Computational methods use information from both in vitro and in vivo assays. The most important artificial intelligence tools and software that provide a description of the state of the art used in drug design and ADMET are summarized in Table 12.1.

12.15 Machine learning algorithms in ADME/Tox Machine learning has become an important tool in pharmacokinetics. It can be used to predict the absorption, distribution, metabolism, and excretion of a drug [298]. It helps determine the dosage and schedule for a drug [299]. Moreover, it can be used to predict the adverse effects of a drug. This is done using a large database of patient information. The machine learning algorithm will analyze these data to identify patterns that are

Role of AI in ADME/Tox toward formulation optimization

Table 12.1 Artificial intelligence tools and software used in drug design and ADMET. Software

Description

References

MedChem Studio

In silico ligand design, clustering/classifying compound libraries, and lead identification and optimization using a cheminformatics platform for computational and medicinal chemists Calculation and prediction of log P, log D, log S, and log W ASNs explicitly correct biases in neural networks The PatchSearch algorithm implements a local search for similar binding sites on protein surfaces with a definite degree of flexibility A system that identifies and prioritizes drug candidates for antibacterial therapy This regression program contains two databases containing more than 21,000 QSAR models Compound promotion and optimization program with interactive, visual displays. This module includes PD and PK parameters as well as physicochemical and ADME modules An application for calculating log Poct/water based on a structure Analyzes the molar refractive index and log P The DSSTox public database is a distributed structuresearchable database of toxicants Research and Education Excel Tools for the PK and ADME fields A system that predicts ADME/Tox properties An algorithm that produces 2D molecular descriptors from 3D molecular interaction energy maps A computation used for predicting the metabolism site of xenobiotics from their 3D structures An algorithm for determining the most energetically favorable sites of binding on molecules with known structures An in silico method for calculating pKa values An algorithm for identifying new drug leads by automatically calculating ADME, performing FIRM analysis, listing virtual libraries, and connecting databases Radioanalytical LIMs system using Excel-based and Excel analysis for ADME/PK simulations with a superior low cost

[271]

ALOGPS 2.1 ASNN PatchSearch

MolScore-Antibiotics cQSAR SeeSAR

clogP ClogP/CMR DSSTOX PK Tutor chem Tree Volsurf MetaSite GRID

Moka Tsar 3.2

Metabase

[272] [273] [274]

[275] [276] [277]

[278] [279] [280] [281] [282] [281] [283] [284]

[285] [286]

[287]

Continued

325

326

A handbook of artificial intelligence in drug delivery

Table 12.1 Artificial intelligence tools and software used in drug design and ADMET—cont’d Software

Description

References

ADME/Toxicity Property Calculator TOPKAT Metabolism ADMET

A knowledge base of ADME/toxicity is used for in silico screening Predictive toxicology An extensive database of metabolic pathways Allows for early elimination of compounds with undesirable ADMET properties to avoid costly reformulation later on as well as the evaluation of proposed structural refinements that aims at improving ADMET properties prior to resource expenditures A rapid ADME prediction is available for drug candidates System based on QSAR for virtual (in silico) screening of compounds to evaluate their properties simultaneously Module of the ADMET Predictor that automatically generates high-quality, predictive structure-property models based on experimental datasets and work seamlessly with ADMET Predictor structure descriptors as inputs Cross-validated models for assessing the toxicity of chemicals based on their molecular structures This collection is designed for determining predicted properties regarding absorption, distribution, metabolism, excretion, and toxicity (ADMET) of molecular collections Predicts drug likeness and ADME based on molecular descriptors

[288]

QikProp ADMEWORKS Predictor ADMET Modeler

Discovery Studio TOPKAT Software Discovery Studio ADMET Software

PreADME

[289] [290] [291]

[292] [293]

[294]

[295] [296]

[297]

associated with adverse effects [300]. Machine learning (ML) is a method of data analysis that enables computers to learn from data without being explicitly programmed [301]. In pharmacokinetics, machine learning can be used to predict the absorption, distribution, metabolism, and excretion of a drug in the body [302]. Machine learning has allowed the development of more accurate and sensitive diagnostic tests and can be used to mine data repositories in search of new leads for drug development [13]. It can also be used to predict the effect of a particular treatment on a disease. Medical data science is the statistical analysis of large quantities of structured and unstructured text-based data, including clinical trial data, clinical notes, imaging data, genomics and pharmacology data, and other types of patient data [303]. Machine learning is a subfield of data science that allows computers to “learn” without being explicitly programmed [304]. The algorithms and methods used in machine learning have many applications in medical research and drug development, including pharmacogenomics,

Role of AI in ADME/Tox toward formulation optimization

deep learning, predictive modeling, natural language processing, and others [302]. There are some main classification techniques of supervised machine learning such as random forest, support vector machine, neural network, k-nearest neighbors, naı¨ve Bayes, and deep learning.

12.15.1 Random forest Random forest (RF) is a popular and versatile machine learning algorithm that has become popular in recent years. It is used for regression, feature selection and dimensionality reduction, and classifying data into categories [305], and is, in fact, a type of decision tree, which is based on a random decision tree that selects the best split at each node and looks at the relationships between predictor variables to find which ones are the most useful in predicting the outcome [306]. Moreover, it can predict outcomes by combining the outputs of a number of tree-based models. The key advantage of RF is its ability to handle high-dimensionality data extremely well with high stability, accuracy, and speed [307]. Its ensemble nature also contributes to its high accuracy, as the individual trees in the forest can vote on the prediction for a given instance. However, it does require more data to achieve good results than some other algorithms [308]. In particular, it can be applied to relatively small datasets without sacrificing predictive accuracy. This makes RF especially well-suited for data exploration and preprocessing tasks [309]. The RF classifier has been used in ADMET/Tox to screen drug candidates in the early stages of development to identify potential toxicity issues and the best set of features for the next generation of computational toxicology models [310]. To this end, the highthroughput screening (HTS) of ADME/Tox assays has, therefore, become increasingly important for compound profiling and prioritization [31]. In addition, it exhibits better predictive performance than do other methods. To date, most pharmacokinetic and toxicity studies in the early developmental phase have been performed using traditional single-point assays [10]. However, RF, a machine learning technique, has begun to be used more often due to its high predictive performance [311]. It is important to mention that this algorithm also provides a richer set of features and variables, which improves prediction accuracy. The RF analysis is also less computationally intense than the other algorithms and thus can be completed more quickly [312]. RF classifiers are based on the idea that if a tree is built for each decision stump, then there may be some redundancy between trees [313]. RF does not generate a single prediction but rather uses different combinations of trees as classification rules.

12.15.2 Support vector machine A support vector machine (SVM) is another classification algorithm that uses the sign of a linear hyperplane to separate the data points in a multidimensional space and is a type of supervised machine learning technique that analyzes data and categorizes them into

327

328

A handbook of artificial intelligence in drug delivery

groups based on an assessment of their features. Besides, it used in regression and classification tasks [314,315]. Moreover, it uses a set of data points to find how these points best separate two classes (by maximizing the margin). An SVM can also be tuned to optimize different goals such as margin, orthogonal distance, or Hamming distance, as it is a strong driver-based classification model [315]. It uses kernel functions that are able to classify both linearly separable and nonlinearly separable data without any difficulty. In recent years, SVMs have become a popular tool for drug discovery and ADME/Tox prediction, as they can be applied to predict drug absorption, distribution, metabolism, and excretion (ADME) and toxicity (Tox) [84]. In the context of ADME/Tox data, an SVM can be used to predict drug absorption, protein-binding affinity, and toxicity [302], as it has been shown to exhibit good performance in ADME/Tox applications when predicting toxicity from toxicity values alone or with the addition of read-across information in highdimensional datasets, making them well-suited for the large datasets typically encountered in ADME/Tox research [316].

12.15.3 Neural network A neural network (NN) is an artificial intelligence and statistical learning approach, which simulates how neurons behave within an artificial intelligence system [317]. It is capable of modeling complex decision-making processes such as finding patterns, classifying data, forecasting trends, making probabilistic decisions, and recognizing images among other tasks [318]. Moreover, it a type of machine learning algorithm that utilizes a set of interconnected artificial neurons to process information in a system known as deep NNs [319]. A NN uses machine learning algorithms to train on data until it is capable of predicting results in new datasets [320]. This type of network learns by comparing inputs with outputs. RF classifiers are helpful for clustering large datasets into groups with similar features [321]. Moreover, neuronal networks can learn from previous experiences and make decisions about new inputs by following an algorithm similar to human brain networks. One example of a NN is the feedforward network in which information moves in only one direction [317]. In recent years, there has been increasing interest in the application of NNs for the prediction of ADME/Tox [322]. ADME/Tox (absorption, distribution, metabolism, excretion, and toxicity) of a molecule is an important consideration in the drug discovery process [323]. The assessment of these properties helps determine a molecule’s safety and efficacy profile in vivo. Prediction of ADME/Tox properties is often undertaken using empirical or quantum chemistry methods; however, such predictions are often limited in their accuracy [293]. Especially, the use of NNs in the early prediction of drug cytotoxicity could help reduce the attrition rate in drug discovery. In this application, a threelayer feedforward NN was implemented and used to predict the cytotoxicity of drugs at an early stage of development, using only physicochemical descriptors as input [324]. The

Role of AI in ADME/Tox toward formulation optimization

performance of the ANN was compared with those of traditional QSAR models. The results showed that the ANN performed better than the QSAR models in predicting drug cytotoxicity [325].

12.15.4 K-nearest neighbors K-nearest neighbor is another learning algorithm that has connections with both artificial intelligence and machine learning [326]. It is classified as an unsupervised machine learning technique that evaluates each input by comparing it to its neighbors, and it is a nonparametric statistical classification technique, whereby each observation is classified by a rule that corresponds to its nearest neighbor in some sense, e.g., the k-most similar observations in terms of one or more attributes [327,328]. Moreover, this method is a decision tree classification technique that works by grouping records based on their similarity to a certain set of training examples and then by identifying clusters with records [329]. In the ADME/Tox context, this technique can be used to predict the toxicity of chemicals, as it is based on their chemical descriptors and can accurately classify toxic and nontoxic chemicals with an accuracy of 99.8% [269].

12.15.5 Naïve Bayes Naı¨ve Bayes (NB) is a predictive model used to classify and identify the toxicity of new drugs and relies on probability calculations, often producing improved results than other algorithms when training datasets with few features [330]. The model predicts the toxicity of a new drug based on three factors: drug type, biochemical or pharmacokinetic properties, and in vitro experimental data [331]. NB assumes that all features are equally important, which can be beneficial when there are many features with similar significance [332]. Moreover, its probabilities are used to predict the potential risk of an adverse event for a given drug design. It also helps identify experimental tests that should be performed during drug development [333].

12.15.6 Deep learning Deep learning (DNN) is a form of machine learning based on NNs and refers to algorithms such as artificial NN, deep belief network, convolutional NN, and recurrent NN [334]. This method consists of multiple layers of information, or neurons, where each layer depends on those below it for processing, which are used to predict unknown attributes based on datasets [335]. Nowadays, deep learning has dramatically changed the manner in which we acquire scientific knowledge. It can be used with chemical compounds in order to understand how they interact with the human body [336]. In the context of ADME/Tox, the potential of deep learning is immense, where it is commonly used in drug discovery and adverse drug reactions/toxicity (ADME/Tox) [337]. With the help of deep NNs, scientists can hope to create new drugs with fewer

329

330

A handbook of artificial intelligence in drug delivery

side effects [299]. It has applications for understanding both chemical compounds and therapeutic candidate molecules [10]. Moreover, it can be applied to both the research process and to the analysis of compounds. Scientists are also looking at how deep learning can be applied as a tool for predicting toxicity and pharmacology as well as determining the structures of new compounds [338].

References [1] E.S. Brunette, R.C. Flemmer, C.L. Flemmer, A review of artificial intelligence, in: 4th International Conference on Autonomous Robots and Agents, 2009. [2] L. Cortellazzo, E. Bruni, R. Zampieri, The role of leadership in a digitalized world: a review, Front. Psychol. 10 (2019), https://doi.org/10.3389/fpsyg.2019.01938. [3] C. Liu, P.P. Constantinides, Y. Li, Research and development in drug innovation: reflections from the 2013 bioeconomy conference in China, lessons learned and future perspectives, Acta Pharm. Sin. B (2014) 112–119, https://doi.org/10.1016/j.apsb.2014.01.002. [4] A. Bohr, K. Memarzadeh, The rise of artificial intelligence in healthcare applications, in: Artificial Intelligence in Healthcare, Elsevier, 2020, pp. 25–60. [5] D. Zhang, S. Surapaneni, ADME-Enabling Technologies in Drug Design and Development, John Wiley and Sons, 2012, https://doi.org/10.1002/9781118180778. [6] T.N. Tozer, M. Rowland, Introduction to Pharmacokinetics and Pharmacodynamics: The Quantitative Basis of Drug Therapy, Lippincott Williams & Wilkins, 2006. [7] J.T. DiPiro, W. Spruill, W. Wade, Introduction to pharmacokinetics and pharmacodynamics, in: Concepts in Clinical Pharmacokinetics, ASHP, 2010, pp. 6–296. [8] C. Palleria, A. Di Paolo, C. Giofre`, C. Caglioti, G. Leuzzi, A. Siniscalchi, G. De Sarro, L. Gallelli, Pharmacokinetic drug-drug interaction and their implication in clinical management, J. Res. Med. Sci. 18 (7) (2013) 600–609. http://journals.mui.ac.ir/jrms/article/view/9310/3947. [9] S. Vangala, J. Pinjari, P. Patole, S. Ravindran, R. Gangal, P. Wangikar, S. Basu, T. Ahmed, H. Rastogi, Translational Drug Discovery Research: Integration of Medicinal Chemistry, Computational Modeling, Pharmacology, ADME, and Toxicology, Wiley, 2012, https://doi.org/10.1002/ 9780470921920.edm038. [10] J.P. Hughes, S.S. Rees, S.B. Kalindjian, K.L. Philpott, Principles of early drug discovery, Br. J. Pharmacol. 162 (6) (2011) 1239–1249, https://doi.org/10.1111/j.1476-5381.2010.01127.x. [11] E.H. Rubin, D.G. Gilliland, Drug development and clinical trials – the path to an approved cancer drug, Nat. Rev. Clin. Oncol. 9 (4) (2012) 215–222, https://doi.org/10.1038/nrclinonc.2012.22. [12] A.D. Hingorani, V. Kuan, C. Finan, F.A. Kruger, A. Gaulton, S. Chopade, R. Sofat, R.J. MacAllister, J.P. Overington, H. Hemingway, S. Denaxas, D. Prieto, J.P. Casas, Improving the odds of drug development success through human genomics: modelling study, Sci. Rep. 9 (1) (2019), https://doi.org/10.1038/s41598-019-54849-w. [13] C. R
eda, E. Kaufmann, A. Delahaye-Duriez, Machine learning applications in drug development, Comput. Struct. Biotechnol. J. 18 (2020) 241–252, https://doi.org/10.1016/j.csbj.2019.12.006. [14] B. Bhhatarai, W.P. Walters, C.E.C.A. Hop, G. Lanza, S. Ekins, Opportunities and challenges using artificial intelligence in ADME/Tox, Nat. Mater. 18 (5) (2019) 418–422, https://doi.org/10.1038/ s41563-019-0332-5. [15] J.W. Cassidy, Applications of machine learning in drug discovery I: target discovery and small molecule drug design, in: Artificial Intelligence in Oncology Drug Discovery and Development, IntechOpen, 2020. [16] D.T. Stanton, QSAR and QSPR model interpretation using partial least squares (PLS) analysis, Curr. Comput. Aided Drug Des. 8 (2) (2012) 107–127, https://doi.org/10.2174/157340912800492357. [17] H. van de Waterbeemd, E. Gifford, ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov. 2 (3) (2003) 192–204, https://doi.org/10.1038/nrd1032.

Role of AI in ADME/Tox toward formulation optimization

[18] T. Ching, D.S. Himmelstein, B.K. Beaulieu-Jones, A.A. Kalinin, B.T. Do, G.P. Way, E. Ferrero, P.M. Agapow, M. Zietz, M.M. Hoffman, W. Xie, G.L. Rosen, B.J. Lengerich, J. Israeli, J. Lanchantin, S. Woloszynek, A.E. Carpenter, A. Shrikumar, J. Xu, et al., Opportunities and obstacles for deep learning in biology and medicine, J. R. Soc. Interface 15 (141) (2018) 20170387, https://doi. org/10.1098/rsif.2017.0387. [19] M. Adenot, R. Lahana, Blood-brain barrier permeation models: discriminating between potential CNS and non-CNS drugs including P-glycoprotein substrates, J. Chem. Inf. Comput. Sci. 44 (1) (2004) 239–248, https://doi.org/10.1021/ci034205d. [20] S. Sakore, B. Chakraborty, In vitro–in vivo correlation (IVIVC): a strategic tool in drug development, J. Bioequivalence Bioavailab. S3 (1) (2011) 1–12. [21] M.L. Chen, V. Shah, R. Patnaik, W. Adams, A. Hussain, D. Conner, M. Mehta, H. Malinowski, J. Lazor, S.M. Huang, D. Hare, L. Lesko, D. Sporn, R. Williams, Bioavailability and bioequivalence: an FDA regulatory overview, Pharm. Res. 18 (12) (2001) 1645–1650, https://doi.org/10.1023/ A:1013319408893. [22] V.R. Devadasu, P.K. Deb, R. Maheshwari, P. Sharma, R.K. Tekade, Physicochemical, pharmaceutical, and biological considerations in GIT absorption of drugs, in: Dosage Form Design Considerations, vol. I, Elsevier, 2018, pp. 149–178, https://doi.org/10.1016/B978-0-12-8144237.00005-8. [23] Q. Bao, B. Newman, Y. Wang, S. Choi, D.J. Burgess, In vitro and ex vivo correlation of drug release from ophthalmic ointments, J. Control. Release 276 (2018) 93–101, https://doi.org/10.1016/j. jconrel.2018.03.003. [24] D.H. Nagadurga, Bioavailability and bioequivalence studies, in: Pharmaceutical Formulation DesignRecent Practices, IntechOpen, London, UK, 2019, p. 71. [25] U. Heigoldt, F. Sommer, R. Daniels, K.G. Wagner, Predicting in vivo absorption behavior of oral modified release dosage forms containing pH-dependent poorly soluble drugs using a novel pH-adjusted biphasic in vitro dissolution test, Eur. J. Pharm. Biopharm. 76 (1) (2010) 105–111, https://doi.org/10.1016/j.ejpb.2010.05.006. [26] S. Nainar, K. Rajiah, S. Angamuthu, D. Prabakaran, R. Kasibhatta, Biopharmaceutical classification system in invitro/in-vivo correlation: concept and development strategies in drug delivery, Trop. J. Pharm. Res. 11 (2) (2012) 319–329, https://doi.org/10.4314/tjpr.v11i2.20. [27] Y. Tsume, D.M. Mudie, P. Langguth, G.E. Amidon, G.L. Amidon, The Biopharmaceutics Classification System: subclasses for in vivo predictive dissolution (IPD) methodology and IVIVC, Eur. J. Pharm. Sci. 57 (1) (2014) 152–163, https://doi.org/10.1016/j.ejps.2014.01.009. [28] J.M. Cardot, E. Beyssac, M. Alric, In vitro-in vivo correlation: importance of dissolution in IVIVC, Dissolution Technol. 14 (1) (2007) 15. [29] N.A. Dura´n-Iturbide, B.I. Dı´az-Eufracio, J.L. Medina-Franco, In silico ADME/Tox profiling of natural products: a focus on BIOFACQUIM, ACS Omega 5 (26) (2020) 16076–16084, https://doi.org/ 10.1021/acsomega.0c01581. [30] S. Ekins, J. Mestres, B. Testa, In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling, Br. J. Pharmacol. 152 (1) (2007) 9–20, https://doi.org/10.1038/sj. bjp.0707305. [31] S. Alqahtani, In silico ADME-Tox modeling: progress and prospects, Expert Opin. Drug Metab. Toxicol. 13 (11) (2017) 1147–1158, https://doi.org/10.1080/17425255.2017.1389897. [32] K. Tsaioun, B.J. Blaauboer, T. Hartung, Evidence-based absorption, distribution, metabolism, excretion (ADME) and its interplay with alternative toxicity methods, ALTEX 33 (4) (2016) 343–358, https://doi.org/10.14573/altex.1610101. [33] F. Wu, Y. Zhou, L. Li, X. Shen, G. Chen, X. Wang, X. Liang, M. Tan, Z. Huang, Computational approaches in preclinical studies on drug discovery and development, Front. Chem. (2020), https:// doi.org/10.3389/fchem.2020.00726. [34] X. Lin, X. Li, X. Lin, A review on applications of computational methods in drug screening and design, Molecules 25 (6) (2020), https://doi.org/10.3390/molecules25061375. [35] B. Qifeng, Drug design progress of in silico, in vitro and in vivo researches, In vitro In-vivo In-silico J. 1 (2018) 16–37.

331

332

A handbook of artificial intelligence in drug delivery

[36] I.M. Kapetanovic, Computer-aided drug discovery and development (CADDD): in silico-chemicobiological approach, Chem. Biol. Interact. 171 (2) (2008) 165–176, https://doi.org/10.1016/j. cbi.2006.12.006. [37] G. Sliwoski, S. Kothiwale, J. Meiler, E.W. Lowe, Computational methods in drug discovery, Pharmacol. Rev. 66 (1) (2014) 334–395, https://doi.org/10.1124/pr.112.007336. [38] A. Tropsha, J. Bajorath, Computational methods for drug discovery and design, J. Med. Chem. 59 (1) (2016) 1, https://doi.org/10.1021/acs.jmedchem.5b01945. [39] R. Urso, P. Blardi, G. Giorgi, A short introduction to pharmacokinetics, Eur. Rev. Med. Pharmacol. Sci. 6 (2) (2002) 33–44. [40] S. Ghaibi, H. Ipema, M. Gabay, ASHP guidelines on the pharmacist’s role in providing drug information, Am. J. Health Syst. Pharm. 72 (7) (2015) 573–577, https://doi.org/10.2146/sp150002. [41] A.H. Vo, T.R. Van Vleet, R.R. Gupta, M.J. Liguori, M.S. Rao, An overview of machine learning and big data for drug toxicity evaluation, Chem. Res. Toxicol. 33 (1) (2020) 20–37, https://doi.org/ 10.1021/acs.chemrestox.9b00227. [42] N.J. White, Pharmacokinetic and pharmacodynamic considerations in antimalarial dose optimization, Antimicrob. Agents Chemother. 57 (12) (2013) 5792–5807, https://doi.org/10.1128/AAC.00287-13. [43] T. Tuntland, B. Ethell, T. Kosaka, F. Blasco, R. Zang, M. Jain, T. Gould, K. Hoffmaster, Implementation of pharmacokinetic and pharmacodynamic strategies in early research phases of drug discovery and development at Novartis Institute of Biomedical Research, Front. Pharmacol. 5 (2014), https:// doi.org/10.3389/fphar.2014.00174. [44] F. Grases, B.M. Simonet, I. Vucenik, R.M. Prieto, A. Costa-Bauza´, J.G. March, A.M. Shamsuddin, Absorption and excretion of orally administered inositol hexaphosphate (IP6 or phytate) in humans, Biofactors 15 (1) (2001) 53–61, https://doi.org/10.1002/biof.5520150105. [45] P.G. Welling, Interactions affecting drug absorption, Clin. Pharmacokinet. 9 (5) (1984) 404–434, https://doi.org/10.2165/00003088-198409050-00002. [46] M.R. Syme, J.W. Paxton, J.A. Keelan, Drug transfer and metabolism by the human placenta, Clin. Pharmacokinet. 43 (8) (2004) 487–514, https://doi.org/10.2165/00003088-200443080-00001. [47] J. Vrbanac, R. Slauter, ADME in drug discovery, A Comprehensive Guide to Toxicology in Nonclinical Drug Development, Elsevier BV, Academic Press, 2017, pp. 39–67. [48] J.K. Patra, G. Das, L.F. Fraceto, E.V.R. Campos, M.d.P. Rodriguez-Torres, L.S. Acosta-Torres, L.A. Diaz-Torres, R. Grillo, M.K. Swamy, S. Sharma, S. Habtemariam, H.-S. Shin, Nano based drug delivery systems: recent developments and future prospects, J. Nanobiotechnol. 16 (1) (2018), https://doi.org/10.1186/s12951-018-0392-8. [49] R. Yang, T. Wei, H. Goldberg, W. Wang, K. Cullion, D.S. Kohane, Getting drugs across biological barriers, Adv. Mater. 29 (37) (2017) 1606596. [50] L.Z. Benet, D. Kroetz, L. Sheiner, J. Hardman, L. Limbird, Pharmacokinetics: the dynamics of drug absorption, distribution, metabolism, and elimination, in: Goodman and Gilman’s the Pharmacological Basis of Therapeutics, vol. 3, McGraw-Hill Inc, 1996, p. e27. [51] M. Caira, C. Ionescu, Drug Metabolism: Current Concepts, Springer, Dordrecht, 2006. [52] B. Agoram, W.S. Woltosz, M.B. Bolger, Predicting the impact of physiological and biochemical processes on oral drug bioavailability, Adv. Drug Deliv. Rev. 50 (1) (2001) S41–S67, https://doi.org/ 10.1016/S0169-409X(01)00179-X. [53] J. Coulson, Nursing Standard (Royal College of Nursing), vol. 35, 1987, pp. 61–66. [54] F. Erdo˝, L.A. Bors, D. Farkas, A´. Bajza, S. Gizurarson, Evaluation of intranasal delivery route of drug administration for brain targeting, Brain Res. Bull. 143 (2018) 155–170, https://doi.org/10.1016/j. brainresbull.2018.10.009. [55] B. Homayun, X. Lin, H.J. Choi, Challenges and recent progress in oral drug delivery systems for biopharmaceuticals, Pharmaceutics 11 (3) (2019), https://doi.org/10.3390/pharmaceutics11030129. [56] M.J. Rathbone, J. Hadgraft, M.S. Roberts, Modified-release drug delivery technology, Drugs and the Pharmaceutical Sciences, vol. 126, Informa Healthcare USA, Inc. CRC Press, 2002, pp. 101–114. [57] S.W. Junod, FDA and clinical drug trials: a short history, in: A Quick Guide to Clinical Trials, Bioplan, Inc., Washington, 2008, pp. 25–55. [58] C. Herkenne, I. Alberti, A. Naik, Y.N. Kalia, F.X. Mathy, V. Pr
eat, R.H. Guy, In vivo methods for the assessment of topical drug bioavailability, Pharm. Res. 25 (1) (2008) 87–103, https://doi.org/ 10.1007/s11095-007-9429-7.

Role of AI in ADME/Tox toward formulation optimization

[59] A.H. Lavan, P. Gallagher, Predicting risk of adverse drug reactions in older adults, Ther. Adv. Drug Saf. 7 (1) (2016) 11–22, https://doi.org/10.1177/2042098615615472. [60] B.J. Bennion, Predicting a drug’s membrane permeability: a computational model validated with in vitro permeability assay data, J. Phys. Chem. B 121 (2017) 5228–5237. [61] I. Tamai, T. Nakanishi, OATP transporter-mediated drug absorption and interaction, Curr. Opin. Pharmacol. 13 (6) (2013) 859–863, https://doi.org/10.1016/j.coph.2013.09.001. [62] H. Wen, H. Jung, X. Li, Drug delivery approaches in addressing clinical pharmacology-related issues: opportunities and challenges, AAPS J. 17 (6) (2015) 1327–1340, https://doi.org/10.1208/s12248015-9814-9. [63] R. Ferrence, Diffusion theory and drug use, Addiction 96 (1) (2001) 165–173, https://doi.org/ 10.1046/j.1360-0443.2001.96116512.x. [64] O. Sten-Knudsen, Passive Transport Processes, Springer Science and Business Media LLC, 1978, pp. 5–113, https://doi.org/10.1007/978-3-642-46370-9_2. [65] W. Zhang, M.R. Prausnitz, A. Edwards, Model of transient drug diffusion across cornea, J. Control. Release 99 (2) (2004) 241–258, https://doi.org/10.1016/j.jconrel.2004.07.001. [66] M. Laksitorini, V.D. Prasasty, P.K. Kiptoo, T.J. Siahaan, Pathways and progress in improving drug delivery through the intestinal mucosa and blood-brain barriers, Ther. Deliv. 5 (10) (2014) 1143–1163, https://doi.org/10.4155/tde.14.67. [67] R. Whittam, K.P. Wheeler, Transport across cell membranes, Annu. Rev. Physiol. 32 (1970) 21–60, https://doi.org/10.1146/annurev.ph.32.030170.000321. [68] M.J. O’Donnell, S.H. Maddrell, Paracellular and transcellular routes for water and solute movements across insect epithelia, J. Exp. Biol. 106 (1) (1983) 231–253. [69] W. Stein, Transport and Diffusion Across Cell Membranes, Elsevier, 2012. 704 pp. [70] Y. Kato, T. Miyazaki, T. Kano, T. Sugiura, Y. Kubo, A. Tsuji, Involvement of influx and efflux transport systems in gastrointestinal absorption of celiprolol, J. Pharm. Sci. 98 (7) (2009) 2529–2539, https://doi.org/10.1002/jps.21618. [71] A.C. Rodrigues, Efflux and uptake transporters as determinants of statin response, Expert Opin. Drug Metab. Toxicol. 6 (5) (2010) 621–632, https://doi.org/10.1517/17425251003713519. [72] V. Fischer, H.J. Einolf, D. Cohen, Efflux transporters and their clinical relevance, Mini-Rev. Med. Chem. 5 (2) (2005) 183–195, https://doi.org/10.2174/1389557053402756. [73] Y. Zhang, B. Hagenbuch, Protein-protein interactions of drug uptake transporters that are important for liver and kidney, Biochem. Pharmacol. 168 (2019) 384–391, https://doi.org/10.1016/j. bcp.2019.07.026. [74] S. Kok-Yong, L. Lawrence, Drug distribution and drug elimination, in: T.A. Ahmed (Ed.), Basic Pharmacokinetic Concepts and Some Clinical Applications, Intech, Rijeka, 2015, pp. 99–116. [75] K.T. Savjani, A.K. Gajjar, J.K. Savjani, Drug solubility: importance and enhancement techniques, ISRN Pharm. (2012) 1–10, https://doi.org/10.5402/2012/195727. [76] A. Siew, Solving poor solubility to unlock a drug’s potential, Pharm. Technol. 39 (7) (2015) 20–27. http://www.pharmtech.com/pharmtech/issue/issueList.jsp?id¼52. [77] J.N. Patel, D.M. Rathod, N.A. Patel, M.K. Modasiya, Techniques to improve the solubility of poorly soluble drugs, Int. J. Pharm. Life Sci. 3 (2) (2012) 1459–1469. [78] C. Awortwe, P.S. Fasinu, B. Rosenkranz, Application of Caco-2 cell line in herb-drug interaction studies: current approaches and challenges, J. Pharm. Pharm. Sci. 17 (1) (2014) 1. [79] H. Sun, E.C.Y. Chow, S. Liu, Y. Du, K.S. Pang, The Caco-2 cell monolayer: usefulness and limitations, Expert Opin. Drug Metab. Toxicol. 4 (4) (2008) 395–411, https://doi.org/10.1517/ 17425255.4.4.395. [80] E. Rutkowska, K. Paja¸k, K. Jo´z´wiak, Lipophilicity – methods of determination and its role in medicinal chemistry, Acta Pol. Pharm. 70 (1) (2013) 3–18. http://www.ptfarm.pl/pub/File/Acta_ Poloniae/2013/1/003-018.pdf. [81] M. Kah, C.D. Brown, Log D: lipophilicity for ionisable compounds, Chemosphere 72 (10) (2008) 1401–1408, https://doi.org/10.1016/j.chemosphere.2008.04.074. [82] S.K. Bhal, LogP—Making Sense of the Value, Advanced Chemistry Development, Toronto, ON, Canada, 2007, pp. 1–4.

333

334

A handbook of artificial intelligence in drug delivery

[83] F.A.P.C. Gobas, J.R. McCorquodale, G.D. Haffner, Intestinal absorption and biomagnification of organochlorines, Environ. Toxicol. Chem. 12 (3) (1993) 567–576, https://doi.org/10.1002/ etc.5620120316. [84] A. Daina, O. Michielin, V. Zoete, SwissADME: a free web tool to evaluate pharmacokinetics, druglikeness and medicinal chemistry friendliness of small molecules, Sci. Rep. 7 (2017), https://doi.org/ 10.1038/srep42717. [85] Y. Wang, J. Xing, Y. Xu, N. Zhou, J. Peng, Z. Xiong, X. Liu, X. Luo, C. Luo, K. Chen, M. Zheng, H. Jiang, In silico ADME/T modelling for rational drug design, Q. Rev. Biophys. 48 (4) (2015) 488–515, https://doi.org/10.1017/S0033583515000190. [86] E.V. Radchenko, A.S. Dyabina, V.A. Palyulin, N.S. Zefirov, Prediction of human intestinal absorption of drug compounds, Russ. Chem. Bull. 65 (2) (2016) 576–580, https://doi.org/10.1007/s11172016-1340-0. [87] M.H. Abraham, Human intestinal absorption – neutral molecules and ionic species, J. Pharm. Sci. 103 (7) (2014) 1956–1966, https://doi.org/10.1002/jps.24024. [88] W.J. Jusko, M. Gretch, Plasma and tissue protein binding of drugs in pharmacokinetics, Drug Metab. Rev. 5 (1) (1976) 43–140, https://doi.org/10.3109/03602537608995839. [89] P.K. Deb, O. Al-Attraqchi, M.R. Prasad, R.K. Tekade, Protein and tissue binding: implication on pharmacokinetic parameters. Implication on pharmacokinetic parameters, in: Dosage Form Design Considerations, vol. I, Elsevier, 2018, pp. 371–399, https://doi.org/10.1016/B978-0-12-8144237.00011-3. [90] G.M. Pacifici, A. Viani, Methods of determining plasma and tissue binding of drugs, Clin. Pharmacokinet. 23 (6) (1992) 449–468, https://doi.org/10.2165/00003088-199223060-00005. [91] S.T. Stern, M.N. Martinez, D.M. Stevens, When is it important to measure unbound drug in evaluating nanomedicine pharmacokinetics? Drug Metab. Dispos. 44 (12) (2016) 1934–1939, https://doi. org/10.1124/dmd.116.073148. [92] J.R. Gillette, Overview of drug-protein binding, Ann. N. Y. Acad. Sci. 226 (1) (1973) 6–17, https:// doi.org/10.1111/j.1749-6632.1973.tb20464.x. [93] R.H. Tajmir, An overview of drug binding to human serum albumin: protein folding and unfolding, Sci. Iran 14 (2) (2007) 87–95. [94] K.K. Jain, Drug Delivery Systems – An Overview, Springer Science and Business Media LLC, 2008, pp. 1–50, https://doi.org/10.1007/978-1-59745-210-6_1. [95] I. Gomez-Orellana, Strategies to improve oral drug bioavailability, Expert Opin. Drug Deliv. 2 (3) (2005) 419–433, https://doi.org/10.1517/17425247.2.3.419. [96] J.K. Seydel, M. Wiese, Drug-Membrane Interactions: Analysis, Drug Distribution, Modeling, first edn, vol. 15, Wiley-VCH, 2009, p. 367. [97] D.R. Abernethy, D.J. Greenblatt, M. Divoll, J.S. Harmatz, R.I. Shader, Alterations in drug distribution and clearance due to obesity, J. Pharmacol. Exp. Ther. 217 (3) (1981) 681–685. [98] C.W. Hwang, D. Wu, E.R. Edelman, Physiological transport forces govern drug distribution for stent-based delivery, Circulation 104 (5) (2001) 600–605, https://doi.org/10.1161/hc3101.092214. [99] D.S. Wishart, Improving early drug discovery through ADME modelling, Drugs R D 8 (6) (2007) 349–362, https://doi.org/10.2165/00126839-200708060-00003. [100] M.A. El Mohsen, J. Marks, G. Kuhnle, K. Moore, E. Debnam, S.K. Srai, C. Rice-Evans, J.P.E. Spencer, Absorption, tissue distribution and excretion of pelargonidin and its metabolites following oral administration to rats, Br. J. Nutr. 95 (1) (2006) 51–58, https://doi.org/10.1079/BJN20051596. [101] T. Sj€ ostrand, Volume and distribution of blood and their significance in regulating the circulation, Physiol. Rev. 33 (2) (1953) 202–228, https://doi.org/10.1152/physrev.1953.33.2.202. [102] N. Holford, D.S. Yim, Volume of distribution, Transl. Clin. Pharmacol. 24 (2) (2016) 74–77, https:// doi.org/10.12793/tcp.2016.24.2.74. [103] A.M. Persky, G. Pollack, Foundations in Pharmacokinetics, 2013. UNC Eshelman School of Pharmacy. [104] R.M. Omar, A.A. Galala, F. Badria, Medicinal plants: dual source of enzymes and enzyme modulators, Polymorphism 3 (2019) 15–31. [105] C.B. Landersdorfer, W.J. Jusko, Pharmacokinetic/pharmacodynamic modelling in diabetes mellitus, Clin. Pharmacokinet. 47 (7) (2008) 417–448, https://doi.org/10.2165/00003088-20084707000001.

Role of AI in ADME/Tox toward formulation optimization

[106] R.B. Kim, Drugs as P-glycoprotein substrates, inhibitors, and inducers, Drug Metab. Rev. 34 (1–2) (2002) 47–54, https://doi.org/10.1081/DMR-120001389. [107] R.B. Kim, C. Wandel, B. Leake, M. Cvetkovic, M.F. Fromm, P.J. Dempsey, M.M. Roden, F. Belas, A.K. Chaudhary, D.M. Roden, A.J.J. Wood, G.R. Wilkinson, Interrelationship between substrates and inhibitors of human CYP3A and P-glycoprotein, Pharm. Res. 16 (3) (1999) 408–414, https://doi.org/10.1023/a:1018877803319. [108] A. Sharma, V.K. Gupta, R. Pathania, Efflux pump inhibitors for bacterial pathogens: from bench to bedside, Indian J. Med. Res. 149 (2) (2019) 129–145, https://doi.org/10.4103/ijmr.IJMR_2079_17. [109] M. Verschraagen, C.H.W. Koks, J.H.M. Schellens, J.H. Beijnen, P-glycoprotein system as a determinant of drug interactions: the case of digoxin-verapamil, Pharmacol. Res. 40 (4) (1999) 301–306, https://doi.org/10.1006/phrs.1999.0535. [110] W.M. Pardridge, Blood-brain barrier delivery, Drug Discov. Today 12 (1–2) (2007) 54–61, https:// doi.org/10.1016/j.drudis.2006.10.013. [111] M.M.A. Almutairi, C. Gong, Y.G. Xu, Y. Chang, H. Shi, Factors controlling permeability of the blood-brain barrier, Cell. Mol. Life Sci. 73 (1) (2016) 57–77, https://doi.org/10.1007/s00018015-2050-8. [112] E. Brouwer, J. Verweij, P. De Bruijn, W.J. Loos, M. Pillay, D. Buijs, A. Sparreboom, Measurement of fraction unbound paclitaxel in human plasma, Drug Metab. Dispos. 28 (10) (2000) 1141–1145. [113] R. Watanabe, T. Esaki, H. Kawashima, Y. Natsume-Kitatani, C. Nagao, R. Ohashi, K. Mizuguchi, Predicting fraction unbound in human plasma from chemical structure: improved accuracy in the low value ranges, Mol. Pharm. 15 (11) (2018) 5302–5311, https://doi.org/10.1021/acs. molpharmaceut.8b00785. [114] C.M. Bowman, L.Z. Benet, An examination of protein binding and protein-facilitated uptake relating to in vitro-in vivo extrapolation, Eur. J. Pharm. Sci. 123 (2018) 502–514, https://doi.org/10.1016/j. ejps.2018.08.008. [115] M.T. Kinirons, M.S. O’Mahony, Drug metabolism and ageing, Br. J. Clin. Pharmacol. 57 (5) (2004) 540–544, https://doi.org/10.1111/j.1365-2125.2004.02096.x. [116] K. Bachmann, Drug metabolism, in: Pharmacology, Elsevier Inc., 2009, pp. 131–173, https://doi. org/10.1016/B978-0-12-369521-5.00008-7. [117] K. Faber, Biotransformations in Organic Chemistry, vol. 4, Springer, Berlin, Heidelberg, 1992, p. 423. [118] G.G. Gibson, P. Skett, Introduction to Drug Metabolism, Springer, 2013. [119] P.K. Gupta, Disposition of toxicants, in: Problem Solving Questions in Toxicology, Springer, Cham, 2020, pp. 57–68. [120] Z. Zhang, W. Tang, Drug metabolism in drug discovery and development, Acta Pharm. Sin. B 8 (5) (2018) 721–732, https://doi.org/10.1016/j.apsb.2018.04.003. [121] M. Holcapek, L. Kola´rova´, M. Nobilis, High-performance liquid chromatography–tandem mass spectrometry in the identification and determination of phase I and phase II drug metabolites, Anal. Bioanal. Chem. 391 (1) (2008) 59–78. [122] X. Gu, J.E. Manautou, Molecular mechanisms underlying chemical liver injury, Expert Rev. Mol. Med. 14 (2012), https://doi.org/10.1017/S1462399411002110. [123] F.P. Guengerich, CYTOCHROME P-450 3A4: regulation and role in drug metabolism, Annu. Rev. Pharmacol. Toxicol. 39 (1) (1999) 1–17, https://doi.org/10.1146/annurev.pharmtox.39.1.1. [124] U.A. Meyer, Overview of enzymes of drug metabolism, J. Pharmacokinet. Biopharm. 24 (5) (1996) 449–459, https://doi.org/10.1007/BF02353473. [125] X. Liu, L. Jia, The conduct of drug metabolism studies considered good practice (I): analytical systems and in vivo studies, Curr. Drug Metab. 8 (8) (2007) 815–821, https://doi.org/10.2174/ 138920007782798153. [126] Jancova´, M. Sˇiller, Phase II drug metabolism, in: J. Paxton (Ed.), Topics on Drug Metabolism, IntechOpen, London, UK, 2012, pp. 35–60. [127] S. Crettol, N. Petrovic, M. Murray, Pharmacogenetics of phase I and phase II drug metabolism, Curr. Pharm. Des. 16 (2) (2010) 204–219, https://doi.org/10.2174/138161210790112674.

335

336

A handbook of artificial intelligence in drug delivery

[128] P. Jancova, P. Anzenbacher, E. Anzenbacherova, Phase II drug metabolizing enzymes, Biomed. Pap. 154 (2) (2010) 103–116, https://doi.org/10.5507/bp.2010.017. [129] A.V. Dmitriev, A.V. Rudik, D.A. Karasev, P.V. Pogodin, A.A. Lagunin, D.A. Filimonov, V.V. Poroikov, Silico prediction of drug–drug interactions mediated by cytochrome P450 isoforms, Pharmaceutics 13 (4) (2021) 538. [130] L. Shargel, Wu-Pong, A. Yu, Drug Elimination and Clearance, The McGraw-Hill Companies, 2012 (Chapter 6). [131] N. Sherman, T.A. Field, Psychopharmacology basics, Neurocounseling: Brain-Based Clinical Approaches, John Wiley & Sons, 2017, pp. 168–178. [132] G. Ghibellini, E.M. Leslie, K.L.R. Brouwer, Methods to evaluate biliary excretion of drugs in humans: an updated review, Mol. Pharm. 3 (3) (2006) 198–211, https://doi.org/10.1021/ mp060011k. [133] M. Werle, A. Bernkop-Schn€ urch, Strategies to improve plasma half life time of peptide and protein drugs, Amino Acids 30 (4) (2006) 351–367, https://doi.org/10.1007/s00726-005-0289-3. [134] A. Calcagno, J. Cusato, L. Marinaro, L. Trentini, C. Alcantarini, M. Mussa, M. Simiele, A. D’Avolio, G. Di Perri, S. Bonora, Clinical pharmacology of tenofovir clearance: a pharmacokinetic/pharmacogenetic study on plasma and urines, Pharmacogenomics J. 16 (6) (2016) 514–518, https://doi.org/ 10.1038/tpj.2015.71. [135] W.M. Kluwe, Renal function tests as indicators of kidney injury in subacute toxicity studies, Toxicol. Appl. Pharmacol. 57 (3) (1981) 414–424, https://doi.org/10.1016/0041-008X(81)90239-8. [136] B.V. Roberts, I. Susano, D.S. Gipson, H. Trachtman, M.S. Joy, Contribution of renal and non-renal clearance on increased total clearance of adalimumab in glomerular disease, J. Clin. Pharmacol. 53 (9) (2013) 919–924, https://doi.org/10.1002/jcph.121. [137] L. Liu, K.S. Pang, An integrated approach to model hepatic drug clearance, Eur. J. Pharm. Sci. 29 (3–4) (2006) 215–230, https://doi.org/10.1016/j.ejps.2006.05.007. [138] G.R. Wilkinson, D.G. Shand, A physiological approach to hepatic drug clearance, Clin. Pharmacol. Ther. 18 (4) (1975) 377–390, https://doi.org/10.1002/cpt1975184377. [139] M.P. Doogue, T.M. Polasek, Drug dosing in renal disease, Clin. Biochem. Rev. 32 (2) (2011) 69–73. http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3100283/pdf/cbr_32_2_69.pdf. [140] R.L. Talbert, Drug dosing in renal insufficiency, J. Clin. Pharmacol. 34 (2) (1994) 99–110, https:// doi.org/10.1002/j.1552-4604.1994.tb03973.x. [141] S.D. Nelson, Metabolic activation and drug toxicity, J. Med. Chem. 25 (7) (1982) 753–765, https:// doi.org/10.1021/jm00349a001. [142] R.E. Aarnoutse, J.M. Schapiro, C.A.B. Boucher, Y.A. Hekster, D.M. Burger, Therapeutic drug monitoring, Drugs 63 (8) (2003) 741–753, https://doi.org/10.2165/00003495-200363080-00002. [143] N.A. Kiani, M.M. Shang, H. Zenil, J. Tegner, Predictive systems toxicology, in: Methods in Molecular Biology, vol. 1800, Humana Press Inc, 2018, pp. 535–557, https://doi.org/10.1007/978-14939-7899-1_25. [144] M.G. Narotsky, R.J. Kavlock, A multidisciplinary approach to toxicological screening: Ii. developmental toxicity, J. Toxicol. Environ. Health 45 (2) (1995) 145–171, https://doi.org/ 10.1080/15287399509531987. [145] R.C. Gupta, A. Srivastava, R. Lall, Toxicity potential of nutraceuticals, in: O. Nicolotti (Ed.), Computational Toxicology. Methods in Molecular Biology, vol. 1800, Humana Press, New York, NY, 2018, pp. 367–394. [146] C.D. Soulsbury, H.E. Gray, L.M. Smith, V. Braithwaite, S.C. Cotter, R.W. Elwood, A. Wilkinson, L.M. Collins, The welfare and ethics of research involving wild animals: a primer, Methods Ecol. Evol. 11 (10) (2020) 1164–1181, https://doi.org/10.1111/2041-210X.13435. [147] J.C. Madden, S.J. Enoch, A. Paini, M.T.D. Cronin, A review of in silico tools as alternatives to animal testing: principles, resources and applications, Altern. Lab. Anim. 48 (4) (2020) 146–172, https://doi. org/10.1177/0261192920965977. [148] A.B. Raies, V.B. Bajic, In silico toxicology: computational methods for the prediction of chemical toxicity, Wiley Interdiscip. Rev. Comput. Mol. Sci. (2016) 147–172, https://doi.org/10.1002/ wcms.1240.

Role of AI in ADME/Tox toward formulation optimization

[149] D.B. Warheit, S.C. Brown, E.M. Donner, Acute and subchronic oral toxicity studies in rats with nanoscale and pigment grade titanium dioxide particles, Food Chem. Toxicol. 84 (2015) 208–224, https://doi.org/10.1016/j.fct.2015.08.026. [150] L.H. Bruner, G.J. Carr, M. Chamberlain, R.D. Curren, Validation of alternative methods for toxicity testing, Toxicol. In Vitro 10 (4) (1996) 479–501, https://doi.org/10.1016/0887-2333(96)00028-8. [151] S.K. Savale, Genotoxicity of drugs: introduction, prediction and evaluation, Asian J. Biomater. Res. 4 (6) (2018) 1–29. [152] S.U. Shah, Importance of genotoxicity & S2A guidelines for genotoxicity testing for pharmaceuticals, IOSR J. Pharm. Biol. Sci. (2012) 43–54, https://doi.org/10.9790/3008-0124354. [153] S.W. Burchiel, C.M. Knall, J.W. Davis, R.S. Paules, S.E. Boggs, C.A. Afshari, Analysis of genetic and epigenetic mechanisms of toxicity: potential roles of toxicogenomics and proteomics in toxicology, Toxicol. Sci. 59 (2) (2001) 193–195, https://doi.org/10.1093/toxsci/59.2.193. [154] A.R. Khuda-Bukhsh, Towards understanding molecular mechanisms of action of homeopathic drugs: an overview, Mol. Cell. Biochem. 253 (1–2) (2003) 339–345, https://doi.org/10.1023/ A:1026048907739. [155] C. Hasselgren, E. Ahlberg, Y. Akahori, A. Amberg, L.T. Anger, F. Atienzar, et al., Genetic toxicology in silico protocol, Regul. Toxicol. Pharmacol. 107 (2019) 4–121. 104403. [156] D.H. Phillips, V.M. Arlt, Genotoxicity: damage to DNA and its consequences, EXS 99 (2009) 87–110, https://doi.org/10.1007/978-3-7643-8336-7_4. [157] Q. Zhang, J. Li, A. Middleton, S. Bhattacharya, R.B. Conolly, Bridging the data gap from in vitro toxicity testing to chemical safety assessment through computational modeling, Front. Public Health (2018), https://doi.org/10.3389/fpubh.2018.00261. [158] H.M. Lee, M.S. Yu, S.R. Kazmi, S.Y. Oh, K.H. Rhee, M.A. Bae, B.H. Lee, D.S. Shin, K.S. Oh, H. Ceong, D. Lee, D. Na, Computational determination of hERG-related cardiotoxicity of drug candidates, BMC Bioinformatics 20 (2019), https://doi.org/10.1186/s12859-019-2814-5. [159] E.R.A. Ferraz, G.A. Umbuzeiro, G. de-Almeida, A. Caloto-Oliveira, F.M.D. Chequer, M.V.B. Zanoni, D.J. Dorta, D.P. Oliveira, Differential toxicity of Disperse Red 1 and Disperse Red 13 in the Ames test, HepG2 cytotoxicity assay, and Daphnia acute toxicity test, Environ. Toxicol. 26 (5) (2011) 489–497, https://doi.org/10.1002/tox.20576. [160] Y. Jing, A. Easter, D. Peters, N. Kim, I.J. Enyedy, In silico prediction of hERG inhibition, Future Med. Chem. 7 (5) (2015) 571–586, https://doi.org/10.4155/fmc.15.18. [161] W.O.K. Grabow, J.L. Slabbert, W.S.G. Morgan, S.A.A. Jahn, Toxicity and mutagenicity evaluation of water coagulated with Moringa oleifera seed preparations using fish, protozoan, bacterial, coliphage, enzyme and Ames Salmonella assays, Water SA 11 (1) (1985) 9–14. [162] M.P. Holt, C. Ju, Mechanisms of drug-induced liver injury, AAPS J. 8 (1) (2006) E48–E54, https:// doi.org/10.1208/aapsj080106. [163] F.F. Hefti, Requirements for a lead compound to become a clinical candidate, BMC Neurosci. 9 (3) (2008), https://doi.org/10.1186/1471-2202-9-S3-S7. [164] L. Kuna, I. Bozˇi
c, T. Kizivat, K. Bojani
c, M. Mrsˇo, E. Kralj, R. Smoli
c, G.Y. Wu, M. Smoli
c, Models of drug induced liver injury (DILI) – current issues and future perspectives, Curr. Drug Metab. 19 (10) (2018) 830–838, https://doi.org/10.2174/1389200219666180523095355. [165] D. Taylor, The pharmaceutical industry and the future of drug development, Pharmaceuticals in the Environment, The Royal Society of Chemistry, 2015, pp. 1–33. [166] R.A. Tariq, R. Vashisht, A. Sinha, Y. Scherbak, Medication Dispensing Errors and Prevention [Updated May 12, 2021]. StatPearls [Internet], StatPearls Publishing, Treasure Island, FL, 2021. [167] S. Greenfield, B. Whelan, E. Cohn, Use of dimensional analysis to reduce medication errors, J. Nurs. Educ. 45 (2006) 91. [168] A.O. Basile, A. Yahi, N.P. Tatonetti, Artificial intelligence for drug toxicity and safety, Trends Pharmacol. Sci. 40 (2019) 624–635. [169] H. Khalil, C. Huang, Adverse drug reactions in primary care: a scoping review, BMC Health Serv. Res. 20 (2020) 1–13. [170] M. Pirmohamed, A.M. Breckenridge, N.R. Kitteringham, B.K. Park, Adverse drug reactions, BMJ 316 (1998) 1295–1298.

337

338

A handbook of artificial intelligence in drug delivery

[171] L.L. Mazaleuskaya, K. Sangkuhl, C.F. Thorn, G.A. FitzGerald, R.B. Altman, T.E. Klein, PharmGKB summary: pathways of acetaminophen metabolism at the therapeutic versus toxic doses, Pharmacogenet. Genomics 25 (2015) 416. [172] J.R. White, A brief history of the development of diabetes medications, Diabetes Spectr. 27 (2014) 82–86. [173] J. Timbrell, Introduction to Toxicology, CRC Press, 2001. [174] F. Keller, M. Maiga, H.-H. Neumayer, H. Lode, A. Distler, Pharmacokinetic effects of altered plasma protein binding of drugs in renal disease, Eur. J. Drug Metab. Pharmacokinet. 9 (1984) 275–282. [175] P. Girard, J. Blin, Environment, health and safety PyNe working group: assessment of bio-oil toxicity for safe handling and transportation, in: A.V. Bridgwater (Ed.), Pyrolysis and Gasification of Biomass and Waste: Proceedings of an Expert Meeting, Strasbourg, France, CPL Press, Newbury, 2002, pp. 155–160. [176] T.F. Pearson, D.G. Pittman, J.M. Longley, Z.T. Grapes, D.J. Vigliotti, S.R. Mullis, Factors associated with preventable adverse drug reactions, Am. J. Health Syst. Pharm. 51 (1994) 2268–2272. [177] R.G. Hughes, M.A. Blegen, Medication administration safety, in: Patient Safety and Quality: An Evidence-Based Handbook for Nurses, Agency for Healthcare Research and Quality (US), 2008. [178] A.M. Bargagli, M. Hickman, M. Davoli, C.A. Perucci, P. Schifano, M. Buster, T. Brugal, J. Vicente, Drug-related mortality and its impact on adult mortality in eight European countries, Eur. J. Public Health 16 (2006) 198–202. [179] D. Lorke, A new approach to practical acute toxicity testing, Arch. Toxicol. 54 (1983) 275–287. [180] M.A. Riedl, A.M. Casillas, Adverse drug reactions: types and treatment options, Am. Fam. Physician 68 (2003) 1781–1790. [181] W.A. Chiu, W. Slob, A unified probabilistic framework for dose–response assessment of human health effects, Environ. Health Perspect. 123 (2015) 1241–1254. [182] R. Sinclair, Alcohol marketing, in: WHO Public Hearing on Harmful Use of Alcohol, vol. 4, World Health Organization, 2009, p. 175. [183] J. Brent, K. Burkhart, P. Dargan, B. Hatten, B. Megarbane, R. Palmer, J. White, Critical Care Toxicology: Diagnosis and Management of the Critically Poisoned Patient, Springer, 2017. [184] A. Mostrag-Szlichtyng, J.-M. Zaldı´var Comenges, A.P. Worth, Computational toxicology at the European Commission’s Joint Research Centre, Expert Opin. Drug Metab. Toxicol. 6 (2010) 785–792. [185] A. Tubaro, V. Dell’Ovo, S. Sosa, C. Florio, Yessotoxins: a toxicological overview, Toxicon 56 (2010) 163–172. [186] S.A. Saganuwan, Toxicity studies of drugs and chemicals in animals: an overview, Bulg. J. Vet. Med. 20 (4) (2017) 291–318. [187] L. Guardabassi, L.B. Jensen, H. Kruse, Guide to Antimicrobial Use in Animals, Wiley Online Library, 2008. [188] T.M. Alshammari, Drug safety: the concept, inception and its importance in patients’ health, Saudi Pharm. J. 24 (2016) 405–412. [189] D.C. Fernandes, A.J. Norman, Drug-induced gastrointestinal disorders, Medicine 47 (2019) 301–308. [190] R.C. Hamdy, Drug facts and comparisons, South. Med. J. 96 (2003) 417–418. [191] D.W. Lachenmeier, J. Rehm, Comparative risk assessment of alcohol, tobacco, cannabis and other illicit drugs using the margin of exposure approach, Sci. Rep. 5 (1) (2015) 1–17. [192] T.A. Hutchinson, K.M. Flegel, M.S. Kramer, D.G. Leduc, H.H.P. Kong, Frequency, severity and risk factors for adverse drug reactions in adult out-patients: a prospective study, J. Chronic Dis. 39 (1986) 533–542. [193] B. Weiss, Behavioral Toxicology, vol. 5, Springer Science & Business Media, 2013. [194] T.C. Marrs, B. Ballantyne, Pesticide Toxicology and International Regulation, John Wiley & Sons, Ltd., Chichester, West Sussex, England, 2004. 554 pp. [195] C.C. Ogu, J.L. Maxa, Drug interactions due to cytochrome P450, Proc. (Bayl. Univ. Med. Cent.) 13 (2000) 421–423. [196] A. Tostmann, M.J. Boeree, R.E. Aarnoutse, W.C. De Lange, A.J. Van Der Ven, R. Dekhuijzen, Antituberculosis drug-induced hepatotoxicity: concise up-to-date review, J. Gastroenterol. Hepatol. 23 (2008) 192–202.

Role of AI in ADME/Tox toward formulation optimization

[197] M. Jaishankar, T. Tseten, N. Anbalagan, B.B. Mathew, K.N. Beeregowda, Toxicity, mechanism and health effects of some heavy metals, Interdiscip. Toxicol. 7 (2014) 60. [198] E.S. Fisher, S.C. Curry, Evaluation and treatment of acetaminophen toxicity, Adv. Pharmacol. 85 (2019) 263–272. [199] J.R. Mitchell, Acetaminophen toxicity, N. Engl. J. Med. 319 (24) (1988) 1601–1602. [200] T.S. Stroup, N. Gray, Management of common adverse effects of antipsychotic medications, World Psychiatry 17 (2018) 341–356. [201] B.M. Francis, Toxic substances in the environment, Environmental Science and Technology: A Wiley-Interscience Series of Texts and Monographs, first edn, John Wiley and Sons, Inc., 1994. [202] H. Pelto, Hypertensive medications in competitive athletes, Curr. Sports Med. Rep. 16 (2017) 45–49. [203] S.R. Veeram Reddy, H.R. Singh, Chest pain in children and adolescents, Pediatr. Rev. 31 (2010) e1–e9. [204] M.A. Wallig, B. Bolon, W.M. Haschek, C.G. Rousseaux, Fundamentals of Toxicologic Pathology, Academic Press, 2017. [205] S. van Tongeren, J.A. Fagerland, M.W. Conner, K. Diegel, K. Donnelly, B. Grubor, A. LopezMartinez, A.P. Bolliger, A. Sharma, S. Tannehill-Gregg, The role of the toxicologic pathologist in the biopharmaceutical industry, Int. J. Toxicol. 30 (2011) 568–582. [206] P.C. Mann, J. Vahle, C.M. Keenan, J.F. Baker, A.E. Bradley, D.G. Goodman, T. Harada, R. Herbert, W. Kaufmann, R. Kellner, International harmonization of toxicologic pathology nomenclature: an overview and review of basic principles, Toxicol. Pathol. 40 (2012) 7S–13S. [207] D.W. Connell, J.Y. Qiming, V. Verma, Influence of exposure time on toxicity—an overview, Toxicology 355 (2016) 49–53. [208] H.K. Hamadeh, R.P. Amin, R.S. Paules, C.A. Afshari, An overview of toxicogenomics, Curr. Issues Mol. Biol. 4 (2002) 45–56. [209] S.Y. Yu, S. Paul, S.Y. Hwang, Application of the emerging technologies in toxicogenomics: an overview, BioChip J. 10 (2016) 288–296. [210] H.-H. Li, J. Aubrecht, A.J. Fornace Jr., Toxicogenomics: overview and potential applications for the study of non-covalent DNA interacting chemicals, Mutat. Res. Fundam. Mol. Mech. Mutagen. 623 (2007) 98–108. [211] T. Mohamed Abd El-Aziz, A.G. Soares, J.D. Stockand, Snake venoms in drug discovery: valuable therapeutic tools for life saving, Toxins 11 (2019) 564. [212] G. Amresh, P.N. Singh, C.V. Rao, Toxicological screening of traditional medicine Laghupatha (Cissampelos pareira) in experimental animals, J. Ethnopharmacol. 116 (2008) 454–460. [213] O.P. Soldin, D.R. Mattison, Sex differences in pharmacokinetics and pharmacodynamics, Clin. Pharmacokinet. 48 (2009) 143–157. [214] P.J. O’Brien, High-content analysis in toxicology: screening substances for human toxicity potential, elucidating subcellular mechanisms and in vivo use as translational safety biomarkers, Basic Clin. Pharmacol. Toxicol. 115 (1) (2014) 4–17. [215] M.P. Holsapple, K.B. Wallace, Dose response considerations in risk assessment—an overview of recent ILSI activities, Toxicol. Lett. 180 (2008) 85–92. [216] M. Moayeri, S.H. Leppla, Cellular and systemic effects of anthrax lethal toxin and edema toxin, Mol. Asp. Med. 30 (2009) 439–455. [217] D. Basak, S. Arrighi, Y. Darwiche, S. Deb, Comparison of anticancer drug toxicities: paradigm shift in adverse effect profile, Life 12 (2021) 48. [218] B. Hassel, Tetanus: pathophysiology, treatment, and the possibility of using botulinum toxin against tetanus-induced rigidity and spasms, Toxins 5 (2013) 73–83. [219] S. Vasan, G.J. Olango, Amphetamine toxicity, in: StatPearls [Internet], StatPearls Publishing, 2021. [220] S. Parasuraman, Toxicological screening, J. Pharmacol. Pharmacother. 2 (2011) 74. [221] J. Lago, L.P. Rodrı´guez, L. Blanco, J.M. Vieites, A.G. Cabado, Tetrodotoxin, an extremely potent marine neurotoxin: distribution, toxicity, origin and therapeutical uses, Mar. Drugs 13 (2015) 6384–6406. [222] M.R. Popoff, B. Poulain, Bacterial toxins and the nervous system: neurotoxins and multipotential toxins interacting with neuronal cells, Toxins 2 (2010) 683–737.

339

340

A handbook of artificial intelligence in drug delivery

[223] A. Eastman, M.A. Barry, The origins of DNA breaks: a consequence of DNA damage, DNA repair, or apoptosis? New drugs, Cancer Invest. 10 (1992) 229–240. [224] A. Lundgren, Environmental hazard classification of chemicals, Toxicol. Lett. 64 (1992) 535–545. [225] A. Kienzler, S.K. Bopp, S. van der Linden, E. Berggren, A. Worth, Regulatory assessment of chemical mixtures: requirements, current approaches and future perspectives, Regul. Toxicol. Pharmacol. 80 (2016) 321–334. [226] E. Trumic, N. Pranjic, L. Begic, F. Becic, M. Asceric, Idiosyncratic adverse reactions of most frequent drug combinations longterm use among hospitalized patients with polypharmacy, Med. Arch. 66 (2012) 243. [227] N.D. Volkow, Drugs, Brains, and Behavior: The Science of Addiction, Retrieved on March 2010, vol. 23, Routledge, 2010. 255-169. [228] H. Hwang, J.Y. Ghee, J.H. Song, S. Piao, C.W. Yang, Comparison of adverse drug reaction profiles of two tacrolimus formulations in rats, Immunopharmacol. Immunotoxicol. 34 (2012) 434–442. [229] J.P. Dworkin, Laryngitis: types, causes, and treatments, Otolaryngol. Clin. N. Am. 41 (2008) 419–436. [230] A.N. Greiner, P.W. Hellings, G. Rotiroti, G.K. Scadding, Allergic rhinitis, Lancet 378 (2011) 2112–2122. [231] R.O. Day, G.G. Graham, Non-steroidal anti-inflammatory drugs (NSAIDs), BMJ 346 (2013) 1–6. [232] K.G. Blumenthal, J.G. Peter, J.A. Trubiano, E.J. Phillips, Antibiotic allergy, Lancet 393 (2019) 183–198. [233] J. Uetrecht, Idiosyncratic drug reactions: current understanding, Annu. Rev. Pharmacol. Toxicol. 47 (2007) 513–539. [234] J. Uetrecht, D.J. Naisbitt, Idiosyncratic adverse drug reactions: current concepts, Pharmacol. Rev. 65 (2013) 779–808. [235] G. Zaccara, D. Franciotta, E. Perucca, Idiosyncratic adverse reactions to antiepileptic drugs, Epilepsia 48 (2007) 1223–1244. [236] H. Rezaee, F. Pourkarim, S. Pourtaghi-Anvarian, T. Entezari-Maleki, T. Asvadi-Kermani, M. Nouri-Vaskeh, Drug-drug interactions with candidate medications used for COVID-19 treatment: an overview, Pharmacol. Res. Perspect. 9 (2021), e00705. [237] R. Bushra, N. Aslam, An overview of clinical pharmacology of ibuprofen, Oman Med. J. 25 (2010) 155. [238] L. Zhang, S.M. Huang, L. Lesko, Transporter-mediated drug–drug interactions, Clin. Pharmacol. Ther. 89 (2011) 481–484. [239] D. Krewski, M.E. Andersen, M.G. Tyshenko, K. Krishnan, T. Hartung, K. Boekelheide, J.F. Wambaugh, D. Jones, M. Whelan, R. Thomas, Toxicity testing in the 21st century: progress in the past decade and future perspectives, Arch. Toxicol. 94 (2020) 1–58. [240] R.J. Kavlock, C.P. Austin, R. Tice, Toxicity testing in the 21st century: implications for human health risk assessment, Risk Anal. 29 (2009) 485. [241] F.A. Al-Mousa, A.M. Gado, A.M. Attia, H.G. Tammam, Medical toxicology experience: poisoning consultations cases registry in Saudi Ministry of Health-2020 annual report, Toxicol. Rep. 8 (2021) 1558–1564. [242] M.R. Meyer, New psychoactive substances: an overview on recent publications on their toxicodynamics and toxicokinetics, Arch. Toxicol. 90 (2016) 2421–2444. [243] J.S. MacDonald, R.T. Robertson, Toxicity testing in the 21st century: a view from the pharmaceutical industry, Toxicol. Sci. 110 (2009) 40–46. [244] S. Dubey, S. Maity, M. Singh, S.A. Saraf, S. Saha, Phytochemistry, pharmacology and toxicology of Spilanthes acmella: a review, Adv. Pharmacol. Pharm. Sci. 2013 (2013) 1–9. [245] C.C. Wright, J. Downing, D. Mungall, O. Khan, A. Williams, E. Fonkem, D. Garrett, J. Aceves, B.F. Kirmani, Clinical pharmacology and pharmacokinetics of levetiracetam, Front. Neurol. 4 (2013) 192. [246] C. Perry, G. Davis, T.M. Conner, T. Zhang, Utilization of physiologically based pharmacokinetic modeling in clinical pharmacology and therapeutics: an overview, Curr. Pharmacol. Rep. 6 (2020) 71–84.

Role of AI in ADME/Tox toward formulation optimization

[247] N. Raj, S. Fernandes, N.R. Charyulu, A. Dubey, R. GS, S. Hebbar, Postmarket surveillance: a review on key aspects and measures on the effective functioning in the context of the United Kingdom and Canada, Ther. Adv. Drug safety 10 (2019). 2042098619865413. [248] P.M. Glassman, V.R. Muzykantov, Pharmacokinetic and pharmacodynamic properties of drug delivery systems, J. Pharmacol. Exp. Ther. 370 (2019) 570–580. [249] J.M. Gallo, Pharmacokinetic/pharmacodynamic-driven drug development, Mt Sinai J. Med. 77 (2010) 381–388. [250] P. Baldrick, Pharmaceutical excipient development: the need for preclinical guidance, Regul. Toxicol. Pharmacol. 32 (2000) 210–218. [251] A. Arntz, Imagery rescripting as a therapeutic technique: review of clinical trials, basic studies, and research agenda, J. Exp. Psychopathol. 3 (2012) 189–208. [252] J. Woodcock, R.E. Behrman, G.J. Dal Pan, Role of postmarketing surveillance in contemporary medicine, Annu. Rev. Med. 62 (2011) 1–10. [253] J. Oates, D. Lopez, Pharmacogenetics: an important part of drug development with a focus on its application, Int. J. Biomed. Invest. 1 (2018) 2–28. [254] R.C. Mohs, N.H. Greig, Drug discovery and development: role of basic biological research, Alzheimer’s Dement.: Transl. Res. Clin. Interv. 3 (2017) 651–657. [255] J.D. McKinney, A. Richard, C. Waller, M.C. Newman, F. Gerberick, The practice of structure activity relationships (SAR) in toxicology, Toxicol. Sci. 56 (2000) 8–17. [256] C.A. Umscheid, D.J. Margolis, C.E. Grossman, Key concepts of clinical trials: a narrative review, Postgrad. Med. 123 (2011) 194–204. [257] J.R. Turner, New drug development, in: New Drug Development, Springer, 2010, pp. 1–10. [258] J.M. Mansur, Medication safety systems and the important role of pharmacists, Drugs Aging 33 (2016) 213–221. [259] K. van Grootheest, S. Olsson, M. Couper, L. de Jong-van den Berg, Pharmacists’ role in reporting adverse drug reactions in an international perspective, Pharmacoepidemiol. Drug Saf. 13 (2004) 457–464. [260] M. Marino, Z. Jamal, P.M. Zito, Pharmacodynamics, StatPearls Publishing, Treasure Island, FL, 2020. [261] C.J. Mbah, N.H. Okorie, Pharmaceutical bioinformatics: its relevance to drug metabolism, Madridge J. Bioinform. Syst. Biol. 1 (1) (2018) 19–26. [262] D. Varghese, C. Ishida, H.H. Koya, Polypharmacy, in: StatPearls [Internet], StatPearls Publishing, 2020. [263] B. Charles, Population pharmacokinetics: an overview, Aust. Prescr. 37 (2014) 210–213. [264] V.L. Patel, E.H. Shortliffe, M. Stefanelli, P. Szolovits, M.R. Berthold, R. Bellazzi, A. Abu-Hanna, The coming of age of artificial intelligence in medicine, Artif. Intell. Med. 46 (2009) 5–17. [265] H.L. Ciallella, H. Zhu, Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity, Chem. Res. Toxicol. 32 (2019) 536–547. [266] Y. Mintz, R. Brodie, Introduction to artificial intelligence in medicine, Minim. Invasive Ther. Allied Technol. 28 (2019) 73–81. [267] S. Kraljevic, P.J. Stambrook, K. Pavelic, Accelerating drug discovery: although the evolution of ‘-omics’ methodologies is still in its infancy, both the pharmaceutical industry and patients could benefit from their implementation in the drug development process, EMBO Rep. 5 (2004) 837–842. [268] J. Strovel, S. Sittampalam, N.P. Coussens, M. Hughes, J. Inglese, A. Kurtz, A. Andalibi, L. Patton, C. Austin, M. Baltezor, M. Beckloff, M. Weingarten, S. Weir, Early drug discovery and development guidelines: for academic researchers, collaborators, and start-up companies, in: S. Markossian, et al. (Eds.), Assay Guidance Manual, Eli Lilly & Company and the National Center for Advancing Translational Sciences, 2012 (Updated July 1, 2016). [269] H. Yang, L. Sun, W. Li, G. Liu, Y. Tang, In silico prediction of chemical toxicity for drug design using machine learning methods and structural alerts, Front. Chem. 6 (2018) 30.

341

342

A handbook of artificial intelligence in drug delivery

[270] T. Allio, Product development under FDA’s animal rule: understanding FDA’s expectations and potential implications for traditional development programs, Ther. Innov. Regul. Sci. 50 (2016) 660–670. [271] S.B. Olasupo, A. Uzairu, G. Shallangwa, S. Uba, QSAR modeling, molecular docking and ADMET/ pharmacokinetic studies: a chemometrics approach to search for novel inhibitors of norepinephrine transporter as potent antipsychotic drugs, J. Iran. Chem. Soc. 17 (2020) 1953–1966. [272] I.V. Tetko, G.I. Poda, Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds, J. Med. Chem. 47 (2004) 5601–5604. [273] I.V. Tetko, Encoding molecular structures as ranks of models: a new secure way for sharing chemical data and development of ADME/T models, in: Papers of the American Chemical Society, 2005, p. 229. [274] P. Tiwari, A. Katyal, M.F. Khan, G.M. Ashraf, K. Ahmad, Lead optimization resources in drug discovery for diabetes, Endocr. Metab. Immune Disord. Drug Targets 19 (2019) 754–774. [275] S. Alrushaid, Pharmaceutical characterization of selected xenobiotics utilizing novel drug delivery systems, A Thesis submitted to the Faculty of Graduate Studies of The University of Manitoba (2018). https://mspace.lib.umanitoba.ca/bitstream/handle/1993/33007/Alrushaid_Samaa.pdf?sequence¼2. [276] K. Hasegawa, K. Funatsu, Data modeling and chemical interpretation of ADME properties using regression and rule mining techniques, Front. Drug Des. Discov. 4 (2009) 378–428. [277] N.T. Tzvetkov, H.-G. Stammler, B. Neumann, S. Hristova, L. Antonov, M. Gastreich, Crystal structures, binding interactions, and ADME evaluation of brain penetrant N-substituted indazole-5carboxamides as subnanomolar, selective monoamine oxidase B and dual MAO-A/B inhibitors, Eur. J. Med. Chem. 127 (2017) 470–492. [278] H. Gao, L. Yao, H.W. Mathieu, Y. Zhang, T.S. Maurer, M.D. Troutman, D.O. Scott, R.B. Ruggeri, J. Lin, In silico modeling of nonspecific binding to human liver microsomes, Drug Metab. Dispos. 36 (2008) 2130–2135. [279] L. Boruah, A. Das, L.M. Nainwal, N. Agarwal, B. Shankar, In-silico drug design: a revolutionary approach to change the concept of current Drug Discovery Process, Indian J. Pharm. Biol. Res. 1 (2013) 60. [280] A.J. Williams, C.M. Grulke, J. Edwards, A.D. McEachran, K. Mansouri, N.C. Baker, G. Patlewicz, I. Shah, J.F. Wambaugh, R.S. Judson, The CompTox Chemistry Dashboard: a community data resource for environmental chemistry, J. Cheminformatics 9 (2017) 1–27. [281] G. Bocci, E. Carosati, P. Vayer, A. Arrault, S. Lozano, G. Cruciani, ADME-Space: a new tool for medicinal chemists to explore ADME properties, Sci. Rep. 7 (2017) 1–13. [282] S. Ekins, J. Rose, In silico ADME/Tox: the state of the art, J. Mol. Graph. Model. 20 (2002) 305–309. [283] H. T’jollyn, K. Boussery, R. Mortishire-Smith, K. Coe, B. De Boeck, J. Van Bocxlaer, G. Mannens, Evaluation of three state-of-the-art metabolite prediction software packages (Meteor, MetaSite, and StarDrop) through independent and synergistic use, Drug Metab. Dispos. 39 (2011) 2066–2075. [284] G. Cruciani, R. Mannhold, H. Kubinyi, G. Folkers (Eds.), Molecular Interaction Fields: Applications in Drug Discovery and ADME Prediction, Methods and Principles in Medicinal Chemistry, vol. 27, Wiley-VCH, 2006. [285] G. Cruciani, F. Milletti, L. Storchi, G. Sforna, L. Goracci, In silico pKa prediction and ADME profiling, Chem. Biodivers. 6 (2009) 1812–1821. [286] D. Fourches, A.J. Williams, G. Patlewicz, I. Shah, C. Grulke, J. Wambaugh, A. Richard, A. Tropsha, Computational tools for ADMET profiling, in: Computational Toxicology: Risk Assessment for Chemicals, first edn, Wiley, 2018, pp. 211–244. [287] E. Lounkine, M.J. Keiser, S. Whitebread, D. Mikhailov, J. Hamon, J.L. Jenkins, P. Lavan, E. Weber, A.K. Doak, S. C^ ot
e, Large-scale prediction and testing of drug activity on side-effect targets, Nature 486 (2012) 361–367. [288] D. Lagorce, O. Sperandio, H. Galons, M.A. Miteva, B.O. Villoutreix, FAF-Drugs2: free ADME/tox filtering tool to assist drug discovery and chemical biology projects, BMC Bioinformatics 9 (2008) 1–9. [289] A.V. Zakharov, M.L. Peach, M. Sitzmann, I.V. Filippov, H.J. McCartney, L.H. Smith, A. Pugliese, M.C. Nicklaus, Computational tools and resources for metabolism-related property

Role of AI in ADME/Tox toward formulation optimization

[290] [291] [292] [293] [294] [295] [296]

[297]

[298]

[299] [300] [301] [302] [303] [304] [305] [306] [307] [308] [309]

predictions. 2. Application to prediction of half-life time in human liver microsomes, Future Med. Chem. 4 (2012) 1933–1944. M.L. Peach, A.V. Zakharov, R. Liu, A. Pugliese, G. Tawa, A. Wallqvist, M.C. Nicklaus, Computational tools and resources for metabolism-related property predictions. 1. Overview of publicly available (free and commercial) databases and software, Future Med. Chem. 4 (2012) 1907–1932. B. Chandrasekaran, S.N. Abed, O. Al-Attraqchi, K. Kuche, R.K. Tekade, Computer-aided prediction of pharmacokinetic (ADMET) properties, in: Dosage Form Design Parameters, Elsevier, 2018, pp. 731–755. F. Ntie-Kang, An in silico evaluation of the ADMET profile of the StreptomeDB database, Springerplus 2 (2013) 1–11. H.K. Shin, Y.-M. Kang, K.T. No, Predicting ADME properties of chemicals, in: Handbook of Computational Chemistry, vol. 59, Springer International Publishing, 2017, pp. 2265–2301. L.L. Ferreira, A.D. Andricopulo, ADMET modeling approaches in drug discovery, Drug Discov. Today 24 (2019) 1157–1165. C. Venkataramana, K.R. Sravani, S.S. Singh, V. Madhavan, In-silico ADME and toxicity studies of some novel indole derivatives, J. Appl. Pharm. Sci. 1 (2011) 159. N.T. El-Shamy, A.M. Alkaoud, R.K. Hussein, M.A. Ibrahim, A.G. Alhamzani, M.M. Abou-Krisha, DFT, ADMET and molecular docking investigations for the antimicrobial activity of 6, 60 -diamino-1, 10 , 3, 30 -tetramethyl-5, 50 -(4-chlorobenzylidene) bis [pyrimidine-2, 4 (1H, 3H)-dione], Molecules 27 (2022) 620. S. Lee, I. Lee, H. Kim, G. Chang, J. Chung, K. No, The PreADME Approach: Web-based program for rapid prediction of physico-chemical, drug absorption and drug-like properties, in: EuroQSAR 2002 Designing Drugs and Crop Protectants: Processes, Problems and Solutions, vol. 2003, Blackwell, 2003, pp. 418–420. F. Lombardo, P.V. Desai, R. Arimoto, K.E. Desino, H. Fischer, C.E. Keefer, C. Petersson, S. Winiwarter, F. Broccatelli, In silico absorption, distribution, metabolism, excretion, and pharmacokinetics (ADME-PK): utility and best practices. an industry perspective from the international consortium for innovation through quality in pharmaceutical development: miniperspective, J. Med. Chem. 60 (2017) 9097–9113. J. Vamathevan, D. Clark, P. Czodrowski, I. Dunham, E. Ferran, G. Lee, B. Li, A. Madabhushi, P. Shah, M. Spitzer, Applications of machine learning in drug discovery and development, Nat. Rev. Drug Discov. 18 (2019) 463–477. I.H. Sarker, Machine learning: algorithms, real-world applications and research directions, SN Comput. Sci. 2 (2021) 1–21. E. Jeong, N. Park, Y. Choi, R.W. Park, D. Yoon, Machine learning model combining features from algorithms with different analytical methodologies to detect laboratory-event-related adverse drug reaction signals, PLoS One 13 (2018), e0207749. V.G. Maltarollo, J.C. Gertrudes, P.R. Oliveira, K.M. Honorio, Applying machine learning techniques for ADME-Tox prediction: a review, Expert Opin. Drug Metab. Toxicol. 11 (2) (2015) 259–271, https://doi.org/10.1517/17425255.2015.980814. S. Dara, S. Dhamercherla, S.S. Jadav, C. Babu, M.J. Ahsan, Machine learning in drug discovery: a review, Artif. Intell. Rev. 55 (2021) 1–53. K.S. Divya, P. Bhargavi, S. Jyothi, Machine learning algorithms in big data analytics, Int. J. Comput. Sci. Eng. 6 (1) (2018) 63–70. N. Mohapatra, K. Shreya, A. Chinmay, Optimization of the random forest algorithm, in: Advances in Data Science and Management, Springer, 2020, pp. 201–208. R.H. Lipsky, M. Lin, Genetic predictors of outcome following traumatic brain injury, Handb. Clin. Neurol. 127 (2015) 23–41. M. Schonlau, R.Y. Zou, The random forest algorithm for statistical learning, Stata J. 20 (2020) 3–29. M.-S. Mushtaq, A. Mellouk, 2-Methodologies for subjective video streaming QoE assessment, in: Quality of Experience Paradigm in Multimedia Services, ISTE Press - Elsevier, 2017, pp. 27–57. L. Cheng, X. Chen, J. De Vos, X. Lai, F. Witlox, Applying a random forest method approach to model travel mode choice behavior, Travel Behav. Soc. 14 (2019) 1–10.

343

344

A handbook of artificial intelligence in drug delivery

[310] P.G. Polishchuk, E.N. Muratov, A.G. Artemenko, O.G. Kolumbin, N.N. Muratov, V.E. Kuz’min, Application of random forest approach to QSAR prediction of aquatic toxicity, J. Chem. Inf. Model. 49 (2009) 2481–2488. [311] M.S. Brieuc, C.D. Waters, D.P. Drinan, K.A. Naish, A practical introduction to Random Forest for genetic association studies in ecology and evolution, Mol. Ecol. Resour. 18 (2018) 755–766. [312] X. Chen, H. Ishwaran, Random forests for genomic data analysis, Genomics 99 (2012) 323–329. [313] V.K. Ayyadevara, Random forest, in: Pro Machine Learning Algorithms, Springer, 2018, pp. 105–116. [314] M. Somvanshi, P. Chavan, S. Tambade, S. Shinde, A review of machine learning techniques using decision tree and support vector machine, in: Proceedings of 2016 International Conference on Computing Communication Control and Automation (ICCUBEA), 2016, pp. 1–7. [315] L. Wang, Support Vector Machines: Theory and Applications, vol. 177, Springer Science & Business Media, 2005. [316] H. Yu, A. Adedoyin, ADME–Tox in drug discovery: integration of experimental and computational technologies, Drug Discov. Today 8 (2003) 852–861. [317] J. Schmidhuber, Deep learning in neural networks: an overview, Neural Netw. 61 (2015) 85–117. [318] P.A. Jansson, Neural networks: an overview, Anal. Chem. 63 (1991) 357A–362A. [319] E. Fiesler, Neural network classification and formalization, Comput. Stand. Interfaces 16 (1994) 231–239. [320] A. Prieto, B. Prieto, E.M. Ortigosa, E. Ros, F. Pelayo, J. Ortega, I. Rojas, Neural networks: an overview of early research, current frameworks and new challenges, Neurocomputing 214 (2016) 242–268. [321] N. Sinha, M. Gupta, D. Rao, Dynamic neural networks: an overview, in: Proceedings of Proceedings of IEEE International Conference on Industrial Technology 2000 (IEEE Cat. No. 00TH8482), 2000, pp. 491–496. [322] D.A. Winkler, Neural networks in ADME and toxicity prediction, Drugs Future 29 (2004) 1043–1057. [323] J. Lin, D.C. Sahakian, S. De Morais, J.J. Xu, R.J. Polzer, S.M. Winter, The role of absorption, distribution, metabolism, excretion and toxicity in drug discovery, Curr. Top. Med. Chem. 3 (2003) 1125–1154. [324] H.E. Webel, T.B. Kimber, S. Radetzki, M. Neuenschwander, M. Nazar
e, A. Volkamer, Revealing cytotoxic substructures in molecules using deep learning, J. Comput. Aided Mol. Des. 34 (2020) 731–746. [325] Q. Su, L. Zhou, QSAR modeling of AT1 receptor antagonists using ANN, J. Mol. Model. 12 (2006) 869–875. [326] Z. Zhang, Introduction to machine learning: k-nearest neighbors, Ann. Transl. Med. 4 (2016) 1–7. [327] O. Kramer, K-nearest neighbors, in: Dimensionality Reduction With Unsupervised Nearest Neighbors, Springer, 2013, pp. 13–23. [328] S. Sun, R. Huang, An adaptive k-nearest neighbor algorithm, in: Proceedings of 2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010, pp. 91–94. [329] B. Zhang, S.N. Srihari, Fast k-nearest neighbor classification using cluster-based trees, IEEE Trans. Pattern Anal. Mach. Intell. 26 (2004) 525–528. [330] A. Ra´cz, D. Bajusz, R.A. Miranda-Quintana, K. H
eberger, Machine learning models for classification tasks related to drug safety, Mol. Divers. 25 (2021) 1409–1424. [331] C. Thiel, H. Cordes, I. Conde, J.V. Castell, L.M. Blank, L. Kuepfer, Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients, Arch. Toxicol. 91 (2017) 865–883. [332] H. Zhang, C.X. Ling, Geometric properties of Naive Bayes in nominal domains, in: Proceedings of European Conference on Machine Learning, 2001, pp. 587–599. [333] B.A. Spilker, The Drug Development and Approval Process, The Pharmaceutical Research and Manufacturers of America, 2000. [334] W. Haoxiang, S. Smys, Overview of configuring adaptive activation functions for deep neural networks—a comparative study, J. Ubiquitous Computing Commun. Technol. 3 (2021) 10–22.

Role of AI in ADME/Tox toward formulation optimization

[335] R. Yamashita, M. Nishio, R.K.G. Do, K. Togashi, Convolutional neural networks: an overview and application in radiology, Insights Imaging 9 (2018) 611–629. [336] A. Karthikeyan, U. Priyakumar, Artificial intelligence: machine learning for chemical sciences, J. Chem. Sci. 134 (2022) 1–20. [337] E. Gawehn, J.A. Hiss, G. Schneider, Deep learning in drug discovery, Mol. Inform. 35 (2016) 3–14. [338] D. Paul, G. Sanap, S. Shenoy, D. Kalyane, K. Kalia, R.K. Tekade, Artificial intelligence in drug discovery and development, Drug Discov. Today 26 (2021) 80.

345

CHAPTER 13

Recent advances in self-regulated drug delivery devices Yixin Wanga,b, Ting-Jing Chen-Mayfielda, Zhaoting Lia,b, and Quanyin Hua,b a

Pharmaceutical Sciences Division, School of Pharmacy, University of Wisconsin-Madison, Madison, WI, United States Carbone Cancer Center, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, United States

b

13.1 Introduction An ideal drug delivery system is one that is long-term and can automatically release the required dose based on the conditions of the disease. Although it may sound complicated, human bodies are born with this feedback-controlled regulation, allowing the on-demand release of hormones, cytokines, and small-molecule chemicals within a reasonable range to maintain homeostasis. By mimicking natural feedback-controlled regulatory functions, self-regulated drug delivery devices are designed to release the required amounts of drugs in order to treat diseases and restore the disrupted balance under pathological conditions [1]. These devices can continuously sense fluctuations in environmental signals from the body, calculate the amount of required therapeutics, and release them, which consequently influence the surrounding environment to generate a feedback [2]. As a result, a closed-loop feedback is formed, which permits the self-regulated devices to respond immediately or even preemptively upon deterioration of the disease. In both medical emergencies and chronic diseases, the timing and dosage of therapeutics are critical factors that determine the prognosis or even survival of patients. Selfregulated drug delivery devices can provide accurate and timely treatment based on on-site diagnosis and manageable therapy. With the gradual maturation of several requisite technologies, self-regulated devices are undergoing explosive growth. Nanotechnology has attracted unprecedented research interest due to its flexibility in synthesis and fabrication, site-specific accumulation, and spatiotemporal structural transformation potential [3–5]. Meanwhile, benefiting from the advanced knowledge and techniques of microtechnology, a new generation of micro-electromechanical systems (MEMSs) with better precision has cropped up [6–8]. Artificial intelligence (AI) technology that can process events and mimic the human brain has also gradually entered our daily lives [9–11]. With the convergence of the exciting advances in these areas, self-regulated micro/nanodevices are undergoing rapid development, providing better therapeutic opportunities for on-demand drug release, reduced administration frequency, and improved patient adherence. A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00012-5

Copyright © 2023 Elsevier Inc. All rights reserved.

347

348

A handbook of artificial intelligence in drug delivery

Fig. 13.1 A schematic illustration of the working principles, design, and technologies of self-regulated drug delivery micro/nanodevices.

With burgeoning theory and technology established for the design and fabrication of self-regulated drug delivery devices, recent years have witnessed a growth in both scientific research and clinical translation. In this chapter, we review the current state of development of self-regulated drug delivery devices based on nanotechnology, microtechnology, and artificial intelligence (Fig. 13.1). In particular, design strategies, construction of devices, and the fundamental technologies of diverse fields are introduced. Applications in the management of various diseases, such as diabetes, infections, and nervous system diseases, are surveyed and analyzed with typical cases. Finally, we discuss the current challenges of their application and the prospective outlook of their future development opportunities and clinical translation.

13.2 Strategies 13.2.1 Nanodevices Motivated by the progresses made in the integration of nanotechnology with drug delivery, researchers have explored various forms of nanodevices, including nanogels,

Recent advances in self-regulated drug delivery devices

nanoparticles, micelles, and liposomes, to meet different therapeutic requirements [12–14]. The safety and efficacy of nanodevices can be greatly enhanced by leveraging various stimuli-responsive materials [15,16]. Considering that many diseases present significant clues such as changes in pH value, redox status, body temperature, enzyme levels, and glucose concentration, self-regulated nanodevices can be designed by integrating responsive materials that sense these physiological indexes and tune the release profile to form a feedback [17–20]. To prepare self-regulated nanodevices, one facile strategy is to use responsive materials as framework, which can directly respond to changes in the environmental index to adjust the drug release rate. Through gradual, consecutive, and chemical and physical structural transformations like erosion, swelling, or degradation of the building matrix, the embedded drugs can be released in a feedback-controlled manner [21]. However, the identification of suitable materials that are sensitive and selective enough to respond to a changed index in certain diseases and the subsequent adjustment of drug release profiles remain a challenge, thus often leading to a rough self-regulation mode. To better eliminate the inaccuracy and lags in a feedback-controlled release, a generalized strategy has been proposed, usually requiring the involvement of two or more types of responsive materials. The first type of responsive material responds to the physiological signal and generates another type of signal that can be recognized by the second material. Through the structural transformation of the second material, the loaded drug could be released. In this process, physiological signals can be amplified or converted, which contributes to a more timely and precise release. Glucose oxidase (GOx) is a glucose-sensing moiety commonly used in developing self-regulated insulin delivery devices, which can catalyze the oxidation of glucose to gluconic acid [22]. Catalase is often incorporated into the system to consume undesired by-products and regenerate oxygen required for the enzymatic conversion of glucose [23]. Since the formed gluconic acid has to overcome physiological buffering effects before reducing the pH in the microenvironment, pH-responsive, material-based nanodevices often suffer from significant delays and slow response in insulin release. By contrast, materials that can respond to hypoxia or hydrogen peroxide rapidly often suffer from a short lifetime. To address this, Volpatti et al. developed self-regulated nanoparticles, allowing both rapid and extended on-demand insulin delivery [24]. Nanoparticles prepared by dextran with a high proportion of acyclic acetal modification displayed rapid release characteristics, whereas those prepared by dextran with a high proportion of cyclic acetal modification displayed slow release characteristics. Therefore, coformulating these two materials in the nanoparticles encapsulating GOx, catalase, and insulin enabled the manipulation of insulin release profiles. The combination of these two materials satisfied the requirements of both rapid-onset and prolonged release kinetics and could maintain normoglycemic conditions for 16 h by a single subcutaneous injection.

349

350

A handbook of artificial intelligence in drug delivery

Beyond chemical or physical structural transformations upon stimulation, biologically intelligent nanodevices have also been designed, which can mimic self-adaptive regulation in organisms that release a desired amount of substance according to environmental demands and continuously received environmental feedback. Mitochondria are able to produce an on-demand ATP supply that meets cell demands [25]. Inspired by the mitochondria, Lin et al. developed biosmart nanoparticles for treating ischemic injury [26]. The nanoparticles were loaded with melatonin in the cores and an oxygensensing circular DNA (cDNA) that expresses VEGF via the host genetic system between two layers of shells. During the acute stage of ischemia that is characterized by vast reactive oxygen species (ROS) generation and apoptotic activation, the nanoparticles promptly released melatonin to scavenge the ROS, prevent cytochrome c release, and provide acute protection. During the chronic stage, the cDNA could sense hypoxia to express VEGF for revascularization. When the blood supply of the ischemic tissue was recovered and the microenvironment was converted to a normoxia situation, the cDNA received the feedback and ceased VEGF secretion. With inspiration from natural organisms, such devices could quickly respond to the deterioration of the disease and decrease adverse effects. Nanodevices have attracted much attention due to their easy synthesis and fabrication, site-specific accumulation, and spatiotemporal drug release potential. Furthermore, their small sizes also allow their application in various administration routes, such as intravenous and subcutaneous administration [27,28]. On that point, self-regulated nanodevices can serve as Lego-like building blocks to construct more complex and multifunctional microdevices together with other fragments. For example, they could be loaded onto microneedle (MN) array patches or other larger devices to realize facile administration [29].

13.2.2 Microdevices Microdevices can satisfy the requirements for real-time monitoring and on-demand release. Self-regulated microdevices typically consist of three core components: a sensor that continuously monitors a signal, a controller that receives input data from the sensor and calculates the required dosage with a delicate algorithm, and a drug release actuator that executes instructions to release the drug. Sensors enable continuous measurement of the target analyte and output a quantifiable signal associated with the concentration. Typical biosensors require two functional units: a receptor for selective recognition and a transducer that converts this biorecognition event to a recognizable signal [30,31]. The design and selection of appropriate sensors is critical for achieving real-time monitoring and on-demand release in self-regulated devices. It is necessary for an ideal sensor to have high sensitivity, good selectivity, and a long lifetime. A great number of biosensor platforms for noninvasive monitoring of

Recent advances in self-regulated drug delivery devices

biofluids have been widely developed [32,33]. Biofluids, such as sweat, tears, and saliva, have been targeted due to their easy accessibility and noninvasive sampling potential, which pose minimal disturbance or harm to the human body [34–37]. Wearable biosensors that can blend into the wearers’ daily life are anticipated to become more streamlined, present in the form of wristbands, patches, textiles, and other subtle accessories [18,38]. Another popular kind of biosensor includes implantable sensors that autonomously provide round-the-clock monitoring after a single surgical implantation [39]. Efforts to prolong the durability of the implant, mitigate the necessity for surgical operation, and reduce the risk and pain in implant placement and extraction processes are of great importance to patient compliance with this invasive sensor. The controller is responsible for determining the drug release profiles and programming dose titration to meet therapeutic demands [40]. This is achieved by receiving signals from the sensor, using an algorithm to predict the required dose to maintain the desired state, and sending signals to the actuator for drug dosing. The controller repeatedly performs its analysis and calculation functions at discrete time intervals. At the same time, the controller can be designed to be wirelessly connected to external devices like smartphones, reporting the information of interest such as the current state of the body or the concentration of specific compounds. A proportional-integral-derivative controller is a commonly used controller that only replies to the signal values and does not need to know either the model or underlying process [41]. It compares the difference between the detected signal and the set value to calculate the output from the information on the current and past differences and a best estimate of future difference. Another commonly used type is a model-predictive controller that requires a dynamic model such as a pharmacokinetic or pharmacodynamic model. This controller can anticipate potential future events and select the optimal output, thus reaching the desired condition. Actuators are essential for self-regulated devices, in which they realize the transformation from “passive” signal reception into “active” drug release. Actuators are usually composed of a drug reservoir, a communication system, a titratable dispensing mechanism, and other supporting units [7]. A great number of portable devices incorporating implantable or wearable actuators have been explored. The insulin pump was employed in the first commercial hybrid closed-loop devices and has been widely applied [42]. Other actuators, such as microneedle arrays, implants, and textile actuators, have also achieved promising therapeutic outcomes at the laboratory level. Since the form of the actuator mainly determines patient adherence, soft and flexible actuators have attracted much attention due to their improved comfort and biocompatibility. So far, numerous self-regulated devices that allow real-time monitoring and on-demand drug release have been developed. For example, Joo et al. developed a personalized biomedical device for simultaneous monitoring and treatment of fatal seizures [43]. The system was composed of a wearable electrophysiology sensor, a power transmitter, and an implantable drug delivery device. The sensor kept monitoring the electroencephalography signals and

351

352

A handbook of artificial intelligence in drug delivery

sent a signal to the transmitter when a fatal epileptic medical emergency occurred. The transmitter then generated radio frequency power to rapidly trigger the on-demand drug release. The devices were demonstrated to effectively decrease brain damage and improve survival rates in a mouse model with status epilepticus. The efficacy and toxicity of therapeutics are both closely tied to their concentrations in the blood. Although most of the current self-regulated devices focus on monitoring certain physiological parameters that can improve the outcomes of many therapeutics, they are of little help for the application of drugs with narrow therapeutic windows. Directly monitoring the circulating drug concentration offers a solution to promote the application of these drugs, yet this method is often technically challenging. To address this, Mage et al. developed a closed-loop device consisting of an aptamer-based biosensor, a controller using a proportional-integral-derivative feedback algorithm, and an infusion pump (Fig. 13.2) [44]. By directly monitoring and controlling circulating drug levels, the device could maintain the desired concentration of doxorubicin in rabbit and rat models. Doxorubicin is known to have a narrow therapeutic window, which

Fig. 13.2 Self-regulated microdevices monitor the circulating drug levels to adjust drug release. (A) A schematic illustration of the construction of a closed-loop control system. (B) Top: the biosensor allows for continuous detection of the drug directly from the whole blood. Bottom: the biosensor enables continuous observation of drug pharmacokinetics in live animals. (C) The feedback loop comprises a controller and an infusion pump, and a real-time biosensor is modeled in silico. (Reproduced with permission from P.L. Mage, B.S. Ferguson, D. Maliniak, K.L. Ploense, T.E. Kippin, H.T. Soh, Closed-loop control of circulating drug levels in live animals, Nat. Biomed. Eng. 1(5) (2017). Copyright 2017, Springer Nature.)

Recent advances in self-regulated drug delivery devices

means that the gap between the minimum amounts that can cause a therapeutic effect and that can cause toxicity is small, and its pharmacokinetics and pharmacodynamics often varied greatly among patients. This device automatically compensated for the pharmacokinetic differences between individuals and achieved a precise dose control of drugs.

13.2.3 AI-based devices AI-based technology promises to redefine self-regulated theranostics, and the popularization of smartphones and the emergence of functional apps both render AI more accessible in our daily lives. AI is a broad concept that encompasses different kinds of computational systems and tools that can process events and mimic the human brain [45]. AI tools accomplish a wide range of functions such as problem-solving, pattern recognition, and knowledge acquisition, similar to human beings. Although the simplest self-regulated microdevices with data collecting, processing, and recording functions can be manually upgraded or updated, AI-based devices still remain appealing due to their self-learning and self-improvement abilities. After continuous running-in, they are competent for making an accurate, timely diagnosis as well as for providing appropriate, effective treatment like private doctors [46]. AI tools have led a new trend in drug discovery and development by maximally reducing experimental works. A deep generative model is one kind of machine learning technique that can produce new data objects using neural networks. Zhavoronkov et al. developed a deep generative model for de novo small molecule design and used the model to discover potent inhibitors of discoidin domain receptor 1 in just 21 days [47]. What is more, many pharmaceutical companies have developed their own AI-based systems for drug discovery or development, which may replace the conventional discovery process of trial and error [48]. AI technology promises to decrease the cost of drug development, reduce the risk of failure, shorten the time of early drug discovery, and ultimately improve the quality of pharmaceutical therapeutics. AI tools permit the prediction of the physicochemical properties, appropriate dose, in vivo response, and pharmacokinetics of new compounds or formulations, thus contributing to enhanced efficiency and reduced cost. Based on the importance and benefits of these prediction functions, utilizing in silico tools for drug discovery and development has become more and more popular. Among all forms of AI, machine learning is the most commonly used form in medical care settings. It allows a powerful interrogation of datasets for ease of identifying undiscovered patterns or underlying relationships between different features in the data. Machine learning offers many appealing capabilities, including providing new insights into the course of a disease, developing a novel therapy, and providing an auxiliary diagnosis. In order to obtain more reliable predictions, various machine learning techniques can be employed, such as random forest, k-nearest neighbor, and artificial neural network [9,49].

353

354

A handbook of artificial intelligence in drug delivery

Tremendous progress made in machine learning has qualitatively extended the designs and functions of micro/nanodevices. Coformulating certain drugs with some excipient can form nanoparticles with high drug-loading capacities, which are promising to overcome the drawbacks of common nanoformulations [50,51]. A poor understanding of this phenomenon makes it difficult to identify which combinations can be used to prepare the desired nanoparticles. Reker et al. reported the integration of machine learning with high-throughput experimentation to realize the large-scale identification of nanoformulations (Fig. 13.3) [52]. The ability to experimentally form coaggregating nanoparticles of 1440 formulations was tested using a high-throughput platform. Molecular dynamics simulations were performed to investigate noncovalent interaction potentials among drugs and excipients. A random forest machine learning model was then employed and trained using 1440 data points acquired from the high-throughput experimentation. As a result, 100 self-assembling drug nanoparticles were identified from 2.1 million pairings of candidate drugs and approved excipients. It is noteworthy that excipients in this study were selected from FDA-approved compounds, which might accelerate the translation of the nanoformulations.

13.3 Applications 13.3.1 Diabetes According to the statistics from the International Diabetes Federation, as of 2019, the number of adults with diabetes worldwide has reached 463 million [53]. It is predicted that by 2045, this number will rise to 700 million. Over the years, the most popular method for the management of diabetes has been frequent insulin injections. The patients have to test their blood sugar levels several times a day to adjust the amount of administered insulin as recommended by their doctors. Even so, this bolus form of administration may lead to blood glucose fluctuations and hypoglycemia episodes. Self-regulated drug delivery devices can release insulin in response to blood glucose, calculate the required amount of drugs, and release them to maintain a normoglycemia level. These devices are also described as artificial pancreas systems as they are designed to mimic the functions of healthy pancreas, delivering exogenous insulin to supplement the deficiency in glucose regulation of the body. Achievements in various fields have accelerated the development of self-regulated insulin delivery devices. The emergence of self-regulated devices is underpinned by the application of a sensor—continuous glucose monitor (CGM) systems—and an actuator—continuous subcutaneous insulin infusion (CSII)—which allow both real-time monitoring and drug infusion at any desired time [54,55]. Once they appeared, CGMs in diabetes quickly attracted great attention due to decreased pain, improved convenience, and reduced social embarrassment compared to that of frequent tests with finger-pricking devices [56]. In the form of a wearable, portable electromechanical

Recent advances in self-regulated drug delivery devices

Fig. 13.3 AI-based large-scale identification of solid drug nanoparticles. (A) Schematic illustrations of using high-throughput experimental workflow to prepare nanoparticles and confirm their self-aggregation propensity. (B) An agreement between real-world high-throughput testing experimentation (left) and computational machine learning-based assessments (right). (C) The relationship between molecular dynamics simulations and machine learning. (D) Performance assessment of a machine learning model. (E) Application of a developed computational prediction model in the prediction of coaggregation propensity in 2.1 million pairs. (Reproduced with permission from D. Reker, Y. Rybakova, A.R. Kirtane, R. Cao, J.W. Yang, N. Navamajiti, A. Gardner, R.M. Zhang, T. Esfandiary, J. L’Heureux, T. von Erlach, E.M. Smekalova, D. Leboeuf, K. Hess, A. Lopes, J. Rogner, J. Collins, S.M. Tamang, K. Ishida, P. Chamberlain, D. Yun, A. Lytton-Jean, C.K. Soule, J.H. Cheah, A.M. Hayward, R. Langer, G. Traverso, Computationally guided high-throughput design of selfassembling drug nanoparticles, Nat. Nanotechnol. 16 (2021) 725–733. Copyright 2021, Springer Nature.)

355

356

A handbook of artificial intelligence in drug delivery

insulin pump, CSII is usually comprised of a computerized control mechanism, a motor, a drug reservoir, and an subcutaneous infusion unit [56]. These components have brought about much convenience over traditional monitoring and dosing methods, and thus the resultant self-regulated devices have finally revolutionized the manner in which diabetes is managed. The FDA approval of the MiniMed 670G pump manufactured by Medtronic in September 2016 marked the beginning of the clinical practice of self-regulated insulin delivery devices [57]. MiniMed 670G is the first hybrid closed-loop system comprising a CGM system, an insulin pump, and a transmitter. Several other commercial closedloop devices were developed by many companies, such as Insulet, Beta Bionics, Tandem Diabetes Care, Roche, and Medtronic [58]. Most current devices display a hybrid self-regulated manner. The users need to input the estimated intake of carbohydrates. The devices then calculate the required amount of insulin and thereafter automatically infuse insulin. Researchers have attempted to develop fully closed-loop systems that automatically deliver appropriate amounts of insulin, even at mealtimes. However, as the insulin pump adopts the subcutaneous administration route, delays in the absorption of insulin may compromise timely glucose control and cause postmeal hyperglycemia. Further improvement and refinement of the first generation of self-regulated insulin devices have been proposed. Dual-hormone devices that deliver both insulin and glucagon represent a feasible method to improve self-regulated diabetes management [59]. Adding glucagon, which counteracts the hypoglycemic effect of insulin, helps reduce the risk of hypoglycemia. When hypoglycemia is detected or predicted, the glucagon in dual-hormone devices is triggered for release. Overall, dual-hormone closed-loop devices contribute to more steady blood glucose levels, increased time in the target range, and shorter time undergoing hypoglycemia with fewer rescue carbohydrates. Correspondingly, such designs add extra complexity and technical difficulty to the system. Furthermore, the long-term stability, safety, and tolerability of glucagon should be thoroughly evaluated before wide application. Although dual-hormone devices have not yet entered the market, companies have already devoted numerous efforts to developing such systems. Beta Bionics and Zealand Pharma are partnering in order to develop a fully automated bionic pancreas, iLet, to deliver both insulin and glucagon (dasiglucagon). This device is expected to control blood glucose levels based on an algorithm without need for patient intervention. Although it is still under development, early trials of home-use studies in patients with T1D have proved that iLet’s algorithms achieved significant improvements compared to a conventional insulin pump [60,61]. Most self-regulated drug delivery nanodevices rely on three categories of glucosesensing moieties—GOx [62], phenylboronic acid (PBA) [63], and glucose-binding proteins [64]. Zuo et al. developed self-assembled multiresponsive nanoparticles loaded with insulin and GOx (Fig. 13.4) [65]. The nanoparticles were self-assembled from an

Recent advances in self-regulated drug delivery devices

Fig. 13.4 Self-regulated insulin delivery devices for treatment of diabetes. (A) Structure and the working mechanism of a multiresponsive diphenylboronic acid guest (B). The host-guest complex self-assembles into vesicles that deliver insulin and GOx. The release of insulin is done in a closedloop regulated manner. (Reproduced with permission from M. Zuo, W. Qian, Z. Xu, W. Shao, X.Y. Hu, D. Zhang, J. Jiang, X. Sun, L. Wang, Multiresponsive supramolecular theranostic nanoplatform based on Pillar[5]arene and diphenylboronic acid derivatives for integrated glucose sensing and insulin delivery, Small 14(38) (2018) e1801942. Copyright 2018, John Wiley & Sons.)

amphiphilic host-guest complex comprising a pillar[5]arene (WP5) host and a diphenylboronic acid guest (G). The integrated theranostic nanoparticles could sense glucose changes and control insulin release. The release of insulin was triggered by high glucose concentration and the GOx-generated H2O2 and gluconic acid. The pH-sensitive WP5 sensed the change in pH, which triggered partial disaggregation of the nanodevice and subsequent release of insulin.

357

358

A handbook of artificial intelligence in drug delivery

Machine learning approaches have also been leveraged to provide decision support for diabetes management [66]. Nimri et al. adopted an automated artificial intelligencebased decision support system to guide insulin doses in youths with type 1 diabetes [67]. The efficacy and safety of AI-based insulin titration was noninferior to the trained physician-guided titration. Tyler et al. reported an algorithm that recommends weekly insulin dosage to patients with type 1 diabetes using multiple daily injection therapy [68]. A virtual platform, a mathematical representation of the glycemic response to food intake, insulin, and exercise in patients were implemented to train a machine learning k-nearest neighbor decision support system (KNN-DSS). The KNN-DSS model could identify the causes of hyperglycemia or hypoglycemia to predict optimal insulin recommendations that improve glycemic outcomes. The algorithm was inputted with CGM data, insulin data, and physical activity metrics. The model determined insulin adjustments from a set of 12 recommendations. The results suggested that the KNNDSS could identify problematic glycemic patterns and avoid life-threatening complications in patients. Given the shortage of specialists in some rural areas and the increasing preference of remote health care, the novel paradigm of medical care provided by self-regulated devices together with AI-based decision support has achieved great success. This mode provides timely insulin adjustments that are hard to achieve by traditional medical care. Besides, by recording and reporting personal data, the system still allows the involvement of specialists to give in-person guidance, thus providing a flexible regulation ability.

13.3.2 Infections Antibiotics have saved numerous lives and are still of great importance to addressing bacterial infections and preventing infections in preoperative or postoperative treatment [69]. However, the abuse of antibiotics can lead to antibiotic resistance, which poses a challenge to their effectiveness. Self-regulated devices are crucial for treating infections for two main reasons: (1) they enable the release of the minimum required amount of the antibiotic that is sufficient to kill the bacteria while avoiding rapid antibiotic resistance and (2) leveraging AI tools helps optimize the antibiotic prescriptions and facilitate the development of new antibiotics. The rapid development of materials science has allowed researchers to explore novel self-regulated devices by taking advantage of the unique features of functional materials. Liquid crystals (LCs) are a special type of anisotropic fluid that integrates the mobility of liquids and the long-range order of crystals, which are widely leveraged to build reconfigurable materials with optical information-report functions [70]. Kim et al. developed a self-reporting and self-regulating antibacterial device based on LCs (Fig. 13.5) [71]. Interfacial interactions could lead to changes in the orientations of LCs and trigger the release of their cargoes. This phenomenon was leveraged to engineer an antibacterial device that

Recent advances in self-regulated drug delivery devices

Fig. 13.5 Self-regulated release of cargo from LCs by heat or bacterial motion that generates interfacial shear stresses in a specific manner. A schematic illustration (A) and photographs (B–D) demonstrating that the heat of a human finger triggers a change in the Bragg diffraction of light and the subsequent release of microdroplets. A schematic illustration (E) and photographs (F–H) demonstrating interfacial shear stresses generated by bacterial motion triggering the self-regulated release of antibacterial drug and red tracer, without bacteria (F), with the presence of motile bacteria (G), and following bacterial death (H). (I) Optical responses of the LC interface corresponding to panels (F–H). (Reproduced with permission from Y.K. Kim, X. Wang, P. Mondkar, E. Bukusoglu, N.L. Abbott, Self-reporting and selfregulating liquid crystals, Nature 557(7706) (2018) 539–544. Copyright 2018, AAAS.)

could release the loaded antibacterial agents only in the presence of bacteria. The motion of the motile bacteria generated interfacial shear stresses when they approached the interface, resulting in reorientations of the LCs at the interface and subsequently triggering the release of required amounts of therapeutic agents to kill the bacteria. The drug release was exhibited in a self-regulated manner and stopped once the bacteria were killed. Along with the release of biocidal agents, the optical appearance of LCs also changed, permitting the self-reporting of the event. This feature can be useful in visualizing the residual amounts of drugs and monitoring the remaining lifetime of the device. Yelin et al. developed machine learning models for personalized predictions of antibiotic resistance on the basis of personal clinical history [72]. Combining the data of demographics, clinical historical factors, and previous use of antibiotics for personalized predictions of resistance, machine-learning-guided prescriptions substantially reduce the

359

360

A handbook of artificial intelligence in drug delivery

frequency of mismatched treatment than do physician-prescribed empirical prescriptions. The abuse of broad-spectrum agents will beget further antibiotic resistance. Uncomplicated urinary tract infection is a common disease related to increasing antibiotic resistance and high probability of applying broad-spectrum second-line agents. Kanjilal et al. developed machine learning models for the prediction of antibiotic susceptibility to recommend the narrowest possible antibiotic [73]. The machine learning model was trained using electronic health record data, and the built decision algorithm reduced the usage of second-line antibiotics and inappropriate treatment compared to clinicians. The machine learning decision algorithm allows minimized usage of broad-spectrum antibiotics while retaining optimal therapeutic outcomes.

13.3.3 Anesthesia and pain relief Self-regulated anesthesia devices performing feedback-controlled administration of anesthetic drugs promise to provide more consistent, precise, and intraoperative sedation to patients. Closed-loop-controlled anesthesia monitors the depth of hypnosis, usually in the form of a processed electroencephalographic (EEG) derivative, such as the bispectral (BIS) index, and releases anesthetics, such as propofol, to maintain a certain level of anesthesia [74,75]. The implementation of closed-loop anesthesia regulation permits continuous patient status monitoring and a more accurate control over drug dosing, thus contributing to better control over anesthesia depth [76,77]. Closed-loop anesthesia also provides benefits, including improved hemodynamic stability, lower drug amounts, and faster postoperative recovery. As an established treatment, spinal cord stimulation has been used for treating chronic back or leg pain for several decades. Neurostimulation is different from other electrical stimulation-based therapies, as it only activates those nerve fibers that provide therapeutic benefits while avoiding those that produce undesirable side effects, thus putting forward high requirements on the degree of stimulation selectivity. A novel system performing real-time, continuous detection of spinal cord activation has been used to deliver closed-loop spinal cord stimulation. The neuromodulation system could continuously measure the spinal cord electrophysiology via evoked compound action potentials (ECAPs). When worked in a closed-loop spinal cord stimulation mode, it could adjust the stimulation according to the difference between the detected ECAP and the desired ECAP amplitude. The system could maintain consistent spinal cord activation within the individuals’ therapeutic window while maximizing the therapeutic effects. Mekhail et al. initiated a randomized, double-blind clinical study to evaluate the long-term safety and efficacy of this system [78]. The results demonstrated that closed-loop stimulation resulted in better spinal cord activation and provided more clinically meaningful pain relief than did open-loop stimulation in 1 year. This system paves a new way for closed-loop neuromodulation devices used for personalized pain relief.

Recent advances in self-regulated drug delivery devices

Although closed-loop systems for muscle relaxants and anesthetic delivery are well-developed, they have never been widely used, presumably due to their inconspicuous therapeutic advantage over traditional methods. Therefore, only limited experience has been acquired regarding the safety, reliability, and outcomes of closed-loop systems in large-scale clinic trials. Besides, only finite scenarios could be assumed in safety evaluation trials. To further improve the safety of closed-loop anesthetic delivery devices, Yousefi et al. developed a formalized safety system to minimize the risk of under/overdosing during closed-loop anesthesia [75]. Their work used a formal model verification technique, and the formalized safety system allowed for blood pressure, plasma, and effect-site concentrations to be maintained within safety limits. The introduction of such safety systems will substantially provide additional guarantee for the safety of self-regulated delivery devices.

13.3.4 Nervous system diseases Nowadays, using self-regulated devices for nervous system disease treatment is no longer a futuristic idea. Since a number of nervous system diseases are chronic with condition fluctuations, self-regulated devices are an ideal treatment option to provide continuous monitoring and timely drug or stimulation administration [79,80]. Self-regulated devices integrating physiological signal sensing and intervention ability are able to provide longterm monitoring and a temporal precision treatment, typically in the form of electrical stimulation. A closed-loop visual-auditory cueing device that captures body motion with an accelerometer was used to help improve gait impairment in patients with Parkinson’s disease (PD) [81]. This device enhanced walking velocity and stride length. Meanwhile, similar to anesthesia, proper drug dosing or stimulation strength based on the monitored personal physiological index provides on-demand treatment and minimizes the side effects of an overdose of drugs or stimulation. The RNS system developed by NeuroPace has gained FDA approval as an implantable, therapeutic, and self-regulated device that delivers responsive neurostimulation to reduce the frequency of seizures [82]. This device can detect parameters with bandpass, line length, and area detectors that can be adjusted by a specialist to be adapted to each individual [83]. When a patient experiences seizures, this device can normalize brain activity by detecting abnormal electrical activity in the brain and can quickly respond by delivering imperceptible electrical stimulation. Aimed at benefiting patients suffering from nervous system diseases in the clinic, self-regulated devices set higher requirements for a well-grounded understanding of pathophysiological brain signals and advanced technology support. To monitor electrophysiological signals, sensors have to be placed near the generator of the pathological signal. Most electrodes used in the clinic are placed on either the surface of the brain or within deep structures. However, they cannot perfectly meet the demand as they are relatively large in size and are not efficient enough to

361

362

A handbook of artificial intelligence in drug delivery

capture high frequency activity. Electrode arrays with enhanced spatiotemporal resolution may offer better properties of sensitivity and specificity for self-regulated devices in treating nervous system diseases [84]. Deep brain stimulation (DBS) is widely used in the management of advanced PD [85]. Under the guidance of a well-designed algorithm, self-regulated devices can perform on-demand stimulation on the basis of ongoing neuronal discharge. Rosin et al. evaluated the therapeutic outcome of the closed-loop stimulation strategy on a primate PD model [86]. After insertion of the recording electrodes into cortical and basal ganglia structures, the recorded electrodes’ analog signal was transported to a digital signal processing chip, and a stimulus could be generated under the guidance of an online real-time algorithm. Pallidal closed-loop stimulation exhibits better performance in improving akinesia and cortical and pallidal discharge patterns when compared to an open-loop device. This work confirms the superiority of closed-loop systems and takes a step forward in understanding the unrevealed pathophysiology of PD, which may provide the foundation for the future development of closed-loop DBS devices. Self-regulated devices have been successfully used to treat several nervous system diseases such as epilepsy, PD, and Alzheimer’s disease; however, most of them require invasive implantation procedures [87,88]. Meanwhile, lack of a deep understanding of the complex pathogenesis and unascertained pathophysiological signals has precluded the extension of their use. Using AI tools to study the pathogenesis and identify the potential signal may provide new opportunities for the refinement of algorithms and the development of self-regulated devices [89]. With a correct algorithm, machine learning will further promote the progress of diagnosis and therapeutics.

13.4 Prospects and challenges The long-awaited arrival of self-regulated devices will bring about a huge revolution in medical care and drug discovery and development. The application of self-regulated devices dovetails with the trend of telemedicine that permits the remote monitoring and transmitting of physiological information for disease management, which may contribute to the ease of burden on medical care. Success in clinical trials of self-regulated delivery in diabetes and anesthesia has encouraged researchers to explore their application in a wide range of diseases. We can anticipate an explosive growth in their clinical translation and commercialization in the next several years. Although only a few self-regulated devices have entered the marketplace, their future development trend could be anticipated by referring to the market of biosensors. Sensors are key components of smart self-regulated devices that can actively interact with the user and environment. According to a report by MarketsandMarkets, it is estimated that biosensors comprise 25.5 billion USD in the 2021 market and will grow at a compound annual growth rate of 7.5% in 5 years [90]. The emergence of nanotechnology-based

Recent advances in self-regulated drug delivery devices

biosensors and other technological advances is accelerating the expansion of the application of biosensors. It is observed that the need for glucose-monitoring biosensors to manage diabetes is still rising. Moreover, due to the COVID-19 pandemic, the demand for home-based point-of-care devices has rapidly increased, indicating the change from centralized hospital-based health care to decentralized home-based personalized medicine. Especially, wearable biosensors that allow the remote, continuous monitoring of the vital health-care index of patients have found increasing opportunities. The future opportunities of self-regulated devices overlap the trend of biosensors to a large extent. They will fundamentally change the medical mode in diagnosis, therapy, and health care when they become commercially available. The commercialization of self-regulated devices may generate a positive feedback in scientific efforts, and increasing market demands will fuel interest and input in developing new technologies. Commercialized devices will spur the initiation of clinical studies on the therapeutic outcomes to present their advantages, which may attract further interest in applying self-regulated devices in more fields beyond the narrow purview of diabetes management. An intriguing field awaiting to be studied is the harnessing of natural cells or engineered cells for self-regulated drug delivery. As autonomous units, cells are able to sense their surrounding environment, interact with each other, and secrete various substances. Through interactions with the environment or the whole organism, cells can be leveraged to generate therapeutics to help the body return to homeostasis. For example, transplanting islets or engineering other cells that produce insulin and mimic natural β-cells allows closed-loop regulation of blood glucose [91,92]. Recent advances in chemistry, synthetic biology, materials science, and drug delivery have enabled researchers to engineer different types of cells. Gene reprogramming, drug loading, and structure modification have brought about new insights into cell engineering studies [93]. Genetically engineered stem cells with CRISPR/Cas9 genome engineering technology have rewired endogenous cell circuits to autonomously sense IL-1- or TNF-a-mediated inflammation and produce their antagonists in a closed-loop release manner [94]. Due to the great potential and implications of this technology, governing engineered cells may help treat a wide range of diseases arising from cell damage or dysfunction. Despite the considerable increase in public interest and intensive scientific efforts, selfregulated devices still need further development. The immaturity of the current selfregulated devices is the most important factor limiting their wide clinical application. Besides, some more realistic considerations, such as high price and low trust from end users, pose large challenges to their commercialization. Accompanied by the introduction of wirelessly communicating technologies, AI tools, and big data analytics, safety and privacy concerns about devices have also increased as eavesdropping and hacking of personal information are always within the central discussion [95–97]. Meanwhile, the lacking complement support from policy, professionals, and service also impacts the facilitation of high-tech product usage.

363

364

A handbook of artificial intelligence in drug delivery

Another factor that impedes the expansion of application of self-regulated devices are the technical challenges of developing practical devices. Self-regulated devices rely on noninvasive or minimally invasive precise detection of physiological indicators, which often present in extremely low concentration in biofluids [98]. Due to the complexity of the human body, corrosion, fouling, malfunction of the devices, and foreign body response (for some implantable devices) are still limiting factors [99]. What is more, the short lifetime and insufficient reliability make the current self-regulated devices far from satisfactory than expected. They still have a high reliance on frequent device calibrations and drug refills. These shortcomings drive the shift of emphasis from idealistic design to realistic practicability. Use of mediator-free or nonenzymatic long-life sensors with a reduced reliance on frequent calibrations may be helpful [100]. Overall, the clinical translation of self-regulated drug devices has a long way to go, needing the support from advances in materials science, nanotechnology, micromanufacturing, and pharmacokinetics.

13.5 Conclusions Although self-regulated drug delivery nano/microdevices have yet to come into clinical application in many aspects, they have the potential to bring about revolutionary progress for medical care. Compared with uncontrollable drug release or conventional controlled release, self-regulated devices allow the real-time monitoring and on-demand release of drugs as soon as disease symptoms appear. Therefore, they demonstrate better versatilities to accommodate various therapeutic demands, providing more access to personalized and precision medicine. Moreover, they bring a new fashion of telemedicine that permits remote monitoring and transmitting of physiological information for disease management externally. The current challenges could be addressed by the convergence of progresses made in nanotechnology, mechanical and electronic fabrication, and AI tools. Continuous and synergistic scientific efforts are expected to devote to a wide range of research fields such as physiological parameters, responsive materials, pharmacokinetic behavior, disease risk assessment, and mechanism of drug action, so as to acquire optimized generic devices that are applicable to different diseases by introducing corresponding components.

Acknowledgments This work was supported by the startup package from the University of Wisconsin-Madison.

Conflict of interest The authors declare no conflict of interest.

Recent advances in self-regulated drug delivery devices

References [1] Q. Pang, D. Lou, S.J. Li, G.M. Wang, B.B. Qiao, S.R. Dong, L. Ma, C.Y. Gao, Z.H. Wu, Smart flexible electronics-integrated wound dressing for real-time monitoring and on-demand treatment of infected wounds, Adv. Sci. 7 (6) (2020). [2] Y. Lu, A.A. Aimetti, R. Langer, Z. Gu, Bioresponsive materials, Nat. Rev. Mater. 1 (2016) 16075. [3] A. Parodi, N. Quattrocchi, A.L. van de Ven, C. Chiappini, M. Evangelopoulos, J.O. Martinez, B.S. Brown, S.Z. Khaled, I.K. Yazdi, M. Vittoria Enzo, L. Isenhart, M. Ferrari, E. Tasciotti, Synthetic nanoparticles functionalized with biomimetic leukocyte membranes possess cell-like functions, Nat. Nanotechnol. 8 (1) (2013) 61–68. [4] R. Weissleder, K. Kelly, E.Y. Sun, T. Shtatland, L. Josephson, Cell-specific targeting of nanoparticles by multivalent attachment of small molecules, Nat. Biotechnol. 23 (11) (2005) 1418–1423. [5] R.M. Williams, S. Chen, R.E. Langenbacher, T.V. Galassi, J.D. Harvey, P.V. Jena, J. BudhathokiUprety, M.K. Luo, D.A. Heller, Harnessing nanotechnology to expand the toolbox of chemical biology, Nat. Chem. Biol. 17 (2) (2021) 129–137. [6] M. Caldorera-Moore, N.A. Peppas, Micro- and nanotechnologies for intelligent and responsive biomaterial-based medical systems, Adv. Drug Deliv. Rev. 61 (15) (2009) 1391–1401. [7] H.J. Lee, N. Choi, E.S. Yoon, I.J. Cho, MEMS devices for drug delivery, Adv. Drug Deliv. Rev. 128 (2018) 132–147. [8] H.B. Zhang, J.K. Jackson, M. Chiao, Microfabricated drug delivery devices: design, fabrication, and applications, Adv. Funct. Mater. 27 (45) (2017), 1703606. [9] P. Hassanzadeh, F. Atyabi, R. Dinarvand, The significance of artificial intelligence in drug delivery system design, Adv. Drug Deliv. Rev. 151–152 (2019) 169–190. [10] D. Ho, Artificial intelligence in cancer therapy, Science 367 (6481) (2020) 982–983. [11] M. Luo, Y.Z. Feng, T.W. Wang, J.G. Guan, Micro-/nanorobots at work in active drug delivery, Adv. Funct. Mater. 28 (25) (2018), 1706100. [12] X. Xu, X.C. Huang, Y. Zhang, S.Y. Shen, Z.Z. Feng, H. Dong, C. Zhang, R. Mo, Self-regulated hirudin delivery for anticoagulant therapy, Sci. Adv. 6 (41) (2020), eabc0382. [13] A.H. Wang, W.W. Fan, T.T. Yang, S.F. He, Y.W. Yang, M.R. Yu, L. Fan, Q.L. Zhu, S.Y. Guo, C.L. Zhu, Y. Gan, Liver-target and glucose-responsive polymersomes toward mimicking endogenous insulin secretion with improved hepatic glucose utilization, Adv. Funct. Mater. 30 (13) (2020), 1910168. [14] G.J. Guan, M.D. Wu, M.Y. Han, Stimuli-responsive hybridized nanostructures, Adv. Funct. Mater. 30 (2) (2020), 1903439. [15] P. Lavrador, M.R. Esteves, V.M. Gaspar, J.F. Mano, Stimuli-responsive nanocomposite hydrogels for biomedical applications, Adv. Funct. Mater. 31 (2020), 2005941. [16] J.Y.C. Lim, S.S. Goh, X.J. Loh, Bottom-up engineering of responsive hydrogel materials for molecular detection and biosensing, ACS Mater. Lett. 2 (8) (2020) 918–950. [17] H.P. Simmen, J. Blaser, Analysis of pH and pO2 in abscesses, peritoneal fluid, and drainage fluid in the presence or absence of bacterial infection during and after abdominal surgery, Am. J. Surg. 166 (1) (1993) 24–27. [18] M. Gong, P.B. Wan, D. Ma, M.J. Zhong, M.H. Liao, J.J. Ye, R. Shi, L.Q. Zhang, Flexible breathable nanomesh electronic devices for on-demand therapy, Adv. Funct. Mater. 29 (26) (2019), 1902127. [19] X. Yang, G. Liu, L. Peng, J. Guo, L. Tao, J. Yuan, C. Chang, Y. Wei, L. Zhang, Highly efficient selfhealable and dual responsive cellulose-based hydrogels for controlled release and 3D cell culture, Adv. Funct. Mater. 27 (40) (2017) 1703174.1–1703174.10. [20] P.M. Arnott, S. Howorka, A temperature-gated nanovalve self-assembled from DNA to control molecular transport across membranes, ACS Nano 13 (3) (2019) 3334–3340. [21] J. Hu, Y. Quan, Y. Lai, Z. Zheng, Z. Hu, X. Wang, T. Dai, Q. Zhang, Y. Cheng, A smart aminoglycoside hydrogel with tunable gel degradation, on-demand drug release, and high antibacterial activity, J. Control. Release 247 (2017) 145–152. [22] A. Fischer, S. Lilienthal, M. Vazquez-Gonzalez, M. Fadeev, Y.S. Sohn, R. Nechushtai, I. Willner, Triggered release of loads from microcapsule-in-microcapsule hydrogel microcarriers: en-route to an “artificial pancreas”, J. Am. Chem. Soc. 142 (9) (2020) 4223–4234.

365

366

A handbook of artificial intelligence in drug delivery

[23] Z. Gu, T.T. Dang, M.L. Ma, B.C. Tang, H. Cheng, S. Jiang, Y.Z. Dong, Y.L. Zhang, D.G. Anderson, Glucose-responsive microgels integrated with enzyme nanocapsules for closed-loop insulin delivery, ACS Nano 7 (8) (2013) 6758–6766. [24] L.R. Volpatti, M.A. Matranga, A.B. Cortinas, D. Delcassian, K.B. Daniel, R. Langer, D.G. Anderson, Glucose-responsive nanoparticles for rapid and extended self-regulated insulin delivery, ACS Nano 14 (1) (2020) 488–497. [25] Y. Yaniv, M. Juhaszova, S.J. Sollott, Age-related changes of myocardial ATP supply and demand mechanisms, Trends Endocrinol. Metab. 24 (10) (2013) 495–505. [26] Y. Lin, J. Liu, R. Bai, J. Shi, X. Zhu, J. Liu, J. Guo, W. Zhang, H. Liu, Z. Liu, Mitochondria-inspired nanoparticles with microenvironment-adapting capacities for on-demand drug delivery after ischemic injury, ACS Nano 14 (9) (2020) 11846–11859. [27] S. Li, Z.T. Bennett, B.D. Sumer, J. Gao, Nano-immune-engineering approaches to advance cancer immunotherapy: lessons from ultra-pH-sensitive nanoparticles, Acc. Chem. Res. 53 (11) (2020) 2546–2557. [28] J.K. Sahoo, M.A. VandenBerg, M.J. Webber, Injectable network biomaterials via molecular or colloidal self-assembly, Adv. Drug Deliv. Rev. 127 (2018) 185–207. [29] W. Chen, R. Tian, C. Xu, B.C. Yung, G. Wang, Y. Liu, Q. Ni, F. Zhang, Z. Zhou, J. Wang, G. Niu, Y. Ma, L. Fu, X. Chen, Microneedle-array patches loaded with dual mineralized protein/peptide particles for type 2 diabetes therapy, Nat. Commun. 8 (1) (2017) 1777. [30] S.A. Abid, A.A. Muneer, I.M.S. Al-Kadmy, A.A. Sattar, A.M. Beshbishy, G.E. Batiha, H.F. Hetta, Biosensors as a future diagnostic approach for COVID-19, Life Sci. 273 (2021) 119117. [31] J.D. Bohbot, S. Vernick, The emergence of insect odorant receptor-based biosensors, BiosensorsBasel 10 (3) (2020) 26. [32] P. Tseng, B. Napier, L. Garbarini, D.L. Kaplan, F.G. Omenetto, Functional, RF-trilayer sensors for tooth-mounted, wireless monitoring of the oral cavity and food consumption, Adv. Mater. 30 (18) (2018), e1703257. [33] S. Guo, K. Wu, C. Li, H. Wang, Z. Sun, D. Xi, S. Zhang, W. Ding, M.E. Zaghloul, C. Wang, F.A. Castro, D. Yang, Y. Zhao, Integrated contact lens sensor system based on multifunctional ultrathin MoS2 transistors, Matter 4 (3) (2020) 969–985. [34] Z. Sonner, E. Wilder, T. Gaillard, G. Kasting, J. Heikenfeld, Integrated sudomotor axon reflex sweat stimulation for continuous sweat analyte analysis with individuals at rest, Lab Chip 17 (15) (2017) 2550–2560. [35] J.T. Reeder, J. Choi, Y. Xue, P. Gutruf, J. Hanson, M. Liu, T. Ray, A.J. Bandodkar, R. Avila, W. Xia, S. Krishnan, S. Xu, K. Barnes, M. Pahnke, R. Ghaffari, Y. Huang, J.A. Rogers, Waterproof, electronics-enabled, epidermal microfluidic devices for sweat collection, biomarker analysis, and thermography in aquatic settings, Sci. Adv. 5 (1) (2019) eaau6356. [36] J. Kim, A.S. Campbell, B.E. de Avila, J. Wang, Wearable biosensors for healthcare monitoring, Nat. Biotechnol. 37 (4) (2019) 389–406. [37] B. Ning, T. Yu, S. Zhang, Z. Huang, D. Tian, Z. Lin, A. Niu, N. Golden, K. Hensley, B. Threeton, C.J. Lyon, X.M. Yin, C.J. Roy, N.S. Saba, J. Rappaport, Q. Wei, T.Y. Hu, A smartphone-read ultrasensitive and quantitative saliva test for COVID-19, Sci. Adv. 7 (2) (2020) eabe3703. [38] C. Wang, C. Pan, Z. Wang, Electronic skin for closed-loop systems, ACS Nano 13 (11) (2019) 12287–12293. [39] D.A. Gough, L.S. Kumosa, T.L. Routh, J.T. Lin, J.Y. Lucisano, Function of an implanted tissue glucose sensor for more than 1 year in animals, Sci. Transl. Med. 2 (42) (2010) 42ra53. [40] J. Li, J.Y. Liang, S.J. Laken, R. Langer, G. Traverso, Clinical opportunities for continuous biosensing and closed-loop therapies, Trends Chem. 2 (4) (2020) 319–340. [41] F.J. Doyle, L.M. Huyett, J.B. Lee, H.C. Zisser, E. Dassau, Closed-loop artificial pancreas systems: engineering the algorithms, Diabetes Care 37 (5) (2014) 1191–1197. [42] R.A. Lal, M. Basina, D.M. Maahs, K. Hood, B. Buckingham, D.M. Wilson, One year clinical experience of the first commercial hybrid closed-loop system, Diabetes Care 42 (12) (2019) 2190–2196. [43] H. Joo, Y. Lee, J. Kim, J.S. Yoo, S. Yoo, S. Kim, A.K. Arya, S. Kim, S.H. Choi, N. Lu, H.S. Lee, S. Kim, S.T. Lee, D.H. Kim, Soft implantable drug delivery device integrated wirelessly with wearable devices to treat fatal seizures, Sci. Adv. 7 (1) (2021) eabd4639.

Recent advances in self-regulated drug delivery devices

[44] P.L. Mage, B.S. Ferguson, D. Maliniak, K.L. Ploense, T.E. Kippin, H.T. Soh, Closed-loop control of circulating drug levels in live animals, Nat. Biomed. Eng. 1 (5) (2017), 0070. [45] J. He, S.L. Baxter, J. Xu, J. Xu, X. Zhou, K. Zhang, The practical implementation of artificial intelligence technologies in medicine, Nat. Med. 25 (1) (2019) 30–36. [46] B.A. Aguado, J.C. Grim, A.M. Rosales, J.J. Watson-Capps, K.S. Anseth, Engineering precision biomaterials for personalized medicine, Sci. Transl. Med. 10 (424) (2018) eaam8645. [47] A. Zhavoronkov, Y.A. Ivanenkov, A. Aliper, M.S. Veselov, V.A. Aladinskiy, A.V. Aladinskaya, V.A. Terentiev, D.A. Polykovskiy, M.D. Kuznetsov, A. Asadulaev, Y. Volkov, A. Zholus, R.R. Shayakhmetov, A. Zhebrak, L.I. Minaeva, B.A. Zagribelnyy, L.H. Lee, R. Soll, D. Madge, L. Xing, T. Guo, A. Aspuru-Guzik, Deep learning enables rapid identification of potent DDR1 kinase inhibitors, Nat. Biotechnol. 37 (9) (2019) 1038–1040. [48] S.L. Rohall, L. Auch, J. Gable, J. Gora, J. Jansen, Y. Lu, E. Martin, M. Pancost-Heidebrecht, B. Shirley, N. Stiefl, M. Lindvall, An artificial intelligence approach to proactively inspire drug discovery with recommendations, J. Med. Chem. 63 (16) (2020) 8824–8834. [49] S. Tian, J.M. Wang, Y.Y. Li, D. Li, L. Xu, T.J. Hou, The application of in silico drug-likeness predictions in pharmaceutical research, Adv. Drug Deliv. Rev. 86 (2015) 2–10. [50] Y. Shamay, J. Shah, M. Isik, A. Mizrachi, J. Leibold, D.F. Tschaharganeh, D. Roxbury, J. Budhathoki-Uprety, K. Nawaly, J.L. Sugarman, E. Baut, M.R. Neiman, M. Dacek, K.S. Ganesh, D.C. Johnson, R. Sridharan, K.L. Chu, V.K. Rajasekhar, S.W. Lowe, J.D. Chodera, D.A. Heller, Quantitative self-assembly prediction yields targeted nanomedicines, Nat. Mater. 17 (4) (2018) 361–368. [51] C.K. McLaughlin, D. Duan, A.N. Ganesh, H. Torosyan, B.K. Shoichet, M.S. Shoichet, Stable colloidal drug aggregates catch and release active enzymes, ACS Chem. Biol. 11 (4) (2016) 992–1000. [52] D. Reker, Y. Rybakova, A.R. Kirtane, R. Cao, J.W. Yang, N. Navamajiti, A. Gardner, R.M. Zhang, T. Esfandiary, J. L’Heureux, T. von Erlach, E.M. Smekalova, D. Leboeuf, K. Hess, A. Lopes, J. Rogner, J. Collins, S.M. Tamang, K. Ishida, P. Chamberlain, D. Yun, A. Lytton-Jean, C.K. Soule, J.H. Cheah, A.M. Hayward, R. Langer, G. Traverso, Computationally guided highthroughput design of self-assembling drug nanoparticles, Nat. Nanotechnol. 16 (2021) 725–733. [53] P. Saeedi, I. Petersohn, P. Salpea, B. Malanda, S. Karuranga, N. Unwin, S. Colagiuri, L. Guariguata, A.A. Motala, K. Ogurtsova, J.E. Shaw, D. Bright, R. Williams, I.D.F.D.A. Committee, Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: results from the International Diabetes Federation Diabetes Atlas, 9(th) edition, Diabetes Res. Clin. Pract. 157 (2019) 107843. [54] J.E. Lane, J.P. Shivers, H. Zisser, Continuous glucose monitors: current status and future developments, Curr. Opin. Endocrinol. Diabetes Obes. 20 (2) (2013) 106–111. [55] M.J. Lenhard, G.D. Reeves, Continuous subcutaneous insulin infusion – a comprehensive review of insulin pump therapy, Arch. Intern. Med. 161 (19) (2001) 2293–2300. [56] D. Slattery, P. Choudhary, Clinical use of continuous glucose monitoring in adults with type 1 diabetes, Diabetes Technol. Ther. 19 (2017) S55–S61. [57] L.H. Messer, G.P. Forlenza, J.L. Sherr, R.P. Wadwa, B.A. Buckingham, S.A. Weinzimer, D.M. Maahs, R.H. Slover, Optimizing hybrid closed-loop therapy in adolescents and emerging adults using the MiniMed 670G system, Diabetes Care 41 (4) (2018) 789–796. [58] C.K. Boughton, R. Hovorka, Advances in artificial pancreas systems, Sci. Transl. Med. 11 (484) (2019) eaaw4949. [59] Z. Wang, J. Wang, H. Li, J. Yu, G. Chen, A.R. Kahkoska, V. Wu, Y. Zeng, D. Wen, J.R. Miedema, J.B. Buse, Z. Gu, Dual self-regulated delivery of insulin and glucagon by a hybrid patch, Proc. Natl. Acad. Sci. U. S. A. 117 (47) (2020) 29512–29517. [60] F.H. El-Khatib, C. Balliro, M.A. Hillard, Home use of a bihormonal bionic pancreas versus insulin pump therapy in adults with type 1 diabetes: a multicentre randomised crossover trial (vol 389, pg 369, 2016), Lancet 389 (10067) (2017) 368. [61] S.J. Russell, M.A. Hillard, C. Balliro, K.L. Magyar, R. Selagamsetty, M. Sinha, K. Grennan, D. Mondesir, L. Ehklaspour, H. Zheng, E.R. Damiano, F.H. El-Khatib, Day and night glycaemic control with a bionic pancreas versus conventional insulin pump therapy in preadolescent children with type 1 diabetes: a randomised crossover trial, Lancet Diabetes Endocrinol. 4 (3) (2016) 233–243.

367

368

A handbook of artificial intelligence in drug delivery

[62] C.R. Gordijo, K. Koulajian, A.J. Shuhendler, L.D. Bonifacio, H.Y. Huang, S. Chiang, G.A. Ozin, A. Giacca, X.Y. Wu, Nanotechnology-enabled closed loop insulin delivery device: in vitro and in vivo evaluation of glucose-regulated insulin release for diabetes control, Adv. Funct. Mater. 21 (1) (2011) 73–82. [63] D.H. Chou, M.J. Webber, B.C. Tang, A.B. Lin, L.S. Thapa, D. Deng, J.V. Truong, A.B. Cortinas, R. Langer, D.G. Anderson, Glucose-responsive insulin activity by covalent modification with aliphatic phenylboronic acid conjugates, Proc. Natl. Acad. Sci. U. S. A. 112 (8) (2015) 2401–2406. [64] M. Brownlee, A. Cerami, A glucose-controlled insulin-delivery system: semisynthetic insulin bound to lectin, Science 206 (4423) (1979) 1190–1191. [65] M. Zuo, W. Qian, Z. Xu, W. Shao, X.Y. Hu, D. Zhang, J. Jiang, X. Sun, L. Wang, Multiresponsive supramolecular theranostic nanoplatform based on pillar[5]arene and diphenylboronic acid derivatives for integrated glucose sensing and insulin delivery, Small 14 (38) (2018), e1801942. [66] C. Perez-Gandı´a, G. Garcı´a-Sa´ez, D. Subı´as, A. Rodrı´guez-Herrero, E.J. Go´mez, M. Rigla, M.E. Hernando, Decision support in diabetes care: the challenge of supporting patients in their daily living using a mobile glucose predictor, J. Diabetes Sci. Technol. 12 (2) (2018) 243–250. [67] R. Nimri, T. Battelino, L.M. Laffel, R.H. Slover, D. Schatz, S.A. Weinzimer, K. Dovc, T. Danne, M. Phillip, N. Consortium, Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes, Nat. Med. 26 (9) (2020) 1380–1384. [68] N.S. Tyler, C.M. Mosquera-Lopez, L.M. Wilson, R.H. Dodier, D.L. Branigan, V.B. Gabo, F.H. Guillot, W.W. Hilts, J. El Youssef, J.R. Castle, P.G. Jacobs, An artificial intelligence decision support system for the management of type 1 diabetes, Nat. Metab. 2 (7) (2020) 612–619. [69] X. Didelot, K.B. Pouwels, Machine-learning-assisted selection of antibiotic prescription, Nat. Med. 25 (7) (2019) 1033–1034. [70] R. Zhang, A. Mozaffari, J.J. de Pablo, Autonomous materials systems from active liquid crystals, Nat. Rev. Mater. 6 (2021) 437–453. [71] Y.K. Kim, X. Wang, P. Mondkar, E. Bukusoglu, N.L. Abbott, Self-reporting and self-regulating liquid crystals, Nature 557 (7706) (2018) 539–544. [72] I. Yelin, O. Snitser, G. Novich, R. Katz, O. Tal, M. Parizade, G. Chodick, G. Koren, V. Shalev, R. Kishony, Personal clinical history predicts antibiotic resistance of urinary tract infections, Nat. Med. 25 (7) (2019) 1143–1152. [73] S. Kanjilal, M. Oberst, S. Boominathan, H. Zhou, D.C. Hooper, D. Sontag, A decision algorithm to promote outpatient antimicrobial stewardship for uncomplicated urinary tract infection, Sci. Transl. Med. 12 (568) (2020). [74] G.A. Dumont, A. Martinez, J.M. Ansermino, Robust control of depth of anesthesia, Int. J. Adapt Control Signal Process. 23 (5) (2009) 435–454. [75] M. Yousefi, K. van Heusden, N. West, I.M. Mitchell, J.M. Ansermino, G.A. Dumont, A formalized safety system for closed-loop anesthesia with pharmacokinetic and pharmacodynamic constraints, Control. Eng. Pract. 84 (2019) 23–31. [76] L. Pasin, P. Nardelli, M. Pintaudi, M. Greco, M. Zambon, L. Cabrini, A. Zangrillo, Closed-loop delivery systems versus manually controlled administration of total IV anesthesia: a meta-analysis of randomized clinical trials, Anesth. Analg. 124 (2) (2017) 456–464. [77] A.R. Absalom, N. Sutcliffe, G.N. Kenny, Closed-loop control of anesthesia using bispectral index: performance assessment in patients undergoing major orthopedic surgery under combined general and regional anesthesia, Anesthesiology 96 (1) (2002) 67–73. [78] N. Mekhail, R.M. Levy, T.R. Deer, L. Kapural, S.A. Li, K. Amirdelfan, C.W. Hunter, S.M. Rosen, S.J. Costandi, S.M. Falowski, A.H. Burgher, J.E. Pope, C.A. Gilmore, F.A. Qureshi, P.S. Staats, J. Scowcroft, J. Carlson, C.K. Kim, M.I. Yang, T. Stauss, L. Poree, D. Brounstein, R. Gorman, G.E. Gmel, E. Hanson, D.M. Karantonis, A. Khurram, D. Kiefer, A. Leitner, D. Mugan, M. Obradovic, J. Parker, P. Single, N. Soliday, E.S. Grp, Long-term safety and efficacy of closed-loop spinal cord stimulation to treat chronic back and leg pain (Evoke): a double-blind, randomised, controlled trial, Lancet Neurol. 19 (2) (2020) 123–134. [79] C.S.L. Arlehamn, R. Dhanwani, J. Pham, R. Kuan, A. Frazier, J.R. Dutra, E. Phillips, S. Mallal, M. Roederer, K.S. Marder, A.W. Amara, D.G. Standaert, J.G. Goldman, I. Litvan, B. Peters, D. Sulzer, A. Sette, Alpha-synuclein-specific T cell reactivity is associated with preclinical and early Parkinson’s disease, Nat. Commun. 11 (1) (2020), 1875.

Recent advances in self-regulated drug delivery devices

[80] A.W.P. Fitzpatrick, B. Falcon, S. He, A.G. Murzin, G. Murshudov, H.J. Garringer, R.A. Crowther, B. Ghetti, M. Goedert, S.H.W. Scheres, Cryo-EM structures of tau filaments from Alzheimer’s disease, Nature 547 (7662) (2017) 185–190. [81] A.J. Espay, Y. Baram, A.K. Dwivedi, R. Shukla, M. Gartner, L. Gaines, A.P. Duker, F.J. Revilla, At-home training with closed-loop augmented-reality cueing device for improving gait in patients with Parkinson disease, J. Rehabil. Res. Dev. 47 (6) (2010) 573–581. [82] K.N. Fountas, J.R. Smith, A.M. Murro, J. Politsky, Y.D. Park, P.D. Jenkins, Implantation of a closedloop stimulation in the management of medically refractory focal epilepsy: a technical note, Stereotact. Funct. Neurosurg. 83 (4) (2005) 153–158. [83] F.T. Sun, M.J. Morrell, The RNS system: responsive cortical stimulation for the treatment of refractory partial epilepsy, Expert Rev. Med. Devices 11 (6) (2014) 563–572. [84] E. Krook-Magnuson, J.N. Gelinas, I. Soltesz, G. Buzsaki, Neuroelectronics and biooptics closed-loop technologies in neurological disorders, JAMA Neurol. 72 (7) (2015) 823–829. [85] D.T. Brocker, B.D. Swan, R.Q. So, D.A. Turner, R.E. Gross, W.M. Grill, Optimized temporal pattern of brain stimulation designed by computational evolution, Sci. Transl. Med. 9 (371) (2017) eaah3532. [86] B. Rosin, M. Slovik, R. Mitelman, M. Rivlin-Etzion, S.N. Haber, Z. Israel, E. Vaadia, H. Bergman, Closed-loop deep brain stimulation is superior in ameliorating parkinsonism, Neuron 72 (2) (2011) 370–384. [87] F.T. Sun, M.J. Morrell, Closed-loop neurostimulation: the clinical experience, Neurotherapeutics 11 (3) (2014) 553–563. [88] S. Senova, A. Chaillet, A.M. Lozano, Fornical closed-loop stimulation for Alzheimer’s disease, Trends Neurosci. 41 (7) (2018) 418–428. [89] M.A. Myszczynska, P.N. Ojamies, A.M.B. Lacoste, D. Neil, A. Saffari, R. Mead, G.M. Hautbergue, J.D. Holbrook, L. Ferraiuolo, Applications of machine learning to diagnosis and treatment of neurodegenerative diseases, Nat. Rev. Neurol. 16 (8) (2020) 440–456. [90] Biosensors Market with COVID-19 Impact by Type, Product (Wearable, Non-wearable), Technology, Application (POC, Home Diagnostics, Research Lab, Environmental Monitoring, Food & Beverages, Biodefense) and Region—Global Forecast to 2026. https://www.marketsandmarkets.com/ Market-Reports/biosensors-market-798.html. [91] A.M.J. Shapiro, C. Ricordi, B.J. Hering, H. Auchincloss, R. Lindblad, R.P. Robertson, A. Secchi, M.D. Brendel, T. Berney, D.C. Brennan, International trial of the Edmonton protocol for islet transplantation, N. Engl. J. Med. 355 (13) (2006) 1318–1330. [92] M. Xie, H. Ye, H. Wang, G.C.E. Hamri, C. Lormeau, P. Saxena, J. Stelling, M. Fussenegger, betacell-mimetic designer cells provide closed-loop glycemic control, Science 354 (6317) (2016) 1296–1301. [93] Z.T. Li, Y.X. Wang, Y.Y. Ding, L. Repp, G.S. Kwon, Q.Y. Hu, Cell-based delivery systems: emerging carriers for immunotherapy, Adv. Funct. Mater. 31 (2021), 2100088. [94] J.M. Brunger, A. Zutshi, V.P. Willard, C.A. Gersbach, F. Guilak, Genome engineering of stem cells for autonomously regulated, closed-loop delivery of biologic drugs, Stem Cell Rep. 8 (5) (2017) 1202–1213. [95] M.M. Zhao, K. Hoti, H. Wang, A. Raghu, D. Katabi, Assessment of medication self-administration using artificial intelligence, Nat. Med. 27 (2021) 727–735. [96] D.W. Bates, D. Levine, A. Syrowatka, M. Kuznetsova, K.J.T. Craig, A. Rui, G.P. Jackson, K. Rhee, The potential of artificial intelligence to improve patient safety: a scoping review, NPJ Digit. Med. 4 (1) (2021) 54. [97] P. Olivera, S. Danese, N. Jay, G. Natoli, L. Peyrin-Biroulet, Big data in IBD: a look into the future, Nat. Rev. Gastroenterol. Hepatol. 16 (5) (2019) 312–321. [98] C.Y. Wang, B.Z. Lin, H.P. Zhu, F. Bi, S.S. Xiao, L.Y. Wang, G.Q. Gai, L. Zhao, Recent advances in phenylboronic acid-based gels with potential for self-regulated drug delivery, Molecules 24 (6) (2019) 1089. [99] J. Coffel, E. Nuxoll, BioMEMS for biosensors and closed-loop drug delivery, Int. J. Pharm. 544 (2) (2018) 335–349. [100] K. Scholten, E. Meng, A review of implantable biosensors for closed-loop glucose control and other drug delivery applications, Int. J. Pharm. 544 (2) (2018) 319–334.

369

CHAPTER 14

Design and control of nanorobots and nanomachines in drug delivery and diagnosis Fulden Ulucan-Karnaka, Gulden Camci-Unalb,c, Beyza Karacaoglua, € u  lud,e € r Seydibeyog and Mehmet Ozg a Department of Medical Biochemistry, Institute of Health, Ege University, Izmir, Turkey Department of Chemical Engineering, University of Massachusetts Lowell, Lowell, MA, United States c Department of Surgery, University of Massachusetts Medical School, Worcester, MA, United States d Department of Materials Science and Engineering, Izmir Katip Celebi University, Izmir, Turkey e Advanced Structures and Composites Center, University of Maine, Orono, ME, United States b

14.1 Introduction Rapid technological advancements and improvements in nanotechnology, robotics, and medicine have never before been witnessed to such an extent as in the current field of nanorobots. Recent developments in the characterization of nanomaterial and nanofabrication techniques have led scientists to develop micro- and nanosized devices and robots for various applications. Since Feynman stated that “there is plenty of room at the bottom” in 1959, there has been a significant body of work reported in nanotechnology, especially after the 2000s. Many commercial products with micro/nanocomponents are now available in sensors, electronics, and nanocomposites. The advanced materials and optimized manufacturing approaches have enabled scientists to develop novel solutions to the main challenges in the fields of micro- and nanotechnology, such as particle growth mechanisms, assembly, stabilization, and reproducibility [1]. Both tissue engineering and regenerative medicine offer tremendous research opportunities to improve human health and quality of life. Currently, a large number of new products are being translated from benchtop to clinic and are also being commercialized. For instance, there have been recent breakthroughs in the understanding of the mechanisms of cancer to provide new solutions for cancer therapy that were not possible 10 years ago [2–4]. Nanomaterials can be utilized for targeting certain cells such as rare cells or cancer cells with special biomarkers, thus enhancing the local concentration of drugs and reducing the toxicity of biomaterials. Delivery of nanomaterials can be achieved through active or passive targeting mechanisms, as illustrated in Fig. 14.1 [5]. Nanobiotechnology utilizes emerging approaches such as nanorobots and nanomachines by combining the principles of electronic engineering, mechanical engineering, mechatronics, materials science, and bioengineering. Drug delivery is a crucial strategy A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00013-7

Copyright © 2023 Elsevier Inc. All rights reserved.

371

372

A handbook of artificial intelligence in drug delivery

Fig. 14.1 A schematic representation of passive and active drug targeting for cancer cells using the enhanced permeability and retention (EPR) effect. (Reproduced with permission P.N. Navya, A. Kaphle, S.P. Srinivas, S.K. Bhargava, V.M. Rotello, H.K. Daima, Current trends and challenges in cancer management and therapy using designer nanomaterials, Nano Converg. 6(1) (2019) 23. Available from: https://nanoconvergencejournal.springeropen.com/articles/10.1186/s40580-019-0193-2. Copyright 2019, Springer.)

to transport the proper amount of a drug to the correct place. Recently, the medical and pharmaceutical industries have suffered from various limitations, including the side effects of drugs, and, thus, many people avoid taking medicines such as chemotherapeutic drugs [6]. Hence, it is crucial to develop drugs that can specifically target the problem with minimum side effects. Nanobiotechnology can be used to address this important limitation.

14.1.1 Drug delivery Drugs and pharmaceutical products are typically administered either orally or intravenously. They typically do not target specific types of tissues. There have been a host of research studies on biodegradable capsules that enable delivery of the active reagent in a controlled manner, although they are still in the developmental stage. Nanorobots that are coupled with biodegradable polymers can potentially play critical roles in improving drug penetration to the diseased tissue [7,8]. Currently, there are challenges in addressing the side effects of drugs that induce damage in healthy cells and cause stomach problems [9]. The common methodology for administering drugs is to deliver them to the whole body rather than targeting only the problematic area. Nanobiotechnology aims to fulfill a big dream of the human race,

Design and control of nanorobots and nanomachines

which is resolving these issues using nanomachines and nanorobots enhanced with biodegradable polymers [10].

14.1.2 Nanotechnology for drug delivery Use of nanorobots and nanomachines as emerging tools in biomedical applications has led scientists to find new solutions to drug delivery problems. Nanomaterials that have less than 100 nm in one dimension provide many new opportunities for biomedical applications. With the use of new nanofabrication methods, nanorobots can be fabricated and used in new medical applications [11,12].

14.2 Nanorobots and nanomachines There is an enormous need for new improvements in nanoscale technologies for biomedical applications. These technologies help improve the bioavailability of drugs when they are used as nanocarriers or determine biomarkers specifically and sensitively at low concentrations in biological fluids. Nanorobots, nanomachines, and other nanosystems, the dimensions of which are on a nanometer scale, are powerful tools for technological enhancements in biomedicine [13–15]. Nanomachines can be divided into three main groups, namely, molecular machines, self-assembled nanomachines, and hybrid inorganic nanomachines. Their size scales can vary between 1 and 100 nm, as seen in Fig. 14.2 [16]. By mimicking the biological organisms in nature, artificial nanomachines are becoming increasingly reflective for understanding the characteristics and challenges in biological systems [17]. Some examples of nanorobots from the literature with different shapes and sizes are provided in Fig. 14.3.

14.2.1 Design and fabrication of nanorobots and nanomachines Nanorobots and nanomachines contain nanoscale components such as biomolecules, synthetic polymers, and molecular constituents. Their sizes are typically either below or in the micrometer range varying from 0.1 to 10 μm. Although nanorobots and nanomachines are sometimes referred to as nanoelectromechanical devices, there are differences between them in terms of functionality and autonomy. Nanomachines are designed to execute their predetermined actuator tasks regardless of the environmental factors, whereas nanorobots are reprogrammable and autonomously controlled [18]. The miniaturization process has various advantages, including portability, small size, less reagent and sample requirement, and cost- and time-effectiveness. These micro- or nanosized vehicles can contain a combination of polymers, nanoparticles, nucleotides, and elements of micro-electromechanical systems (MEMSs). Micro- and nanofabrication techniques can facilitate chemical, biological, and physical interactions and responses at

373

374

A handbook of artificial intelligence in drug delivery

Fig. 14.2 Molecular machines and nanomachines that are classified based on their length scales and material systems. (Reproduced with permission E. Ellis, S. Moorthy, W.-I.K. Chio, T.-C. Lee, Artificial molecular and nanostructures for advanced nanomachinery, Chem. Commun. 54(33) (2018) 4075–4090. Available from: http://xlink.rsc.org/?DOI¼C7CC09133H. Copyright 2018, RCS.)

the cellular and molecular scales through miniaturization or improvements in size, dimension, organization, morphology, and functionality [19]. The most widely studied nanorobots are the organic and inorganic ones. Organic nanorobots, also known as bionanorobots, are generated using viral and bacterial DNA. They have low toxicity for the target organism [20]. Inorganic nanorobots can be generated from complementary metal oxide semiconductors (CMOSs), carbon nanotubes, diamondoids, and fullerenes, and they are commonly known as nanoelectromechanical systems (NEMSs) [21,22]. Inorganic nanorobots may not be biocompatible. To tackle this hurdle, nanorobots can be encapsulated in materials such as alumina layers [23] or the nitrocellulose membrane [24]. Brownian motion and Reynolds number are important parameters in designing nanorobots. To address the limitations in Brownian motion and low Reynolds numbers, an autonomous power supply and nanomotors should be integrated into these systems. Substructures such as swimming tail, payload, and other nanoscale components are required for synchronized and properly operating components [25].

Design and control of nanorobots and nanomachines

Fig. 14.3 Examples of nanorobots from the literature. (A) A 200–300-nm glass propeller. (B) A 200nm-thick artificial bacterial flagellum. (C) An antiseptic drug-loaded gold (Au)-nickel (Ni)-gold (Au)polymer (PPyPSS) nanorod. (D) A fish-like 200-nm-wide nanorod. (E) A hexagonal cage-like DNA robot. (F) DNA nanorobots consisting of 90nm  50nm  2 nm sheets. (Reproduced with permission R. Arvidsson, S.F. Hansen, Environmental and health risks of nanorobots: an early review, Environ. Sci. Nano 7(10) (2020) 2875–2886. Available from: http://xlink.rsc.org/?DOI¼D0EN00570C. Copyright 2020, RCS.)

Choosing the proper fabrication technique is a crucial factor for designing micro- and nanorobotic devices. Conventional microfabrication techniques include lithography, nanoimprinting, thin-film deposition, chemical etching, and electrodeposition. To address the low-resolution limitations, self-assembly, dip-pen lithography, and directed self-assembly techniques have been developed [26].

375

376

A handbook of artificial intelligence in drug delivery

Table 14.1 Major lithography techniques and their applications [27]. Techniques

Applications

Photolithography

Laboratory-level patterning and production of various MEMS devices, commercial electronic products, CPU chips Mask production, R&D of photonic crystals, nanofluidic channels

Electron beam lithography Ion beam lithography Soft lithography Nanoimprint lithography Dip-pen lithography

R&D of hole arrays, plasmonic lens, bull’s eye structure Lab-on-a-chip applications Biosensors, lab-on-a-chip, and nanodevices Biosensors, gas sensors, bioelectronics

Micro- and nanolithography technologies are utilized for creating patterns in microand electromechanical systems, including NEMS- and MEMS-based devices. The main lithography techniques can be classified as photolithography, electron beam lithography, ion beam lithography, soft lithography, nanoimprint lithography, and dip-pen lithography. These techniques and their applications are reported in the literature as listed in Table 14.1 [27]. The term “nanorobot” was first used in the late 1990s. This term has occurred in Requicha et al.’s book chapter in 1999 [28], in Sitti and Hashimoto’s paper in 1998 [29], and in Freitas’ book on nanomedicine in 1999 [30,31]. The construction of miniaturized machines like nanorobots has evolved after discoveries of fullerenes and carbon nanotubes. To date, this field has been rapidly progressing with new technological advances [32]. Although interest in nanorobots has been increasing in the scientific world since 2016, the Nobel Prize in chemistry was awarded to a group of engineers from Hong Kong for developing a nanorobot as the world’s smallest machine with controllable movements, which can perform a task when energy is added [33]. Clean-room facilities and lengthy procedures are commonly used to fabricate micro- and nanorobots. Another drawback of such small-scale robots in biomedical applications is their biocompatibility and biodegradation behavior. To address the design challenges of micro- and nanorobots, engineers, physicists, applied mathematicians, and biologists have been working together using interdisciplinary approaches [34].

14.2.2 Actuation mechanisms of nanorobots and nanomachines The actuation mechanisms of nanorobots and nanomachines are classified as physical, chemical, biological, and hybrid. To obtain power, one of these actuation methods is chosen based on the target application [35]. For physical actuation methods, magnetic

Design and control of nanorobots and nanomachines

Light Energy Electric Field

Ultrasound Energy

Magnetic Field

Chemical Reaction H2O2 ® H2O + O2

N S

Micro/nanorobot Model

Targeted Drug Delivery

Fig. 14.4 Main actuation types of micro- and nanorobots. (Reproduced with permission M. Hu, X. Ge, X. Chen, W. Mao, X. Qian, W.E. Yuan, Micro/nanorobot: a promising targeted drug delivery system, Pharmaceutics 12(7) (2020) 1–18. Copyright 2020, MDPI.)

and electric fields, heat, acoustic wave, and light energy are used as the external power source (Fig. 14.4) [36]. According to their energy supply, nanorobots can be divided into self-propelled and external field-propelled types. In self-propelled nanorobots, energy is generally supplied by water, H2O2 solution, or acidic solutions. H2O2 typically lowers the interfacial tension of the aqueous solution. When there is a gradient in oxygen concentration, a net axial propelling force is generated [37]. Similarly, the gradient of water surface tension is affected by acidic solutions, which can induce self-propulsion [38]. External fieldpropelled nanorobots are driven by external magnetic, electric, or ultrasonic fields. In Table 14.2, the actuation mechanisms, main properties, and potential biomedical applications are summarized according to the literature paper by Wang et al. [10]. The selection of the right actuation methods for developing a novel nanorobot can increase the nanorobots’ ability of transport and 3D assembly [39]. To achieve the drag force of a nanorobot in viscous fluids, it is a must to generate a sustainable and powerful actuation force. It is known that there are three main factors that significantly affect the actuation force of motion: (1) actuation mechanism, (2) structure, and (3) light wavelength [40]. All of these properties should be considered while selecting the actuation methods, which could be integrated into the developed system to meet the demanded requirements.

377

378

A handbook of artificial intelligence in drug delivery

Table 14.2 Actuation methods and potential biomedical applications. Actuation mechanism

Main properties

Possible biomedical applications

Magnetic field

High penetration Multiple modes of motion Precise and multiple degrees of freedom control of tiny robots Biocompatible High penetration Fast response and long lifetime Biocompatible and safe for patient use Can be used for multiple agents High-speed motion of individual robots Biochemical fuels (enzymeinduced, catalysts) No external actuation setup is required Rapid motion Chemotaxis-guided motion Contact-free Low cost

Suitable for patients without metal implants

Acoustic field

Light field

Fuel

Electric field

Marangoni effect

Surface energy-guided motion

Magnetic field + acoustic field

High penetration of the tissue and body Reversible swarming behavior Safe and biocompatible High penetration of the tissue and body

Magnetic field + fuel

Can be examined in in vivo applications

Suitable for sensing and diagnosis

Bubble-propelled motors

Can be used in an ionic biological medium such as interstitial fluid and blood Suitable for in vitro tissue model applications and liquid/air or solid/ liquid interfaces Suitable for patients without metal implants

Suitable for patients without metal implants

This table is modified according to B. Wang, K. Kostarelos, B.J. Nelson, L. Zhang, Trends in micro-/nanorobotics: materials development, actuation, localization, and system integration for biomedical applications, Adv. Mater. 33(4) (2021) 2002047. Available from: https://onlinelibrary.wiley.com/doi/10.1002/adma.202002047.

14.2.3 Nanotechnology-enabled artificial blood: Respirocytes, clottocytes, and microbivores Nanotechnology is also utilized to design artificial blood for biomedical applications. This type of nanoengineered material is also called a “nanobot,” and it has been proven to act intelligently in the treatment of diseases such as cancer, kidney diseases, chronic heart

Design and control of nanorobots and nanomachines

diseases, etc. Nanobots can be classified into respirocytes, microbivores, and clottocytes [41]. Respirocytes are nanobots that are artificial red blood cells, and they aim to mimic the function of the red blood cells in the body. They can carry O2 and CO2 and are designed for treatment of anemia and lung problems with their high cargo capacities [42]. A respirocyte structure includes different parts such as molecular motors, a power generator, sensors, and a receiver. They are designed to work on a molecular scale with sizes ranging between 0.2 and 1 μm in diameter. Respirocytes can be designed for various purposes such as for the release of stored oxygen, capture of oxygen and carbon dioxide in the bloodstream, and uptake and release of glucose from the bloodstream as a source of energy [43]. Respirocytes have been suggested to be a possible source of oxygen availability on Mars to generate an alternative life platform [44]. Microbivores are biomimetic medical nanobots that can act as artificial white blood cells and can be used for therapeutic purposes. They are designed to attach to a specific target surface of the pathogens and destroy them, similar to white blood cells [42]. A microbivore has a spheroid shape and is made of diamond and sapphire. It measures 3.4 μm in diameter along its major axis and 2 μm in diameter along its minor axis. Microbivores are responsible for phagocytosis and can also be used for digestion of biofilms. It is known that microbivores are up to 1000 times faster than natural or antibiotic-assisted biological phagocytosis and also 80 times more efficient than macrophages, in terms of volume/second digestion of bacterial components into harmless and nonantigenic molecules per unit volume of a phagocytic agent [45]. Clottocytes are artificial mechanic platelets, and they can be used for clotting to decrease the natural bleeding time and to avoid hemostatic problems [41]. They are designed to be biomimetically the same size as natural platelets with a diameter of 2 μm. They include biodegradable fiber meshes and soluble coatings. They can act 100–1000 times faster than natural platelets [46]. Nanorobots may travel in and out of the skin and interact with the digestive system. Apart from the respirocytes, microbivores, and clottocytes, there are also vasculocytes and cleaners for artery repair, and chromallocytes that are used as gene delivery vectors for chromosome replacement. A schematic representation of these nanobots is shown in Fig. 14.5 [47].

Fig. 14.5 A schematic representation of different types of medical nanorobots. (Reproduced with permission M. Swan, Engineering life into technology: the application of complexity theory to a potential phase transition in intelligence, Symmetry (Basel) 2(1) (2010) 150–183. Available from: http:// www.mdpi.com/2073-8994/2/1/150. Copyright 2010, MDPI.)

379

380

A handbook of artificial intelligence in drug delivery

The major limitations of the use of nanobots are their challenging design and developing proper manufacturing techniques. Their manufacturing processes are time-consuming and expensive. Nevertheless, the use of nanobots in biomedicine, especially in the diagnosis and treatment of diseases, provides a basis for potential in vivo applications. Previous studies have suggested that nanobots can play an important role in increasing life expectancy and improving human health [48].

14.2.4 Nanorobots and nanomachines as smart biosensors Robotic platforms have the advantages of miniaturization of treatment and diagnostics and facilitation of easy and remote access to body sections for surgeons. In the last few decades, medical nanorobots and nanomachines have found widespread clinical use. They can be integrated into drug delivery, surgery, imaging, and diagnostic applications in precision medicine (Fig. 14.6) [49]. The use of nanorobots and nanomachines as smart biosensors is an emerging trend in biosensor development. Recent studies in this field have been divided into three categories according to different sensing mechanisms: 1. Chemical sensing-based 2. Electrochemical sensing-based

Fig. 14.6 A schematic of the applications of micro- and nanorobotics in precision medicine. (Reproduced with permission F. Soto, J. Wang, R. Ahmed, U. Demirci, Medical micro/nanorobots in precision medicine, Adv. Sci. 7(21) (2020) 2002203. Available from: https://onlinelibrary.wiley.com/doi/ 10.1002/advs.202002203. Copyright 2020, Wiley.)

Design and control of nanorobots and nanomachines

3. Fluorescent, electrochemiluminescent (ECL), and colorimetric sensing-based microand nanorobots [50] Different types of biomolecules/biomarkers can be determined by nanosensors through the differences in their binding sites toward different analytes. The sensing ability of nanorobots enables detection and identification of targeted molecules continuously in real time for in vivo prognosis purposes. Generally, nanorobots with embedded nanobiosensors have the advantages of finding, treating, and monitoring diseases and therapeutic treatments as well as monitoring neuro-electrical signals [51]. DNA is a biopolymer that has been suggested as a powerful tool for generation of nanostructures in different geometric shapes and dimensions. Single-strand DNAs (ssDNAs) are relatively flexible and can act as flexible joints and hinges. Double-strand DNAs (dSDNAs) are rigid and can act as arms or limbs in nanomachine designs. Other parts of DNA, such as hairpin, which is single-strand loop structure, are also important because they help maintain stability and conformational switching kinetics. DNAzymes (deoxyribozymes) are enzymes that are capable of catalyzing chemical reactions, including ribonucleases, DNA and RNA ligases, and MNAzymes. Therefore, DNA can be useful to several parts in terms of their properties in the development of DNA-based biosensors [52]. Biosensors that are based on DNA walkers have shown promise, although their amplification efficiency is limited because of their slow walking and low processivity properties. Wu et al. (2020) designed a high-speed and high-processive DNA rolling machine (a biosensor that is constructed with hairpin-loaded Au nanoparticles (NPs) (hpDNA@AuNPs) as a DNA walker and AgNC-decorated magnetic NPs (AgNCs@MNPs) as a DNA rolling machine). Concentration was found to be in a linear range of 0.5–500 fmol L1, and the detection limit was found to be 119 fmol L1 for the p53 cancer-related gene. p53 recovery within the range of 98%–106% was achieved, along with relative standard deviation (RSD) of 2.8%–7.1%. These results have proved that the designed DNA nanomachine biosensor is a candidate in the assay of the p53 gene from complex biological samples [53]. Another DNA-based biosensor was reported by Zheng et al. [54]. The researchers used an HIV nucleic acid as a biomarker. They designed a DNA nanomachine on gold nanoparticles (AuNPs) and DNA walker cascade amplification for ultrasensitive detection of HIV nucleic acids. This sensing strategy resulted in a detection limit of 1.46 fM. This system can also determine the target DNA with high selectivity and sensitivity. Moreover, the DNA nanomachine-based biosensor could determine a single-base mismatch in lower than 3.5 pM concentrations. The results suggested that the DNA-based biosensor has high potential for clinical applications of HIV DNA determination [54]. In another study, DNA walking and rolling nanomachines were used in electrochemical biosensors for miRNA detection by Miao et al. [55]. DNA nanotechnology exhibits great potential in the fabrication of electrochemical response-amplified miRNA biosensors. This research group developed a novel DNA walking and rolling nanomachine for

381

382

A handbook of artificial intelligence in drug delivery

highly sensitive and selective detection of miRNA with a limit of detection as low as 39  1018 M. This biosensor was suggested to be useful for miRNA assays in clinical diagnostics [55].

14.3 Applications of nanorobots and nanomachines in targeted drug delivery 14.3.1 In vitro applications Targeted drug delivery can significantly benefit from the advances in nanorobotic chemotaxis. For example, a nanomotor was developed to meet the specifications of 308-nm size, 78.1% loading capacity, and directional motion functionalized with platinum nanoparticles (PtNPs) and loaded with doxorubicin (DOX), which is a model anticancer drug. It has been reported that this nanomotor exhibited chemotactic movements in the presence of hydrogen peroxide. This can be useful for the delivery of drugs to the late stages of tumor tissues of organs because, in nanomotors, chemotactic movements require a higher concentration of fuels for higher speeds and longer distances [56,57]. Magnetotactic bacteria (MTB) that naturally produce magnetic iron oxide nanoparticles were combined with liposomes loaded with therapeutic agents in vitro to overcome the diffusion limits of large drug molecules in hypoxic regions. Magnetococcus marinus MC-1 can be potentially used as a therapeutic nanorobot. This study reported an approach for bacteria-conjugated liposomes (MTB-LP). Larger amounts of therapeutics can be released by magnetotactic bacteria (Gram-negative bacteria that move in response to Earth’s magnetic field) to overcome the diffusion limits against large drug molecules in hypoxic areas (by bacteria with a low oxygen requirement: microaerophilic) within solid tumors that are difficult to treat. The main advantage of bacteria-conjugated drug-loaded liposomes is their binding of therapeutic cargo, without significantly affecting the bacteria’s capacity to perform as delivery agents. Cytotoxicity experiments were performed with three different cell types using J774, NIH/3T3, and Colo205 cells. It had been found that liposomal binding to the MTB formulation increased the biocompatibility of MTB, whereas binding did not interfere in liposomal uptake [58]. In another approach, sensory-based and directionally controlled agents in the magnetotactic bacterial (MTB) form of the MC-1 strain were investigated for their effectiveness as therapeutic nanorobots in cancer treatment. Using the magnetotactic displacement behavior, appropriate ranges of the directional magnetic field are defined to direct the MC-1 cells toward the tumor volume. The microaerophilic response of drug-loaded magnetotactic bacterial cells (MC-1) can be applied in tumoral interstitial fluid microenvironments, after computer-based magnetotactic control, to reach the tumor site. In this manner, the swimming paths will be guided by the reduction of oxygen concentration toward the hypoxic areas. Nevertheless, the implementation of such a targeting strategy requires a method to switch from a computer-assisted magnetotactic displacement

Design and control of nanorobots and nanomachines

control to an autonomous aerotactic displacement control. Consequently, the magnetoaerotactic displacement behavior of self-propelled microaerophilic MC-1 bacteria is possibly the standard for carrying therapeutic loads to the hypoxic areas of solid tumors, following peritumoral injections. Therefore, the buoyancy response of these agents to directional magnetic fields of various sizes has become a key feature for the implementation of a successful targeting strategy. This is followed by an autonomous aerotacticbased displacement of the hypoxic regions toward lower oxygen concentration. Determination of magnetic field ranges will help create requirements for future clinical platforms that can take advantage of the displacement behavior of MC-1 cells, maximize therapeutic effects while minimizing systemic toxicity, and more effectively target hypoxic areas of solid tumors [59]. In this study, a FePd nanobot that can target specific regions in the cancer cell population and perform bioorthogonal organometallic activation reactions of the commercial prodrug Pro-5-FU in vitro was published for the first time. For targeted and bioorthogonally powered activation of the suppressed chemotherapeutic prodrug 5-fluoro-1-propargyl-uracil (Pro-5-FU) in in vitro and in vivo cancer models, a hybrid nanowire (NW) is recommended. The NWs composed of Fe and Pd entities were synthesized by template-assisted electrodeposition. Consequently, the biorthogonal conversion of the latent Pro-5-FU into the active chemotherapeutic 5-FU in the presence of FePd NWs by initiating important cell death in a breast cancer in vitro cell model—the MDA-MB-231 cell line—and in vitro tests did not show any important cytotoxic effect by the NWs. The magnetic properties of Fe discovered in nanowires have allowed the in vitro study to be expanded to prove targeted cancer treatment. Using the ferromagnetic properties of Fe within the structure, wireless locomotion and applied this property to prove spatially targeted activation of Pro-5 FU in vitro. Magnetic fields were used to attract FePd nanowires to predefined cancer regions. This triggered the local activation of Pro-5-FU and subsequent cell death in this region alone [60]. Hortela˜o et al. manufactured and tested in vitro nanobots operated by urease as the enzyme for doxorubicin (Dox) anticancer drug loading, release, and effective delivery to cells. Mesoporous silica-based core-shell nanobots self-propagate in an ionic environment (PBS buffer). To prove the capability of the enzyme-driven nanobots to improve the release of the drug, Dox-loaded nanobots were dispersed in solutions of an ionic buffer without urea and in solutions containing different concentrations of urea. Furthermore, Dox-loaded MSNP-NH2 was also located in PBS to compare the nanobots to the conventional MSNP-NH2 carriers. As a result, a fourfold rise in drug release was seen in nanobots compared to their passive equivalent carriers. Combined with the synergistic impact of ammonia produced in high concentrations of the urea substrate, the use of enzyme-driven drug-loaded nanobots suggests improved drug release and anticancer efficacy against HeLa cells in vitro. Future studies will be conducted on fuel-dependent targeting and cellular uptake to develop intelligent and self-propelled drug delivery carriers

383

384

A handbook of artificial intelligence in drug delivery

based on enzyme catalysis. These studies also need to address newly induced release mechanisms and in situ guidance methods such as the use of pH and thermal or chemical gradients to attract and drive nanomotors in vitro and in vivo [61]. Serra` et al. [62] manufactured multisegmented, biocompatible, and nontoxic (Au/ Ni-NiO)8 nanowires (NWs), which are 100 nm in diameter and a few micrometers in length, by the electrochemical synthesis procedure in a single bath. A single electrolytic solution containing both gold (i.e., Au (III) chloride trihydrate) and nickel (i.e., Ni(II) chloride tetrahydrate) salts was used to develop multisegmented (Au/Ni)8 NWs inside the nanochannels of a polycarbonate membrane. The magnetic propulsion of the (Au/Ni-NiO)8 NWs stimulates their vibration and motion inside HeLa cells in vitro and thus increases programmed cell death, without any proof of mechanical demolition. So, multisegmented (Au/Ni-NiO)8 NWs can be applied in therapeutics as two types of drug carriers and can cause cell death via magnetic propulsion. In addition, this method increases the effect of treatments such as hyperthermia and photodynamic therapy and the release of some drugs [62]. In another in vitro application of nanorobots for targeted drug delivery, FeGa@P (VDF-TrFE), a hybrid magnetoelectric core-shell composite nanowire (NW) with a magnetostrictive core and a piezoelectric shell, is designed and manufactured. First, ferroelectric P(VDF-TrFE) nanotubes are acquired by the melt-wetting of anodic aluminum oxide (AAO) templates. The following electrodeposition of FeGa allows conformal development of the magnetostrictive core. The manufacturing strategy allows easy adjustment of the geometric parameters (i.e., length and diameter) of the NW and, more importantly, superior interfacing between the piezoelectric and magnetostrictive phases. Structural characterization tests with IR, XRD, and EDX have confirmed that FeGa is in the appropriate piezoelectric and magnetostrictive phase. Ferroelectricity within the nanotube is also verified using piezoresponse force microscopy. NWs can charge the anticancer drug paclitaxel after surface functionalization using polydopamine and release the drug upon application of alternative magnetic areas caused by the magnetoelectric impact. Hence, cancer cells are destroyed by the released drug. Precise magnetic maneuvering abilities have been demonstrated using low-amplitude, rotating magnetic areas. This proven precision control strategy has provided NWs with an enhanced targeting capability. Different magnetic stimuli originating from the same energy source allow these devices to efficiently transport and deliver drugs to the targeted site while minimizing the side effects of systemically administered drugs. In summary, it has been demonstrated that FeGa@P(VDF-TrFE) can be activated and triggered using various magnetic areas for targeted drug delivery [63]. To transport and release drugs activated by alternating magnetic fields (AMFs), coreshell nanoparticles are used in vitro as supramolecular valves that include a bulky cucurbit uril (CB) ring and an alkylammonium thread attached on the surface of mesoporous silica nanoparticles (MSNs), which act as thermosensitive gatekeepers. Leading to the

Design and control of nanorobots and nanomachines

separation of CB from the thread, the binding constant between the CB ring and the alkylammonium thread reduces when the temperature rises, followed by drug delivery. The delivery of drugs was stimulated by localized heating produced from the interior magnetic cores and not by bulk heating from the solutions. In this study, DOX released intracellularly by AMF stimulation has been shown to effectively kill MDA-MB-231 breast cancer cells in vitro and, subsequently, enable spatial, temporal, and dose control of drug delivery using superparamagnetic MnFe2O4@CoFe2O4 nanoparticles with a high specific power loss and high saturation magnetization. In vitro studies demonstrate that the drug delivery system is biocompatible, DOX-loaded nanocarriers do not kill the cells without AMF stimulation, and cell death is correlated with the AMF exposure time [64].

14.3.2 In vivo applications The clinical applications of micro/nanorobots are still affected by the advances in nanotechnology and bioengineering. In Fig.14.7A, their in vivo applications in humans can be

Fig. 14.7 (A) A schematic of the medical perspectives in micro/nanorobotics for in vivo human applications. (B) The cumulative number of published articles. (C) The impact factor of those publications. (Reproduced with permission F. Soto, R. Chrostowski, Frontiers of medical micro/ nanorobotics: in vivo applications and commercialization perspectives toward clinical uses, Front. Bioeng. Biotechnol. 6(November) (2018) 1–12. Copyright 2018, Frontiers.)

385

386

A handbook of artificial intelligence in drug delivery

subcategorized into biopsy, delivery, healing, and retention. In Fig. 14.7B, in vivo studies that were published between 2006 and 2018 can be seen with an increasing rate. The impact factor of these studies is also shown in Fig. 14.7C. As can be seen, these strategies and studies were accepted from high-impact journals [65]. Nanorobotics are highly common in tumor in vivo applications because medical nanorobotics hold significant capability to deliver drugs with a higher degree of precision and speed than do traditional methods [66]. Thus, targeted delivery has been developed by current in vitro studies in nanorobotic chemotaxis [57] and material research using stimuli-triggered drug delivery [65,67,68]. The programmability of chemical or biosensing effects of in vivo sensing and actuation purposes are achieved [69]. The use of magnetic nanobots to activate prodrugs in chemotherapy is an encouraging alternative to the existing chemotherapeutics. Magnetically driven nanorobots were used to deliver the fluorouracil drug to decrease the tumor growth in an in vivo nude mice model of MDA-MB-231 human breast cancer xenografts. In this chapter, the experiment was carried out between five groups. Groups 4 and 5 received an intratumoral injection of a FePd nanowire suspension in phosphate-buffered saline (PBS), whereas Groups 1, 2, and 3 were injected with the same volume of PBS. The intraperitoneal injections of active 5-FU (Group 3) and inactive Pro-5-FU (Groups 2 and 5) were administered three times a week for 2 weeks. Consequently, when compared to the control mice models, the groups treated with the active drug 5-FU (Group 3) and FePd nanowires with Pro-5-FU (Group 5) exhibited a major decline in tumor growth. Furthermore, tumor growth inhibition of the triggered prodrug was comparable to the active chemotherapeutic agent. This study validates the in vivo activation of Pro-5FU by FePd nanowires injected into the tumor. Magnetococcus marinus strain MC-1 was used to transport drug-loaded nanoliposomes to the hypoxic areas of the tumor. First, the HCT116 cell suspension was subcutaneously injected into SCID beige mice to create a tumor colorectal xenograft model. The two experimental groups, which were subjected to a magnetic field, and the control group were randomized following measurement of the tumor sizes. Liposomes were chosen as nanocarriers because of their biocompatible, low immunogenicity, and high flexibility features. Moreover, they protect the body from potential toxic cargo. In this study, it has been found that harnessing swarms of microorganisms exhibiting magneto-aerotactic behavior can importantly recover the therapeutic index of various nanocarriers in tumor hypoxic regions when MC-1 cells bearing covalently bound drug-containing nanoliposomes were injected near the tumor in severe combined immunodeficient and magnetically directed mice [70]. In a study with in vitro and in vivo applications, a multifunctional nanorobot structure was developed. The aim of this study was to develop clinically useful systems for cancer treatment or other biomedical applications, as the developed nanorobot system increases the therapeutic effectiveness of cancer and overcomes biological barriers. The nanorobot structure for tumor therapy was mainly comprised of a multifunctional nanorobot

Design and control of nanorobots and nanomachines

structure (MF-NRS), an electromagnetic actuation (EMA) module, and an NIR light exposure module. This system combined the application, detection, drug release, and diagnostic functions for cancer treatment. The advanced nanorobot structure showed tumor-targeting ability of more than 71.8% in in vivo rat liver models. The results demonstrated the attractive potential of targeting MF-NRS in vivo. Furthermore, MF-NRS proved to have the promising capability to be applied as an MR and a CT imaging contrast agent. This is useful for monitoring MF-NRS biodistribution, analyzing tumor size, and estimating tumor therapeutic efficacy. In the future, real-time visual feedback of tumor targeting and in vivo therapeutic efficiency can be achieved after the nanorobot system is properly upgraded [71].

14.4 Self-driven and bioinspired nanorobots and nanomachines for drug delivery Nanorobots and nanomachines have been declared as next-generation tools, which can be guided by endogenous and exogenous stimuli. Different propulsion mechanisms can be evaluated to gain energy such as self-propulsion, external stimulus-based propulsion, and biohybrid strategies. All of these strategies can be combined to swim and penetrate targeted cells/tissues and perform their tasks [72]. Self-propelling nanorobots have the ability to intrinsically navigate in biological fluids with enhanced pharmacokinetics and penetrate deeper tissues, especially in targeted therapies [73].

14.4.1 Autonomous nanorobots and nanomachines as drug delivery vehicles The low efficacy of biological agents and drugs has enforced researchers to develop alternative strategies against traditional ones for efficient drug targeting and delivery. To this end, self-navigating, autonomous drug delivery systems have cropped up. They have the ability to select targeted peptides, undergo vasculature-directed in vivo phage screening, and penetrate into the tissue. These systems can be utilized for designing novel medications and imaging probes for precision and personal medicine [74]. Active transport using self-propelled or autonomous nanorobots or nanomachines propose increasing the diffusion of the drug to the targeted locations [75]. Autonomous nanorobots can produce results using multiple disease markers with increasing theragnostic intelligence levels. Therefore, design of smart nanorobots with biocomputing capability, which can autonomously produce results in the disease microenvironment, is a vital step in personalized and intelligent disease treatment [76]. DNA nanorobotics in biomedical applications such as drug delivery have shown exciting developments in the past decade in terms of hosting molecular payloads and have switched them between open and closed conformations [77].

387

388

A handbook of artificial intelligence in drug delivery

Using the DNA origami strategy, an autonomous DNA robot can be constructed. Li et al. developed a nanorobot that is functionalized on the outside with a DNA aptamer that binds nucleolin. Using tumor-bearing mouse models, they showed that intravenously injected DNA nanorobots deliver thrombin specifically to tumor-associated blood vessels and induce intravascular thrombosis. This developed nanorobot proved that it is safe and immunologically inert in mice and Bama miniature pigs [78]. In another study, Yang et al. presented a DNA nanorobot that can regulate thrombin functions. This nanorobot does not work under normal coagulation conditions. Therefore, this nanorobot is especially functional for autonomous anticoagulation in various medical situations. A tunable threshold control can be useful for the management of thrombin-related health issues [79]. Lopez-Ramirez reported a study about developing microneedles. Microneedles can carry out painless localized delivery of drugs across the skin. This group developed an autonomous and degradable active microneedle delivery platform and they employed magnesium microparticles loaded within the microneedle patch. These particles interacted with the interstitial fluid and provided the necessary force to reach the dermal barriers. In vivo experiments using a B16F10 mouse melanoma model showed that the active delivery of anti-CTLA-4 (a checkpoint inhibitor drug) results in greatly enhanced immune response and significantly longer survival. Therefore, this study can be regarded as an important study of active and passive microneedles with a rapid burst response along with a slow and sustained release [80].

14.4.2 Biologically inspired nanorobots as drug delivery vehicles The feasibility of nanomachines or nanorobots is inspired by biological organisms at the same size scales performing extremely efficiently and intelligently. Biomimicking of Brownian motion, elasto-hydrodynamic interaction, diffusion, or capillary effects are the main inspiration sources for designing biologically inspired nanorobots [32]. Biological inspiration of nonreciprocal motion can be the rotation of helical flagella used by bacteria. Ali et al. showed that bacteria-inspired nanorobots have the ability to fabricate, visualize, and actuate fuel-free stimuli-responsive nanoswimmers. With magnetic nanoparticles, the “body” of a swimmer can be easily changed using different sizes or compositions. Moreover, it can be self-assembled. With the aid of the rapidly changing morphology, it is possible to develop advanced navigation strategies for dynamic biological environments on demand in real time. As a consequence, in response to changes in the fluidic environment, these swimmers can be utilized as nanoscale probes [81]. The inspiration source can be extremely different from nature. For example, in a study by Deng et al., natural killer cells were the source of inspiration for developing nanorobots. The natural-killer-cell-mimicking nanoparticles were designed by wrapping a natural killer cell membrane on a polymeric nanoendoskeleton. The developed

Design and control of nanorobots and nanomachines

nanorobots showed a superior and bright NIR-II fluorescence effect. They can cross the blood-brain barrier (BBB) in a self-help manner and specifically accumulate in the brain tumors. They can be used for fluorescence imaging and photothermal therapy of the skull and scalp [82]. It is highly important to discuss that computationally powered machine learning (ML) and artificial intelligence (AI) technologies can be integrated with these robotics to design novel and next-generation nanorobots to across the blood-brain barrier with no damage, toxicity, or malfunctions [83]. Another interesting study was conducted by Singh et al. They designed a new class of microrobots called spermbots. These were created by coupling sperm cells with mechanical loads and were utilized with the aid of the flagellar movement of the sperm cells. They are biocompatible and show considerable speed and motion. These spermbots are important for drug delivery to overcome fertilization problems and enhance human producibility [84].

14.5 Challenges and future outlook A detailed summary of the recent developments in the area of nanomachines and nanorobots has been presented in this chapter, but, there are still many challenges in the field. Like in conventional drug research, long-term clinical tests, including different phases and human trials, are needed for the real-time use of these methods and solutions. In the area of nanobiotechnology, there is an issue of nanotechnology and nanomaterial toxicology, which is not discussed in depth. In the area of nanomaterials and chemicals, the logistics of the sales and distribution of nanoparticles and their use in real-time applications require caution, particularly with regard to nanotoxicology, as nanoparticles can penetrate through the skin and reach the cells. In the cosmetic industry, there are regulations on the use of nanosized materials and there are restrictions on the use of nanomaterials in cosmetic products. On the other hand, there is a great volume of papers, book chapters, and books on nanobiotechnology, and we believe that the toxicology perspective should be taken into account in the nanorobot and nanomachine research. Furthermore, the advancements in microscopy and nanomanufacturing techniques have enabled scientists to discover many new solutions, including subatomic particles, artificial intelligence, and deep learning techniques. Future research will be toward the use of artificial intelligence in these nanorobots that can manipulate the motion of robots in the body with changing conditions. There are new approaches in the manufacturing industry such as deep learning and machine learning, and these methods can also be used for the nanorobot research. The use of biodegradable polymers that can be degraded at the end of drug delivery will be also a key issue in these applications. The use of new nanoparticles and subatomic materials will also help for the design of these next-generation robots.

389

390

A handbook of artificial intelligence in drug delivery

14.6 Conclusions The search for new drugs is an ongoing process, and the use of medicines dates back to ancient times when human beings used plant extracts for healing certain diseases. The use of plant extracts and natural drugs is still an interesting field known as “phytotherapy,” and it is gaining importance as an alternative medicine for curing many diseases. Modern technology has led to the use of advanced chemical synthesis of new drugs using plant extracts, and various characterization tools help the human race in understanding the chemistry and biology of these new strategies. The latest technology is to combine these developments with the use of nanotechnology through nanorobots and nanomachines for targeted delivery to the problem area.

References [1] Nanotechnology and Nanomaterials, 2006, pp. 1–69. (Chapter 1). Available from: https://linkinghub. elsevier.com/retrieve/pii/S1383730306800025. [2] J.K. Patra, G. Das, L.F. Fraceto, E.V.R. Campos, M.d.P. Rodriguez-Torres, L.S. Acosta-Torres, et al., Nano based drug delivery systems: recent developments and future prospects, J. Nanobiotechnol. 16 (1) (2018) 71. Available from: https://jnanobiotechnology.biomedcentral.com/articles/10.1186/s12951018-0392-8. [3] E.M. Melchor-Martı´nez, N.E. Torres Castillo, R. Macias-Garbett, S.L. Lucero-Saucedo, R. ParraSaldı´var, J.E. Sosa-Herna´ndez, Modern world applications for nano-bio materials: tissue engineering and COVID-19, Front. Bioeng. Biotechnol. 9 (2021). Available from: https://www.frontiersin. org/articles/10.3389/fbioe.2021.597958/full. [4] P. Boisseau, B. Loubaton, Nanomedicine, nanotechnology in medicine, C. R. Phys. 12 (7) (2011) 620–636. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1631070511001538. [5] P.N. Navya, A. Kaphle, S.P. Srinivas, S.K. Bhargava, V.M. Rotello, H.K. Daima, Current trends and challenges in cancer management and therapy using designer nanomaterials, Nano Converg. 6 (1) (2019) 23. Available from: https://nanoconvergencejournal.springeropen.com/articles/10.1186/ s40580-019-0193-2. [6] O.D. Farokzhad, R. Langer, Impact of nanotechnology on drug delivery, ACS Nano 3 (1) (2009) 16–20. [7] G. Tiwari, R. Tiwari, S. Bannerjee, L. Bhati, S. Pandey, P. Pandey, et al., Drug delivery systems: an updated review, Int. J. Pharm. Investig. 2 (1) (2012) 2. Available from: http://www.jpionline.org/ index.php/ijpi/article/view/58. [8] A. Chowdhury, S. Kunjiappan, T. Panneerselvam, B. Somasundaram, C. Bhattacharjee, Nanotechnology and nanocarrier-based approaches on treatment of degenerative diseases, Int. Nano Lett. 7 (2) (2017) 91–122. Available from: http://link.springer.com/10.1007/s40089-017-0208-0. [9] D. Taylor, The Pharmaceutical Industry and the Future of Drug Development, 2015, pp. 1–33. Available from: http://ebook.rsc.org/?DOI¼10.1039/9781782622345-00001. [10] B. Wang, K. Kostarelos, B.J. Nelson, L. Zhang, Trends in micro-/nanorobotics: materials development, actuation, localization, and system integration for biomedical applications, Adv. Mater. 33 (4) (2021) 2002047. Available from: https://onlinelibrary.wiley.com/doi/10.1002/adma.202002047. [11] Y. Saadeh, D. Vyas, Nanorobotic applications in medicine: current proposals and designs, Am. J. Robot Surg. 1 (1) (2014) 4–11. Available from: http://openurl.ingenta.com/content/xref?genre¼article& issn¼2374-0612&volume¼1&issue¼1&spage¼4. [12] A. El-Sayed, M. Kamel, Advances in nanomedical applications: diagnostic, therapeutic, immunization, and vaccine production, Environ. Sci. Pollut. Res. 27 (16) (2020) 19200–19213. Available from: http://link.springer.com/10.1007/s11356-019-06459-2.

Design and control of nanorobots and nanomachines

[13] B. Senthilnathan, J. Bejoy, S. Robertson, Nanorobots—a hypothetical concept of interest, Pharma Sci. Monit. 7 (3) (2016) 70–84. [14] E.R. Kay, D.A. Leigh, Lighting up nanomachines, Nature 440 (7082) (2006) 286–287. Available from: http://www.nature.com/articles/440286b. [15] H.H. Park, A.C. Jamison, T.R. Lee, Rise of the nanomachine: the evolution of a revolution in medicine, Nanomedicine 2 (4) (2007) 425–439. Available from: https://www.futuremedicine.com/doi/ 10.2217/17435889.2.4.425. [16] E. Ellis, S. Moorthy, W.-I.K. Chio, T.-C. Lee, Artificial molecular and nanostructures for advanced nanomachinery, Chem. Commun. 54 (33) (2018) 4075–4090. Available from: http://xlink.rsc.org/? DOI¼C7CC09133H. [17] H. Wang, M. Pumera, Micro/nanomachines and living biosystems: from simple interactions to microcyborgs, Adv. Funct. Mater. 28 (25) (2018) 1705421. Available from: http://doi.wiley.com/10.1002/ adfm.201705421. [18] A. Loukanov, H. Gagov, S. Nakabayashi, Artificial nanomachines and nanorobotics, in: The Road From Nanomedicine to Precision Medicine, Jenny Stanford Publishing, 2019, pp. 515–532. Available from: https://www.taylorfrancis.com/books/9781000020939/chapters/10.1201/9780429295010-14. [19] T. Betancourt, L. Brannon-Peppas, Micro- and nanofabrication methods in nanotechnological medical and pharmaceutical devices, Int. J. Nanomedicine 1 (4) (2006) 483–495. Available from: http://www. atypon-link.com/DMP/doi/abs/10.2147/nano.2006.1.4.483. [20] G.V. da Silva Luz, K.V.G. Barros, F.V.C. de Arau´jo, G.B. da Silva, P.A.F. da Silva, R.C.I. Condori, et al., Nanorobotics in drug delivery systems for treatment of cancer: a review, J. Mater. Sci. Eng. A 6 (3) (2016). Available from: http://www.davidpublisher.org/index.php/Home/Article/index? id¼28232.html. [21] A. Cavalcanti, W. Wood, L. Kretly, B. Shirinzadeh, Computational nanomechatronics: a pathway for control and manufacturing nanorobots, in: 2006 International Conference on Computational Intelligence for Modelling Control and Automation and International Conference on Intelligent Agents Web Technologies and International Commerce (CIMCA’06), IEEE, 2006, p. 185. Available from: http:// ieeexplore.ieee.org/document/4052804/. [22] S. Mitthra, A. Karthick, B. Anuradha, R. Mensudar, K. Ramkumar Sadhana, V.G. Nidhya, Nanorobots—a small wonder, Biosci. Biotechnol. Res. Asia 13 (4) (2016) 2131–2134. Available from: http://www.biotech-asia.org/vol13no4/nanorobots-a-small-wonder/. [23] H. Jo, W. Choi, Encapsulation of NEM memory switches for monolithic-three-dimensional (M3D) CMOS–NEM hybrid circuits, Micromachines 9 (7) (2018) 317. Available from: http://www.mdpi. com/2072-666X/9/7/317. [24] J.H. Kim, J.K. Cha, M.H. Cho, H. Kim, H.-M. Shim, S.H. Kim, Thermal reactions of nitrocelluloseencapsulated Al/CuO nanoenergetic materials fabricated in the gas and liquid phases, Mater. Chem. Phys. 238 (2019) 121955. Available from: https://linkinghub.elsevier.com/retrieve/pii/ S0254058419307540. [25] A. Loukanov, S. Nikolova, C. Filipov, S. Nakabayashi, Nanomaterials for cancer medication: from individual nanoparticles toward nanomachines and nanorobots, Pharmacia 66 (3) (2019) 147–156. Available from: https://pharmacia.pensoft.net/article/37739/. [26] B.J. Nelson, L. Dong, F. Arai, Micro/nanorobots, in: Springer Handbook of Robotics, Springer Berlin Heidelberg, Berlin, Heidelberg, 2008, pp. 411–450. Available from: http://link.springer.com/10. 1007/978-3-540-30301-5_19. [27] A. Pimpin, W. Srituravanich, Reviews on micro- and nanolithography techniques and their applications, Eng. J. 16 (1) (2012) 37–55. [28] A.A. Requicha, Nanorobotics. Handbook of industrial robotics, Handbook of Industrial Robotics, Wiley, 1999, pp. 199–210. [29] M. Sitti, H. Hashimoto, Tele-nanorobotics using atomic force microscope, Proceedings of the 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications 3 (1998) 1739–1746. [30] C. Mavroidis, A. Ferreira, Nanorobotics: past, present, and future, in: Nanorobotics, Springer New York, New York, NY, 2013, pp. 3–27. Available from: http://link.springer.com/10.1007/978-14614-2119-1_1.

391

392

A handbook of artificial intelligence in drug delivery

[31] R.A. Freitas, R.A. Freitas, Nanomedicine, Volume I: Basic Capabilities, Bioscience, Georgetown, TX: Landes, 1999, pp. 210–219. [32] N.N. Sharma, R.K. Mittal, Nanorobot movement: challenges and biologically inspired solutions, Int. J. Smart Sens. Intell. Syst. 1 (1) (2008) 87–109. [33] J.M. Buriak, Chemistry nobel prize—molecular machines are real, Chem. Mater. 28 (20) (2016) 7179–7180. Available from: https://pubs.acs.org/doi/10.1021/acs.chemmater.6b04254. [34] A.V. Singh, M.H.D. Ansari, P. Laux, A. Luch, Micro-nanorobots: important considerations when developing novel drug delivery platforms, Expert Opin. Drug Deliv. 16 (11) (2019) 1259–1275. Available from: https://www.tandfonline.com/doi/full/10.1080/17425247.2019.1676228. [35] J. Yang, C. Zhang, X.D. Wang, W.X. Wang, N. Xi, L.Q. Liu, Development of micro- and nanorobotics: a review, Sci. China Technol. Sci. 62 (1) (2019). [36] M. Hu, X. Ge, X. Chen, W. Mao, X. Qian, W.E. Yuan, Micro/nanorobot: a promising targeted drug delivery system, Pharmaceutics 12 (7) (2020) 1–18. [37] Y. Wang, R.M. Hernandez, D.J. Bartlett, J.M. Bingham, T.R. Kline, A. Sen, et al., Bipolar electrochemical mechanism for the propulsion of catalytic nanomotors in hydrogen peroxide solutions, Langmuir 22 (25) (2006) 10451–10456. Available from: https://pubs.acs.org/doi/10.1021/la0615950. [38] M. Frenkel, L. Dombrovsky, V. Multanen, V. Danchuk, I. Legchenkova, S. Shoval, et al., Selfpropulsion of water-supported liquid marbles filled with sulfuric acid, J. Phys. Chem. B 122 (32) (2018) 7936–7942. Available from: https://pubs.acs.org/doi/10.1021/acs.jpcb.8b06136. [39] C. Hu, S. Pane, B.J. Nelson, Soft micro- and nanorobotics, Annu. Rev. Control Robot. Auton. Syst. 1 (1) (2018) 53–75. Available from: https://www.annualreviews.org/doi/10.1146/annurev-control060117-104947. [40] Z. Zhan, F. Wei, J. Zheng, W. Yang, J. Luo, L. Yao, Recent advances of light-driven micro/nanomotors: toward powerful thrust and precise control, Nanotechnol. Rev. 7 (6) (2018) 555–581. Available from: https://www.degruyter.com/document/doi/10.1515/ntrev-2018-0106/html. [41] B. Jacob, V.L. Madhavi, V. Chandy, Nanobots—the artificial blood, Trends Drug Deliv. 5 (20–22) (2018). Available from http://openurl.ingenta.com/content/xref?genre¼article&issn¼2374-0612& volume¼1&issue¼1&spage¼4. [42] G. Barbara, V.B. Carlos, R.C.U. Yendry, V.C. Stephanie, J.R.V. Baudrit, Nanobots: development and future, Int. J. Biosens. Bioelectron. 2 (5) (2017). Available from: https://medcraveonline.com/ IJBSBE/nanobots-development-and-future.html. [43] Y. Muhammad, S. Liya, S. Saeed, A. Yakubu, A. Habeeb, M. Bk, et al., Nanotechnology and artificial blood; future revolution in modern transfusion medicine, Chin. J. Med. Res. 3 (March) (2020) 23–27. [44] M.A.L. Leal, D. Tovar, J.S. Nieves, M.T.C. Ramı´rez, I.C.R. Jimenez, M.L. Leal, R.A.R. Quin˜onez, I.M. Cadena, A.E.Q. Toro, L.F.d.C. Francisco Jose Reyes Torr, Mars dichotomy: prospects for human life on Mars, Earth Arxiv (2021). [45] P.S. Shabnashmi, S. Naga Kani, V. Vithya, B. Vijaya Lakshmi, R. Jasmine, Therapeutic applications of nanorobots- respirocytes and microbivores, J. Chem. Pharm. Res. 8 (5) (2016) 605–609. [46] N. Save, S. Deshpande, Nanorobot—a prospective outlook in medicine, BioChem. Indian J. 11 (1) (2017) 1–10. [47] M. Swan, Engineering life into technology: the application of complexity theory to a potential phase transition in intelligence, Symmetry (Basel) 2 (1) (2010) 150–183. Available from: http://www.mdpi. com/2073-8994/2/1/150. [48] T. Rifat, M.S. Hossain, M.M. Alam, A.S.S. Rouf, A review on applications of nanobots in combating complex diseases, Bangladesh Pharm. J. 22 (1) (2019) 99–108. Available from: https://www.banglajol. info/index.php/BPJ/article/view/40081. [49] F. Soto, J. Wang, R. Ahmed, U. Demirci, Medical micro/nanorobots in precision medicine, Adv. Sci. 7 (21) (2020) 2002203. Available from: https://onlinelibrary.wiley.com/doi/10.1002/advs. 202002203. [50] L. Kong, J. Guan, M. Pumera, Micro- and nanorobots based sensing and biosensing, Curr. Opin. Electrochem. 10 (2018) 174–182. Available from: https://linkinghub.elsevier.com/retrieve/pii/ S2451910318300759.

Design and control of nanorobots and nanomachines

[51] D.P. Modi, V.P. Patel, R.B. Patel, J.N. Patel, B.V. Bhimani, R.R. Shah, Nanorobots: the emerging tools in medicinal applications—a review, Int. J. Drug Dev. Res. 5 (3) (2013) 105–119. [52] F. Mollarasouli, U. Badilli, N.K. Bakirhan, S.A. Ozkan, Y. Ozkan, Advanced DNA nanomachines: strategies and bioapplications, J. Drug Deliv. Sci. Technol. 61 (2021) 102290. Available from: https://linkinghub.elsevier.com/retrieve/pii/S1773224720315793. [53] N. Wu, K. Wang, Y.-T. Wang, M.-L. Chen, X.-W. Chen, T. Yang, et al., Three-dimensional DNA nanomachine biosensor by integrating DNA walker and rolling machine cascade amplification for ultrasensitive detection of cancer-related gene, Anal. Chem. 92 (16) (2020) 11111–11118. Available from: https://pubs.acs.org/doi/10.1021/acs.analchem.0c01074. [54] J. Zheng, X. Ji, M. Du, S. Tian, Z. He, Rational construction of a DNA nanomachine for HIV nucleic acid ultrasensitive sensing, Nanoscale 10 (36) (2018) 17206–17211. Available from: http://xlink.rsc. org/?DOI¼C8NR05206A. [55] P. Miao, Y. Tang, DNA walking and rolling nanomachine for electrochemical detection of miRNA, Small 16 (47) (2020) 2004518. Available from: https://onlinelibrary.wiley.com/doi/10.1002/smll. 202004518. [56] Jaiswal, 基因的改变 NIH Public Access, Bone 23 (1) (2014) 1–7. Available from https://www.ncbi. nlm.nih.gov/pmc/articles/PMC3624763/pdf/nihms412728.pdf. [57] F. Peng, Y. Tu, J.C.M. van Hest, D.A. Wilson, Self-guided supramolecular cargo-loaded nanomotors with chemotactic behavior towards cells, Angew. Chem. 127 (40) (2015) 11828–11831. [58] S. Taherkhani, M. Mohammadi, J. Daoud, S. Martel, M. Tabrizian, Covalent binding of nanoliposomes to the surface of magnetotactic bacteria for the synthesis of self-propelled therapeutic agents, ACS Nano 8 (5) (2014) 5049–5060. [59] S. Martel, M. Mohammadi, Switching between magnetotactic and aerotactic displacement controls to enhance the efficacy of MC-1 magneto-aerotactic bacteria as cancer-fighting nanorobots, Micromachines 7 (6) (2016) 1–12. [60] M. Hoop, A.S. Ribeiro, D. R€ osch, P. Weinand, N. Mendes, F. Mushtaq, et al., Mobile magnetic nanocatalysts for bioorthogonal targeted cancer therapy, Adv. Funct. Mater. 28 (25) (2018) 1–8. ` . Blanco, S. Sa´nchez, Enzyme-powered nanobots [61] A.C. Hortela˜o, T. Patin˜o, A. Perez-Jimenez, A enhance anticancer drug delivery, Adv. Funct. Mater. 28 (25) (2018) 1–10. [62] A. Serra`, G. Va´zquez-Marin˜o, J. Garcı´a-Torres, M. Bosch, E. Valles, Magnetic actuation of multifunctional nanorobotic platforms to induce cancer cell death, Adv. Biosyst. 2 (2) (2018) 1–9. € [63] X.Z. Chen, M. Hoop, N. Shamsudhin, T. Huang, B. Ozkale, Q. Li, et al., Hybrid magnetoelectric nanowires for nanorobotic applications: fabrication, magnetoelectric coupling, and magnetically assisted in vitro targeted drug delivery, Adv. Mater. 29 (8) (2017) 1–7. [64] W. Chen, C.A. Glackin, M.A. Horwitz, J.I. Zink, Nanomachines and other caps on mesoporous silica nanoparticles for drug delivery, Acc. Chem. Res. 52 (6) (2019) 1531–1542. [65] F. Soto, R. Chrostowski, Frontiers of medical micro/nanorobotics: in vivo applications and commercialization perspectives toward clinical uses, Front. Bioeng. Biotechnol. 6 (November) (2018) 1–12. [66] P. Erkoc, I.C. Yasa, H. Ceylan, O. Yasa, Y. Alapan, M. Sitti, Mobile microrobots for active therapeutic delivery, Adv. Ther. 2 (1) (2019) 1800064. [67] G.G. Genchi, A. Marino, C. Tapeinos, G. Ciofani, Smart materials meet multifunctional biomedical devices: current and prospective implications for nanomedicine, Front. Bioeng. Biotechnol. 5 (December) (2017) 1–8. [68] N. Vijayakameswara Rao, H. Ko, J. Lee, J.H. Park, Recent progress and advances in stimuli-responsive polymers for cancer therapy, Front. Bioeng. Biotechnol. 6 (August) (2018) 110–125. [69] S. Dolev, R. Narayanan, Towards radio transceiving in-vivo nano-robots, SN Appl. Sci. 1 (9) (2019) 969. Available from: http://link.springer.com/10.1007/s42452-019-1001-7. [70] O. Felfoul, M. Mohammadi, S. Taherkhani, D. De Lanauze, Y. Zhong Xu, D. Loghin, et al., Magneto-aerotactic bacteria deliver drug-containing nanoliposomes to tumour hypoxic regions, Nat. Nanotechnol. 11 (11) (2016) 941–947. [71] Z. Jin, K.T. Nguyen, G. Go, B. Kang, H.K. Min, S.J. Kim, et al., Multifunctional nanorobot system for active therapeutic delivery and synergistic chemo-photothermal therapy, Nano Lett. 19 (12) (2019) 8550–8564.

393

394

A handbook of artificial intelligence in drug delivery

[72] V. Agrahari, V. Agrahari, M.-L. Chou, C.H. Chew, J. Noll, T. Burnouf, Intelligent micro-/ nanorobots as drug and cell carrier devices for biomedical therapeutic advancement: promising development opportunities and translational challenges, Biomaterials 260 (2020) 120163. Available from: https://linkinghub.elsevier.com/retrieve/pii/S0142961220304099. [73] S.S. Andhari, R.D. Wavhale, K.D. Dhobale, B.V. Tawade, G.P. Chate, Y.N. Patil, et al., Selfpropelling targeted magneto-nanobots for deep tumor penetration and pH-responsive intracellular drug delivery, Sci. Rep. 10 (1) (2020) 4703. Available from: http://www.nature.com/articles/ s41598-020-61586-y. [74] V.A. Petrenko, Autonomous self-navigating drug-delivery vehicles: from science fiction to reality, Ther. Deliv. 8 (12) (2017) 1063–1075. Available from: http://www.future-science.com/doi/10. 4155/tde-2017-0086. [75] X. Ma, S. Sa´nchez, Self-propelling micro-nanorobots: challenges and future perspectives in nanomedicine, Nanomedicine 12 (12) (2017) 1363–1367. Available from: https://www.futuremedicine.com/ doi/10.2217/nnm-2017-0104. [76] M. Luo, Y. Feng, T. Wang, J. Guan, Micro-/nanorobots at work in active drug delivery, Adv. Funct. Mater. 28 (25) (2018) 1706100. Available from: http://doi.wiley.com/10.1002/adfm.201706100. [77] M. Li, N. Xi, Y. Wang, L. Liu, Progress in nanorobotics for advancing biomedicine, I.E.E.E. Trans. Biomed. Eng. 68 (1) (2021) 130–147. Available from: https://ieeexplore.ieee.org/document/ 9078884/. [78] S. Li, Q. Jiang, S. Liu, Y. Zhang, Y. Tian, C. Song, et al., A DNA nanorobot functions as a cancer therapeutic in response to a molecular trigger in vivo, Nat. Biotechnol. 36 (3) (2018) 258–264. Available from https://doi.org/10.1038/nbt.4071. [79] L. Yang, Y. Zhao, X. Xu, K. Xu, M. Zhang, K. Huang, et al., An intelligent DNA nanorobot for autonomous anticoagulation, Angew. Chem. 132 (40) (2020) 17850–17857. Available from: https://onlinelibrary.wiley.com/doi/10.1002/ange.202007962. [80] M.A. Lopez-Ramirez, F. Soto, C. Wang, R. Rueda, S. Shukla, C. Silva-Lopez, et al., Built-in active microneedle patch with enhanced autonomous drug delivery, Adv. Mater. 32 (1) (2020) 1905740. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.201905740. [81] J. Ali, U.K. Cheang, J.D. Martindale, M. Jabbarzadeh, H.C. Fu, M. Jun Kim, Bacteria-inspired nanorobots with flagellar polymorphic transformations and bundling, Sci. Rep. 7 (1) (2017) 14098. Available from: http://www.nature.com/articles/s41598-017-14457-y. [82] G. Deng, X. Peng, Z. Sun, W. Zheng, J. Yu, L. Du, et al., Natural-killer-cell-inspired nanorobots with aggregation-induced emission characteristics for near-infrared-II fluorescence-guided glioma theranostics, ACS Nano 14 (9) (2020) 11452–11462. Available from: https://pubs.acs.org/doi/10.1021/ acsnano.0c03824. [83] A.V. Singh, V. Chandrasekar, P. Janapareddy, D.E. Mathews, P. Laux, A. Luch, et al., Emerging application of nanorobotics and artificial intelligence to cross the BBB: advances in design, controlled maneuvering, and targeting of the barriers, ACS Chem. Nerosci. (2021), https://doi.org/10.1021/ acschemneuro.1c00087. acschemneuro.1c00087. Available from. [84] A. Singh, M. Ansari, M. Mahajan, S. Srivastava, S. Kashyap, P. Dwivedi, et al., Sperm cell driven microrobots—emerging opportunities and challenges for biologically inspired robotic design, Micromachines 11 (4) (2020) 448. Available from: https://www.mdpi.com/2072-666X/11/4/448.

CHAPTER 15

Artificial intelligence (AI) in drug product designing, development, and manufacturing Shivang Chaudharya, Prakash Muthudossb, Thiagarajan Madheswaranc, e Amrit Paudeld, and Vinod Gaikwad a b

QbD-Expert, Ahmedabad, Gujarat, India A2Z4.0 Research and Analytics Private Limited, Chennai, Tamil Nadu, India International Medical University, Kuala Lumpur, Malaysia d Research Center Pharmaceutical Engineering GmbH (RCPE), Graz, Austria e Department of Pharmaceutics, National Institute of Pharmaceutical Education and Research (NIPER), Hajipur, Bihar, India c

15.1 Introduction Product quality does not occur by accident; instead, quality should be planned and built into the product from the designing and developmental stages itself. In the past, overall pharmaceutical development was mainly empirical and entire developmental research studies were often conducted one factor at a time (OFAT) with a quality-by-testing (QbT) system. In earlier QbT systems, manufacturing processing parameters were completely fixed and process validation was principally performed based on primary full-scale batches, which only emphasized reproducibility. Process controls were finalized through in-process offline analytical tests primarily for go/no-go decisions. However, nowadays, in a quality-by-design (QbD) system, overall pharmaceutical development is based on a systematic multivariate design of experiments (DoE) to build mathematical relationships linking critical material attributes and process parameters to drug product CQAs for establishing a flexible design space [1]. In QbD, manufacturing processing parameters are totally adjustable within a flexible design space, and, moreover, statistical process control (SPC) and process analytical technology (PAT) tools are utilized for tracking, trending, and controlling the process parameters to support postapproval continual improvement efforts with proper feedforward and feedback controls. In QbD, drug product quality is ensured by a risk-based control strategy, in which quality controls are shifted upstream, with the possibility of real-time release testing or reduced end-product testing, and product specifications are also part of the overall quality control strategy. In this design-based developmental system, product life cycle management is mainly preventive and is based on continual improvement of the commercial manufacturing process [2]. An enhanced, systematic quality-by-design approach to pharmaceutical product development includes the following elements, as represented step by step in Fig. 15.1: (i) defining the “quality target product profile” (QTPP) as it relates to quality, safety, A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00015-0

Copyright © 2023 Elsevier Inc. All rights reserved.

395

396

A handbook of artificial intelligence in drug delivery

Fig. 15.1 Quality by design (QbD) and artificial intelligence (AI)-assisted pharmaceutical product development and commercial manufacturing.

and efficacy, considering the route of administration, dosage form, bioavailability, strength, and stability; (ii) determination and measurement of the potential “critical quality attributes” (CQAs) of the drug product so that these product attributes that have an impact on product quality can be studied and controlled, after which relevant prior knowledge about the drug substance, potential excipients, and process operations should be gathered to establish a knowledge space; (iii) risk analysis and identification of all the risk factors, i.e., “critical material attributes” (CMAs) and “critical process parameters” (CPPs), which can have an effect on product CQAs through risk assessment with systematic identification, analysis, and evaluation for refining and prioritizing formulation material attributes and manufacturing process parameters based on prior knowledge, scientific rationale, and initial experimentations; (iv) design of experiments (DoE) for establishing

AI in drug product designing, development, and manufacturing

functional model relationships that link CMAs and CPPs to product CQAs through multivariate data analysis for development of a design space, i.e., multidimensional flexible ranges of CMAs and/or CPPs within which all the CQAs will meet their predefined specifications; (v) implementation of a “control strategy” for each and every CMA and CPP to ensure batch-to-batch consistency in in-process and finished product CQAs during commercial manufacturing, considering expected scale-up changes; and (vi) continual improvement of the process with continuous trend analysis of CMAs, CPPs, and CQAs and updating the process to ensure consistent quality throughout the product’s life cycle. Thus, pharmaceutical QbD is a systematic approach to product development, which begins with predefined objectives in the form of QTPP and emphasizes product and process understanding in the form of CMAs, CPPs, and CQAs and their controls based on sound science and quality risk management [3]. Artificial intelligence (AI) refers to intelligence demonstrated by machines, in contrast to the natural human intelligence that perceives its environment and takes actions that maximize its chance of successfully achieving its goals. AI can manage the scanning of large data of customer requirements and drug pharmacokinetic- toxicological profiling, which can help identify the CQAs connected to the desired QTPP. An ideal QTPP of a drug product is not only restricted to pharmacological or drug delivery features but also to any characteristic crucial for its efficacy, safety, and intended use [4]. From this, a prototype drug formulation can be designed and characterized. Thereafter, a reproducible process that is capable of manufacturing a robust product with the defined characteristic is required to be developed. At this stage, AI could also support the process engineering, its scale-up and validation through exploring material attributes, and process parameter ranges, which can independently evaluate their impact on the CQAs. Machine learning (ML) is a subset of AI, which provides statistical tools to explore and analyze data, whereas deep learning (DL) is a subset of ML based on a multineural network architecture that enables the machine to learn in the same manner through which the human brain tries to learn new things. DL mimics the human brain to analyze data, thus creating models, and to take appropriate action [5]. By means of a science and risk-based systematic approach to development through QbD, a control strategy will be defined for each of the CMAs and CPPs after screening through risk assessment tools and optimization through DoE and other MVDA techniques with respect to each in-process and finished product CQAs. These QbD-defined control ranges of individual CMAs and CPPs will be fed into machines by means of machine learning and language; so, during routine commercial manufacturing, a machine can automatically analyze and control the critical variables by means of PAT tools with artificial intelligence. The main goal of analyzing data through machine learning and developing a multineural network architecture through deep learning is to derive an AI application, which automatically analyzes all types of data and, accordingly at the same time, takes appropriate actions. Thus, in future, “quality will be controlled and assured automatically, when no one is looking”[6].

397

398

A handbook of artificial intelligence in drug delivery

15.2 Impact and risk assessment of material attributes and process parameters For implementation of a QbD system, “what customers want” should be first defined from the target product profile (TPP), which records the voice of the customers. Because quality does not happen accidently, it must be designed by planning. A QTPP is a prospective summary of the quality characteristics and quality attributes (QAs) of a drug product, which will ideally be achieved to ensure the desired quality, taking into account the safety and efficacy of the drug product. A quality target product profile forms the basis of design for the development of a product [1]. Out of all the quality attributes (QAs) of a QTPP, critical quality attributes (CQAs) are determined, on the basis of: (1) an impact analysis by a change in the formulation and/ or process variables (i.e., whether there is any impact of change on any formulation or process variable on this quality attribute) and (2) severity of harm to a patient (i.e., how severe the consequences are on patient health), resulting from failure to meet that quality attribute of the drug product. CQAs are generally associated with the drug substance, excipients, intermediates (in-process materials), and drug product. Critical quality attributes (CQAs) are physical, chemical, biological, or microbiological properties or characteristics, which may be impacted by any change in the formulation or process variables that should be within an appropriate limit, range, or distribution to ensure the desired product quality, considering severity of its harm on a patient’s health [1]. From the perspective of pharmaceutical product development, a list of CQAs of the drug product will be determined and investigated, which have a high potential to be impacted by the formulation and/or process variables. Potential relevant CQAs can be identified and prioritized by an iterative process of quality risk management and experimentation that assesses the extent to which their variation can have an impact on the quality of the drug product. For example, if we wanted to check whether dissolution is a CQA, then both the criteria for determination of CQAs are required to be checked. So, first, if formulation factors, i.e., API particle size distribution, binder concentration, disintegrant concentration, and/or process variables (kneading time during granulation, compression force during tablet compression), are changed, then it will have impact on the dissolution. Moreover, failure to meet specifications of the percentage of dissolution will severely impact patient safety (if dissolution >USL) and/or efficacy (if dissolution .1, indicating that the selected model fits the actual response behavior and the selected model actually fits the observed data); (iii) adequate precision >4 (reproducibility of prediction values); (iv) a highest R2 unadjusted value with R2 adjusted and R2 predicted values within a difference of 0.2; and (v) well-behaved residuals (¼ actual experimental values
model predicted values) without any specific particular trend. When there are several variables, like in multivariate investigations, regression analysis can also be performed with the help of partial least squares or principal component analysis [27]. 15.3.3.2 Graphical analysis of a mathematical regression model Model graphs paint a clear picture of how a response will behave at different levels of factors at a time through a prediction model. (a) A one-factor main effect plot shows the linear effect of changing the level of a single factor, whereas an interaction plot reveals that an interaction occurs with two nonparallel cross-lines. (b) A 2D contour plot reveals the effect of two independent factors on one response at a time. (c) A 3D response surface plot reveals the effect of two independent factors on one response at a time. (d) A cube plot reveals the effect of three independent factors on one response at a time, while keeping the magnitude of response and other factors constant.

15.3.4 Development and verification of the ideal region of a design space After analysis of the responses, a design space, which is a multidimensional combination and interaction of input variables, i.e., CMAs and CPPs, in which all the specifications of the individual responses, i.e., CQAs, meet the predefined targets, will be developed to ensure quality. Movement within the design space is not considered as a change.

AI in drug product designing, development, and manufacturing

Movement outside of the design space is considered to be a change and would initiate a regulatory postapproval change process. For numerical optimization, the best combination of multiple responses is determined by a geometric mean function called the overall desirability. In this method, depending upon the goals, the values of each response for a given combination of factors is first translated into a number between 0 and 1, known as individual desirability (di), and, then, the overall desirability (D) is calculated by taking the geometric mean of all individual desirabilities. For “graphical optimization,” contour plots of individual responses are superimposed or overlaid on top of each other with respect to their individual set specifications to yield a “sweet spot in the overlay plot,” where all the responses simultaneously meet their set specifications as predefined in the QTPP [28]. “Checkpoints” or “verification runs” are terms used to describe such formulations. Six verification runs are usually recommended, but a minimum of three runs should be performed to quantify the variability in that region. In addition, the atmosphere and circumstances ought to be similar to those used in the original study. The results of these milestones are then compared to the model-predicted results, and a residual analysis is carried out. The residuals are often plotted against the data that have already been collected to look for patterns like lines that go up or down or cycles. The difference between a single-surface response map and a real process design space is that the latter is dynamic, starting when the drug is conceptualized and evolving throughout the product’s life cycle [19].

15.3.5 Control space implementation for CMAs and CPPs Through prior art, scientific rationale, and OFAT/DoE-based proven acceptable ranges and design space for different CMAs and CPPs, control strategies for each and every CMA and CPP are proposed for future “commercial manufacturing” to ensure batchto-batch consistency in product quality. A “control strategy” is a planned set of controls derived from current product and process understanding during the laboratory-scale developmental stage and the scale-up exhibit-submission stage, which ensures consistent process performance and product quality during commercial manufacturing. The controls of commercial manufacturing include [1,3]: 1. Control of input material attributes (e.g., drug substance, excipients, primary packaging materials) based on an understanding of their impact on process ability or product quality. 2. Controls for processing parameters of unit operations that have an impact on downstream processing or product quality (e.g., the impact of drying on degradation, particle size distribution of the granulate on dissolution, impact of machine speed on weight variation, impact of blending or mixing time on content uniformity). 3. In-process or real-time release testing in lieu of end-product testing (e.g., measurement and control of CQAs during processing).

413

414

A handbook of artificial intelligence in drug delivery

4. A monitoring program (e.g., full product testing at regular intervals) for verifying multivariate prediction models. 5. Finished product specification(s). For finalizing and implementing a control strategy for each and every individual CMA and/or CPP, ranges studied at the laboratory-scale developmental stage will be reviewed with a pilot plant scale-up and pivotal scale exhibit batches to ensure batch-to-batch consistent quality and performance of the finished product during commercial manufacturing, as summarized in Table 15.2. Thus, a control strategy is an integrated overview of how quality is assured for commercial batches based on the current process and product knowledge gained from the QbD-DoE-PAT system. Individual ranges of CMAs of APIs and excipients and CPPs of manufacturing processes are reviewed, and ranges for commercial batches are proposed for batch-to-batch consistency of the respective CQAs. All the ranges are systematically tabulated with a purpose, i.e., (i) ranges studied at the laboratory scale; (ii) ranges studied during exhibit batches; and (iii) ranges proposed for commercial batches. A control strategy is built upon the outcomes of extensive product and process understanding studies. These studies investigated the material attributes and process parameters that were deemed high-risk to the CQAs of the drug product during the initial risk assessment. In some cases, variables considered medium-risk were also investigated. All variables that ranked as high-risk, i.e., those critical in the initial risk assessment, are included in the control strategy because the conclusion of the experiments was dependent on the range(s) studied and the complex multivariate relationship between variables. Thus, a control strategy is an integrated overview of how quality is assured based on the current process and product knowledge. A control strategy can include different elements. For example, one element of a control strategy could rely on end-product testing, whereas another could depend on real-time release testing. The rationale for using these alternative approaches should be described in the submission. Understanding sources of variability and their impact on downstream processes or processing, in-process materials, and drug product quality can provide an opportunity to shift controls upstream and minimize the need for end-product testing. Product and process understanding, in combination with quality risk management, will support the control of the process such that the variability (e.g., of raw materials) can be compensated for in an adaptable manner to deliver consistent product quality. This process understanding can enable an alternative manufacturing paradigm in which the variability of input materials could be less tightly constrained. Instead, it can be possible to design an adaptive process step (one that is responsive to the input materials) with appropriate process control to ensure consistent product quality. A control strategy may be further refined based on additional experience gained during the commercial life cycle of a product. However, any postapproval changes should be reported to the agency in accordance with CFR 314.70 and should follow the steps

Table 15.2 Control strategies for CPPs involved in the unit operations of granulation processes.

Process Parameter (s)

Ranges studied at Laboratory Scale

Actual data for Exhibit batches

Proposed range for Commercial batch

Purpose of Control

___#

___#

___#

To ensure batch to batch consistency in Particle size Distribution and Density of Powder

Rate of Binder Addition Wet Mixing-Impeller Speed Wet Mixing-Kneading Time

[ ___-___ ] min [ ___-___ ] RPM [ ___-___ ] min

{___-___} min {___-___} RPM {___-___} min

(__-__)min (__-__)RPM (__-__)min

%Fill Level in Mixer Product Temperature Inlet Air Volume %Fill Level in Bowl

[ ___-___ ] % [ ___-___ ]°C [ ___-___ ] cfm [ ___-___ ] %

{___-___} % [ ___-___ ] °C {___-___} cfm {___-___} %

(__-__)% [ ___-___ ] °C (__-__) cfm (__-__) %

[ ___-___ ] °C

{___-___} °C

(__-__) °C

[ ___-___ ] cfm

{___-___} cfm

(__-__)cfm

[ ___-___ ] %

{___-___} %

(__-__) %

CPPs

Sieve size Co-Sifting

Wet Granulation

Drying

Inlet Air Temperature for Drying Inlet Air Volume for Drying %Fill Level in Bowl for Drying

To ensure batch to batch consistency in Granule Size Distribution and Density in order to warrant Uniform Flow property and Disintegration of Granules

To ensure batch to batch consistency in Water content in granules in order to prevent In Process impurity, Microbial growth and Compression defects (Sticking/ Picking)

Continued

Table 15.2 Control strategies for CPPs involved in the unit operations of granulation processes—cont’d Sizing

Mill Speed for Sizing Screen Size for Sizing

[ ___-___ ] RPM [ ___-___ ] mm

{___-___} RPM {___-___} mm

(__-__) RPM (__-__) mm

To ensure consistency in PSD, BD, TD of granules

Lubrication

Blending Speed Blending Time %Fill Level in Blender

[ ___-___ ] RPM [ ___-___ ] min [ ___-___ ] %

{___-___} RPM {___-___} min {___-___} %

(__-__) RPM (__-__) min (__-__) %

To ensure batch to batch consistency in Blend Uniformity and Dissolution

Compression-Turret Speed Compression - Feed Frame Paddle Speed Compressed Tablet Thickness

[ ___-___ ] RPM

{___-___} RPM

(__-__) RPM

[ ___-___ ] RPM

{___-___} RPM

(__-__) RPM

[ ___-___ ] mm

{___-___} mm

To ensure batch to batch consistency in Hardness, Weight and Disintegration in order to ensure Minimum %Friability Loss, CU and Desired Dissolution without any Compression defects

[ ___-___ ] kN

{___-___} kN

[ ___-___ ] kN

{___-___} kN

[ ___-___ ] %

{___-___} %

[ ___-___ ] °C

{___-___} °C

[ ___-___ ] %RH

{___-___} %RH

Compression

Pre Compression Force Main Compression Force %Fill Level in Hopper

Environmental Conditions

Temperature Relative Humidity (%RH)

(__-__) mm (__-__) kN (__-__) kN (__-__) % (__-__) °C (__-__) %RH

To ensure batch to batch consistency in Physical and Chemical Stability

AI in drug product designing, development, and manufacturing

outlined by the guidance used for scale-up and postapproval changes. An enhanced understanding of product performance can justify the use of alternative approaches to determine whether the material is meeting its quality attributes. The use of such alternatives could support real-time release testing. For example, disintegration could serve as a surrogate for the dissolution for fast-disintegrating solid forms with highly soluble drug substances. Unit dose uniformity that is performed in-process (e.g., using weight variation coupled with a near-infrared (NIR) assay) can enable real-time release testing and provide an increased level of quality assurance compared to the traditional endproduct testing using compendia content uniformity standards. Real-time release testing can replace end-product testing but cannot replace the review and quality control steps called for under GMP to release the batch.

15.4 Continuous inline/online analysis and controlling with PAT PAT is a system for designing, analyzing, and controlling manufacturing through timely measurements (i.e., during processing) of critical quality and performance attributes of raw and in-process materials and processes with the goal of ensuring the final product’s quality. Thus, through PAT, an online/inline/at-line analyzing system for individual in-process and/or finished product CQAs can be developed with a feedback or feedforward controlling system for relevant CMAs and/or CPPs in an individual unit operation of the manufacturing process.

15.4.1 Classification of inline/online process sensors In a manufacturing environment, four main categories of PAT sensor deployment based on accessibility can be defined: offline, at-line, inline, and online. The simplest sensor deployment is offline, which is also considered as a centralized system. These sensors, when placed near the process area, becomes at-line, yet sampling would be manual. Both these types are flexible and can be placed in a dust-free environment. Being manual, both these sensors do not qualify as a QbD tool, that is, real-time data acquisition is required for the QbD to be successfully implemented. Data gathering in real time (either online or inline) is an important leap in the development of a QbD tool [29]. Inline analysis is embedding an analytical sensor directly into a process or product flow, allowing data to be collected in real time. On the other hand, diverting samples from the process/product flow is required for online analysis, which uses an analytical equipment to provide real-time data. Depending on the application, the diverted sample may be reintroduced or discarded as waste. Comprehensively, the implementation of a PAT framework during various stages of product development could assist in enhancing the process robustness, quality assurance, productivity, and profitability [30].

417

418

A handbook of artificial intelligence in drug delivery

15.4.2 PAT tools In a traditional quality-by-testing (QbT) approach, the performance of the finished dosage formulations is assessed using sieve analysis, dissolution apparatus, high-performance liquid chromatography (HPLC), and gas chromatography (GC). One of the most important requirements of QbD is the infrastructure to extract real-time information in order to control variability and incorporate process/product robustness. None of the aforementioned systems provide real-time data. PAT tools have been investigated and developed to solve these challenges in order to acquire the critical quality attributes (CQAs). PAT tools in the pharmaceutical industry have a wide range of applications, including (i) monitoring process conditions (temperature, humidity, dissolved oxygen, blending time, blend uniformity, granulation), (ii) material properties (polymorphism, particle size), (iii) process properties and characterization (powder density, porosity, wettability), and (iv) product performance testing (assay, content uniformity, dissolution, related substance, real-time release testing, etc.). Popular methods for assessing the CQAs of pharmaceutical processes include (i) single-purpose sensors (temperature, humidity, dissolved oxygen, pH monitor), (ii) optical and diffraction probes for monitoring particle size, and (iii) physico-chemical monitoring sensors like NIRS, Raman, FTIR, Terahertz, NMR, and UV-vis imaging. The pharmaceutical industry has used PAT devices in a variety of applications throughout the drug substance manufacturing process. Blend/content homogeneity, loss on drying, polymorphism stability, particle size fluctuations, and powder density are all monitored in real time using these technologies, as mentioned in Table 15.3. Although manufacturing costs have dramatically decreased, it is important to note that product quality assurance has not been compromised, and, hence, the usage of PAT tools has dramatically increased. In order to ensure the final product’s quality, the USFDA has taken steps to promote the use of PAT tools in pharmaceutical manufacturing [31]. As summarized in Table 15.3, process analyzers measure the physical, chemical, and biological properties of materials and collect both quantitative data and qualitative data. Data collection can be nondestructive, require minimal sample preparation, and have rapid or real-time responses when compared to traditional methods. Online and inline process analyzers have the greatest potential to reduce operating costs and improve quality; both minimize sample requirements and sample handling compared to their at-line and offline counterparts.

15.4.3 Designing of sampling strategies and location of sensors Samples are collected at various stages of the pharmaceutical manufacturing process to manage process parameters and assess pharmaceutical product quality. Hence, both the location of sensors and sampling strategies are considered pivotal. In terms of sampling, heterogeneity and representation are strongly intertwined. According to the

AI in drug product designing, development, and manufacturing

Table 15.3 PAT tools used in the analyzing phase of drug product manufacturing.

Unit Operations API and Excipients Release Granulation

In-Process or Finished Product CQAs Particle Size Distribution

FT-IR

☑

☑

Particle Polymorphism

☑

☑

☑ ☑

Particle Size Distribution Particle Polymorphism

☑

Moisture Determination

☑

Compression

Content Uniformity

Coating

Color Shade Variation

Packaging

Identification

FBRM ☑

☑

Blending

Milling

Raman

Purity of API and Excipients

Particle Size Distribution Impurities/Related Substances Particle Size Distribution Impurities/Related Substances Blend Uniformity

Drying

NIR

☑

☑ ☑ ☑

☑

☑ ☑

☑

☑

☑

☑

☑

☑ ☑

☑

☑

☑

theory of sampling (TOS), there are two sorts of errors: (i) sampling errors and (ii) nonsampling errors. A sampling error is a form of error that happens when the sample chosen does not accurately reflect the population. The outcome is inaccuracy, that is, there is difference between the sample mean and the population mean. This can be overcome by choosing a larger sample size, wherein the chance of an error is reduced. When conducting PAT or any analytical measurements, a nonsampling error occurs when an issue arises due to variables other than sampling. As a result, there is data inadequacy and inaccuracies in data analysis. Nonsampling errors can be due to changes in material properties during processing, artifacts associated with instrumental setup, or environmental issues as a result of changes in humidity, temperature, etc. [32]. To circumvent such mishaps, data (as measurements) collected using various sensors in the form of “variables,” which can take any value based on a concept or a characteristic, need to be understood. In this context, general statistics (like data types, understanding the target variable, generation of summary statistics) coupled with probability distribution is essential, as the decision is mostly made based on sample data to make an inference about the population. In order to infer the population from the sample, the

419

420

A handbook of artificial intelligence in drug delivery

quality of input data or data quality is considered as a major challenge. This can be satisfied with few assumptions such as sample representativeness, sample randomness, normality, sample independence, homoscedasticity, and sample size. To avoid errors, it is frequently advised that statistical samples be examined, sample representativeness, sample randomness, and sample heterogeneity be checked, evaluation through univariate and multivariate process control and process capacity analysis relative to specification be carried out, and so on. The relationship between experiments, statistics, and probability is demonstrated for qualitative identification and quantitative estimation application in Fig. 15.5.

15.4.4 Analyzing and controlling CMAs, CPPs, and CQAs using PAT and data science The outcome of individual PAT sensors is investigated using statistics and chemometrics. Since the data generated using PAT tools are analyzed using descriptive or inferential statistics, the decision obtained is binary (either pass or fail, go/no go, etc.). Similarly, data analysis and models are selected by an expert, which could lead to one or more cognitive biases. However, sometimes, predictive modeling is required because the data generated from PAT sensors need to be considered big data based on 5Vs. According to € ose et al., further classification of data can be based on four dimensions or “5Vs,” Ozk€ namely, volume (quantity or size of data, in gigabytes/GB, terabytes/TB, petabytes/ PB, exabytes/EB, zettabytes/ZB, yottabytes/YB), velocity (speed of data collection), value (data should enrich the process under study), variety (format of data, i.e., structured or unstructured), and veracity (quality of data) of the format [33]. For example, “big data” are defined as those that require more storage space, appear at a faster rate/streaming, and involve data cleaning and data preparation as additional steps. Mining big data without having an a priori hypothesis, on the other hand, are considered statistical theatrics; however, such analysis can also help in generation of unknown information. Apart from the 5Vs, other information about big data include variability, visualization, location (local PC vs cloud or multiple serves), longevity (specific duration vs infinite time), reproducibility in case of loss (easy with small data, whereas difficult with big data), and stakeholder risk. That said, big data must be derived from high-quality small data in order to produce meaningful insights [34]. As discussed in the prior sections, the QbD process generates a large amount of data. By integrating digitization, automation, and big data, Pharma 4.0 has begun to change the manufacturing business. The idea is to connect units and create a new generation of smart factories. Machine learning (ML) is a subset of AI, which provides statistical tools to explore and analyze data, whereas deep learning (DL) is a subset of ML based on a multineural network architecture to make the machine learn in the same manner through which the human brain tries to learn new things. DL mimics the human brain to analyze data, thus creating models, and to take an appropriate action.

AI in drug product designing, development, and manufacturing

Fig. 15.5 Application of PAT for qualitative identification and quantitative estimations.

421

422

A handbook of artificial intelligence in drug delivery

Data science approaches have been widely employed to understand powder blending, powder homogeneity/blend uniformity, granulation, coating, final product characteristics like content uniformity, assay, tensile strength, disintegration, in vitro dissolution, and real-time release testing [35]. Detailed discussions on data analytics are provided in the modeling and simulation sections.

15.4.5 Digital twins, Internet of Things, and outlook A close integration of physical resources and the virtual environment through intertwined communication/cloud storage has led to the crucial development of a digital twin (DT) architecture, as demonstrated in Fig. 15.6. In various industries, rapid development of numerous applications utilizing Internet of Things (IoT) technologies has been achieved. Quality by design (QbD), PAT deployment, modeling and simulation, continuous manufacturing (CM), continuous process verification (CPV), and flowsheet modeling have all been used in the pharmaceutical industry to provide real-time system monitoring and control [36]. However, the progress of DT is lagging for both pharmaceutical and biopharmaceutical manufacturing due to the late/partial adoption of digital transformation and/or Pharma 4.0 concepts.

Fig. 15.6 A general digital twin (DT) framework encompassing the physical components, virtual components, and a data management platform.

AI in drug product designing, development, and manufacturing

Although Pharma 4.0 has been declared a new industrial revolution, its implementation will more likely resemble an evolution in which digitalization and automation meet highly complex product portfolios and life cycles. It is therefore important to achieve an accepted understanding of readiness and maturity, starting with additional digital enablers and elements added to the ICH Q10: the pharmaceutical quality system along with the product’s life cycle.

15.5 Simulation tools in drug product designing and manufacturing process development 15.5.1 Introduction and need for simulation tools A drug’s development cycle, including commercialization, can take years (7–12 years) and could cost significant investments ranging between $100 million and $500 million. That said, 80%–85% of items entering clinical trials would fail, resulting in significant costs [37]. In such scenarios, employing modeling and simulation (M&S) at early stages can help the abovementioned process become faster and more cost-effective. M&S are R&D methodologies for describing physical systems or processes in which mathematical equations (referred to as “models”) are numerically/analytically solved through simulations [38]. Recognizing these benefits, modeling and simulation are being used by organizations such as the US Food and Drug Administration (USFDA) and academic pharmaceutical programs all over the world. The focus of this section is on the role of M&S in accelerating QbD [39]. A large majority of drug products are manufactured with raw materials (RMs) like active pharmaceutical ingredients (APIs) and excipients, the quality of which are core to the manufacturing of efficacious finished dosage forms. Most manufacturing processes involve sifting, flowing of solids, mixing of solids, wetting and drying, milling, compression, coating, etc. Based on the manufacturing route, one or more of these steps could be optional. A thorough raw material understanding and providing a correlative approach to selecting the process to tailor the prerequisite product performance is deemed pivotal [40]. The material properties of APIs or excipients like polymorphism, shape, size, adhesiveness, morphology, roughness, wettability, density, surface chemistry, plasticity, brittleness, and hygroscopicity play an important role in the dosage form design. Thenceforth, characterization/simulation of the structural and chemical properties of the active ingredient(s) and excipients is essential for successful product development. Unfortunately, there is no “one-size-fits-all” tool for approaching these complex analyses. How efficiently and reproducibly these operations can be performed is dependent on the characteristics of the solids involved [41]. Furthermore, qualifying raw materials should be considered as the first step in achieving the “right first time and right every time” (RFT-RET) approach. Hence, in this chapter, an en suite of simulation tools have been described to help in constructing a strategy that would

423

424

A handbook of artificial intelligence in drug delivery

Fig. 15.7 A pictorial representation of the correlation between material and process with first principles, simulation, and data science.

compensate for the variabilities (like raw material, formulation composition, process, equipment characteristics, etc.) encountered during pharmaceutical operations right from dispensing to performing product performance testing, as presented in Fig. 15.7. Within the context of digital pharmaceutical development, elucidating the structureprocess-property relationship across the molecular level to the nano-, meso-, and microlevel hierarchy of pharmaceutical materials and formulated systems and underlying processes enables developing the desired functionality in crystals, particles, and formulated products, thereby improving the process and product performance as well as the therapeutic outcome [40]. A wider range of time and length scales must be considered

AI in drug product designing, development, and manufacturing

Table 15.4 Classification of simulation tools.

Empirical approaches

First principles

Simulation studies

Data science

Hybrid simulation

BCS (molecular-level) SeDeM (particle-level) MCS (particle-level) Materials science tetrahedron (ingredient-level) Bonding area and bonding strength (matrix-level) Crystal structure predictions Kawakita equation Heckel equation Partial differential equations Froude number Molecular dynamics Finite element analysis Discrete element analysis Computational fluid dynamics Hybrid approaches Statistics Chemometrics Artificial intelligence Artificial neural networks and machine learning Deep learning Data science with first principles Material informatics (integrated materials and data science)

when simulating and forecasting the performance of active pharmaceutical components, excipients, manufacturing processes, and finished dosage formulations. The various simulation tools employed during product development, right from API/excipient raw material selection to product performance testing, are shown in Table 15.4. The generally employed methodologies, such as empirical and first principles, can clarify certain recognized concepts, but they may be inconsequential in deriving notions of a complicated nature. Advanced simulation approaches (ranging from the quantum to macroscopic scale) are used to achieve this, which include density functional theory (DFT), molecular dynamics (MD) simulation, computational fluid dynamics (CFD), discrete element modeling (DEM), finite element method (FEM), population balance model (PBM), and physiologically based pharmacokinetic (PBPK) modeling [42,43]. These M&S techniques aid in the mechanistic understanding of the critical physics and chemistry involved in a specific material, its formulation, and/or process. All numerical/analytical simulations and models assume certain things, and mismatches in the input data can have a significant impact on the conclusion [44]. That is, although there is growing interest in the development and deployment of MandS within the pharmaceutical industry, ensuring input data quality and the right selection of models are both extremely

425

426

A handbook of artificial intelligence in drug delivery

important prerequisites for MandS. It is worth mentioning that one should be aware that MandS adheres to the well-known analogy, garbage in, garbage out (GIGO). It is necessary to have a thumb rule while selecting M&S tools, be it empirical or mechanistic modeling or data-driven approaches [45]. For example, if the requirement is to derive molecular-level properties like polymorphism or melting point, then the empirical information required are topological surface area, number of H-bond acceptors, number of H-bond donors, atom-atom interactions, orientational entropy, conformation, and orientation of the molecule. The modeling and simulation approaches can be density functional theory, molecular mechanics, and molecular dynamics. At the particle, bulk, or powder level, DEM is recognized as a powerful tool, which simulates the interaction and flow of dry materials in various manufacturing unit operations [4]. When it comes to simulating or modeling fluid flow and predicting the interaction of dry materials with aqueous and/or nonaqueous fluids, both CFD and FEM are viable approaches. Population balance modeling (PBM) has been extensively used to model particle evolution as a function of drug substance crystallization and particle size reduction as a function of milling, blending, and granulation processes [46]. PBM is also computationally cost-effective. All these abovementioned models work at the quantum, atomistic/molecular, microscale-mesoscale, or continuum/macroscale, as shown in Fig. 15.8. The

Fig. 15.8 A pictorial representation of the correlation between material, process, and characterization with first principles, simulation, and data science.

AI in drug product designing, development, and manufacturing

drawback is predicting the interplay of phenomena involving the mechanical and physico-chemical properties of APIs and excipient materials in a particular unit operation at distinct spatial and temporal scales and in correlating them with biopharmaceutics. In such scenarios, multiscale or hybrid modeling is used. Modeling and simulations, as well as data science-based approaches, are simply an approximation of reality and are only valid in a limited set of circumstances. Using sophisticated experimental methodologies, material properties, process parameters, and endproduct testing approaches must be established. Multiscale modeling is a type of modeling that allows understanding relationships by combining several models to explain a system at the same time scale but with varying resolution ranges [47]. Coupling of the (a) DEM and CFD, (b) PBM and DEM, and (c) DEM, CFD, and PBM is well-known for its application in describing the movement of particles in a granulator, particle evolution during crystallization or granulation, particle size reduction during milling, dissolution, residence time distribution, etc. Palanisamy et al. [48] sequentially published research articles in which the researchers used a kinetics-based experimental technique to study swelling followed by multiscale modeling involving CFD-DEM to study the swelling of starch granules. One of the most successful approaches that helps in connecting the data from material properties (APIs and excipients) to formulations is integrated multiscale modeling. The relationship between material properties and residence time distribution (RTD) with respect to process parameters has also been investigated using semiempirical hybrid models.

15.5.2 Data-driven modeling: AI/ML/DL for product and process development In many scenarios, researchers who create large datasets from a variety of sources and resolutions are frequently constrained in their capacity to successfully integrate them to derive a more logical design and development [43]. That is, the routine use of such elaborate and high-resolution models to address CMC cases is often hindered by prohibitively computational expenses and lengthy run times. In this context, bridging mechanistic modeling and machine learning approaches can help unravel various unanswered issues within pharmaceutical development. That is, a large amount of data gathered using experimental and/or MandS approaches can be integrated into AI/ML/DL models, resulting in data-driven modeling strategies. In pharmaceutical manufacturing employing QbD approaches, multivariate statistical analysis, Monte Carlo, machine learning, artificial neural networks (ANNs), deep learning, time-series forecasting, and other methodologies are often employed. Due to their high level of sophistication, linear and nonlinear handling abilities, and the ability to extrapolate and interpolate with enough available information, data-driven approaches have quickly gained prominence for the study of structure-property correlations, process, and performance prediction of materials within formulation research [49]. Lattice energy and atom-atom interaction predictions are effortlessly demonstrated by data-driven modeling

427

428

A handbook of artificial intelligence in drug delivery

Fig. 15.9 Integration of QbD parameters for the modeling of pharmaceutical unit operations to predict and correlate various properties to process and/or performance.

approaches. Homogeneous mixing of solid particles is one of the most basic determinants for attaining content homogeneity and reproducible batch manufacturing processes such as drying, blending, and granulation. Kumar et al. utilized integrated approaches involving discrete element modeling (DEM), machine learning, and time-series forecasting via autoregressive integrated moving average (ARIMA) models to simulate a complex pharmaceutical problem that involved developing an agitation protocol to ensure uniform solid mixing [50]. Pharma 4.0 and Industry 4.0 as well as the developments in computing hardware have made inroads into big data analytics. That said, not all domains or applications boast of large data both in quality and quantity. In such scenarios, design of experiments coupled with machine learning could be envisaged. According to a recent study, self-validated ensemble modeling (SVEM) can be leveraged to reliably achieve good prediction even when data are sparse. Because data-driven techniques lack a grasp of the underlying physics, chemistry, and biology, generalizations and transferability of models outside the input dataset are frequently disappointing. To address such setbacks, a recent tendency has been to develop new hybrid modeling methodologies, as shown in Fig. 15.9.

AI in drug product designing, development, and manufacturing

15.5.3 Flowsheet modeling for product and process development A patient-centric approach involving a continuous manufacturing (CM) platform is being considered in order to reduce the finished product’s variability and improve its affordability and accessibility to pharmaceuticals [37]. CM is a fully integrated process that converts raw materials into tablets without requiring any intermediate isolation (i.e., individual unit operations are connected in a series). The output from equipment becomes the input of the next unit operation, and, mathematically, the result of the preceding unit’s model becomes the input of the next unit operation. Software like ASPEN Plus, CHEMCAD, and gPROMS are some of the flowsheet modeling packages that are extensively used in the chemical and petrochemical industries for processes involving fluids. For continuous pharmaceutical manufacturing, flowsheet models have recently been developed to efficiently represent integrated process dynamics involving API purification, direct compression, dry granulation, and wet granulation. Nagy et al. [51] demonstrated and implemented flowsheet modeling for the first time to assess a CM finished product’s performance using dissolution. Using integrated flowsheet models, CM bridges the long-standing gap between QbD theory and implementation, signaling a departure from traditional pharmaceutical manufacturing [39]. Through a data-driven environment, this risk-based strategy encourages enhanced product and process expertise.

15.5.4 Limitations of simulation tools in product designing and process development Getting away from a checklist attitude is the first barrier to M&S or QbD implementation in general. The second barrier is determining the benefits or tangibility or the rewards. The time required for MandS, which is exponentially proportional to the problem’s complexity, is the third significant barrier [38]. The risks associated with M&S, which include accuracy, generalizability, closeness of the outcome to the physical domain, and output unpredictability, are the next barriers. To overcome one or more of these obstacles, data-driven modeling, hybrid modeling (multiscale modeling), and/or flowsheet modeling are used. However, these also have real-time monitoring flaws, and, hence, we discuss some outlook in the following sections.

15.5.5 Data science, machine learning, and outlook Throughout the commercialization process, M&S and AI/ML/DL tools can be immensely beneficial, as represented in Fig. 15.10. They are becoming more popular in QbD as a measure to gauge, collate, communicate, document, and reduce risks as part of cost-avoidance efforts. It is anticipated that these tools would have infinite possibilities from an industrial standpoint in terms of PAT, transitioning from development to manufacturing, facilitating technology transfer, predictive control, automation, and so

429

430

A handbook of artificial intelligence in drug delivery

Fig. 15.10 AI-ML-DL-based design analysis and control phases of manufacturing.

on. Despite the fact that QbD was first introduced in the early 2000s, it is now extensively and progressively adopted in design courses at reputable universities. We anticipate that bridging first-principles simulations and machine learning models, as well as forecasting approaches, such as ARIMA, will aid in overcoming lengthy computational constraints and hope that more researchers will be interested in this ingenious approach. Very recently, a quantum computer has been able to complete a task in just 36 μs that would otherwise take a supercomputer 9000 years. We expect that in future, modeling, simulation, and data science can be a real-time enabling application for advanced feedback and controls, continuous manufacturing, and optimization. As a result, batch operations or continuous endto-end manufacturing processes will be more resilient, adaptable, and flexible, resulting in higher-quality final products that can be manufactured faster yet at a lower cost.

15.6 Artificial intelligence in drug product commercial manufacturing and analysis For decades, the pharmaceutical manufacturing industry has been and will continue to be a significant contributor to society in the future and will enable people to live longer, better, and healthier lives. Pharmaceutical manufacturing is a critical process due to

AI in drug product designing, development, and manufacturing

the simultaneous consideration and optimization of several factors that potentially influence the process and product quality [52–55]. AI simulates the human intelligence processes (learning, reasoning, and self-correction) with the help of a computer system (a machine). The pharmaceutical industry has explored AI for different applications such as process control, prediction systems and modeling, defect detection, and data and pharmaceutical supply chain management. Mainly, AI has already been proven to be an efficient tool in drug discovery and development with advancements in technology. It helps accelerate drug discovery research by continuously providing, analyzing, and interpreting the new data collected from clinical trial studies, with multiple participants undergoing different treatments along with a better knowledge of adverse reactions or side effects arising thereof. Moreover, AI may have a significant pharmacoeconomic impact through reduction of errors, time, and cost incurred on unnecessary formulation trials, allowing to focus on more important tasks like improving formulations for better performance and efficacy. Consequently, AI is now being continuously explored for its significant use in the development of AI-powered pharmaceutical manufacturing processes and plants, which might completely transform the pharmaceutical industry. Therefore, AI is seen as the way of the future in the pharmaceutical manufacturing industry [52–55]. As discussed earlier, the pharmaceutical industry should improve the production process to bring about scalability and efficiency to the manufacturing of APIs. AI predictive analytics can be integrated for maximum utilization of resources that help in the reduction of drug manufacturing costs. This is achieved with the use of AI thorough evaluation of data generated from prior batches manufactured using the same facilities at the plant to maximize the cost-effective production. Therefore, AI can suggest an operation mechanism for the efficient running of the manufacturing process and for improvements in the design of equipment for better output [52,53]. Moreover, AI, along with advanced technology platforms, which provide digital information about market demand, the status of inventory, and the production capacity of the industry, can help estimate a drug’s production schedule in advance. This will help in designing an ideal production layout for improved output along with the reduction in waste and the demand-supply gap of pharmaceuticals. AI also assists manufacturers in the execution of good manufacturing practices and in the manufacture of cost-effective and efficient pharmaceuticals and pharmaceutical products by detecting trends in a large data pool [52,54,56]. The pharmaceutical industry manufactures APIs on a large scale, which holds more than half of the drug market, to fulfill the growing demands of consumers. The physicochemical properties of APIs tend to change over time or with a change in the supplier and hence need to be accurately controlled within the predefined acceptance criteria, along with the dose of the drug to ensure the quality, safety,

431

432

A handbook of artificial intelligence in drug delivery

and efficacy of the final pharmaceutical product [52,55]. Implementation of AI technology could help improve accurate dosing and batch consistency. Moreover, as discussed earlier, machine learning can analyze the data from previous batches for optimization of the process parameters that have been proven to be critical to the quality and performance of the final product. The AI predictive modeling approach can be applied to optimize repeatable manufacturing processes by developing a system based on the evaluation and comparison of real-time data from the manufacturing process and data of previous batches for detection of outliers having a significant impact on product quality and performance. Taken together, this will contribute to enhancing the predictability toward the efficiency and effectiveness of the drug manufacturing process. The AI technique also assists the pharmaceutical industry in meeting demands by reducing the hold periods between manufacturing phases for continued supply without interruption and delay caused by process-related paperwork errors or manual processing [52,54,55].

15.6.1 AI in the drug product batch manufacturing process A batch manufacturing process involves multiple unit operations or steps that can occur at different manufacturing plants and can take months to manufacture the final product. Although it is the preferred method of manufacturing most pharmaceuticals, some of the pharmaceuticals are manufactured through a continuous manufacturing process. In batch manufacturing, after each stage, the production is kept on hold for some time (hold period) while samples are evaluated offline for quality. The procedure can be complicated and time-consuming, with substantial gaps between the process stages. However, sometimes, the material is stored or transported to other facilities during this holding period to execute the selected steps of the manufacturing process. This is of major concern for APIs, which are highly susceptible to environmental degradation. Therefore, the implementation of AI in the individual steps of manufacturing will benefit the batch drug production process for APIs [52]. Several steps, including timely recording of pressures or temperatures, equipment performance monitoring, etc., need to be completed during the manufacturing process in the production of the final product. This can be potentially achieved with the implementation of AI technology for the individual steps in the process together with human supervision, which could help improve the efficiency and productivity of the manufacturing process with a reduction of errors. Therefore, a high level of standardization can be achieved with AI-assisted batch manufacturing. Moreover, this will assist the pharmaceutical industry in better achieving supply expectations necessary for end-toend quality control, including [52]: • Decreasing the hold period between steps, • Reducing errors caused by human processing or paperwork problems, and • Increasing operating flexibility during transportation and storage procedures.

AI in drug product designing, development, and manufacturing

15.6.2 AI in the drug product continuous manufacturing process Pharmaceutical manufacturing intends to produce a product that consistently meets quality criteria at every single stage of the process [52]. AI allows pharmaceutical makers to improve their operations and find scope for enhancement in the continuous pharmaceutical manufacturing process (manufacturing of products continually on a single line without a pause or break). Usually, a continuous manufacturing process has a short supply chain and is carried out at a single location and can take up to days to manufacture the final product, which is less than the batch process [52,54]. Moreover, AI technology can be explored for controlling the process parameters and for observing the performance parameters of the equipment (levels of temperature and pressure, comparing cleaning procedures with factory specifications, and detecting probable sources of contamination through readings obtained from sensors installed all over the premises). Furthermore, using cameras positioned over sink areas or ladders, pharmaceutical manufacturers can observe precise details relevant to employee compliance, such as handwashing frequency that helps reduce the potential threat of product contamination [52].

15.6.3 AI in drug delivery and drug product research and development 15.6.3.1 AI in drug delivery AI is explored in drug delivery modeling, along with its use in the understanding of the biological system, and further using the same information to improve the designing of nanosystems or for modeling of drug delivery systems [57,58]. For a better understanding and for drug delivery, the human body is simplified by dividing into several compartments separated by biological membranes or barriers. Permeation through these biological barriers can be achieved through two main mechanisms, viz., active and passive. Passive diffusion of a drug is mainly dependent on its molecular properties, which tend to get altered with exposure to the physiological environment, and, hence, predictive or in silico modeling of the drug distribution process is complex. Conversely, active diffusion involves an energy-activated transporter system that transports the drug across biological membranes. Such biological interaction between membrane transporters and drug molecules allows the modeling of the transporter systems through computing several parameters [57–60]. AI tools can be integrated into the early stages of the drug discovery and development process for drug repurposing purposes [53–55,61]. The artificial neural network (ANN) modeling approach was employed for understanding and establishing the quantitative relationship between different formulations and processing variables with the in vitro and in vivo characteristics of the formulations. This led to the generation of predictive models for accurate estimation of the formulation property of interest based on the inputs for the design, such as material and process attributes. The information so obtained was further used for the optimization of formulation properties for desired applications [62–67]. Moreover, trained ANN models have also been explored for the prediction of moisture content and viscosity, glass transition

433

434

A handbook of artificial intelligence in drug delivery

temperatures, and water-uptake profile of polymers [74], which will help collect more precise information in the preformulation stage and in other aspects of preformulation studies of the product development process [62,64]. Previous studies have reported integration of the AI modeling approach for prediction of formulation properties such as the dissolution profile, transportability of acidic, basic, and neutral drugs, suitable polymer composite, suitable grade of HPMC for desired biopharmaceutical properties, hydrophilic drug entrapment in liposomes developed for lung targeted delivery, etc. [60]. AI is an emerging tool for predicting the interactions of drug molecules with other components in genomic and phenotypic research, which could accelerate the development of personalized drug delivery systems [54,55]. Some of the most critical steps in this process are the selection, evaluation, and simulation of the factors and type of model, in addition to designing the quality attributes before experimentation or identification of the critical attributes along with the observed results of the experiment [62]. AI technology is implemented in the development of microfluidic lab-on-a-chip models to perform assays for membrane penetration and cellular or enzymatic interactions as well as in the development of organ-on-a-chip models. Moreover, AI is also explored for predicting drug pharmacokinetics and toxicity and how it gets affected by the use of different types of drug delivery systems [54,57,58]. AI technology can be used to establish molecular requirements based on the assessment of the known molecular properties that are predicted and proved to be effective in the desired treatment. This could help in a small increase of local concentration but a tremendous improvement in bioavailability and therapeutic effect in the case of potent drug candidates. The results of precise mechanistic modeling can have a significant impact on future product development. Therefore, an AI tool not only collects data using several sources but also generates indications for drugs that could be more useful in the development of a drug delivery system. Moreover, by reviewing molecular, pharmacokinetics, and patient information, the most important problems in the development of a drug delivery system could be identified [57,58]. The main issue in the integration of AI technology into the current system is the availability of quality and accurate databases with relevant and consistent data. Moreover, a large portion of previous data results is insufficient to allow impartial development and evaluation of AI models. Consequently, AI tools could be employed to create or verify the current database for compilation of the necessary data in the development of AI models for future applications. This will also help understand the current loopholes in the required knowledge and data as input for the development of AI models to be used in the development of a new product with desired quality attributes [57,58].

AI in drug product designing, development, and manufacturing

15.6.3.2 AI in product research and development The product research and development and prototyping stage occurs after recognizing the mechanism of action, metabolism, obstacles, and delivery strategy for the selected drug [57,58]. Apart from manufacturing development and control, AI is also extended in prototyping as well as in the early and late stages of product research and development. An early stage refers to the transformation of a concept into a prototype, along with its testing and consistent refinement [61]. An adequate quantity of products for early testing and evaluation is manufactured using laboratory-scale manufacturing processes [61–65]. Moreover, early definition of product properties and scalable methods, accurate recognition of prototype, and early confirmation of safety and efficacy are all required. However, late-phase research and development relates to the robustness and scalability of the actual manufacturing process for the production of a large quantity of quality products, which is one of the most important components to be considered in product management throughout its life cycle [65]. Therefore, AI-powered technology could prove to be a turning point for pioneer industries in the pharmaceutical sector for early-stage design and development to reduce the failures while shifting to clinical testing and for smart manufacturing for desired drug delivery objectives to improve the scalability, robustness, and ease of transfer in the manufacturing process [54,55,65,66]. Conversely, there are several challenges in the adoption of AI technology in drug delivery and research and development applications. Developing a highly specific and customized AI technology for defined technical applications is much needed. For this, transparent and unbiased use of AI platforms by scientists having specific technical understanding along with average data handling or IT skills is highly needed. Moreover, the safety and security of highly precious data and knowledge is a matter of top priority and a great concern for developers in high-tech organizations [57,58].

15.6.4 Challenges in the implementation of AI Before integrating AI into pharmaceutical manufacturing processes, several potential barriers must be overcome, such as the worth brought by AI to the industry as well as working and addressing the AI process [52]. 15.6.4.1 High AI implementation cost Implementation of AI is not an easy task, and it will necessitate substantial expenditures. It is extremely important to understand in advance the quantification of return on investment from the integration of AI into a given process or product. However, it is extremely difficult to achieve a positive and anticipated return on investment from the implementation of AI technology [52,54].

435

436

A handbook of artificial intelligence in drug delivery

15.6.4.2 Time constraints in AI development Pharmaceutical manufacturers are already engaged in meeting the demand for new drugs, drug products, and safety recalls, by consistently meeting regulatory requirements, and, hence, they may not spend additional time on the development and implementation of AI technology for the defined manufacturing process [52,54]. 15.6.4.3 Inadequate expertise of pharmaceutical IT teams The pharmaceutical sector is facing a deficit in AI skills due to insufficiently trained people or experts in AI. This may also be due to the increasing expansion and growing competition for available skilled individuals. Moreover, the pharmaceutical sector is facing AI personnel shortages more than other sectors [52,54]. 15.6.4.4 Inadequate clarity on the correct use and implementation of AI in the existing process Pharmaceutical manufacturers have no clarity regarding the potential use and applications of AI technology in the development and manufacturing of drug substances and products because they are unaware of the current technological possibilities and may have unrealistic expectations [52]. 15.6.4.5 Proper feed of well-organized data The quality and accuracy of input data are highly recommended for the development of highly predictive and powerful AI models for a given process. Although many industries started after the digital transformation era, they have been unable to collect data in an organized manner and hence are not successful in the integration of AI technology. For the development of successful AI models with high accuracy and minimum prediction errors, one needs an input of a large amount of data, and, hence, the pharmaceutical industry needs to redesign data collection and information storage systems for manufacturing operations [52,54]. 15.6.4.6 Compliance with regulatory requirements While integrating AI into various processes, pharmaceutical manufacturers must comply with the regulatory requirements for data security, which help ensure data integrity, safety standards, and related systems engineering requirements, as well as by the FDA [52]. 15.6.4.7 Legal challenges associated with process Pharmaceutical manufacturers must resolve some legal challenges associated with data privacy, data security, and, more importantly, ownership of intellectual property rights before deploying AI systems [52].

AI in drug product designing, development, and manufacturing

15.6.4.8 Harmonization of requirements for AI implementation Different countries have different regulations on the use of AI in the manufacturing process. Some regulatory agencies completely allow some restrictions, and some completely prohibit the use of AI. Therefore, global harmonization of the regulatory requirements regarding how and when to use AI in the pharmaceutical manufacturing process is highly needed for its successful adoption [52].

15.6.5 Future scope for the implementation of AI 15.6.5.1 For compliance with regulatory requirements and quality assurance With the implementation of the AI system in the production process and in product research and development, pharmaceutical manufacturers can monitor activities and discover any deviations to ensure improved compliance against regulatory requirements, quality assurance, and consumer expectations. AI can focus on monitoring manufacturing processes such as controlling the quality of raw materials and their influence on the finished product’s quality. Therefore, AI-assisted quality assurance could detect the defects in raw materials in advance and hence promote the development of the process. Moreover, AI can also be combined with the IoT technology on manufacturing lines to help in boosting consumer satisfaction by detecting faulty items or packaging in real time [52,53,67]. 15.6.5.2 For predictive maintenance of the manufacturing line AI could be applied to detect issues in equipment performance and regulatory compliance. Integration of AI-based predictive maintenance in the assessment of manufacturing line performance could help in the early identification of equipment wear down or repair of parts if any. This will help reduce the downtime with cost benefits, which will prove more important in cases in which enhanced levels of product quality assurance at all manufacturing stages are required with the introduction of new regulations. Moreover, AI also helps in swiftly responding to the ever-changing needs for new drugs along with surprising safety concerns (drug recalling due to the adverse effects) attributed to its ability to process large data in extremely less time [52–54,56,67]. Fig. 15.11 illustrates the use of AI-powered technology for quality control on the production line of pharmaceutical products. 15.6.5.3 Planning of production activities Pharmaceutical manufacturers may explore AI in the effective management of purchase orders, consumer needs, and market demand for the planning of production activities and for sequentially improving their manufacturing process. For this, they must consider the available number of products and requirements to meet the ever-increasing demands. Moreover, the pharmaceutical industry can explore AI systems to track the status of the inventory and product quantity in hand at any given time before it reaches a critical level,

437

438

A handbook of artificial intelligence in drug delivery

Fig. 15.11 Manufacturing line embedded with an AI-powered quality control system [52].

allowing uninterrupted and on-track supply of the product. This will help manufacturers in improving the planning efficiency without raw material or finished product shortages, along with less wastage and fewer delays due to overstocking or stockouts [52–54,56].

15.7 Conclusions Manufacturing a drug substance or a drug product is a critical process that involves systematic planning along with the assurance of efficient monitoring of all processes involved therein for better output. Moreover, due to the huge time and cost expenditure on drug research and development, more innovative procedures and strategies need to be invented and introduced. AI technology has enormous potential for processing and analyzing a large amount of multivariate data, accelerating drug discovery, establishing a correlation between processing variables and formulation characteristics, and finding a solution for complex problems in formulation development. Early adoption of AI by the pharmaceutical industry appears to be rich in opportunity and promise as it offers several advantages, including better productivity, quality control, cost savings, and overall efficiency over potential competitors. By means of a science and risk-based systematic approach to development through QbD, a control strategy will be defined for each of the CMA and CPP after screening through risk assessment tools and optimization through DoE and other MVDA techniques with respect to each in-process and finished product CQA. These QbD-defined control ranges of individual CMAs and CPPs will be fed into machines by means of machine learning and language; so, during routine commercial manufacturing, a machine can automatically analyze and control critical variables by means of PAT tools with artificial intelligence. The main goal of analyzing data through machine learning and developing a multineural network architecture through deep learning is to derive an AI application, which automatically analyzes all types of data as well as takes appropriate

AI in drug product designing, development, and manufacturing

actions accordingly at the same time. Thus, in future, “quality will be controlled and assured 24  7 automatically, when no one is looking.” However, pharmaceutical manufacturers need to overcome several obstacles stepwise before the adoption of AI, and, hence, they are still in doubt about the integration of AI technology into the manufacturing and research and development processes to improve their productivity. Surprisingly, the most probable and primary impediments to the integration of AI into the pharmaceutical industry are the two major nontechnological components of AI, viz., deficient standardized databases and the conservative regulatory approach in the conventional pharmaceutical manufacturing process. Although AI is still a relatively new concept in the pharmaceutical sector, its use in the API manufacturing process and product research development could prove to be a turning point in the growth of the business. Therefore, pharmaceutical companies need to spend more time researching the best usability processes, along with learning about the present AI competencies, to prepare the industry for this future system.

References [1] L.X. Yu, G. Amidon, M.A. Khan, S.W. Hoag, J. Polli, G.K. Raju, et al., Understanding pharmaceutical quality by design, AAPS J. 16 (2014) 771–783. [2] V. Mishra, S. Thakur, A. Patil, A. Shukla, Quality by design (QbD) approaches in current pharmaceutical set-up, Expert Opin. Drug Deliv. 15 (2018) 737–758. [3] S. Chaudhary, QbD and PAT in granulation, in: Handbook of Pharmaceutical Granulation Technology, fourth ed., CRC Press, 2021, pp. 827–866. [4] D.L.H. van der Haven, F.H. Ørtoft, K. Naelap€a€a, I.S. Fragkopoulos, J.A. Elliott, Predictive modelling of powder compaction for binary mixtures using the finite element method, Powder Technol. 403 (2022), 117381. [5] D. Paul, R. Tekade, Artificial intelligence in drug discovery and development, Drug Discov. Today 26 (1) (2021) 80–93. [6] S. Colombo, Applications of artificial intelligence in drug delivery and pharmaceutical development, in: Artificial Intelligence in Healthcare, Academic Press, 2020, pp. 85–116. [7] H.G. Claycamp, R. Kona, R. Fahmy, S.W. Hoag, Quality-by-design II: application of quantitative risk analysis to the formulation of ciprofloxacin tablets, AAPS PharmSciTech 17 (2) (2016) 233–244. [8] H. Ando, Y. Hara, K. Sato, M. Dohi, T. Hakomori, E. Yonemochi, New approach to optimizing risk management of the sticking problem using scale-independent critical material attributes and the quantitative process parameter, Int. J. Pharm. 577 (2020) 1–8. [9] T. Waghule, N. Dabholkar, S. Gorantla, V.K. Rapalli, R.N. Saha, G. Singhvi, Quality by design (QbD) in the formulation and optimization of liquid crystalline nanoparticles (LCNPs): a risk based industrial approach, Biomed. Pharmacother. 141 (2021) 1–15. [10] Z. Nemeth, E. Pallagi, D.G. Dobo´, G. Kozma, Z. Ko´nya, I. Cso´ka, An updated risk assessment as part of the QbD-based liposome design and development, Pharmaceutics 13 (7) (2021) 1–28. [11] J. Kushner, B.A. Langdon, J.I. Hiller, G.T. Carlson, Examining the impact of excipient material property variation on drug product quality attributes: a quality-by-design study for a roller compacted, immediate release tablet, J. Pharm. Sci. 100 (6) (2011) 2222–2239. [12] A. Benedetti, J. Khoo, S. Sharma, P. Facco, M. Barolo, S. Zomer, Data analytics on raw material properties to accelerate pharmaceutical drug development, Int. J. Pharm. 563 (2019) 122–134. [13] A. Bejarano, C. Hewa Nadungodage, F. Wang, A.C. Catlin, S.W. Hoag, Decision support for excipient risk assessment in pharmaceutical manufacturing, AAPS PharmSciTech 20 (6) (2019) 1–16.

439

440

A handbook of artificial intelligence in drug delivery

[14] I. Akseli, A. Stecuła, X. He, N. Ladyzhynsky, Quantitative correlation of the effect of process conditions on the capping tendencies of tablet formulations, J. Pharm. Sci. 103 (6) (2014) 1652–1663. [15] ICH Guideline Q8(R2), Pharmaceutical Development, in: ICH Harmonised Tripartite Guideline, August, International Conference on Harmonisation, 2009, pp. 1–24. https://database.ich.org/sites/ default/files/Q8_R2_Guideline.pdf. (Accessed 30 December 2022). [16] Ruey-ching Hwang, Donna Kowalski, Design of Experiments for Formulation Development, in: Pharmaceutical Technology, 7th, PharmTech, 2005. https://www.tandfonline.com/doi/abs/10. 1080/03639045.2017.1291672?journalCode¼iddi20. (Accessed 25 December 2022). [17] A.J. Hickey, H.D.C. Smyth, Considerations in monitoring and controlling pharmaceutical manufacturing, Springer, 2020, pp. 31–38. [18] B. Singh, R. Kumar, N. Ahuja, Optimizing drug delivery systems using systematic “design of experiments.” Part I: Fundamental aspects, Crit. Rev. Ther. Drug Carrier Syst. 22 (2005) 27–105. [19] M. von Stosch, R. Schenkendorf, G. Geldhof, C. Varsakelis, M. Mariti, S. Dessoy, et al., Working within the design space: do our static process characterization methods suffice? Pharmaceutics 12 (6) (2020) 1–15. [20] S.N. Politis, P. Colombo, G. Colombo, D.M. Rekkas, Design of experiments (DoE) in pharmaceutical development, Drug Dev. Ind. Pharm. 43 (2017) 889–901. [21] S. Beg, S. Swain, Introduction to the application of experimental designs in pharmaceutical product development, in: Design of Experiments for Pharmaceutical Product Development, Springer, 2021, pp. 1–17. [22] I.M. Fukuda, C.F.F. Pinto, C.D.S. Moreira, A.M. Saviano, F.R. Lourenc¸o, Design of experiments (DoE) applied to pharmaceutical and analytical quality by design (QbD), Braz. J. Pharm. Sci. 54 (2018) 1–16. [23] G. Bai, Z. Chen, K. Raines, H. Chen, K. Dave, H.P. Lin, et al., Assessment of applications of design of experiments in pharmaceutical development for oral solid dosage forms, J. Pharm. Innov. 15 (4) (2020) 547–555. [24] A. Badr, General introduction to design of experiments (DOE), in: Wide Spectra of Quality Control, IntechOpen, 2011. [25] R. Lee, Statistical design of experiments for screening and optimization, Chem.Ing..Tech. 91 (2019) 191–200. [26] M.J. Anderson, P.J. Whitcomb, DOE Simplified: Practical Tools for Effective Experimentation, third ed., CRC Press, 2017, pp. 1–252. [27] B. Durakovic, Design of experiments application, concepts, examples: state of the art, Period Eng. Nat. Sci. 5 (3) (2017) 421–439. [28] A. Rogers, M.G. Ierapetritou, Mathematical tools for the quantitative definition of a design space, in: Methods in Pharmacology and Toxicology, Springer, 2016, pp. 225–279. [29] E.J. Kim, J.H. Kim, M.S. Kim, S.H. Jeong, D.H. Choi, Process analytical technology tools for monitoring pharmaceutical unit operations: a control strategy for continuous process verification, Pharmaceutics 13 (6) (2021) 1–45. [30] C. McSweeney, Process Analytical Technology: Innovation Supporting Right First Time in Pfizer Global Manufacturing, IMB/Industry Meeting October, 2008. http://www.hpra.ie/docs/defaultsource/default-document-library/9-pat–conor-mcsweeney.pdf. (Accessed 30 December 2022). [31] G. Gerzon, Y. Sheng, M. Kirkitadze, Process analytical technologies – advances in bioprocess integration and future perspectives, J. Pharm. Biomed. Anal. [Internet] 207 (2022), 114379, https://doi.org/ 10.1016/j.jpba.2021.114379. [32] A. Schmidt, H. Helgers, L.J. Lohmann, F. Vetter, A. Juckers, M. Mouellef, et al., Process analytical technology as key-enabler for digital twins in continuous biomanufacturing, J. Chem. Technol. Biotechnol. 97(9) (December) (2021) 2336–2346. € ose, E.S. Arı, C. Gencer, Yesterday, today and tomorrow of Big Data, Procedia Soc. Behav. Sci. [33] H. Ozk€ 195 (2015) 1042–1050. [34] S.Y. Teng, M. Tousˇ, W.D. Leong, B.S. How, H.L. Lam, V. Ma´ˇsa, Recent advances on industrial datadriven energy savings: digital twins and infrastructures, Renew. Sust. Energy Rev. 135 (2021) 1–22 (February 2020).

AI in drug product designing, development, and manufacturing

[35] L. Aristodemou, F. Tietze, The state-of-the-art on Intellectual Property Analytics (IPA): a literature review on artificial intelligence, machine learning and deep learning methods for analysing intellectual property (IP) data, World Pat. Inf. [Internet] 55 (February) (2018) 37–51, https://doi.org/10.1016/ j.wpi.2018.07.002. [36] S. Laske, A. Paudel, O. Scheibelhofer, S. Sacher, T. Hoermann, J. Khinast, et al., A review of PAT strategies in secondary solid oral dosage manufacturing of small molecules, J. Pharm. Sci. [Internet] 106 (3) (2017) 667–712, https://doi.org/10.1016/j.xphs.2016.11.011. [37] J. Rantanen, J. Khinast, The future of pharmaceutical manufacturing sciences, J. Pharm. Sci. [Internet] 104 (11) (2015) 3612–3638, https://doi.org/10.1002/jps.24594. [38] R. Schenkendorf, D.I. Gerogiorgis, S.S. Mansouri, K.V. Gernaey, Model-based tools for pharmaceutical manufacturing processes, Processes 8 (1) (2020) 2–5. [39] N. Jiwa, Y. Ozalp, G. Yegen, B. Aksu, Critical tools in tableting research: using compaction simulator and quality by design (QbD) to evaluate lubricants’ effect in direct compressible formulation, AAPS PharmSciTech 22 (4) (2021) 151. [40] I. Tho, A. Bauer-Brandl, Quality by design (QbD) approaches for the compression step of tableting, Expert Opin. Drug Deliv. 8 (12) (2011) 1631–1644. [41] M. Leane, K. Pitt, G.K. Reynolds, N. Dawson, I. Ziegler, A. Szepes, et al., Manufacturing classification system in the real world: factors influencing manufacturing process choices for filed commercial oral solid dosage formulations, case studies from industry and considerations for continuous processing, Pharm. Dev. Technol. [Internet] 23 (10) (2018) 964–977, https://doi.org/ 10.1080/10837450.2018.1534863. [42] D. Wilson, S. Wren, G. Reynolds, Linking dissolution to disintegration in immediate release tablets using image analysis and a population balance modelling approach, Pharm. Res. 29 (1) (2012) 198–208. [43] P. Pandey, R. Bharadwaj, Predictive modeling of pharmaceutical unit operations, in: Predictive Modeling of Pharmaceutical Unit Operations, Woodhead Publishing, 2016, pp. 1–437. [44] H. Leuenberger, M. Lanz, Pharmaceutical powder technology – from art to science: the challenge of the FDA’s process analytical technology initiative, Adv. Powder Technol. [Internet] 16 (1) (2005) 3–25, https://doi.org/10.1163/1568552053166683. [45] N. Boruah, H. Sarma, H.K. Sharma, Computational formulation development and drug delivery, Adv. Sci. Technol. 1 (May) (2019) 191–195. [46] X. Zhang, R.A. Lionberger, B.M. Davit, L.X. Yu, Utility of physiologically based absorption modeling in implementing quality by design in drug development, AAPS J. 13 (1) (2011) 59–71. [47] Beck Ron, Munoz Gerardo, Hybrid Modeling: AI and Domain Expertise Combine to Optimize Assets. AspenTech, 2020. Accessed Online: https://www.aspentech.com/en/resources/whitepapers/hybrid-modeling-ai-and-domain-expertise-combine-to-optimize-assets-cxo. [48] A. Palanisamy, D. Flick, et al., Kinetic modelling of individual starch granules swelling, Food Struct. 26 (October 2020) 1–27. https://www.sciencedirect.com/science/article/abs/pii/ S2213329120300150. [49] P.G. Jamkhande, M.H. Ghante, B.R. Ajgunde, Software based approaches for drug designing and development: a systematic review on commonly used software and its applications, Bull. Fac. Pharm. Cairo Univ. [Internet] 55 (2) (2017) 203–210, https://doi.org/10.1016/j.bfopcu.2017.10.001. [50] P. Kumar, K. Sinha, N.K. Nere, Y. Shin, R. Ho, L.B. Mlinar, et al., A machine learning framework for computationally expensive transient models, Sci. Rep. 10 (1) (2020) 1–11. [51] B. Nagy, Dynamic flowsheet model development and digital design of continuous pharmaceutical manufacturing with dissolution modeling of the final product, Chem. Eng. J., 419 (September) (2021) 1–19, doi:10.1016/j.cej.2021.129947. [52] D. Owczarek, The Future of Pharmaceutical Manufacturing Process: Artificial Intelligence, Nexocode, 2021. [53] S. McGrail, AI in the Pharma Industry: Current Uses, Best Cases, Digital Future, PharmaNewsIntel, 2021. https://pharmanewsintel.com/news/ai-in-the-pharma-industry-current-uses-best-casesdigital-future. (Accessed 27 December 2022).

441

442

A handbook of artificial intelligence in drug delivery

[54] K. Walch, The increasing use of AI in the pharmaceutical industry, Forbes, 2020. https://www.forbes. com/sites/cognitiveworld/2020/12/26/the-increasing-use-of-ai-in-the-pharmaceutical-industry/? sh¼47d428ee4c01. (Accessed 28 December 2022). [55] Sartorius, The trending role of artificial intelligence in the pharmaceutical industry, August, Sartorius Science Snippets Blog, 2020. https://www.forbes.com/sites/cognitiveworld/2020/12/26/theincreasing-use-of-ai-in-the-pharmaceutical-industry/?sh¼47d428ee4c01. (Accessed 28 December 2022). [56] D. Greenfield, How and why pharmaceutical manufacturers are applying artificial intelligence, July, Automation World, 2021. https://www.automationworld.com/analytics/article/21578476/howand-why-pharmaceutical-manufacturers-are-applying-artificial-intelligence. (Accessed 29 December 2022). [57] A. Bohr, K. Memarzadeh, Artificial Intelligence in Healthcare, Academic Press, Cambridge, US, 2020. [58] P. Hassanzadeh, F. Atyabi, R. Dinarvand, The significance of artificial intelligence in drug delivery system design, Adv. Drug Deliv. Rev. 151–152 (2019) 169–190. € Ozer, € [59] B. Aksu, A. Paradkar, M. De Matas, O. T. G€ uneri, P. York, A quality by design approach using artificial intelligence techniques to control the critical quality attributes of ramipril tablets manufactured by wet granulation, Pharm. Dev. Technol. 18 (1) (2012) 236–245. [60] G. Troiano, J. Nolan, D. Parsons, H.C. Van Geen, S. Zale, A quality by design approach to developing and manufacturing polymeric nanoparticle drug products, AAPS J. 18 (6) (2016) 1354–1365. [61] A.S. Rathore, H. Winkle, Quality by design for biopharmaceuticals, Nat. Biotechnol. 27 (1) (2009) 26–34. [62] L.X. Yu, Pharmaceutical quality by design: product and process development, understanding, and control, Pharm. Res. 25 (4) (2008) 781–791. [63] A.S. Rathore, Roadmap for implementation of quality by design (QbD) for biotechnology products, Trends Biotechnol. 27 (9) (2009) 546–553. [64] X. Xu, M.A. Khan, D.J. Burgess, A quality by design (QbD) case study on liposomes containing hydrophilic API: I. Formulation, processing design and risk assessment, Int. J. Pharm. 419 (1–2) (2011) 52–59. [65] M.A.W. Eaton, L. Levy, O.M.A. Fontaine, Delivering nanomedicines to patients: a practical guide, Nanomedicine 11 (4) (2015) 983–992. [66] H. Ragelle, F. Danhier, V. Preat, R. Langer, D.G. Anderson, Nanoparticle-based drug delivery systems: a commercial and regulatory outlook as the field matures, Expert Opin. Drug Deliv. 14 (7) (2016) 851–864. [67] J. Spinner, Artificial intelligence can elevate pharma manufacturing, May, Outsourcing Pharma, 2021. https://www.outsourcing-pharma.com/Article/2021/05/17/Artificial-intelligence-can-elevatepharma-manufacturing. (Accessed 30 December 2022).

Further reading [68] M. Li, Y. Du, Q. Wang, C. Sun, X. Ling, B. Yu, et al., Risk assessment of supply chain for pharmaceutical excipients with AHP-fuzzy comprehensive evaluation, Drug Dev. Ind. Pharm. 42 (4) (2016) 676–684. [69] B. Carlin, Quality risk management of compliant excipients, J. Excip. Food Chem. 3 (4) (2012) 143–153. [70] Y. Cui, X. Song, K. Chuang, C. Venkatramani, S. Lee, G. Gallegos, et al., Variable selection in multivariate modeling of drug product formula and manufacturing process, J. Pharm. Sci. 101 (12) (2012) 4597–4607. [71] A.S. Dhoot, G.J. Fernandes, A. Naha, M. Rathnanand, L. Kumar, Design of experiments in pharmaceutical development, Pharm. Chem. J. 53 (8) (2019) 730–735. [72] V. Vanhoorne, C. Vervaet, Recent progress in continuous manufacturing of oral solid dosage forms, Int. J. Pharm. 579 (2020). https://doi.org/10.1016/j.ijpharm.2020.119194.

CHAPTER 16

Impact of AI on drug delivery and pharmacokinetics: The present scenario and future prospects Jigna B. Prajapatia, Himanshu Paliwalb, Surovi Saikiac, Bhupendra G. Prajapatib, Dhvanil N. Prajapatid, Anil K. Philipe, and Md. Faiyazuddinf,g a

Faculty of Computer Applications, Ganpat University, Mehsana, Gujarat, India Department of Pharmaceutics and Pharmaceutical Technology, Shree S.K. Patel College of Pharmaceutical Education and Research, Ganpat University, Mehsana, Gujarat, India c Translation Research Laboratory, Department of Biotechnology, Bharathiar University, Coimbatore, Tamil Nadu, India d Department of Information Technology, LDRP Institute of Technology and Research, Gandhinagar, Gujarat, India e School of Pharmacy, University of Nizwa, Nizwa, Oman f School of Pharmacy, Al-Karim University, Katihar, Bihar, India g Nano Drug Delivery® (A product development partnership company), Raleigh-Durham, NC, United States b

16.1 Introduction Artificial intelligence is applied in both drug delivery technology and pharmaceutical development as well as for the genetic alteration of cells for the industrial-scale production of high-quality macromolecules, although, it is difficult to imagine deriving several therapeutic benefits from proteins and nucleic acids even if one has faith that this can help avoid the molecular fragility that undermines the stability and efficacy of biological agents. However, the majority of humanized monoclonal antibodies are currently available as a suspension or lyophilized powder for naked protein injection or infusion [1]. The primary goal of formulation stability in these products is to ensure retention of quality during manufacturing, marketing, logistics, and use. However, the use of a bare antibody in an intravenous injection as a therapeutic extension may no longer be sufficient. In reality, it severely limits the regulation of pharmacokinetics and the management of the product throughout its life cycle, once the patent expires. The fabrication of oral or subcutaneous insulin administration methods, for example, aims to eliminate the inconveniences and limitations of injections in chronic situations. When a protein medicine is subjected to gastric acid conditions or a protease-rich derma, engineering is used to avoid natural breakdown. It is also utilized to improve medication penetration across the physical barriers that isolate the medicine from its target [2]. Drug build-up at the site of action is another typical goal related to drug delivery. Regulated localization of medications is achieved by distributing them to the site of action for enhancing therapeutic efficacy while lowering toxicity. For directing a cytotoxic chemical to preferentially target tumor cells, for example, it can boost its biological activity on A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00016-2

Copyright © 2023 Elsevier Inc. All rights reserved.

443

444

A handbook of artificial intelligence in drug delivery

tumors while avoiding harmful effects on healthy organs. As a result, improving a medicine’s safety and potency is an equally significant goal of modern drug delivery research [3,4]. Simple molecular conjugates, atom clusters, membranes, particles, microdevices, and an extensive series of absorption enhancers are all examples of drug delivery systems, with the goal of conferring a distinct pharmacokinetic feature on the drug payload in comparison with the bare drug material [5,6]. Irrespective of the chemistry and mechanism of any delivery system, they all have one thing in common, which is that they must intermingle with the biological model of action. As a result, compatibility has become a critical component in determining the attributes of a system. The real size of a system that circulates in the circulatory system is limited to the micro to nanoscale, regardless of the administration method. Nanotechnology is particularly well-suited to the preparation of delivery systems, as it permits the system to mingle with cellular/subcellular structures, molecularly interact with proteins and nucleic acids, and avoid activation of the immune system, which could result in severe side effects. Human physiology has also evolved to deal with issues on such a small scale such as rapid clearance of microparticles through filtration of the blood in the immune system and liver, whereas filtration through the kidneys effectively removes the nanosized charged molecules [7–11]. This implies that the “absorption, distribution, metabolism, and excretion” (ADME) profile must be carefully considered throughout the creation of a delivery system. A submicron-characterized system, along with a stable colloidal solution and a submicron synthetic or biological structure, is theoretically included in nanomedicine. In the same manner, drug delivery can be considered as a stabilizing method that has a good impact on molecular pharmacokinetics, and, therefore, a large number of previous galenic formulations could be given this moniker. A nanomedicine, on the other hand, is a medication or a therapeutic molecule that benefits from nanofeatures in its final formulation to improve its pharmacokinetic features in comparison with its original form. A monoclonal antibody solution is in the form of colloidal dispersion, as the colloidal form simply maintains rather than improves the predicted activity. Contrarily, a drug delivery system is the active regulation of pharmacokinetics carried out by controlling the colloidal or physical attributes of the active substance. Abraxane, for example, needs albumin to actively collect paclitaxel (an antitumor agent) predominantly at the vicinity of the cancer site, whereas liposomes consist of distinct structures with sizes ranging from the nanometer to micrometer range to deliver the drug load to the specific site or confer a particular mechanism of action. Nanomedicine is a system in which nanoscale elements are actively included in a system’s planned qualities and mechanism of action, whereas an improved drug delivery system has quality attributes that influence product efficacy [12–15]. AI can improve nanosystem design by allowing for a deeper awareness of the biological environment, which can then be used to nanoengineer medicinal products. The

Impact of AI on drug delivery and pharmacokinetics

human body is a complex structure that is typically simplified for medication delivery into a compartmentalized system separated by biological membranes. The term “biological membranes” refers to the physicochemical features of the barriers that separate different biological compartments, therefore classifying a wide range of epithelial habitats. An orally taken medicine, for example, is exposed to the stomach tract and must permeate the intestinal or gastric epithelium to reach the circulation. Then, it targets a specific structure in the body, which might be a tissue, a cellular membrane component, or even an intracellular molecule. Active and passive transport are two types of biological barrier permeation. Passive diffusion is controlled by physicochemical gradients and is largely dependent on a drug’s molecular properties. The presence of an active drug in a biological environment actively interferes with the applications of artificial intelligence in drug delivery and pharmaceutical drug development, rendering in silico drug distribution prediction difficult. Furthermore, unless mediated by particular drug delivery mechanisms, passive permeation is often ineffective in biological and other kinds of small compounds. Active diffusion relies on intricate biological interactions and substantially higher energy expenditure since it is mediated by energy-activated cellular mechanisms, such as membrane transport. This could potentially broaden the scope of system modeling by allowing for the computation of a greater number of specific parameters [16].

16.2 Applications of artificial intelligence in drug delivery In recent times, drug delivery technologies have been primarily focusing on designing novel systems for the targeted delivery of active drugs to produce desired therapeutic benefits without causing any unwanted effects [17,18]. The limitations of conventional drug delivery systems have been subdued by developing controlled delivery systems for dealing with drugs associated with the narrow therapeutic index, systemic toxicity, chronic therapy, etc. [19,20]. The use of AI can be crucial for developing nanotechnology-based delivery systems by imparting radical information about the biological milieu. The intricacies of the human body are simplified by compartmentalizing them according to biological membranes. However, in silico computational predictability is still extremely complicated as the biological environment tends to impede the molecular characteristics of the drug. Therefore, there is a need for expanding models by augmenting the number of specific parameters to compute for accommodating the complexity of the process [16]. The application of artificial intelligence could be significantly useful in recognizing, developing, and describing various mechanisms in simulated human settings. The implementation of an automated system allows the development of a highly systematic model and parameter characterization, which enables better searching, simulating, and refinement of the literature search [16,21,22]. In recent times, there have been several cases in which neural network approaches and software providers have been utilized for

445

446

A handbook of artificial intelligence in drug delivery

In silico Model

Drug Properes as Theorecal Parameters a

Collecon of Input Parameters Computaon Parsimony Criterion Model Construcon and Refinement

a Outcomes of aIn vitro cell a culture studies

In vitro Release Studies/ Membrane Permeability studies a

Outcomes of In vivo Pharmacokinec Studies

Predicon of Pharmacokinec Performance of the Delivery System

Generate simulaon based outputs for Clinical Research & Development

a

Fig. 16.1 A schematic diagram indicating the process of in silico simulations for a drug delivery system.

pharmacokinetics evaluation [23,24] and for analysis of complex input parameters arising from drug interactions and phenotypic, chemical, and genomic databases [25,26]. Fig. 16.1 depicts the process of in silico simulations for a drug delivery system.

16.2.1 Significance of AI in drug delivery Generation of novel sources of information, acquisition of a greater level of precision, in silico simulations, system-generated predictions, and faster detection and monitoring of diseased conditions are few of the benefits of incorporating AI, with the inclusion of statistical pattern recognition approaches, computational intelligence, artificial neural networks, probability principles, etc. for accuracy in the analysis of data, followed by better management and/or interpretation of data and complex functions [27,28]. On the basis of the predictability of the pharmacokinetic profile of a drug product, the implementation of in silico methods may lead to enhancement of efficiency and minimization of the cost of developing a drug delivery system [29]. Models based on ANNs are categorized depending upon their utility in associating type, feature-extracting type, and nonadaptive-type networks [28,30]. Since process factors, formulation factors, and release characteristics of drug delivery systems do not show linearity, the fabrication and optimization of controlled release products are preferably managed with associating networks [31]. A multilayer perceptron (MLP) network is a type of neural network that involves the use of multiple layers of nodes, and each layer is linked to the next one, thus eliciting the identification of specific elements [32,33]. MLP has been implemented for the development of controlled release drug products and for the optimization and estimation of their

Impact of AI on drug delivery and pharmacokinetics

dissolution behavior [34,35]. Dynamic neural networks utilize the knowledge gathered from the past to assess the current and future conditions of a system, therefore finding its usefulness in modeling the release of drugs from controlled release products [36,37]. The predictive capacity of an ANN is even better than that of the response surface methodology (RSM) due to its intensified estimation of the design space of the formulation process and optimization of the product [38,39]. Apart from the application of ANNs in the design of formulations and for the prediction of the bioavailability and performance of drugs, they have also been recommended as efficient tools for simulating cause-and-effect relationships and in vitro-in vivo correlations [40,41]. The establishment of an IVIVC in conjunction with ANNs facilitates the interpolation of the pharmacokinetic profile and exploration of their complex interactions [40].

16.2.2 Artificial intelligence in the development of a drug delivery system: A research outlook AI approaches are used for therapeutic applications in the diagnosis of diseases, identification of metabolic pathways, and development of individual-based therapies. The percentage confidence level of predictability of an ANN is up to 90%, which is considerably higher compared to those of commonly used methods [42,43]. Harrison et al. utilized a convolutional neural network for extracting time-lapse data from HepG2 cells, which are introduced with green fluorescent protein (GFP)-expressing mRNA-containing lipid nanoparticles. The outcomes of their study indicated a high predictive power, and, especially, for the consideration of temporal dynamics, the performance was greatly enhanced [44]. AI-based approaches are useful for the fast estimation and categorization of pain, choosing suitable therapeutic interventions, which are tailored for individuals. It has been suggested that the automated Mouse Grimace Scale is a reliable marker for pain research, especially for long-term monitoring of pain [45]. ANNs were implemented for modeling the aerosols of salbutamol sulfate administered to an asthmatic patient by exploring the input variables such as aerodynamic particle size (APS), body surface area (BSA), age, pretreatment forced expiratory volume in one second (FEV (1)), forced vital capacity (FVC), cumulative emitted drug dose (CEDD), and bronchodilator reversibility (BR). The outcomes of the study showed that the alterations in APS are a good indicator of bronchodilation responses produced by aerosols, whereas the determination of the clinical response in patients can be carried out by examining patient attributes [46]. A novel prediction model for examining the stability of solid dispersion was performed using eight machine learning methods. The random forest (RF) model showed the best accuracy of prediction in comparison with other models. Scientists have developed a robust model, which can be used for the prediction of the stability of solid dispersion in an experiment, and this approach can be further used for other dosage forms [47]. Gao et al. assessed the dissolution profile of solid dispersions in which a random forest algorithm was utilized for designing a classification model for differentiating two types of dissolution profiles, namely, “spring-and-parachute” and “maintain

447

448

A handbook of artificial intelligence in drug delivery

supersaturation.” A regression model was constructed for predicting the time-dependent dissolution profiles [48]. The machine learning technique based on LightGBM models was explored upon nanocrystals. Nanocrystals prepared by different methods, such as ball wet milling (BMW), high-pressure homogenization (HPH), and antisolvent precipitation methods, were analyzed for the prediction of the size and polydispersity index (PDI) of the nanoparticles. The model indicated that the nanocrystals prepared by BMW and HPH showed better performance with significantly low mean absolute error (MAE) values [49]. Gao et al. prepared self-emulsifying drug delivery systems (SEDDs) using machine learning algorithms for the comparison and selection of suitable combinations of excipients. A pseudo-ternary diagram was predicted and validated with high prediction accuracy. A molecular dynamic simulation was successfully carried out to predict the molecular interaction between drugs and excipients and the role of the cosurfactant [50]. Attention-based DNNs were designed by Ye et al. for the formulation of cyclodextrin (CD) inclusion complexes. The model was able to differentiate between the set of inputs with considerable high accuracy, and the proposed method showed reliability in depicting both sample- and feature-level interpretations [51]. A generative adversarial network for distribution analysis (GANDA) was designed to elucidate and produce intratumoral quantum dot (QD) distribution after an intravenous injection. The model was reliable in generating the image of QD distribution for tumor vessels and cell nuclei with a similar spatial resolution. Such a deep generative model may be useful for furnishing possibilities of examining the factors that may affect the distribution of nanoparticles in tumor cells and for monitoring the optimization of nanomedicine [52]. Computational tools have been thoroughly investigated for single drugs and combinations, which are useful against infection-causing pathogens. Machine learning tools have been reported to show a reliable correlation between formulation parameters and the release behavior of drugs used for the treatment of infections [53]. A female-controlled drug delivery system (FcDD), formulated as a mucoadhesive gel, was investigated using an ANN. A reliable quantitative relationship was established between the formulation and physiological variables and release profiles of sodium dodecyl sulfate (SDS) from the formulation. The formulation variables were dominated by the impact of external factors on the diffusion coefficient. Furthermore, it was clear from the study that model-based strategies are suitable for the estimation of the diffusion coefficient of the drugs loaded in FcDD-like delivery systems [54]. Nemati et al. conducted an ANN-based prediction of the release profile of dexamethasone (DEX) and formulation variables of a silicone rubber containing porous monolithic devices. The outcomes of their study showed that the release of drugs from the device was modeled with high accuracy. This can allow the prediction of optimal levels of formulation parameters, leading to the definite release of the drug. The models were also useful in deciphering the process of formulation in a short time, along with furnishing personalized drug delivery regimens [55]. Table 16.1 summarizes the investigations based on AI.

Table 16.1 Summary of the investigations based on artificial intelligence using machine learning models for the development of drug delivery systems. ML method

Dataset

Input

Output

Performance

References

Convolutional neural network Convolutional neural network ANN

5771

Automated Mouse Grimace Scale

Pearson’s r ¼ 0.75

[44]

774

Imaging channels (counterstain, bright field, and lipid nanoparticles) APS, BSA, age, FEV (1), FVC, CEDD, and BR

Binary pain/no-pain assessment, and confidence score Temporal dynamics

Accuracy: 0.718–0.965

[45]

[46]

Random forest

646

Nonlinear determinants (R2): 0.87 and 0.89 Prediction accuracy: 82.5%

Random forest

674

LightGBM

1251

Random forest

4495

216

Formulation composition, environmental conditions, preparation process, and component properties Composition of formulations, dissolution test condition, in vitro dissolution sampling time, and molecular descriptors Properties of active pharmaceutical ingredients (APIs) and stabilizers

Composition of SEDDs and physicochemical properties of the selected oils, surfactant, and cosurfactant

Bronchodilator responses (ΔFEV1(%) at T10 and T20) Amorphous molecular solid dispersion potential and crystallization tendency of compounds Percentage of the dissolved drug substance

Particle size and PDI of nanocrystals

Pseudo-ternary phase diagram, molecular interaction between the drug and excipient, and the role of the cosurfactant

[47]

Accuracy: 0.73–0.87

[48]

MAE Size: 94.8–111.7 PDI: 0.068–0.105 Accuracy: 89.51%

[49]

[50]

Continued

Table 16.1 Summary of the investigations based on artificial intelligence using machine learning models for the development of drug delivery systems—cont’d ML method

Dataset

Input

Output

Performance

References

Attention-based deep neural network (AttPharm) Generative adversarial network for distribution analysis (GANDA) ANN

1320

Properties of APIs and CD

CD binding-free energy

[51]

27,775 patches of tumor vessels and cell nuclei 96

Whole-slide images of four T1 breast cancer cells and cell nuclei

Complex tumornanoparticle interactions and intratumoral NP distribution

Determination coefficient (R2): 0.71–0.81 Intraclass correlation (ICC): 0.94–0.99

Diffusivity (diffusion coefficient)

Diffusion coefficient: 0.95

[54]

ANN

44

Vaginal fluid pH, vaginal fluid secretion rate, formulation loading weight, loaded doses, and inserting position DEX release curves and function coefficients

DEX loading percentage, porogen type, and porogen content in the device

MAE: 0.39–0.59 MSE: 0.21–0.54

[55]

[52]

Impact of AI on drug delivery and pharmacokinetics

Table 16.2 Baseline parameter values essential for generating Bayesian prior distribution in calibrated PK/PD models [59]. Parameters

Mean value

Amount of the drug absorbed Oral absorption rate of the drug (1/day) Total drug clearance (L/day/kg0.75) Muscle/blood partition coefficient

1 38.64 0.96 0.76

16.3 Applications of artificial intelligence in pharmacokinetics Drugs with poor pharmacokinetic profiles are very much less likely to be persuaded by pharmaceutical companies and researchers due to the cost and time involved in their development and discovery, which is economically unfeasible [56]. However, the recent trend has shown that there has been an increase in the use of academic and new industrial models for studying the pharmacokinetic profile of drugs like ADMET (absorption, distribution, metabolism, excretion, and toxicity) [57]. Undesirable pharmacokinetic properties limit the development of new drugs for which there has been an increasing demand for a novel method to predict the same [58]. Table 16.2 shows the baseline parameter values essential for generating Bayesian prior distribution in calibrated PK/PD models. The use of new technologies in the ADMET prediction of drugs has taken drug development to the next level. In search of novel methods, in silico methods have been introduced in drug discovery and development as an ADMET predictor tool in the early stages [56]. Fig. 16.2 displays the process of in silico simulations in pharmacokinetics.

16.4 Computational pharmacokinetic modeling Several studies were conducted to describe the relationship between the biological and physicochemical properties of drug molecules. Manipulation of physicochemical properties can be carried out by changing the chemical structures for optimized effects [60]. The pharmacological effect of a drug depends on the spatial rearrangement of atoms in the ligand and their interaction with the target. The computational side of chemistry can categorize these dynamics and interactions, structures, and energies associated with target-ligand interactions. Melting point, boiling point, partition coefficient, water solubility, and bioconcentration factors are some of the physiochemical properties on which the behavior of a drug depends, which further affects the pharmacokinetic properties such as permeability, transfer, drug bioavailability, etc. [61]. The pharmacokinetic properties of a drug during the early stage of drug discovery depend on the interpretation of these physicochemical properties. So, computational methods such as in silico tools can be applied to predict both the physiochemical properties and their side effects following administration [60].

451

452

A handbook of artificial intelligence in drug delivery

Fig. 16.2 A schematic diagram indicating the process of in silico simulations in pharmacokinetics.

16.4.1 In silico physicochemical property prediction 16.4.1.1 Hydrogen bonding A driving factor in the partitioning of active molecules is the obvious role played by hydrogen bonds. The interaction between an H-bond acceptor and an H-bond donor is reflected by hydrogen bonds [62]. The permeability aspect of substances across a biological membrane is determined by the hydrogen-bonding capacity of a bioactive compound, and it is important for the compound to break the hydrogen bonds in order to cross the biological membrane. This aspect is inversely proportional to the degree of permeability and absorption factor of a compound [63]. Studies have shown the relationship between QSAR and hydrogen bonds, and, so, it is critical to measure the strength of hydrogen bonds, which is a critical state for structure-based drug designing [62]. 16.4.1.2 Lipophilicity “Log P” is the most commonly used term to refer to the lipophilicity representing the concentration equilibrium of a certain compound between two phases: oil and liquid phases [64]. Various pharmacokinetic properties like absorption, permeability, distribution, and routes of drug clearance are influenced by log P, which is an important consideration for the development of new drugs [65]. In order to achieve the desired potency and selectivity, there has been an increasing demand for designing drugs with high

Impact of AI on drug delivery and pharmacokinetics

lipophilicity, owing to the lipid origin of the biological targets. For example, intracellular, anatomical targets, and targets from the neurotransmitter pathway require the binding of agonists of a lipophilic nature to gain the desired results [66]. Optimal oral absorption with the desired activity and deposition is only possible for those drugs that show an acceptable aqueous solubility and lipophilicity. Thus, at integrated drug discovery stages, computational measurements for lipophilicity, degree of ionization, and aqueous solubility are used [63]. The need for an accurate in silico model to predict lipophilicity is the current trend due to the importance of the lipophilicity parameter and its role in learning the pharmacokinetic properties of drug molecules. A log P prediction model has been observed to aid in the process of drug designing, which has further helped in model designing for multiple atoms and fragments [60]. 16.4.1.3 Permeability The passive diffusion mechanism helps drug molecules cross the BBB and the intestinal epithelium barrier from where substances are transported via a concentration gradient. Paracellular transport and the transcellular transport mechanism are types of passive diffusion, whereas drugs are transported either through PGP- or carrier-mediated transport [67]. Studies have shown that hydrogen bonding determines the permeability of drugs, and it was found that the descriptor of a hydrogen bond acceptor (HBA) is associated with less significance in the prediction of permeability for the human intestinal epithelium [68]. Several in silico models were introduced for accurate prediction of membrane permeability at par with their lipophilicity profiles, capacity of H-bonding, molecular size, and polar surface area. These prediction models have shown drug development to be a less time-consuming affair [69]. 16.4.1.4 Solubility Every stage of drug discovery involves aqueous solubility as a fundamental property as it is involved in the estimation of drug intake, elimination, and transfer in the body [70]. Aqueous solubility of a drug determines its efficiency, as compounds showing low dissolution or poor solubility will be eliminated before entering the blood circulation, which results in no pharmaceutical effects [66]. The list of poorly soluble drugs has been growing with poor absorbance, lack of pharmacokinetic details, and after-food effects [71]. The drug development process can be significantly facilitated by enough solubility data, and, in many cases, it is extremely difficult to find drugs with good solubility data. In these cases, computational methods are used for predicting the solubility and enhancing the absorption of drugs [72]. Compactness of crystalline structures and lipophilicity of a compound are the two important factors that determine the solubility of a compound, and both factors are inversely related to solubility [64]. Solubility was never considered as a parameter under ADMET calculations for a drug, but it seems to be a major factor in determining the oral absorption of a drug, as compounds exhibiting low gut absorption will have low permeability and hence low

453

454

A handbook of artificial intelligence in drug delivery

absorption. This observation has made drug developers assign critical importance to solubility during the development process [60]. Although solubility plays an important role in all the stages of drug discovery, it lacks consistency and reliable data for the solubility prediction of a drug.

16.5 Molecular modeling Computational prediction of solubility based on a molecular structure is achieved by a multivariate data analysis (MVA), which includes models based on random forest (RF), artificial neural network (ANN), partial least squares (PLSs), and support vector regression (SVR) [73]. A useful computational methodology has been demonstrated by a recent in silico study, capable of predicting the effect of solubility on solid-state materials. In future, it is expected that this type of method, along with others like those on quantum and molecular mechanics, will provide more accuracy in prediction. These methods are also capable of accurately predicting crystal lattices and their impact on solubility and drug dissolution [74].

16.5.1 QSAR modeling The quantitative structure-activity relationship (QSAR) modeling technique is used to reveal the relationships between the structural properties of chemical compounds and their computationally derived biological activities. The underlining phenomenon in QSAR is that there occur changes in biological activities with a change in the structural properties of chemical compounds [75]. QSAR is important for drug discovery, but it comes with many constraints, which can be overcome by ensemble-based ML methods. Here, the structural properties indicate the physiochemical properties, and the biological properties refer to the pharmacokinetic ADMET properties. As an in silico method, QSAR modeling favors compounds amidst a large number of chemicals based on desired biological activities. This helps in reducing the number of chemical entities to be tested in in vivo experiments, and thus QSAR modeling is an inevitable part of the pharmaceutical industry [76]. An extremely large number of compounds are included in QSAR, and each of them can be represented by certain chemical descriptors, fingerprints commonly used, and some with highly correlated structures; it is assumed that datasets contain some errors as their actual relationship is tested in situ. A reliable prediction score is not delivered by QSAR due to the abovementioned constraints due to which ML approaches are applied to the QSAR prediction model such as linear regression, Bayesian neural networks, and RF, which is the gold standard in this field [77]. There has been extensive use of ensemble learning methods in drug research such as neural networks based on bootstrap sampling in QSAR [78], drug-drug interaction ensemble learning methods [79], QSAR tools in Bayesian ensembles [80], quantitative- and qualitative-based

Impact of AI on drug delivery and pharmacokinetics

SAR ensemble models [81], QSAR hybrid models [82], boosting method-enabled ensembles [83], QSAR modeling for hybrid feature selection and learning [84], and ensembles for diverse chemicals for prediction of carcinogenicity [85].

16.5.2 ADME modeling In silico ADME modeling plays a significant role in early pharmaceutical research, particularly in the prediction of ADME/Tox properties. These models are labor-, time-, and cost-effective during the early stages of drug discovery and development [86]. The application of these models has been on a substantial rise in recent times and is expected to increase further. At present, models are available both in the literature and commercially for successful and effective prediction of the ADME/Tox properties of chemicals. These properties include pKa, solubility, oral bioavailability, permeability, skin permeation, BBB penetration, volume of distribution, clearance, transporters, metabolism, and its pathway along with several other models of drug toxicity prediction [87].

16.5.3 Molecular and pharmacophore modeling Several new ML methods have been developed in recent years using the concept of 3D pharmacophores. HS-Pharm (hot-spots-guided receptor-based pharmacophores) trains ML models so that features are reduced from apo-based 3D pharmacophore models [88]. Bayesian classifiers and decision trees were used to predict binding cavities in three pharmaceutically relevant targets, resulting in a 3D pharmacophore that showed better performances than docking in two cases. Pharm-IF was considered as an input for ML algorithms for ranking the docking process of small molecules [89]. In a combined study, it was found that Pharm-IF in combination with a support vector machine (SVM) produces the best scoring, leaving behind other ML algorithms and scoring functions. DeepSite and related software use convolutional neural networks to visualize images for the prediction of druggability in the binding pockets of proteins [90]. DeepSite was found to outperform other algorithms and other state-of-the-art cavity detection algorithms [90]. Other such algorithms include LigDream [91], LigVoxel [92], and KDEEP [93] for binding affinity prediction and designing novel molecules. The approaches mentioned earlier are integrated with some software packages for local installations and are also available for free use in the form of a web application.

16.6 Mathematical modeling Nowadays, registration authorities and pharmaceutical industries are stressing upon the pharmacokinetic release and dissolution of a drug. The prediction of the drug release rate is made easy by mathematical modeling that helps formulate highly effective drug formulations and dosing to save both time and money [94]. Basically, the amount of drug

455

456

A handbook of artificial intelligence in drug delivery

dissolved “D” from one solid dosage is a function of time t, which is f ¼ D (t). The underlying mechanism is still unknown, but some semiempirical equations build on fundamental functions such as exponentials, polynomials, etc. A significant number of mathematical models exist in the literature, but the use of the best-fit model for certain datasets remains questionable. One way to sort this out is to either systematically compare all the existing models or to use statistical methods to select models [95]. Another common method is to consider the minimization of the coefficient of determination (R2) or the adjusted coefficient of determination (R2adjusted ¼ 1  (1  R2)(N  1)/(N  m)) for models with a varying number of parameters, where m is the number of parameters and N represents the number of experimental points. The six most renowned mathematical models are as follows: (i) Zero-order model: Q(t) ¼A + Bt, where A and B are free parameters (ii) First-order model: Q(t) ¼ Q0 exp (kt/2.303), where Q0 and k are two free parameters (iii) Higuchi model [96]: Q(t) ¼ k(t)1/2, where k is single free parameter (iv) Hixson-Crowell model [97]: Q(t) ¼ (A + Bt)3, where A and B are free parameters (v) Power-Law model [98]: Q(t) ¼ Atn, where A and n are free parameters (vi) Hopfenberg model [99]; flat geometry for n ¼ 1; Q(t) ¼ kt, where k is a single parameter

16.7 Process simulation in pharmacokinetics Simulations in the field of pharmacokinetics are the current need of the pharmaceutical field, and the current field is still under development. Pharmacokinetic simulation models require the availability of the literature and pharmacokinetic studies and also the parameters used for pharmacodynamics [100]. The success of such simulations is highly dependent on the quality of data inputs, and previous studies are taken as a reference point for predicting the simulation demonstrating the pharmacokinetic parameters of drugs [101]. Clinical pharmacists and scientists practice computational techniques along with applied skills and knowledge, which can be applied when dealing with pharmacokinetic problems. To solve complicated pharmacokinetic problems, equations and pharmacokinetic processes software modeling are used such as population pharmacokinetics, ADME pharmacokinetics, individual pharmacokinetics, WinNolin (Window-based Non-linear model fitting), SAS (Statistical Analysis Software), PK solution, NONMEM (Non-linear Mixed Effects Modeling), etc. A robust software solution that is easy to use and is capable

Impact of AI on drug delivery and pharmacokinetics

of addressing the three main parts of a PK/PD workflow, namely, data management, analysis, and reporting, helps researchers predict the ADME properties of a new compound. Pharmacokinetic studies conducted with WINBUGS are considered to be the best software, as there exists no limitation with the size of the problem and the dimensional array, but, for individual pharmacokinetic studies, TDMS is considered to be the best software, in which least squares and Bayesian methods can be used for curve fitting with both linear and nonlinear pharmacokinetic data [102].

16.8 The current status of AI in pharmacokinetics Preclinical pharmacokinetics and pharmacodynamics (PK/PD) is a critical tool for research analysis and is a proof of mechanism or concept in animals during translational research. The therapeutic areas that are commonly considered for preclinical PK/PD analysis are inflammation, metabolic diseases, neurology, and other therapeutic areas such as oncology and infectious diseases. It is found that companies using preclinical PK/PD analysis to translate to human PK/PD analysis for dose/regime prediction usually exhibit better communication with other R&D departments like clinical pharmacology. Recently, PK/PD analysis has been one of the quantitative and rational approaches recommended by the regulatory agencies for pharmaceutical industries to incorporate into their drug development [103]. Table 16.3 shows the various ML methods, along with their merits and demerits, for use in drug delivery studies. AI-assisted ML methods very well aid in drug delivery for infectious diseases as they can apply their features to large-scale and complex datasets for accurate predictions without the difficulty of repeating the biological tests. These have become desirable tools for analyzing the PK aspects for drug development, such as optimization of drug dosing, selection of drug combinations etc., which require a large test space covering all the possible therapeutic compounds, needed replicates, and concentration matrices for screening, including the diversity of pathogenic strains. The resulting data may result in unseen patterns and form new laws, thus providing us with the underlying hidden knowledge of the data. This will help the research team detect the antimicrobial resistance (AMR) that skipped prior genomic knowledge and a lengthy phenotypic assay of the microbe. Due to the extraordinary data processing and analysis speed, it is possible to predict the adverse reactions in advance during an antiinfective treatment in order to develop an in-time effective treatment for stopping the AMR infections [105]. Moreover, operations can be performed at a point of care by connecting the computational software to mobile or clinical devices, which is practical for clinical decision-making for the drug delivery of antiinfective drugs [106]. These AI-based methods also have the potential to include and learn from new variables of patients and microorganisms to generate flexible drug delivery and also to refine their real-time performance, by which continuous and common pathogen evolution can be included.

457

458

A handbook of artificial intelligence in drug delivery

Table 16.3 Popular ML (machine learning) methods with their merits and demerits in drug delivery [104]. ML methods

Merits

Artificial neural networks (ANNs)

Large input variables Highly complex models can be obtained by a nonlinear relationship with input variables Inputs can be independent Covariates and predictors are included

Boost

Decision trees

Demerits

Hidden layers and neurons need calculation Explicit interpretation of output is not possible due to independent inputs Underlying model assumptions are few for a functional and distributional relationship between covariates and predictors Understanding and interpretation is Overfitting problem in small datasets simple Output-driven No assumptions or prior knowledge

Feedback system control (FSC) k-nearest neighbor Classifiers are simple and (kNN) nonparametric

The instances of k should be mentioned All training samples need storage Logistic regression Categorical variables can be dealt Binary outcomes for models with flexibly Sensitive toward dependent variables Naı¨ve Bayes Efficient, simple, and performs well Assumption that data attributes are for multiclass predictions independent Set covering Simple and easy interpretation Frequent occurrence of symmetrical machine (SCM) Few training errors loss

A recent application of AI in PK has mainly focused on overcoming the limitations of the current challenges that exist in both in vivo and allometric methods, QSPkR (a structure-pharmacokinetic relationship) modeling, and QBPM (physiologically based pharmacokinetic models). The basic pharmacokinetic parameters for in vivo and allometric methods can be easily calculated by AI from the basic measurable physiochemical parameters, thus reducing both time and cost. AI has the potential to revolutionize the computational pharmacokinetic model, as, unlike traditional modeling, AI provides the future value estimation via processing multiple observations, thereby discovering the underlying pattern amidst the data. This helps in the estimation of pharmacokinetic parameters without being aware of the rules of drug deposition in the body as explained elsewhere [107–110]. High-throughput experimental techniques and computational modeling hold the promise of cost reduction to launch new drugs in the market. Toxicity prediction is one of the critical components of modern-day drug discovery projects. ℯToxPred is a new approach used to reliably estimate the synthetic accessibility and toxicity of small

Impact of AI on drug delivery and pharmacokinetics

organic compounds, which employs ML algorithms to test their performances through multiple databases. The mean square error is as low as 4% for synthetic accessibility and 72% accuracy for toxicity. ℯToxPred can be utilized to build custom libraries by incorporating it into protocols for virtual screening projects and is also freely available as a stand-alone software [111]. Deep learning approaches have been recently used in drug design and discovery such as generative adversarial network (GAN) frameworks, which is an emerging technique in AI and ML research. GAN architecture has been successfully employed for de novo molecular design [112] and in deep adversarial autoencoders, variational autoencoders [113], etc. The recent years have witnessed the integration of many multiscale data from molecular databases and clinical databases with the use of DL models to calibrate the different data types. Academic research has seen a drastic increase in the use of DL and ML, which is expected to increase in future. Drugs once approved by the agencies, the drug monitoring will use the databases automatically to collect the adverse effects and AI plays a significant role in this premarket safety [114].

16.9 Future prospective Explainable AI is an emerging topic that aims to develop a set of techniques that allow interpretability and, at the same time, can maintain the high-performance aspect of AI [115,116]. The use of t-distribution stochastic neighborhood embedding is one such method of visualizing inputs and predictions [117]. Along these lines, TreeExplainer is another that allows detectable computation of local optimal explanations, and visual reasoning and explanations are case-based [118]. Insights into the rationale of AI decision-making can be gained by understanding the input features and decisions of an AI model [119]. Deep learning is powered by large amounts of information; the future direction is to use the MIMO (multiple-input multiple-output) model of deep learning to provide guidance for drug delivery. Collection and merging of public datasets are other future directions to create a large dataset, leading to a standard ML method of training models to help in clinical decision-making [120]. Transfer learning is an option for dealing with small-scale data; deep learning methods, which are benchmarked AI models, are trained on large datasets and the learned parameters obtained are transferrable and can boost the performance of AI models on different small datasets. In this type of transfer learning, most parameters obtained from an AI model trained on large datasets can be reused on other datasets. Fine-tuning of only a few parameters is required for new datasets and tasks to improve their performance and avoid the problem of overfitting in an AI model in small datasets [121]. Transfer learning thus helps in using the same AI model using different datasets, and models trained on patients of one hospital can be used on patients in another health-care facility.

459

460

A handbook of artificial intelligence in drug delivery

Federated learning is recommended to solve the problem of small datasets located at many different locations and owned by entities of different kinds [122]. Traditional methods solve this kind of small data problem by centralizing the data, but federated learning methods come into play when it is difficult to share data between hospitals, leading to privacy issues, and have been used in the studies of medical reports [123]. Flexibility is an advantage of federated learning, which can adapt itself to various hospital settings, and, also, the model can be trained on decentralized data, thus keeping the privacy intact. Thus, further development and practical use of AI in dealing with pharmacokinetic applications require increased AI model interpretability, AI model usage guidance, improvement in feature engineering selection, and further optimization of source data quality.

Acknowledgments We thank the vice-chancellor of Ganpat University, Gujarat, India, and Bharathiar University, Coimbatore641046, Tamil Nadu, India, for support. Dr. Surovi is thankful to UGC-New Delhi, for the Dr. D S Kothari Fellowship (No.F-2/2006 (BSR)/BL/20-21/0396).

References [1] Y. Cui, P. Cui, B. Chen, S. Li, H. Guan, Monoclonal antibodies: formulations of marketed products and recent advances in novel delivery system, Drug Dev. Ind. Pharm. 43 (4) (2017) 519–530. [2] C.Y. Wong, J. Martinez, C.R. Dass, Oral delivery of insulin for treatment of diabetes: status quo, challenges and opportunities, J. Pharm. Pharmacol. 68 (9) (2016) 1093–1108. [3] T. Lammers, F. Kiessling, M. Ashford, W. Hennink, D. Crommelin, G. Storm, Cancer nanomedicine: is targeting our target? Nat. Rev. Mater. 1 (9) (2016) 16069. [4] J.I. Hare, T. Lammers, M.B. Ashford, S. Puri, G. Storm, S.T. Barry, Challenges and strategies in anticancer nanomedicine development: an industry perspective, Adv. Drug Deliv. Rev. 108 (2017) 25–38. [5] I. Tuncer Degim, N. Celebi, Controlled delivery of peptides and proteins, Curr. Pharm. Des. 13 (1) (2007) 99–117. [6] H. Rosen, T. Abribat, The rise and rise of drug delivery, Nat. Rev. Drug Discov. 4 (5) (2005) 381–385. [7] H.R. Lakkireddy, D.V. Bazile, Nano-carriers for drug routeing – towards a new era, J. Drug Target. 27 (5–6) (2019) 525–541. [8] A. Dowling (Ed.), Nanoscience and Nanotechnologies, July 2004, The Royal Society & The Royal Academy of Engineering, 2006, pp. 1–115. [9] National Research Council, Committee to Review the National Nanotechnology Initiative, A Matter of Size: Triennial Review of the National Nanotechnology Initiative, National Academies Press, 2006. [10] E.H. Chang, J.B. Harford, M.A.W. Eaton, P.M. Boisseau, A. Dube, R. Hayeshi, et al., Nanomedicine: past, present and future – a global perspective, Biochem. Biophys. Res. Commun. 468 (3) (2015) 511–517. [11] V. Wagner, A. Dullaart, A.-K. Bock, A. Zweck, The emerging nanomedicine landscape, Nat. Biotechnol. 24 (10) (2006) 1211–1217. [12] S. Colombo, M. Beck-Broichsitter, J.P. Bøtker, M. Malmsten, J. Rantanen, A. Bohr, Transforming nanomedicine manufacturing toward quality by design and microfluidics, Adv. Drug Deliv. Rev. 128 (2018) 115–131. [13] V. Weissig, D. Guzman-Villanueva, Nanopharmaceuticals (part 2): products in the pipeline, Int. J. Nanomedicine 10 (2015) 1245. [14] V. Weissig, T.K. Pettinger, N. Murdock, Nanopharmaceuticals (part 1): products on the market, Int. J. Nanomedicine 9 (2014) 4357.

Impact of AI on drug delivery and pharmacokinetics

[15] M.L. Etheridge, S.A. Campbell, A.G. Erdman, C.L. Haynes, S.M. Wolf, J. McCullough, The big picture on nanomedicine: the state of investigational and approved nanomedicine products, Nanomedicine 9 (1) (2013) 1–14. [16] B. Bhhatarai, W.P. Walters, C.E.C.A. Hop, G. Lanza, S. Ekins, Opportunities and challenges using artificial intelligence in ADME/Tox, Nat. Mater. 18 (5) (2019) 418–422. [17] D. Jain, R. Raturi, V. Jain, P. Bansal, R. Singh, Recent technologies in pulsatile drug delivery systems, Biomatter 1 (1) (2011) 57–65. [18] L.A. Villarruel Mendoza, N.A. Scilletta, M.G. Bellino, M.F. Desimone, P.N. Catalano, Recent advances in micro-electro-mechanical devices for controlled drug release applications, Front. Bioeng. Biotechnol. 8 (2020) 827. [19] N. Reddy Dumpa, S. Bandari, M.A. Repka, Novel gastroretentive floating pulsatile drug delivery system produced via hot-melt extrusion and fused deposition modeling 3D printing, Pharmaceutics 12 (1) (2020) 52. [20] A. Maroni, L. Zema, M.D. Del Curto, G. Loreti, A. Gazzaniga, Oral pulsatile delivery: rationale and chronopharmaceutical formulations, Int. J. Pharm. 398 (1–2) (2010) 1–8. [21] L. Vanneschi, Improving genetic programming for the prediction of pharmacokinetic parameters, Memetic Comput. 6 (4) (2014) 255–262. [22] L.X. Yu, C.D. Ellison, A.S. Hussain, Predicting human oral bioavailability using in silico models, in: Applications of Pharmacokinetic Principles in Drug Development, Springer, 2004, pp. 53–74. [23] K.-K. Mak, M.R. Pichika, Artificial intelligence in drug development: present status and future prospects, Drug Discov. Today 24 (3) (2019) 773–780. [24] P. Hassanzadeh, F. Atyabi, R. Dinarvand, The significance of artificial intelligence in drug delivery system design, Adv. Drug Deliv. Rev. 151–152 (2019) 169–190. [25] F. Cheng, Z. Zhao, Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties, J. Am. Med. Inform. Assoc. 21 (e2) (2014) e278–e286. [26] J.Y. Ryu, H.U. Kim, S.Y. Lee, Deep learning improves prediction of drug-drug and drug-food interactions, Proc. Natl. Acad. Sci. U. S. A. 115 (18) (2018) E4304–E4311. [27] S. Russell, D. Dewey, M. Tegmark, Research priorities for robust and beneficial artificial intelligence, AI Mag. 36 (4) (2015) 105–114. [28] W. Duch, R. Setiono, J.M. Zurada, Computational intelligence methods for rule-based data understanding, Proc. IEEE 92 (5) (2004) 771–805. [29] Y. Sakiyama, The use of machine learning and nonlinear statistical tools for ADME prediction, Expert Opin. Drug Metab. Toxicol. 5 (2) (2009) 149–169. [30] T. Hastie, R. Tibshirani, J. Friedman, Linear methods for classification, in: T. Hastie, R. Tibshirani, J. Friedman (Eds.), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer New York, New York, NY, 2009, pp. 101–137. [31] S. Nihar, P. Nishith, K.R. Patel, A sequential review on intelligent drug delivery system, J. Pharm. Sci. Biosci. Res. 3 (5) (2013) 158–162. [32] M.K. Gupta, S. Gupta, R.K. Rawal, Impact of artificial neural networks in QSAR and computational modeling, in: M. Puri, Y. Pathak, V.K. Sutariya, S. Tipparaju, W. Moreno (Eds.), Artificial Neural Network for Drug Design, Delivery and Disposition, Academic Press, Boston, 2016, pp. 153–179 (Chapter 8). [33] J. Taskinen, J. Yliruusi, Prediction of physicochemical properties based on neural network modelling, Adv. Drug Deliv. Rev. 55 (9) (2003) 1163–1183. [34] S. Wang, J. Di, D. Wang, X. Dai, Y. Hua, X. Gao, A. Zheng, J. Gao, State-of-the-art review of artificial neural networks to predict, characterize and optimize pharmaceutical formulation, Pharmaceutics 14 (1) (2022) 183. [35] K.K. Peh, C.P. Lim, S.S. Quek, K.H. Khoh, Use of artificial neural networks to predict drug dissolution profiles and evaluation of network performance using similarity factor, Pharm. Res. 17 (11) (2000) 1384–1388. [36] J. Petrovi
c, S. Ibri
c, G. Betz, Z. Đuri
c, Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees, Int. J. Pharm. 428 (1–2) (2012) 57–67.

461

462

A handbook of artificial intelligence in drug delivery

[37] J. Petrovi
c, S. Ibri
c, G. Betz, J. Parojci
c, Z. Đuri
c, Application of dynamic neural networks in the modeling of drug release from polyethylene oxide matrix tablets, Eur. J. Pharm. Sci. 38 (2) (2009) 172–180. [38] T. Sova´ny, Z. Tisl
er, K. Kristo´, A. Kelemen, G. Regdon, Estimation of design space for an extrusion– spheronization process using response surface methodology and artificial neural network modelling, Eur. J. Pharm. Biopharm. 106 (2016) 79–87. [39] K. Veerubhotla, R.B. Walker, Application of quality by design principles for optimizing process variables of extrusion and spheronization of a captopril pellet formulation, Indian J. Pharm. Sci. 29 (1) (2020) 76–84. [40] A. Mendyk, P. Tuszy
nski, S. Polak, R. Jachowicz, Generalized in vitro-in vivo relationship (IVIVR) model based on artificial neural networks, Drug Des. Devel. Ther. 7 (2013) 223. [41] P.K. Tuszy
nski, J. Szlęk, S. Polak, R. Jachowicz, A. Mendyk, In vitro-in vivo correlation (IVIVC): from current achievements towards the future, Dissolut. Technol. 25 (3) (2018) 20–27. [42] H. Chen, O. Engkvist, Y. Wang, M. Olivecrona, T. Blaschke, The rise of deep learning in drug discovery, Drug Discov. Today 23 (6) (2018) 1241–1250. [43] A.W. Przybyszewski, M. Kon, S. Szlufik, A. Szymanski, P. Habela, D.M. Koziorowski, Multimodal learning and intelligent prediction of symptom development in individual Parkinson’s patients, Sensors (Basel) 16 (9) (2016) 1498. [44] P.J. Harrison, H. Wieslander, A. Sabirsh, J. Karlsson, V. Malmsj€ o, A. Hellander, et al., Deep-learning models for lipid nanoparticle-based drug delivery, Nanomedicine 16 (13) (2021) 1097–1110. [45] A.H. Tuttle, M.J. Molinaro, J.F. Jethwa, S.G. Sotocinal, J.C. Prieto, M.A. Styner, et al., A deep neural network to assess spontaneous pain from mouse facial expressions, Mol. Pain 14 (2018), 1744806918763658. [46] M. de Matas, Q. Shao, M.F. Biddiscombe, S. Meah, H. Chrystyn, O.S. Usmani, Predicting the clinical effect of a short acting bronchodilator in individual patients using artificial neural networks, Eur. J. Pharm. Sci. 41 (5) (2010) 707–715. [47] R. Han, H. Xiong, Z. Ye, Y. Yang, T. Huang, Q. Jing, et al., Predicting physical stability of solid dispersions by machine learning techniques, J. Control. Release 311–312 (2019) 16–25. [48] H. Gao, W. Wang, J. Dong, Z. Ye, D. Ouyang, An integrated computational methodology with datadriven machine learning, molecular modeling and PBPK modeling to accelerate solid dispersion formulation design, Eur. J. Pharm. Biopharm. 158 (2021) 336–346. [49] Y. He, Z. Ye, X. Liu, Z. Wei, F. Qiu, H.-F. Li, et al., Can machine learning predict drug nanocrystals? J. Control. Release 322 (2020) 274–285. [50] H. Gao, H. Jia, J. Dong, X. Yang, H. Li, D. Ouyang, Integrated in silico formulation design of selfemulsifying drug delivery systems, Acta Pharm. Sin. B 11 (11) (2021) 3585–3594. [51] Z. Ye, W. Yang, Y. Yang, D. Ouyang, Interpretable machine learning methods for in vitro pharmaceutical formulation development, Food Front. 2 (2) (2021) 195–207. [52] Y. Tang, J. Zhang, D. He, W. Miao, W. Liu, Y. Li, et al., GANDA: a deep generative adversarial network conditionally generates intratumoral nanoparticles distribution pixels-to-pixels, J. Control. Release 336 (2021) 336–343. [53] R. Widyati, A. Ashari, Afiahayati, A review of using data mining and machine learning for predicting drug loading modeling in solid lipid nanoparticles containing curcumin, J. Phys. Conf. Ser. 1918 (4) (2021), 042015. [54] Y. Lee, A. Khemka, J.-W. Yoo, C.H. Lee, Assessment of diffusion coefficient from mucoadhesive barrier devices using artificial neural networks, Int. J. Pharm. 351 (1) (2008) 119–126. [55] P. Nemati, M. Imani, F. Farahmandghavi, H. Mirzadeh, E. Marzban-Rad, A.M. Nasrabadi, Artificial neural networks for bilateral prediction of formulation parameters and drug release profiles from cochlear implant coatings fabricated as porous monolithic devices based on silicone rubber, J. Pharm. Pharmacol. 66 (5) (2014) 624–638. [56] A. Boobis, U. Gundert-Remy, P. Kremers, P. Macheras, O. Pelkonen, In silico prediction of ADME and pharmacokinetics. Report of an expert meeting organised by COST B15, Eur. J. Pharm. Sci. 17 (4–5) (2002) 183–193. [57] S. Ekins, C.L. Waller, P.W. Swaan, G. Cruciani, S.A. Wrighton, J.H. Wikel, Progress in predicting human ADME parameters in silico, J. Pharmacol. Toxicol. Methods 44 (1) (2000) 251–272.

Impact of AI on drug delivery and pharmacokinetics

[58] C. Balakumar, M. Ramesh, C.L. Tham, S.P. Khathi, F. Kozielski, C. Srinivasulu, et al., Ligand- and structure-based in silico studies to identify kinesin spindle protein (KSP) inhibitors as potential anticancer agents, J. Biomol. Struct. Dyn. 36 (14) (2018) 3687–3704. [59] M.A. Lyons, B. Reisfeld, R.S.H. Yang, A.J. Lenaerts, A physiologically based pharmacokinetic model of rifampin in mice, Antimicrob. Agents Chemother. 57 (4) (2013) 1763–1771. [60] M.C. Wenlock, P. Barton, In silico physicochemical parameter predictions, Mol. Pharm. 10 (4) (2013) 1224–1235. [61] Q. Zang, K. Mansouri, A.J. Williams, R.S. Judson, D.G. Allen, W.M. Casey, et al., In silico prediction of physicochemical properties of environmental chemicals using molecular fingerprints and machine learning, J. Chem. Inf. Model. 57 (1) (2017) 36–49. [62] J.A.H. Schw€ obel, R.-U. Ebert, R. K€ uhne, G. Sch€ uu €rmann, Prediction models for the Abraham hydrogen bond donor strength: comparison of semi-empirical, ab initio, and DFT methods, J. Phys. Org. Chem. 24 (11) (2011) 1072–1080. [63] H. van de Waterbeemd, E. Gifford, ADMET in silico modelling: towards prediction paradise? Nat. Rev. Drug Discov. 2 (3) (2003) 192–204. [64] A.V. Lyubimov, T. Bohnert, C. Prakash, ADME profiling in drug discovery and development: an overview, in: A.V. Lyubimov (Ed.), Encyclopedia of Drug Metabolism and Interactions, John Wiley & Sons, 2012, pp. 1–42. https://doi.org/10.1002/9780470921920.edm021. [65] P. Dwivedi, R. Kumar Tekade, J.N. Kumar, Nanoparticulate carrier mediated intranasal delivery of insulin for the restoration of memory signaling in Alzheimer’s disease, Curr. Nanosci. 9 (1) (2013) 46–55. [66] C.A.S. Bergstr€ om, W.N. Charman, C.J.H. Porter, Computational prediction of formulation strategies for beyond-rule-of-5 compounds, Adv. Drug Deliv. Rev. 101 (2016) 6–21. [67] T. Hou, J. Wang, W. Zhang, W. Wang, X. Xu, Recent advances in computational prediction of drug absorption and permeability in drug discovery, Curr. Med. Chem. 13 (22) (2006) 2653–2667. [68] H.H. Refsgaard, B.F. Jensen, P.B. Brockhoff, S.B. Padkjaer, M. Guldbrandt, M.S. Christensen, In silico prediction of membrane permeability from calculated molecular parameters, J. Med. Chem. 48 (3) (2005) 805–811. [69] P.V. Balimane, S. Chong, R.A. Morrison, Current methodologies used for evaluation of intestinal permeability and absorption, J. Pharmacol. Toxicol. Methods 44 (1) (2000) 301–312. [70] J. Kayat, V. Gajbhiye, R.K. Tekade, N.K. Jain, Pulmonary toxicity of carbon nanotubes: a systematic report, Nanomedicine 7 (1) (2011) 40–49. [71] N. Soni, N. Soni, H. Pandey, R. Maheshwari, P. Kesharwani, R.K. Tekade, Augmented delivery of gemcitabine in lung cancer cells exploring mannose anchored solid lipid nanoparticles, J. Colloid Interface Sci. 481 (2016) 107–116. [72] K. L€ uder, L. Lindfors, J. Westergren, S. Nordholm, R. Kjellander, In silico prediction of drug solubility. 3. Free energy of solvation in pure amorphous matter, J. Phys. Chem. B 111 (25) (2007) 7303–7311. [73] S. Kwon, H. Bae, J. Jo, S. Yoon, Comprehensive ensemble in QSAR prediction for drug discovery, BMC Bioinformatics 20 (1) (2019) 521. [74] R.B. Von Dreele, P.W. Stephens, G.D. Smith, R.H. Blessing, The first protein crystal structure determined from high-resolution X-ray powder diffraction data: a variant of T3R3 human insulin-zinc complex produced by grinding, Acta Crystallogr. D Biol. Crystallogr. 56 (Pt 12) (2000) 1549–1553. [75] J. Verma, V.M. Khedkar, E.C. Coutinho, 3D-QSAR in drug design—a review, Curr. Top. Med. Chem. 10 (1) (2010) 95–115. [76] A. Golbraikh, X.S. Wang, H. Zhu, A. Tropsha, Predictive QSAR modeling: methods and applications in drug discovery and chemical risk assessment, in: J. Leszczynski (Ed.), Handbook of Computational Chemistry, Springer Netherlands, Dordrecht, 2016, pp. 1–48. [77] J. Ma, R.P. Sheridan, A. Liaw, G.E. Dahl, V. Svetnik, Deep neural nets as a method for quantitative structure-activity relationships, J. Chem. Inf. Model. 55 (2) (2015) 263–274. [78] D.K. Agrafiotis, W. Ceden˜o, V.S. Lobanov, On the use of neural network ensembles in QSAR and QSPR, J. Chem. Inf. Comput. Sci. 42 (4) (2002) 903–911.

463

464

A handbook of artificial intelligence in drug delivery

[79] J. Xu, P. Yang, S. Xue, B. Sharma, M. Sanchez-Martin, F. Wang, K.A. Beaty, E. Dehan, B. Parikh, Translating cancer genomics into precision medicine with artificial intelligence: applications, challenges and future perspectives, Hum. Genet. 138 (2) (2019) 109–124. [80] P. Pradeep, R.J. Povinelli, S. White, S.J. Merrill, An ensemble model of QSAR tools for regulatory risk assessment, J. Cheminformatics 8 (2016) 48. [81] N. Basant, S. Gupta, K.P. Singh, Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches, Comput. Biol. Chem. 61 (2016) 178–196. [82] W. Wang, M.T. Kim, A. Sedykh, H. Zhu, Developing enhanced blood-brain barrier permeability models: integrating external bio-assay data in QSAR modeling, Pharm. Res. 32 (9) (2015) 3055–3065. [83] L.T. Afolabi, F. Saeed, H. Hashim, O.O. Petinrin, Ensemble learning method for the prediction of new bioactive molecules, PLoS One 13 (1) (2018), e0189538. [84] I. Ponzoni, V. Sebastia´n-P
erez, C. Requena-Triguero, C. Roca, M.J. Martı´nez, F. Cravero, et al., Hybridizing feature selection and feature learning approaches in QSAR modeling for drug discovery, Sci. Rep. 7 (1) (2017) 2403. [85] L. Zhang, H. Ai, W. Chen, Z. Yin, H. Hu, J. Zhu, et al., CarcinoPred-EL: novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods, Sci. Rep. 7 (1) (2017) 2118. [86] S. Alqahtani, In silico ADME-Tox modeling: progress and prospects, Expert Opin. Drug Metab. Toxicol. 13 (11) (2017) 1147–1158. [87] J. Ghosh, M.S. Lawless, M. Waldman, V. Gombar, R. Fraczkiewicz, Modeling ADMET, Methods Mol. Biol. 1425 (2016) 63–83. [88] C. Barillari, G. Marcou, D. Rognan, Hot-spots-guided receptor-based pharmacophores (HS-Pharm): a knowledge-based approach to identify ligand-anchoring atoms in protein cavities and prioritize structure-based pharmacophores, J. Chem. Inf. Model. 48 (7) (2008) 1396–1410. [89] T. Sato, T. Honma, S. Yokoyama, Combining machine learning and pharmacophore-based interaction fingerprint for in silico screening, J. Chem. Inf. Model. 50 (1) (2010) 170–185. [90] J. Jim
enez, S. Doerr, G. Martı´nez-Rosell, A.S. Rose, G. De Fabritiis, DeepSite: protein-binding site predictor using 3D-convolutional neural networks, Bioinformatics 33 (19) (2017) 3036–3042. [91] H.I. Umar, A. Ajayi, R.O. Bello, H.O. Alabere, A.A. Sanusi, O.O. Awolaja, et al., Novel molecules derived from 3-O-(6-galloylglucoside) inhibit main protease of SARS-CoV 2 in silico, Chem. Pap. 76 (2) (2022) 785–796. [92] M. Skalic, A. Varela-Rial, J. Jim
enez, G. Martı´nez-Rosell, G. De Fabritiis, LigVoxel: inpainting binding pockets using 3D-convolutional neural networks, Bioinformatics 35 (2) (2019) 243–250. [93] J. Jim
enez, M. Sˇkalic, G. Martı´nez-Rosell, G. De Fabritiis, KDEEP: protein–ligand absolute binding affinity prediction via 3D-convolutional neural networks, J. Chem. Inf. Model. 58 (2) (2018) 287–296. [94] H.K. Shaikh, R.V. Kshirsagar, S.G. Patil, Mathematical models for drug release characterization: a review, World J. Pharm. Pharm. Sci. 4 (4) (2015) 324–338. [95] H. Lokhandwala, A. Deshpande, S. Deshpande, Kinetic modeling and dissolution profiles comparison: an overview, Int. J. Pharm. Bio. Sci. 4 (1) (2013) 728–773. [96] T. Higuchi, Mechanism of sustained-action medication. Theoretical analysis of rate of release of solid drugs dispersed in solid matrices, J. Pharm. Sci. 52 (12) (1963) 1145–1149. [97] A.W. Hixson, J.H. Crowell, Dependence of reaction velocity upon surface and agitation, Ind. Eng. Chem. 23 (8) (1931) 923–931. [98] R.W. Korsmeyer, R. Gurny, E. Doelker, P. Buri, N.A. Peppas, Mechanisms of solute release from porous hydrophilic polymers, Int. J. Pharm. 15 (1) (1983) 25–35. [99] H.B. Hopfenberg, Controlled release from erodible slabs, cylinders, and spheres, in: Controlled Release Polymeric Formulations, ACS Symposium Series, vol. 33, American Chemical Society, 1976, pp. 26–32. [100] B.J. Anderson, N.H.G. Holford, Rectal paracetamol dosing regimens: determination by computer simulation, Pediatr. Anesth. 7 (6) (1997) 451–455. [101] S.L. Shafer, J.R. Varvel, Pharmacokinetics, pharmacodynamics, and rational opioid selection, Anesthesiology 74 (1) (1991) 53–63.

Impact of AI on drug delivery and pharmacokinetics

[102] V. Dave, R.B. Yadav, S. Yadav, S. Sharma, R.K. Sahu, A.F. Ajayi, A critique of computer simulation software’s used in pharmacokinetics and pharmacodynamics analysis, Curr. Clin. Pharmacol. 13 (4) (2018) 216–235. [103] A. Goto, S. Abe, S. Koshiba, K. Yamaguchi, N. Sato, Y. Kurahashi, Current status and future perspective on preclinical pharmacokinetic and pharmacodynamic (PK/PD) analysis: survey in Japan Pharmaceutical Manufacturers Association (JPMA), Drug Metab. Pharmacokinet. 34 (2) (2019) 148–154. [104] S. He, L.G. Leanse, Y. Feng, Artificial intelligence and machine learning assisted drug delivery for effective treatment of infectious diseases, Adv. Drug Deliv. Rev. 178 (2021), 113922. [105] V.M. Patel, B.G. Prajapati, M.M. Patel, Formulation, evaluation, and comparison of bilayered and multilayered mucoadhesive buccal devices of propranolol hydrochloride, AAPS PharmSciTech 8 (1) (2007) E147–E154. [106] V.M. Patel, B.G. Prajapati, M.M. Patel, Design and characterization of chitosan-containing mucoadhesive buccal patches of propranolol hydrochloride, Acta Pharma. 57 (1) (2007) 61–72. [107] M.E. Brier, J.M. Zurada, G.R. Aronoff, Neural network predicted peak and trough gentamicin concentrations, Pharm. Res. 12 (3) (1995) 406–412. [108] H.-H. Chow, K.M. Tolle, D.J. Roe, V. Elsberry, H. Chen, Application of neural networks to population pharmacokinetic data analysis, J. Pharm. Sci. 86 (7) (1997) 840–845. [109] R.G. Hall 2nd, J.G. Pasipanodya, C. Meek, R.D. Leff, M. Swancutt, T. Gumbo, Fractal geometrybased decrease in trimethoprim-sulfamethoxazole concentrations in overweight and obese people, CPT Pharmacometrics Syst. Pharmacol. 5 (12) (2016) 674–681. [110] R.G. Hall, J.G. Pasipanodya, M.A. Swancutt, C. Meek, R. Leff, T. Gumbo, Supervised machinelearning reveals that old and obese people achieve low dapsone concentrations, CPT Pharmacometrics Syst. Pharmacol. 6 (8) (2017) 552–559. [111] L. Pu, M. Naderi, T. Liu, H.-C. Wu, S. Mukhopadhyay, M. Brylinski, eToxPred: a machine learning-based approach to estimate the toxicity of drug candidates, BMC Pharmacol. Toxicol. 20 (1) (2019) 2. [112] J. Vamathevan, D. Clark, P. Czodrowski, I. Dunham, E. Ferran, G. Lee, et al., Applications of machine learning in drug discovery and development, Nat. Rev. Drug Discov. 18 (6) (2019) 463–477. [113] E. Lin, S. Mukherjee, S. Kannan, A deep adversarial variational autoencoder model for dimensionality reduction in single-cell RNA sequencing analysis, BMC Bioinformatics 21 (1) (2020) 64. [114] A.O. Basile, A. Yahi, N.P. Tatonetti, Artificial intelligence for drug toxicity and safety, Trends Pharmacol. Sci. 40 (9) (2019) 624–635. [115] S. Reddy, S. Allan, S. Coghlan, P. Cooper, A governance model for the application of AI in health care, J. Am. Med. Inform. Assoc. 27 (3) (2020) 491–497. [116] A. Holzinger, G. Langs, H. Denk, K. Zatloukal, H. M€ uller, Causability and explainability of artificial intelligence in medicine, Wiley Interdiscip. Rev. 9 (4) (2019), e1312. [117] D. Kobak, P. Berens, The art of using t-SNE for single-cell transcriptomics, Nat. Commun. 10 (1) (2019) 5416. [118] S.M. Lundberg, G. Erion, H. Chen, A. DeGrave, J.M. Prutkin, B. Nair, et al., From local explanations to global understanding with explainable AI for trees, Nat. Mach. Intell. 2 (1) (2020) 56–67. [119] J.-B. Lamy, B. Sekar, G. Guezennec, J. Bouaud, B. S
eroussi, Explainable artificial intelligence for breast cancer: a visual case-based reasoning approach, Artif. Intell. Med. 94 (2019) 42–53. [120] S.C. Huang, A. Pareek, S. Seyyedi, I. Banerjee, M.P. Lungren, Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines, npj Digit. Med. 3 (2020) 136. [121] M. Delavy, L. Cerutti, A. Croxatto, G. Prod’hom, D. Sanglard, G. Greub, et al., Machine learning approach for Candida albicans fluconazole resistance detection using matrix-assisted laser desorption/ ionization time-of-flight mass spectrometry, Front. Microbiol. 10 (2019) 3000. [122] Y. Gao, Y. Cui, Deep transfer learning for reducing health care disparities arising from biomedical data inequality, Nat. Commun. 11 (1) (2020) 5131. [123] D.S. Kermany, M. Goldbaum, W. Cai, C.C.S. Valentim, H. Liang, S.L. Baxter, et al., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell 172 (5) (2018) 1122–1131. e9.

465

CHAPTER 17

Artificial intelligence in vaccine development: Significance and challenges ahead Shantani Kannana, Kannan Subbaramb, and Md. Faiyazuddinc,d a

Department of Electronics and Communications Engineering, Kumaraguru College of Technology, Coimbatore, India School of Medicine, The Maldives National University, Mal
e, Maldives c School of Pharmacy, Al-Karim University, Katihar, Bihar, India d Nano Drug Delivery® (A product development partnership company), Raleigh-Durham, NC, United States b

17.1 Machine learning approaches in vaccine development A vaccine provides hope to eliminate and prevent diseases by preparing the immune system to develop antibodies to fight pathogens. It is healthy, simple to use, has no side effects, and is used for the treatment of many illnesses. Vaccines can be of various forms, such as live attenuated vaccines, DNA vaccines, etc. The vaccine production process is a long-term one, involving highly advanced laboratories and clinical trials. Sometimes, even after these clinical trials, they fail to produce the desired results. The vaccine development process appears to be straightforward; it is simply identification of the virus and extraction of the inactive proteins that generate the immune response. Unfortunately, the reality is far more complicated than just that. In order to activate the immune response, specific parts of the virus must be exposed to antibodies [1]. Machine learning has truly transformed the field of science and engineering. It has changed our lives and has set foot into every aspect, ranging from automated robots to voice assistants, facial recognition, etc. If well-utilized in the field of vaccine discovery, then it can prove to be highly impactful. Machine learning methods like clustering, classification, and association can be used in different stages of clinical trials. These techniques are carried out without much time consumption and provide greater accuracy and sensitivity. VaxiJen was the first application of ML technology for antigen prediction. Recently, graphical features have been introduced into feature antibody structures in a more expert manner [2]. Based on the learning system, machine learning can be classified as: • Supervised learning: 8In this system, the labeled inputs are classified according to their desired outputs. It is based on a set of training data examples. Supervised learning, as the name suggests, is the existence of a supervisor or a teacher. Usually, supervised learning is a process in which we either instruct or train a computer using data that are well-labeled, which means that certain data are already labeled with the correct response. After that, a A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00017-4

Copyright © 2023 Elsevier Inc. All rights reserved.

467

468

A handbook of artificial intelligence in drug delivery

new collection of examples is fed into the computer such that the supervised learning algorithm evaluates the training data and produces the outcome. • Semisupervised learning: A small amount of labeled data are trained with unlabeled data, basically a combination of supervised and unsupervised learning. Semisupervised learning offers the advantages of both unsupervised and supervised learning while avoiding the difficulties of locating a large number of labeled results. This means that we can teach the algorithm to classify data without having to use too many training data. • Unsupervised learning: The machine gets assigned to inputs without the desired outputs, which is basically a clustering and association rule. Unsupervised learning is training using data that are neither categorized nor labeled and that requires an algorithm to operate on that information without supervision. The machine’s job here is to group unsorted data according to similarities, trends, and differences without any previous data preprocessing. • Reinforcement learning: It is the training of intelligent models in a dynamic environment in order to maximize the probability of obtaining a reward. This type of learning differs from supervised learning in such a way that, in supervised learning, the training data hold the key such that the model is conditioned with the right answer, whereas in reinforcement learning, there is no answer, but the agent knows what to do to execute the task. In the nonexistence of a training dataset, the agent is restricted to learn from previous experience [3].

17.1.1 Supervised classification in bioinformatics 17.1.1.1 Proteomics Several applications have been rendered by the nearest neighbor to predict the secondary protein structure. Support vector machines and Bayesian classifiers can be used to predict the surface residues of proteins involved in protein-protein interactions. Fuzzy k-nearest neighbor algorithms solve the problem of automatically estimating the subcellular protein’s position from its sequence. Reconstruction of amino acid sequences using spectral features has been attempted using dynamic programming. Dynamic programming is also an effective algorithm type for RNA secondary structure prediction. 17.1.1.2 Genomics One of the most useful applications of artificial intelligence (AI) techniques can be found in the case of gene detection. It is one of the best methods of genetic prediction. Classification trees can be used when checking for protein coding regions in human DNA. Feature subset selection has been used to solve the issue of gene discovery. Optimization techniques should be used to settle the issue of splice site prediction. Classification methodologies are also used in the search for RNA genes. Support vector machines and neural networks are used for the predictive recognition of functional RNA genes and

Artificial intelligence in vaccine development

identification of genes, likely to be associated with genetic disorders, taking various conservation levels and chromosome length as the predictive variables. 17.1.1.3 Pattern recognition A support vector machine is often used to choose an optimal classifier and an optimal subset of cancer diagnostic genes on the basis of the expression results. This will assist in vaccination research on the disease. Nearest neighbor algorithms can also be used in this method. A k-nearest neighbor algorithm is used in combination with a genetic algorithm in the gene selection wrapper strategy. Ensemble learning such as boosted decision trees works better than do single class trees in the classification of malignant gene expression profiles [4].

17.1.2 Employing proteomics for gonorrhea antigen mining A comparison of the number of antigens tested for the serogroup B vaccine with the number currently being studied for a gonorrhea vaccine reveals how much gonorrhea testing lags behind meningitis research and highlights the need for new methods to widen the pool of vaccine candidates under consideration. A groundbreaking approach to bridging this gap is reverse vaccinology antigen mining using subcellular fractionation, followed by high-throughput quantitative proteomics and bioinformatics, which have established various stably expressed proteins and indicated that the design of a subunit vaccine against gonorrhea would be effective. Proteomic analyses show the biologically important population of proteins that are released through exposure to the conditions under analysis. Proteomic methods can also be used to classify surface-exposed proteins without the need for detailed bioinformatic predictions. Surface-localized proteins are promising vaccine candidates because they are effective immune system foci and play critical roles in bacterial physiology and host-pathogen interactions. Surface-localized proteins, other outer membrane and periplasm-derived proteins, and, usually, cytoplasmic proteins are used in naturally elaborated MVs (NeMVs) and in MVs isolated from cell envelopes (CE) [5].

17.2 The basic workflow of a machine learning algorithm for classification The concept behind a machine learning algorithm is that it is not explicitly programmed, but the model learns how to map an input to output. 1. Classification by prediction: Classification works by identifying the target class for each case. It is a data mining method. From a modeling point of view, classification includes a training dataset with several samples of inputs and outputs from which to learn. The algorithm would use the training dataset to determine how best to map examples of input data to particular class labels. As such, the training dataset must be sufficiently reflective of the problem and must include several representations of each class name. It is one of the best approaches to be used in the health-care field

469

470

A handbook of artificial intelligence in drug delivery

to forecast diseases. There are many machine learning algorithms for classification like J48 decision tree, random forest, naı¨ve Bayes, etc. 2. Association rules: Depending upon the data, hidden correlations, and other insights, the association rules are identified. Researchers could use association rules to classify the possible side effects and risk factors. There are also aspects that need to be weighed before making a diagnosis, since many illnesses share symptoms. By applying correlation rules and machine-led data processing, this will assess the conditional likelihood of a given disease by analyzing symptom associations in data from previous events. 3. Clustering: In clustering, a set of observations are grouped into subsets, also known as clusters. Clustering consists of partitioning a group of elements into subsets according to the discrepancies between them. In other words, it is the act of grouping together similar elements. The key distinction from supervised grouping is that there is no information on how many groups there are in clustering. Observations in the same cluster will have similar characteristics. There are many clustering methods like: • Model-based: In a model-based clustering method, data are interpreted as originating from a mixture of probability distributions, all of which represent a particular cluster. In other words, in model-based clustering, data are supposed to be produced by a mixture of probability distributions in which each part represents a separate cluster. • Centroid-based: Centroid-based clustering arranges data into nonhierarchical clusters, as opposed to the hierarchical clustering mentioned in the following. K-means is the most commonly used algorithm for this purpose. Centroid-based algorithms are effective but vulnerable to original and external conditions. • Grid-based: In this approach, the data space is formulated into a finite number of cells, forming a grid-like structure. Both clustering operations conducted on these grids are fast, regardless of the number of data items. Quick processing time is the key benefit of this approach. It depends only on the number of cells in each dimension of the quantitative space. • Partition-based: Partition clustering attempts to achieve partitioning of the data. Each point belongs to a single cluster. It is a common approach to correct the number of clusters, but some algorithms can look for the most suitable number of clusters when assigning items to various clusters. A k-means algorithm is an effective algorithm to perform this type of clustering. • Hierarchy-based: Hierarchical clustering consists of structures called hierarchical trees. A hierarchical tree is a nested partition set represented by a tree diagram or a dendrogram. The division of a tree at a specific stage generates a division into disjoint classes of K. If two groups are selected from opposite partitions, then they are disjointed or one group entirely contains the other. It is basically the measure of

Artificial intelligence in vaccine development

dissimilarity between two partitions. There are many algorithms like agglomerative algorithms, divisive algorithms, etc. • Density-based: Density-based clustering links regions of high density to clusters. This results in random distributions as long as dense areas can be associated. These algorithms have difficulties with data of differing densities and high dimensions [6].

17.2.1 K-means clustering algorithms Clustering is one of the most popular exploratory data processing methods used to gain insights into the nature of data. The role of defining subgroups in data can be characterized such that pieces of data in the same subgroup (cluster) are highly similar, whereas data points in different clusters are highly unique. In other words, we attempt to explore homogeneous subgroups within data in such a way that the data points in each cluster are as close as possible to a feature vector, such as a Euclidean-based distance or a correlation-based distance. The judgment on the similarity of the test to be used is application-specific [7]. A k-means clustering algorithm is one of the best, accurate, and easy methods to use clustering algorithms. It is a nonhierarchical clustering method and can accept input data even without labels. It can be carried out as follows: 1. Assign K data points plotted on “x” and “y” coordinates. 2. Perform clustering by allocating a centroid. 3. Compute the distance between the centroid and data points. 4. Reposition till the final converged cluster is obtained. Open-source software like WEKA, Cluster, etc. can also perform k-means clustering. They can perform a wide number of tasks ranging from preprocessing, classification, regression, and clustering to association rules [8].

17.2.2 Requirements of clustering 1. High dimensionality: A clustering algorithm should be able to manage lowdimensional data and high-dimensional space simultaneously. 2. Interpretable results: The results obtained after clustering should be interpretable, understandable, and usable. 3. Ability to overcome noisy data: Databases are often subjected to missing, noisy information. Some of the algorithms can be affected by these interruptions and may lead to poor results. 4. Adaptable to an arbitrary shape: It should be capable of detecting an arbitrary shape and not be bounded to a particular shape. 5. Applicable to a variety of data: It should have the capability of being applied to all types of data like numerical, binary, etc. 6. Power of scalability: The algorithm should have high scalability so that it can be applied to large datasets [9].

471

472

A handbook of artificial intelligence in drug delivery

17.2.3 Logistic regression Logistic regression is a form of supervised classification algorithm. It is necessary to carry out a regression analysis in which the dependent variable is dualistic (binary). Logistic regression can be implemented in such cases. For other regression analyses, logistic regression is a statistical analysis. It is used to characterize data and illustrate the relationship between one dependent binary variable and one or more standard, ordinal, interval, or ratio-level independent variables. Regression analysis can be used for three purposes: 1. Forecasting the consequences or influencing specific changes. For example, if a vaccine production firm tries to estimate how many units of a given vaccine it needs to manufacture in order to satisfy the current demand, then, in this context, both forecasting and influencing are factors, which are particularly important at times of a crisis or a pandemic. 2. Forecasting developments and future values. For example, determining how high Pfizer’s stock price is going to be in 6 months will provide an insight into future values and their forecast. 3. Determining the power of various predictors or, in other words, determining the extent to which independent variables have an effect on the dependent variables. For example, if a vaccine or drug manufacturer is considering a specific community or country of people, then they would like to decide whether any other factors, such as environmental or genetic, are accounted for.

17.2.4 Regression approaches to the assessment of influenza vaccine effectiveness The influenza vaccine is the most practical means available for the prevention of influenza virus infection and is commonly practiced in many countries. Since vaccine components and circulating strains are constantly evolving, it is important to continuously track the vaccine’s efficacy (VE). A test-negative design is typically used to assess the VE. In this design, patients who follow the same clinical case description are enrolled and screened for influenza; those who test positive are the cases and those who test negative form the reference category. The standard approach to assessing the effectiveness in these experiments is to use logistic regression to correct for confounding effects. Although the coverage of vaccines and the occurrence of influenza vary from season to season, a time frame is included in these confounding effects. Although most experiments use definitive logistic regression, some use time adjustment, an alternative method, which involves utilizing conditional logistic regression at the right time. In this case, simulation data may be used to analyze the ability of all regression methods to determine robustness and efficacy. In cases in which the coverage of a vaccine varies over the influenza season, both conditional and unconditional models can be modified on a weekly basis and the use of the spline feature for a week will have more precise outcomes [10].

Artificial intelligence in vaccine development

17.2.5 Prediction of vaccination outcomes by neural networks and logistic regression The most difficult aspect of vaccine development is predicting vaccine potency. The aim of most researchers is to create a model that could successfully forecast the outcome of a vaccination based on real-world medical evidence. Neural network architectures like multilayer perceptron, radial base, and probabilistic can be validated in combination with parameter optimization and regularization techniques to construct a vaccination model that could be used for prediction purposes in the medical practice of primary health-care physicians, where the vaccine is normally delivered. The input variables can be chosen based on a model of the vaccine strain, which has been regularly updated and on which a weak vaccine response is predicted. The cumulative hit rate of negative and positive vaccine outcomes can be used to evaluate the model results [11].

17.2.6 Naïve Bayesian classification A naı¨ve Bayes algorithm can be applied to evaluate the performance of vaccine models. It is based on the principle of Bayes and can be used for classifying data with the help of class identifiers. The assumption of this algorithm is that the result of a functional value on a given class is independent of the value of the other features. This presumption is called conditional independence, which is why it is called naı¨ve. After learning the model from the results, i.e., learning the structure and estimating the parameters, we use inference algorithms to obtain the likelihood of an occurrence of interest (e.g., disease contraction) on the basis of a series of data (e.g., male, vaccinated or not). This method is also known as probabilistic reasoning or the inference method [12].

17.2.6.1 Applications of Naïve Bayes algorithms 1. Real-time prediction: Naı¨ve Bayes is a fast-learning classifier that can learn quickly. It could therefore be used in real time to make predictions. 2. Multiclassification: The multiclass provision feature of this algorithm is also known. The probability of multiple target classes can be predicted here. 3. Text classification/spam filtering/sentiment analysis: Classifiers of Naı¨ve Bayes, which are often used in text classification, have higher performance levels compared with those of other algorithms due to better multiclass problems and their principles. 4. Recommendation framework: A Naı¨ve Bayes classifier and collaborative filtering can work together to create a recommendation system that uses machine learning and data mining techniques to filter unseen knowledge and determine whether a user would like a given resource or not [13].

473

474

A handbook of artificial intelligence in drug delivery

17.2.7 Implementation of a vaccine development model 1. Preprocessing data: The foremost requirement would be to remove all unnecessary data, like noise and repetition. This improves the accuracy of the model. 2. Testing and training data: Data will be split into training data and testing data. A machine learning algorithm will be applied to both the datasets. The training data are used for training the model to make decisions, whereas the testing data are used for making predictions. 3. Cross-validation or model assessment: In this, a validation computer check is used to check whether the data entered are logical and reasonable. Many of these approaches require high computational efficiency. Thus, it should also reduce the resource usage. It works best depending on the form of data and the purpose of the project, experiment, and test algorithm performance. 4. Decision trees: A decision tree is a widely used method of classification as it is extremely easy to use and accurate. It contains a set of questions about the attributes of the test record. It keeps receiving the answer till the final conclusion of the class label (Yes or No) is met. If “Yes” is found in the antigen, then the particular antigen will be considered for vaccine development [14].

17.3 Neural networks Neural networks have achieved massive popularity over the past decade. They are a series of algorithms that mimic human brain operations to identify relationships between massive amounts of data. They view sensory data by tagging or clustering raw information using machine perception. The patterns they know are numerical and are stored in vectors, into which all real-world data must be converted, whether images, sound, text, or time series. Neural networks assist in clustering and classifying results. However, early versions of neural networks could only be applied using normal or Euclidean data, although many data in the real world have underlying graph structures that are non-Euclidean. The lack in regularity of data structures has contributed to the recent developments in graph neural networks (GCNNs). Modified aspects of graph neural networks have been developed over the last few years, with graph convolutional neural networks being one of them. GCNNs are often known to be one of the fundamental variants of graph neural networks [15].

17.3.1 Graph convolutional neural networks Graph convolutional neural networks (GCNNs) were introduced in the year 2017 by Kipf and Welling. They have the ability to handle graphs and predict useful information. When a new illness is found, scientists first look at the processes that cause it: Is it a form of bacteria? Is it virus-infected? What kind is it? What does it do inside the human body?

Artificial intelligence in vaccine development

What are its serious effects (like mortality rate etc.)? When the condition is well-known, the next step in designing a cure is to establish a target. A target is a molecule in the body, usually a protein, which is associated with a specific disorder that a drug may influence to achieve the desired therapeutic result. Following the identification of an optimal target, the next step is to determine substances that may be associated with this target within the body. This is where things get particularly complicated, so scientists must decide which compounds among hundreds of millions of potential candidates will bind to the desired target. A compound is the one that can be conveniently and cheaply synthesized or collected from nature to be mass-generated and used as a medicine. Despite the fact that scientific intuition and previous science play a role here, this phase is essentially accomplished by brute force. Simply placed, high-throughput screening involves using robotics to combine hundreds of thousands of different substances at varying concentrations with the target and testing how well they bind. High-throughput screening is a drug discovery procedure that allows for the automated testing of a wide number of chemical and/or biological substances for a particular biological target. This is arguably the costliest aspect of the drug development process, and it is here that machine learning can really help. The final calculation of how well a compound bonds with a target is known as the dissociation constant, normally denoted by Kd, which is a real-number calculation used to describe the binding affinity of a drug to its target. The lower the value, the stronger is the binding. Successful prediction using GCNNs has been employed in the study of drug properties, interaction between the vaccine and the target, protein surface estimation, and prediction of reactivity. mRNA degradation can also be predicted using GCNNs and recurrent neural networks (RNNs). Due to the advent of many new diseases, researchers need to work on an effective vaccine in a short span of time in order to save lives. mRNA vaccines, for instance, face the challenge of losing stability over time. They spontaneously degrade over time unless kept under constant refrigeration. In order to find a stable mRNA sequence, GCNNs and RNNs can be used [16].

17.3.2 Epidemic graph convolutional networks Recently, there has been an increasing movement to leverage the structure of today’s big data, where most of the data can be interpreted as graphs. Simultaneously, graph convolutional networks (GCNs) were proposed and have seen steady growth since then. Because of the scalability problems that occur when attempting to use these influential models on real-world data, methodologies have lately attempted to employ sampling techniques. Mini batches of nodes are created, and, then, sets of nodes are sampled to aggregate from in one or more layers. Usually, GCN models are built in such a way so as to use a convolutional operator to allow the neural network weights to be shared around the graph. This provides a wide range of advantages over more simplistic systems (e.g., a completely linked network), including the ability to accommodate networks of

475

476

A handbook of artificial intelligence in drug delivery

varying sizes, preventing possible overfitting and explosion parameters, while, at the same time, offering a mechanism for studying inductive node representations that can be used in a wide variety of network-related functions, e.g., grouping of nodes. Usually, epidemic models are used for modeling processes such as the distribution of infection across the nodes of the network or in an area. Some of the popular and classically used epidemic models are the susceptible-infected-susceptible (SIS) model, susceptible-infectiousrecovered (SIR) model, and susceptible-infectious (SI) model [17].

17.3.3 Recurrent neural networks RNNs are used for modeling sequential data, and they have been known to produce predictive results while handling sequential data when compared to other classes of neural networks. The structure of an RNN model is an encoder-decoder architecture, enabling performance of sequence-to-sequence operation. It is more robust and helps in singlestep prediction. Using this technique, existing viral strains can be matched to provide ease in vaccine selection and surveillance. RNNs bring about an intriguing twist to simple neural networks. A standard neural network uses a fixed-size vector as an input that restricts its use in cases that require an input form “sequence” with no predetermined size. A RNN remembers the past, and its actions are guided by those past experiences [18].

17.3.4 Long short-term memory networks Another type of deep learning model that is widely employed in mRNA-based vaccine technologies is long short-term memory (LSTM) networks. Such a network is a type of RNN, which was proposed by Sepp Hochreiter and J€ urgen Schmidhuber in 1977. It cannot only process single data but also multiple data sequences at the same time. These networks work tremendously well on a wide range of issues and are now widely used. LSTM networks were deliberately designed to prevent long-term dependence issues. Their default behavior is retaining information for long periods of time. Both recurring neural networks have the shape of a chain of recurring neural network modules. It informs the next network about the output of the first one, essentially allowing the information to remain till the end. LTSM networks are applied to detect protein homology, predict subcellular localization of proteins, drug design, etc. An LSTM network has the ability to remove or add information to a cell state. This process is monitored by structures called gates. Gates are a way to let information pass through. They consist of a sigmoid neural net layer and a point-to-point multiplication operation. So, when we move from an RNN to an LSTM (long short-term memory), we keep introducing more and more control knobs to control the flow as well as mixing of inputs as per trained weights. So, an LSTM network exerts the most influence and thus produces the best results, but it also comes with more ambiguity and operating costs [19].

Artificial intelligence in vaccine development

17.3.5 Deep convolutional neural networks One of the greatest challenges faced in the field of vaccine development is the rapid evolution of viruses. To predict the antigenicity, various machine learning models can be used. One such model is a deep convolutional neural network (DCNN). This type of neural network can analyze the amino acid properties and amino acid substitution matrices for prediction. Many studies have been performed using DCCNs, and the results show that they are highly effective in prediction, outperforming the WHO recommendation. Such a network is more cost-effective, has an extensive pipeline system, and has the potential to improve the vaccine-recommendation process. It is widely employed in visual analysis and natural language processing. A DCNN consists of layers of networks arranged in a hierarchical pattern and is assembled using simpler patterns. Initially, a filter size is chosen and the input is systematically scanned using a sliding window to identify the important features. This is followed by pooling and dropout. Multiple kernels can also be included. The structural design, however, is critical for its performance. The layers, kernels, size, and pooling approach need to be tuned [20].

17.3.6 Computational protein design using deep neural networks A computational protein architecture has a wide range of uses. Despite its impressive progress, the design of a protein for a given structure and purpose is still a daunting task. On the other hand, the number of protein structures that have been resolved is rapidly increasing, whereas the number of unique protein folds has reached a stable number, indicating that more structural information is being collected. Proteins perform a complex variety of roles in cells, including signal transduction, DNA replication, catalyzing reactions, etc. Engineering and designing proteins for a particular structure and function not only deepen our understanding of the difference between the protein sequence and structure but also have wide implications in chemistry, biology, and medicine. Unlike other standard machine learning algorithms, deep learning networks automatically extract features without human intervention. Given that feature extraction will take teams of data scientists years to complete, deep learning is a way to avoid the bottleneck of small experts. It augments the capabilities of small data science teams, which, by definition, do not grow. As trained on unlabeled data, each node layer in a deep network automatically learns features by repeatedly attempting to reproduce the input from which it derives its samples, attempting to reduce the discrepancy between the network’s guesses and the probability distribution of the input data. A neural network is an efficient method to train such a large dataset and has demonstrated superior success in many machine learning fields. Unprecedented successes have been achieved in protein engineering, of which some of the designs have been driven by analytical methods. Examples of several recent effective computational protein designs include novel folds, novel enzymes, vaccines, antibodies, novel protein assemblies, ligand-binding proteins, and membrane proteins.

477

478

A handbook of artificial intelligence in drug delivery

The deep learning neural network methodology can be extended to the design of computational proteins to estimate the likelihood of 20 natural amino acids per protein residue. A broad collection of protein structures may be designed on a multilayer neural network. Better precision is also obtained [21].

17.4 Design of epitope-based vaccines using deep learning Since traditional approaches have been proved to be less efficient and antigen collection is quite random, the concept of creating a vaccine from a select few epitopes has become a more rational option. In the last 5 years, several new vaccine candidates based on B-cell epitopes (BCEs) and T-cell epitopes (TCEs) have been proposed. This method of rapidly identifying immunogenic epitopes is based on numerical assumptions, which make use of advanced algorithms and an expanding epitope database. Epitope prediction is a critical component of an in silico vaccine design, but it is based on antigen detection and, more specifically, epitope selection for a successful immune response. B-cell epitopes play a crucial role in the design of epitope-based vaccines. The aggregation of epitope sample data makes it possible to forecast epitopes by machine learning techniques like LSTM networks. Compared to experimental studies, computational approaches are simpler and more economical. Computational vaccinology refers to the process of developing vaccines in silico. The use of high-throughput data analysis tools for rapid antigen recognition, molecular docking, and simulation models to assess immunological responses is a benefit of computational vaccinology. This approach will test multiple antigen candidates and whole proteomes for antigenicity and efficacy in an extremely short period of time. Several deep learning statistical approaches have been used to boost the precision of epitope predictions. Fortunately, owing to increasingly usable validated epitope databases, scientists can use machine-based learning algorithms with all curated data to design an enhanced predictive platform for vaccine researchers. In a recent effort to locate vaccine candidates, researchers have been using various approaches to classify the most potent antigens using KEGG (Kyoto Encyclopedia of Genes and Genomes) to find protein-protein interactions. VaxiJen2.0 is an alignment-independent antigen prediction server that uses auto cross covariance (ACC) to predict a protein sequence’s physicochemical properties. Yet, another web provider, Jenner-Predict, predicts antigens dependent on the functional domains of proteins implicated in host-pathogen interactions. Similarly, using subtractive reverse vaccinology, VacSol can forecast future therapeutic targets. AntigenDB is a database of historically validated antigens, which contains information from other databases such as Swiss-Prot, MHCBN, AntiJen, IEDB, and BCIPEP. These techniques may aid in the detection of the most likely antigens capable of eliciting the desired immune response. These algorithms have been improved numerous times in epitope prediction, and several milestones have been achieved [22].

Artificial intelligence in vaccine development

17.5 Reverse vaccinology Reverse vaccinology is an improvement in the field of vaccinology with the help of bioinformatics. It was first used against the serogroup B meningococcal disease. By the end of the 1990s, infections caused by Neisseria meningitidis (MenB) serogroup B strains, a significant cause of meningococcal meningitis and septicemia, had defied all conventional vaccine production measures. Vaccines for other meningococcal strains, dependent on their capsular polysaccharides, have been available since the 1960s; however, MenB’s capsular polysaccharide has been found to be of low immunity due to its similarity to the human autoantigen. This proved to be a great challenge in vaccine development in addition to the wide genetic variation. This led to a new technique, which involves a bottom-to-top approach from genome to vaccine. Bioinformatic techniques are used here to scan the whole genome of pathogens to decide whether the protein is a suitable vaccine candidate. With the advancement of genomics in the development of vaccinations, it is important to study the whole genotype and environmental sensitivity. It has also been used against many other bacterial vaccines and employs many machine learning methods of classification like support vector machines (SVMs) [23].

17.5.1 Reverse vaccinology prediction using VAXIGEN-ML VAXIGEN-ML is an online ML-based vaccine candidate prediction. It employs the principle of reverse vaccinology. Any protein sequence of a particular pathogen can be uploaded in the tool. The proteomes will be analyzed using a VAXIGEN reverse vaccinology pipeline. It can predict many biological features like transmembrane helix, immunogenicity and adhesin probability, etc. Physicochemical features such as compositions, distributions, polarity, etc. are also analyzed. VAXIGEN uses machine learning classification algorithms like random forest, logistic regression, k-means clustering, support vector machine, etc. Finally, it predicts a protegenicity score, which is the percentile rank of an ML classification model. If a particular protein has a higher protegenicity score, then it is considered as a suitable candidate for vaccination with effective protection [24].

17.6 Random forest analysis On one hand, although there is a growing need to discover new vaccines, there is a problem too. The problem lies in the sufficiency of vaccines. Vaccines, once discovered, have to be manufactured in sufficient quantities. In the case of viral vaccines, it becomes complex, as viruses are grown in cell culture. So, our ultimate goal would be “prediction and explanation.” We need to predict the yield based on process variables. A random forest is a type of classification algorithm consisting of many decision and regression trees. It is a supervised learning algorithm. The “forest” that it produces is an ensemble of decision trees, which are normally trained using the “bagging” process. The bagging approach is

479

480

A handbook of artificial intelligence in drug delivery

based on the premise that mixing learning models boosts the final result. The significant benefit of a random forest is that it can be used for both classification and regression problems, which make up the vast majority of problems. It was first created by Tin Kam Ho in 1995. The trees vote on the value of classification or regression. It can produce better results when compared to a single tree. In other words, a random forest can scrutinize large number of values to find the most important ones. This will help identify crucial variables in vaccine development.

17.7 Support vector machine-based prediction of binding peptides In machine learning, SVMs are supervised learning models, which are associated with particular learning algorithms. Developed at AT&T Bell Laboratories by Vapnik in the late 1990s, SVMs are considered to be a robust form of prediction method. They are preferred by many as they provide high accuracy with considerably less computation power. They find applications in many areas like T-cell epitope prediction, binding peptide prediction, etc. For instance, T-cell receptors, play a major role in the process of antigen-specific T-cell activation. A support vector machine can be developed to predict a T-cell epitope by training it with datasets. On the other hand, some species of bacteria like Shigella can cause diarrhea in humans. The peptide fragments of this particular bacteria can be studied with SVMs to understand the vaccine design and approach. Support vector machines depend on data preprocessing to represent patterns in a high dimension—usually much higher than the original space function. For an effective nonlinear mapping of a sufficiently high dimension, data from two groups can often be divided by a hyperplane. The decision can also be influenced by the designer’s understanding of the problem domain. Defining the margin as some positive distance from the decision hyperplane, the task of the training support vector machines is to locate the most marginally separating hyperplane. It is expected that the greater the margin, the higher is the generalizability of the classifier. Support vectors are patterns near the hyperplane. They determine the optimum hyperplane separation and are the most difficult patterns to distinguish. They are the most informative structures for the execution of classification tasks [25].

17.8 Recursive feature elimination As datasets continue to increase, it is important to select the correct feature from the original dataset for effective identification of the target, which is a paramount process in order to hamper the infection rate of a particular pathogen. The innate immune system of the host fights against particular infected cells by releasing antibodies such as B and T cells. Recursive feature elimination (RFE) is a basic and important tool for identifying antigens from protein sequences. It works by selecting the most appropriate features and removing

Artificial intelligence in vaccine development

the weakest feature till a specified number of features is reached. RFE is common because it is easy to customize and use and because it is accurate in selecting specific features (columns) in a training dataset, which are becoming more and more important for predicting the target variable. There are two important configuration possibilities when using RFE: the choice of the number of features to be chosen and the choice of the algorithm used to help select the features. Both of these hyperparameters can be tested, but the accuracy of the method is not highly reliant on the specification of these hyperparameters [16].

17.9 AI in the vaccine adverse event reporting system The Vaccine Adverse Event Reporting System (VAERS) is a vaccine safety monitoring system, which is jointly run by the Centers for Disease Control (CDS) and the Food and Drug Administration (FDA). It is an early warning system that reports adverse reactions or health issues faced after a vaccination. It detects possible “signals” relating to the aftermath of a vaccination. With the help of these signals, researchers will be able to conduct various studies and detect the actual risks associated with the vaccine. Advanced AI tools can be used in adverse event case processing to extract viable information from source documents to identify the adverse cases. This information can be trained using machine learning algorithms with the source documents. This can be implemented during emergency situations as a substitute for time-consuming manual handling of source documents. The following are machine learning algorithms used to extract data from the source data: 1. Sentence classification: This is used to predict whether a sentence within a case is related to an AE. 2. Entity recognition: This is used to perform prediction of adverse reactions at the token level. 3. Table pattern recognition: This is used to predict whether a particular table cell contains a specific information. 4. Pattern matching based on a rule: This is used to extract specific information like age, initial, patient name, etc. [26].

17.10 An AI-powered vaccine safety data Bank: The key to vaccine development AI has the capability to power medical breakthroughs by providing high-quality data, eliminating challenges faced, and accelerating the pathway to clinical trials and phases. This will eventually lead to quicker vaccine development. In order to develop a vaccine, it is most likely that a combination of drugs is required. Thus, analysis of multiple drug pairs from the existing approved drugs in the market is required. An AI-based analysis is an effective tool to aid in this process. AI models can process through many

481

482

A handbook of artificial intelligence in drug delivery

pharmaceutical compounds and predict which combination works better. However, they require high-quality datasets during training so that the AI model keeps learning constantly [27].

17.11 mRNA- and protein-based vaccines in collaboration with the AI ecosystem Currently, many leading vaccine developing companies like Pfizer and BioNTech, who are the leading candidates for mRNA- and protein-based vaccines, have established collaborations with AI innovation laboratories such as InstaDeep, DeepMind, Nvidia, etc. This will lead to advancements in the field of vaccine development with promising initiatives in protein engineering and target identification. Most laboratories focus on the following three key research areas: 1. Protein engineering: The pipeline of mRNA vaccines will be integrated with an AI protein design platform to engineer mRNA sequences and encode antibodies. An AI model will understand the language of biology in order to develop proteins in a controllable manner. AI systems have a high potential to be tested in the available protein databases. They resolve one of the most daunting problems in science and show that large-scale generative modeling will unleash the ability of protein engineering to change synthetic biology, materials science, and human health. 2. Manufacturing of drugs and autonomous decision-making: There are many AI-driven initiatives on which the pharmaceutical industry may have a substantial effect. For example, the existing inefficiencies and vulnerabilities along supply chains—whether in manufacturing procedures, distribution logistics, traceability, or storage—not only influence the accessibility, affordability, and consistency of drugs but can also pose major costs for companies. Supply chain processes can be automated with the help of machine learning algorithms. This will deliver a highly efficient logistics and manufacturing technology. So, the drugs can be supplied with more ease and efficiency. 3. Advanced analysis: Patient datasets can be analyzed with greater efficiency and anonymity with the help of machine learning and edge learning. Health care requires data of different kinds, such as health data, omics data, and sensor data. Medical records contain electronic health records that store medical records obtained during clinical care. Omics data are one of the high-dimensional data types of gene, transcriptome, and proteome data. Sensor data are obtained from a number of wearable and wireless sensor systems. Manually processing these raw data is highly complicated. Machine learning has emerged as a powerful method for data mining. It uses various mathematical methods and sophisticated algorithms to forecast health data outcomes more accurately [28].

Artificial intelligence in vaccine development

17.12 Advanced deep Q learning network with fragment-based drug design An advanced deep Q learning network with a fragment-based drug design (ADQN-FBDD) can be developed using deep reinforcement learning for finding compounds targeting viruses such as SARS-CoV-2. A fragment-based approach to drug discovery (FBDD) has been developed as an important method for creating new drugs. The principle of FBDD is that proper optimization of each particular interaction at the binding site and eventual integration into a single molecular object can yield a compound with a binding affinity, which is the sum of the individual interactions. Currently, some known and approved drugs like ritonavir and lopinavir have failed in the treatment of SARS-CoV-2. Companies like IBM and Insilico Medicine are currently working on this approach to drug design. IBM has generated as many as 3000 molecules using models against targets. Similarly, in silico medicine has identified 97 potential molecules [29].

17.13 Challenges of implementing an AI-based vaccine development model In the field of health care and vaccine development, AI serves as a powerful catalyst, speeding up vaccine research. It has the ability to draw different insights by combining machine learning technologies with laboratory technologies and different real-world sources. As the superiority of AI unfolds, scientists expect it to play a major role in the vaccine development of incurable diseases like HIV or cancer. The anticipation surrounding AI research also comes with a caveat. AI tools cannot speed up the trivial processes in vaccine development. Human effort is required for clinical trials, with scientists, health-care workers, and volunteers participating in real time. Computational processes can only optimize the workflow. AI may aid in the prediction of antigens, but understanding the biological working of the immune system and what it can do in a live human is beyond the capabilities of computers and algorithms. It requires human intelligence and reasoning [30].

17.13.1 Machine learning platforms for vaccine development 17.13.1.1 SIMON: Sequential iterative modeling OverNight SIMON is an open-source platform for integrating machine learning with bioinformatics. It has aided researchers in researching flu vaccine responses. The flu vaccine remains an important problem to solve. Every year, millions of people are infected with the influenza virus, commonly known as the flu. Although short-term, it can be fatal in some cases and in vulnerable people. These strains continue to mutate, posing a challenge to vaccine development. An effective vaccine needs to be developed in order to prevent the risk of a pandemic. SIMON is able to recognize immune cell subsets that are involved in those

483

484

A handbook of artificial intelligence in drug delivery

that will react well to the vaccine, as defined by the American Association of Immunologists in one of the most widely read publications of 2019 and 2020. These findings came from the FluPrint project, in partnership with Stanford University, which studied five influenza clinical trials from 2007 to 2015. The idea behind this development to enhance the flu vaccine is that the types of cells that respond well in certain people can be boosted in those that do not respond well. This brings about tremendous potential to increase the efficacy of the flu vaccine. SIMON can be used to predict whether a person can favorably react to a vaccine based on a series of immune system parameters. Using SIMON, researchers have identified immune cell subsets that have not been set earlier to provide protection against a virus. These studies are essential for the development of the next generation of vaccines and have the potential to drastically transform vaccines [31]. 17.13.1.2 MIT’s OptiVax Scientists at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a combined machine learning framework to develop, assess, and broaden vaccine designs with a strong focus on COVID-19. The design framework, called OptiVax, uses machine learning to pick short strings of amino acids called peptides, which are predicted to have a high population coverage in a proposed vaccine. It incorporates approaches to develop innovative peptide vaccines, test current vaccines, and improve existing vaccine designs. In this method, peptides are analyzed via machine learning by their ability to be shown to produce an immune response and are then chosen to increase the coverage of the population that could benefit from the vaccine. In designing their model, the team first tweaked their predictive models and used numerous models to prototype the vaccine. Taking into account the enormous variations in the human DNA, the researchers paid great attention to the genetic composition of different communities in order to increase the probability that individuals with unusual genes would still be protected by the vaccine [32].

References [1] A. Schuchat, Human vaccines and their importance to public health, Procedia Vaccinol. 5 (2011) 120–126, https://doi.org/10.1016/j.provac.2011.10.008. [2] F. Feijoo, M. Palopoli, J. Bernstein, S. Siddiqui, T.E. Albright, Key indicators of phase transition for clinical trials through machine learning, Drug Discov. Today 25 (2) (2020) 414–421, https://doi.org/ 10.1016/j.drudis.2019.12.014. [3] T.O. Ayodele, Types of machine learning algorithms, in: New Advances in Machine Learning, vol. 3, 2010, pp. 19–48. [4] M.J. Cunningham, Genomics and proteomics: the new millennium of drug discovery and development, J. Pharmacol. Toxicol. Methods 44 (1) (2000) 291–300, https://doi.org/10.1016/ S1056-8719(00)00111-8. [5] B.I. Baarda, F.G. Martinez, A.E. Sikora, Proteomics, bioinformatics and structure-function antigen mining for gonorrhea vaccines, Front. Immunol. 9 (2018) 2793, https://doi.org/10.3389/ fimmu.2018.02793.

Artificial intelligence in vaccine development

[6] C.C. Aggarwal, P.S. Yu, Data mining techniques for associations, clustering and classification, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 1574, Springer Verlag, 1999, pp. 13–23, https://doi.org/ 10.1007/3-540-48912-6_4. [7] T. Kanungo, D.M. Mount, N.S. Netanyahu, C. Piatko, R. Silverman, A.Y. Wu, Analysis of a simple k-means clustering algorithm, in: Proceedings of the Annual Symposium on Computational Geometry, ACM, 2000, pp. 100–109, https://doi.org/10.1145/336154.336189. [8] S. Srivastava, Weka: a tool for data preprocessing, classification, ensemble, clustering and association rule mining, Int. J. Comput. Appl. 10 (2014) 1–4. [9] L. Rokach, O. Maimon, Clustering Methods, Springer Science and Business Media LLC, 2005, pp. 321–352, https://doi.org/10.1007/0-387-25465-x_15. [10] H.S. Bond, S.G. Sullivan, B.J. Cowling, Regression approaches in the test-negative study design for assessment of influenza vaccine effectiveness, Epidemiol. Infect. 144 (8) (2016) 1601–1611, https://doi. org/10.1017/S095026881500309X. [11] L. Trtica-Majnaric, M. Zekic-Susac, N. Sarlija, B. Vitale, Prediction of influenza vaccination outcome by neural networks and logistic regression, J. Biomed. Inform. 43 (5) (2010) 774–781, https://doi.org/ 10.1016/j.jbi.2010.04.011. [12] L.Y. Bai, H. Dai, Q. Xu, M. Junaid, S.L. Peng, X. Zhu, Y. Xiong, D.Q. Wei, Prediction of effective drug combinations by an improved naı¨ve Bayesian algorithm, Int. J. Mol. Sci. 19 (2) (2018), https:// doi.org/10.3390/ijms19020467. [13] K. Srinivas, B.K. Rani, A. Govrdhan, Applications of data mining techniques in healthcare and prediction of heart attacks, Int. J. Comput. Sci. Eng. 2 (2) (2010) 250–255. [14] E.K. Lee, H.I. Nakaya, F. Yuan, T.D. Querec, G. Burel, F.H. Pietz, B.A. Benecke, B. Pulendran, Machine learning for predicting vaccine immunogenicity, Interfaces 46 (5) (2016) 368–390, https://doi.org/10.1287/inte.2016.0862. [15] M.H. Hassoun, N. Intrator, S. McKay, W. Christian, Fundamentals of artificial neural networks, Comput. Phys. 137 (1996), https://doi.org/10.1063/1.4822376. [16] S. Zhang, H. Tong, J. Xu, R. Maciejewski, Graph convolutional networks: algorithms, applications and open challenges, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11280, Springer Verlag, 2018, pp. 79–91, https://doi.org/10.1007/978-3-030-04648-4_7. [17] T. Derr, Y. Ma, W. Fan, X. Liu, C. Aggarwal, J. Tang, Epidemic graph convolutional network, in: WSDM 2020—Proceedings of the 13th International Conference on Web Search and Data Mining, Association for Computing Machinery, 2020, pp. 160–168, https://doi.org/ 10.1145/3336191.3371807. [18] L.R. Medsker, L.C. Jain, Recurrent Neural Networks. Design and Applications, vol. 5, CRC Press, 2001. [19] S.K. Sahu, A. Anand, Drug-drug interaction extraction from biomedical texts using long short-term memory network, J. Biomed. Inform. 86 (2018) 15–24, https://doi.org/10.1016/j.jbi.2018.08.005. [20] H.C. Shin, H.R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, R.M. Summers, Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning, IEEE Trans. Med. Imaging 35 (5) (2016) 1285–1298, https://doi.org/ 10.1109/TMI.2016.2528162. [21] A.G. Street, S.L. Mayo, Computational protein design, Structure 7 (5) (1999) R105–R109, https:// doi.org/10.1016/S0969-2126(99)80062-8. [22] S. Parvizpour, M.M. Pourseif, J. Razmara, M.A. Rafi, Y. Omidi, Epitope-based vaccine design: a comprehensive overview of bioinformatics approaches, Drug Discov. Today 25 (6) (2020) 1034–1042, https://doi.org/10.1016/j.drudis.2020.03.006. [23] R. Rappuoli, Reverse vaccinology, Curr. Opin. Microbiol. 3 (5) (2000) 445–450, https://doi.org/ 10.1016/S1369-5274(00)00119-3. [24] E. Ong, M.F. Cooke, A. Huffman, Z. Xiang, M.U. Wong, H. Wang, M. Seetharaman, N. Valdez, Y. He, Vaxign2: the second generation of the first web-based vaccine design program using reverse vaccinology and machine learning, Nucleic Acids Res. (2021), https://doi.org/10.1093/nar/gkab279.

485

486

A handbook of artificial intelligence in drug delivery

[25] P. Gonzalez-Dias, E.K. Lee, S. Sorgi, D.S. de Lima, A.H. Urbanski, E.L. Silveira, H.I. Nakaya, Methods for predicting vaccine immunogenicity and reactogenicity, Hum. Vaccin. Immunother. 16 (2) (2020) 269–276, https://doi.org/10.1080/21645515.2019.1697110. [26] R.T. Chen, S.C. Rastogi, J.R. Mullen, S.W. Hayes, S.L. Cochi, J.A. Donlon, S.G. Wassilak, The vaccine adverse event reporting system (VAERS), Vaccine 12 (6) (1994) 542–550, https://doi.org/ 10.1016/0264-410X(94)90315-8. [27] T.T. Shimabukuro, M. Nguyen, D. Martin, F. DeStefano, Safety monitoring in the vaccine adverse event reporting system (VAERS), Vaccine 33 (36) (2015) 4398–4405, https://doi.org/10.1016/j. vaccine.2015.07.035. [28] N. Pardi, M.J. Hogan, F.W. Porter, D. Weissman, mRNA vaccines—a new era in vaccinology, Nat. Rev. Drug Discov. 17 (4) (2018) 261–279, https://doi.org/10.1038/nrd.2017.243. [29] K. Raza, Artificial Intelligence Against COVID-19: A Meta-Analysis of Current Research, Springer Science and Business Media LLC, 2020, pp. 165–176, https://doi.org/10.1007/978-3-030-552589_10. [30] E. Racine, W. Boehlen, M. Sample, Healthcare uses of artificial intelligence: challenges and opportunities for growth, Healthc. Manage. Forum 32 (5) (2019) 272–275, https://doi.org/ 10.1177/0840470419843831. [31] A. Tomic, I. Tomic, Y. Rosenberg-Hasson, C.L. Dekker, H.T. Maecker, M.M. Davis, SImon, an automated machine learning system, reveals immune signatures of influenza vaccine responses, J. Immunol. 203 (3) (2019) 749–759, https://doi.org/10.4049/jimmunol.1900033. [32] A. Rojas, Artificial intelligence in the COVID-19 era, Artif. Intell. 27 (2020) 8.

CHAPTER 18

AI-enabled quadrupole stimuliresponsive targeted polymeric nanodrug delivery for cancer therapy George Kordas Sol-Gel Laboratory, INN, NCSR Demokritos, A. Paraskevi Attikis, Greece

18.1 Introduction In chemotherapy, most drugs fail due to their undesirable side effects and limited effectiveness. The latter is due to the limited proportion of drugs that reaches tumors, and the rest goes to healthy tissues. Due to these side effects, many oncological treatments available today that are administered to subpopulations do not exert their full effect. The effectiveness of existing and experimental drugs requires the creation of “artificially intelligent” drug delivery systems (DDSs) to transport medicines in appropriate volume for their safe targeting of a tumor. The entrapment of drugs in DDSs has emerged as a promising approach to significantly improving local bioavailability. These DDSs can protect oncology drugs from degradation and healthy tissues from toxic effects. Despite this promise, most treatments that rely on nanocarriers and have reached the market are still disappointing. In addition, there are still unresolved problems, such as their stability in the blood circulation and the failure to totally eliminate the drug from the tumor because these carriers cannot recognize it. Despite the promising improvement in the safety and efficacy of nanomedicines, the US FDA has approved only six of them [1]. We note that two are antibody-drug conjugates (ADCs), conceptually one of the simplest nanomedicines with an anticancer drug bonded via a binding molecule to a targeting molecule. Except for ADCs, all currently marketed nanomedicines lack “active targeting” ligands and instead rely on “passive targeting” through the leaky vasculature surrounding the tumors, described as the enhanced permeability and retention (EPR) effect [2]. Furthermore, large-scale production of nanomedicines is still severely hampered by the poor reproducibility and limited shelf stability of the final product. Doxil is a notable example with manufacturing problems [3], threatening its manufacturing continuation [4].

A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00018-6

Copyright © 2023 Elsevier Inc. All rights reserved.

487

488

A handbook of artificial intelligence in drug delivery

18.2 Existing pharmaceuticals Currently, compounds such as Taxol are highly effective in treating cancer. However, these are delivered not only to cancer cells and tissues but also to healthy cells, thus leading to side effects. Numerous companies are trying to find ways to deliver drugs only to cancer cells. Moreover, although some drugs are much more effective than the known ones, they cannot be delivered to their targets due to toxicity. Other formulations worth several hundred billion dollars, which cannot be adequately delivered to the target cells/tissues, remain unused, and the industry wants to unlock and explore this valuable intellectual property. New drug delivery compounds extend the life cycle of drugs and patents, creating new chemical entities (NCEs) through reconfiguring existing and orphan compounds and, subsequently, creating value for pharmaceutical companies and their shareholders [5]. There are plenty of opportunities for better administration of anticancer drugs, as the market for oncology drugs is enormous and highly lucrative. Many existing chemotherapeutic drugs and newly developed anticancer compounds of small molecules have high lipophilic and low water solubility [5,6]. These drugs are generally dissolved using high concentrations of surface-active substances and cosolvents, which often lead to adverse side effects. Therefore, nanopharmaceuticals that can deal with these intractable compounds (thus eliminating the need for surfactants and cosolvents) and reduce their toxicity are highly sought after. A proven example of this is the success of Abraxane (paclitaxel bound to albumin nanoparticles), which was designed to overcome the extremely low solubility of paclitaxel. To date, paclitaxel (known as Taxol) is the best-selling cancer drug ever made, but the main advantage of Abraxane over paclitaxel is that it avoids the hypersensitivity reaction associated with Cremophor EL, a solvent used in conventional paclitaxel therapy [7,8].

18.3 Nanomedicines Today, we have six FDA-approved nanopharmaceuticals for cancer treatment: brentuximab vedotin, trastuzumab emtansine, Doxil, DaunoXome, Marqibo, and Abraxane [1]. Brentuximab vedotin and trastuzumab emtansine are antibody-drug conjugates (ADCs). These are anticancer drugs conjugated to a targeting molecule. Brentuximab targets the protein CD30. Trastuzumab targets human epidermal growth factor receptor 2 (HER2) overexpressed in HER2-positive breast cancer cells. Doxil, DaunoXome, and Marqibo are liposomal formulations of doxorubicin, daunorubicin, and vincristine, respectively. The FDA approves Doxil for treating ovarian cancer, Kaposi’s sarcoma associated with AIDS, and multiple myeloma. In addition, DaunoXome is FDA-approved for HIV-related Kaposi’s sarcoma and Marqibo for rare leukemia (Philadelphia chromosome-positive acute lymphoblastic leukemia) (Table 18.1).

AI-enabled quadrupole stimuli

Table 18.1 Available platforms. Platform

Class

Drug

Problem addressed

d (nm)

Brentuximab vedotin Trastuzumab emtansine Doxil

ADC

Liposome

Doxorubicin

DaunoXome

Liposome

Daunorubicin

Marqibo

Liposome

Vincristine

Monomethyl auristatin E (MMAE) is too toxic to be used alone Mertansine is too toxic to be used alone Drug toxicity and adverse cardiac side effects Drug toxicity and adverse cardiac side effects Drug toxicity and adverse side effects

10

ADC

Monomethyl auristatin E Mertansine

10 100 50 100

Abraxane, or nab-paclitaxel (nanoparticle albumin bound), is lyophilized human serum albumin nonspecifically bound to paclitaxel. It is approved for breast cancer, nonsmall cell lung cancer, and pancreatic cancer. Although no active targeted anticancer nanopharmaceuticals are currently commercially available, at least five targeted liposomes, one retroviral vector, and two targeted therapies of polymer nanocarriers have reached clinical development stages [9]. One of these two targeted polymer nanocontainers (CALAA-01) entered phase I clinical trials for the treatment of melanoma, but the follow-up trial was discontinued for reasons that were not made fully clear [10]. Consequently, there is currently only one targeted polymeric anticancer nanomedicine progressing through clinical trials at this moment: BIND-014 [10].

18.4 Properties of cancer cells Cancer cells have properties that differ from those of the remaining healthy cells/tissues, such as a lower pH (
7.0), a higher temperature (37–41.5°C), and a different redox (GSH) environment [11–14]. Cancer cells are overexpressed by a receptor different from a healthy cell [15]. Once the targeted quadrupole stimuli-responsive nanocontainers reach this environment and are attached to cancer cells via the receptors, one can expect that this DDS will expand and locally release the anticancer drug to achieve efficient chemotherapy. Drug delivery will not occur in places like sypsemia where one parameter stimulates the DDS. This device has the analogue of a “logical circuit” [4]. In such a logical circuit, the output “opens” when all four input commands are in the “on” state; only then is the drug released. When the “logical circuit” attaches to cancer cells through the receptor, it behaves as if it is in a cancer environment and releases the drug (all four input states are in the “on” condition). If the “logical circuit” is located in an area of sypsemia that has an elevated temperature environment, then we have a command in the “on” state

489

490

A handbook of artificial intelligence in drug delivery

with the rest in the “off” state, and, as such, the drug stays in the DDS. The author named this DDS as a chemical equivalent of a Nano4XX platform. When the platform is found in a sypsemia environment, the T-sensitive polymer expands, but the rest are in the same state and block the release of anticancer drugs. On the other hand, if the platform can access the cancer cells through the receptor and is encapsulated in them, then all four conditions are stimulated (in the “on” state), resulting in the platform expanding and releasing the drug. Such a device should be between 100 and 200 nm to use the enhanced permeability and retention (EPR) effect [16]. This Nano4XX platform [17] can be charged with anticancer drugs such as doxorubicin Nano4Dox and can be attached to all known targeting groups. Fig. 18.1 presents the principle of functioning of the Nano4XX platform. The production procedure of this platform has been recently discussed many times [4].

18.5 The Nano4XX (XX 5 Dox, Cis, etc.) platform Recently, a Nano4XX (XX ¼ Dox, Cis, etc.) platform has been developed, exhibiting four stimuli (pH, temperature, reducing environments, and alternating magnetic fields) grafted with a targeting group to attack cancer cells [4,18–22]. This platform was loaded with doxorubicin with a loading capacity (LC%) and an encapsulation efficiency (EE%) of 94.7% and 94.7%, respectively [4]. The hydrodynamic diameter of this system depends on the pH value, temperature (T), redox environment, etc. (Fig. 18.2A). Fig. 18.2B and C presents some of the release experiments that were conducted in this sample. A maximum release of 75% is observed at T ¼ 42°C and pH ¼ 4.5 after 38 h. However, at pH ¼ 7.4 and T ¼ 25°C, the release is less than 12%. The use of polymers Ν realized the targeted Nano4XX platform, Νdiethylaminomethyl methacrylate for temperature sensor, methyl methacrylate, acrylic acid, or N-(2-hydroxypropyl) methacrylamide for pH sensor and N, N0 (2,20 -disulfanediylbis (ethane-2,1-diyl)) bis (2-methyl acrylamide) for redox sensor. On the surface of the Nano4XX platform, magnetic, gold, gadolinium, and radionuclide nanoparticles are grafted to monitor their effects on living organisms or cells with various spectroscopy techniques, such as NMR, PET, etc. In addition, we incorporate one or two anticancer drugs into the targeted quadrupole stimuli-responsive nanocontainers to fight cancer cells and face drug resistance effectively. The cancer-targeting molecule can be folic acid for breast cancer, or leuprolide for prostate cancer [4,20]. Fig. 18.3 shows the TEM micrographs of two nanocontainers, one without iron (A) and the other with iron (B) nanoparticles. The nanocontainers with iron nanoparticles exhibit dark spots (Fig. 18.3B) on their surface (Fig. 18.3A) due to the presence of the iron nanoparticles. One can observe from Fig. 18.3A and B that the diameter of nanoparticles is
310 nm, whereas the iron oxide particles have a diameter of
20 nm [18].

Fig. 18.1 Principle of the Nano4XX platform.

A handbook of artificial intelligence in drug delivery

A

Release [%]

T = 25°C

1300

pH = 7.4 T = 25°C

pH = 7.4 T = 42°C

pH = 4.5 T = 25°C

pH = 4.5 T = 42°C

60

75

T = 42°C

1040

C

70

50 % Release

1560

B

100

1820

Size [nm]

492

50

25 780

40 30 20

pH = 7.4, RT pH = 7.4, Hyperthermia, GSA

10 2

3

4

5

6

7

8

pH

0

0

10

20

30

40

50

0 10

20

Time [h]

30

40

50

60

Time (minutes)

Fig. 18.2 (A) The hydrodynamic diameter of quadrupole stimuli-responsive nanocontainers as a function of pH and temperature (T). (B) Release of Dox from nanocontainers for different pH levels and temperatures in a time-related function. (C) Dox release from nanocontainers with GSH and hyperthermia at pH ¼ 7.4. In all, 70% doxorubicin release was achieved after 60 min at pH ¼ 7.4, hyperthermia, and redox.

Fig. 18.3 TEM images of nanocontainers (A) grafted with iron nanoparticles (B).

18.6 Cell intercalation Folic acid (FA) can functionalize nanocontainers to target cancer cells overexpressed on the surface of an explicit hormone. Fig. 18.4 shows the attachment of folic acid-grafted nanocontainers to cervical cancer cells (HeLa). Later, internalization of the nanocontainers into cancer cells occurs, illuminating the cells red. On the contrary, the nanocontainers agglomerate (Fig. 18.4A) without (FA) targeting. The drug is released inside the cancer cell when the nanocontainers are entrapped. In this manner, DNA replication is

AI-enabled quadrupole stimuli

Cell studies for targeting to HeLa cells by confocal Without Folic Acid

With Folic Acid

Folic Acid Folic Acid Receptor

Encapsulation

Drug release Nucleus NC degradation

A

B

C

Red: Lyso-tracker Green: Fitc-targeted nanocontainers Fig. 18.4 NCs are agglomerated outside HeLa cells (A). FA-targeted NCs are intercalated in the cells (B). Incorporation of the nanocontainers into cancer cells (C).

inhibited through the intercalation mode. Fig. 18.4A and B show the FA-targeted nanocontainers functionalized with Fitc, efficient target after 15 min of treatment the cancer cells in contrast to the nonfunctionalized NCs.

18.7 Cytotoxicity An MTT test was conducted to study the cytotoxicity of empty Nano4XX, Nano4Dox, and doxorubicin concentrations for free Dox (0,01, 0,1, 1, 5, 10, and 30 μM) in MCF-7 cell lines (breast carcinoma) and HeLa cells (cervical carcinoma) [23]. The FA receptor recognizes HeLa cells that bind to their surface [24–28]. The incubation time of cells in the presence of NCs with or without FA was 72 h. Fig. 18.5 shows the toxicity of MCF-7 cells even at high concentrations. On the other hand, cell viability in Nano4Dox, once encapsulated in cells, is exercised out of cytotoxicity almost equal to that of the free drug. The same results were obtained in HeLa cells.

18.8 Biodistribution of FA-targeted nanocontainers in HeLa tumor-bearing mice Fig. 18.6 presents the distribution of nanocontainers with or without FA-targeting groups via PET in various organs and tumors after 24-h accumulation. One can observe a negligent concentration of the nanocontainers on the tumor. On the contrary, the concentration of the grafted nanocontainers on the side of cancer is 3.5% after just 1 h of accumulation [5,18].

493

494

A handbook of artificial intelligence in drug delivery

Fig. 18.5 Cytotoxicity of FA-Nano4XX, FA-Nano4Dox, and free Dox on MCF-7 cells repeated three times.

Fig. 18.6 In vivo uptake at 1-h postinjection (PI) for the radiolabeled nanocontainers with or without grafting of FA.

18.9 Switching effect The efficacy of FA-targeted nanocontainers loaded with Dox was compared to that of FA without grafting nanocontainers loaded with Dox. For this study, HeLa cervical tumor-bearing SCID mice were used to monitor cancer volume as a function of time in different groups. Fig. 18.7A shows the findings of these experiments. The points were fitted with simple lines to simplify the discussion. One can observe a growth of cancer

AI-enabled quadrupole stimuli

Fig. 18.7 Performance of the Nano4Dox (A) and Nano4Cis (B) platforms.

volume with time in SCID mice treated with nanocontainers loaded with Dox. Contrary to this observation, cancer volume is reduced to 20% when the nanocontainers are loaded with Dox and grafted with FA. The therapy outcome is further improved when hyperthermia is applied to the nanocontainers during treatment. Quadrupole stimuli-targeted nanocontainer technology has seen substantial development, and it is the first to integrate four stimuli (pH, temperature, reducing environments, and alternating magnetic fields) with proprietary targeting capabilities [22]. The “active targeting” feature of our nanocontainers results from the surface attachment of specific ligands that bind to proteins overexpressed on tumor cells. It has been demonstrated to improve the target specificity and advance therapeutic activity. Due to this targeting, 3.5% of the compound reaches the tumor; this is the absolute best in class. The earlier finding supports that the developed platform, which we refer to from now on as Nano4DOX, exhibits better tumor efficiency than do commercial drugs, e.g., Doxil©. Experiments were also carried out on the Nano4Cis platform, compared with cisplatin and lipoplatin (Fig. 18.7B). The drug was loaded onto the platform using PBS at pH ¼ 7.4 for 24-h treatment. We obtained a loading capacity of 82% and EE% of 92%. Animals carrying a tumor from HeLa cells were evaluated with a volume efficacy test. Three groups of animals were used here. Fig. 18.7B shows the control group, lipoplatin, and the Nano4Cis platform. The results showed that Nano4Cis behaves better than do lipoplatin and cisplatin. In conclusion, in mouse experiments, we observed that the drug candidate Nano4Dox (doxorubicin loaded onto our Nano4Dox platform) has proven to be significantly more safe and effective in vivo than the current gold standard Doxil© (liposomal doxorubicin), an absolute blockbuster nanomedicine in oncology [20,21,29].

495

496

A handbook of artificial intelligence in drug delivery

18.10 Conclusions The Nano4XX platform is a tunable product designed to provide optimal control over therapeutic agents’ release kinetics and bioactivity, thereby improving pharmacotherapeutic efficacy and reducing side effects. The platform can accommodate therapeutic agents, including small molecules, peptides, and proteins. The active targeting Nano4Dox platform reaches about 3.5% of the tumor. It improves the therapeutic window and increases drug efficacy, as proven in cancer models, compared to Doxil. The Nano4Dox platform improves the in vivo safety profile compared to currently marketed products, allowing dosing at higher levels. Significantly lower levels of doxorubicin in the kidneys were measured after administration of our doxorubicin-loaded NCs compared to Doxil, which could result in reduced doxorubicin nephrotoxicity—a known side effect of doxorubicin. Mice treated with Nano4Dox lived 10 times longer than those treated with Doxil (both sets of mice were treated with an equal concentration of doxorubicin). The Nano4DOX platform accommodates a broad diversity of drug molecules with a loading capacity of up to 98%. In addition, the manufacturing process is generic and independent of the type of drug molecule. The technology is patented and is available for commercial evaluation [22].

Acknowledgments This work was conducted with the support of the European Research Council (ERC) under the IDEAS program under contracts: 1. Advanced Grant: “A Novel Nanocontainer Drug Carrier for Targeted Treatment of Prostate Cancer Nanotherapy” No. 232959 and 2. Proof-of-concept Grant No. 620238. The financial support is appreciated.

References [1] C.M. Dawidczyk, L.M. Russell, P.C. Searson, Nanomedicines for cancer therapy: state-of-the-art and limitations to pre-clinical studies that hinder future developments, Front. Chem. 2 (August) (2014) 1–13. [2] R. van der Meel, L.J.C.C. Vehmeijer, R.J. Kok, et al., Ligand-targeted particulate nanomedicines undergoing clinical evaluation: current status, Adv. Drug Deliv. Rev. 65 (10) (2013) 1284–1298. [3] Www.ashpfoundation.org, ASHP Foundation report, Am. J. Health Syst. Pharm. 52 (22) (1995) 2611. [4] G. Kordas, Quadrupole stimuli-responsive targeted polymeric nanocontainers for cancer therapy: artificial intelligence in drug delivery systems, in: S. Jana, S. Jana (Eds.), Nanoengineering of Biomaterials, vol. 1, Wiley, London, 2022, pp. 505–522. https://doi.org/10.1002/9783527832095.ch16. [5] M. Caraglia, G. De Rosa, G. Salzano, D. Santini, M. Lamberti, P. Sperlongano, A. Lombardi, A. Abbruzzese, R. Addeo, Nanotech revolution for the anti-cancer drug delivery through blood-brain barrier, Curr. Cancer Drug Targets 12 (3) (2012) 186–196, https://doi.org/ 10.2174/156800912799277421. [6] M. Narvekar, H.Y. Xue, J.Y. Eoh, et al., Nanocarrier for poorly water-soluble anticancer drugs – barriers of translation and solutions, AAPS PharmSciTech 15 (4) (2014) 822–833. [7] National Cancer Institute, Taxol® US National Institute of Health. Success Story, 2022, Available from: https://dtp.cancer.gov/timeline/flash/success_stories/s2_taxol.htm.

AI-enabled quadrupole stimuli

[8] W.E. Bawarski, E. Chidlowsky, D.J. Bharali, et al., Emerging nanopharmaceuticals, Nanomedicine 4 (4) (2008) 273–282. [9] N. Bertrand, J. Wu, X. Xu, et al., The impact of passive and active targeting in the era of modern cancer biology, Cancer Nanotechnol. 66 (2) (2014) 2–25. [10] J. Wright, Deliver on a promise, Nature 509 (2014) 58–59. [11] X. Zhang, Tumor pH and its measurement Xiaomeng, J. Nucl. Med. 176 (3) (2010) 139–148. [12] J. Wang, X. Sun, W. Mao, et al., Tumor redox heterogeneity-responsive prodrug nanocapsules for cancer chemotherapy, Adv. Mater. 25 (27) (2013) 3670–3676. [13] X. Wu, X. Sun, Z. Guo, et al., In vivo and in situ tracking cancer chemotherapy by highly photostable NIR fluorescent theranostic prodrug, J. Am. Chem. Soc. 136 (9) (2014) 3579–3588. [14] B. Yu, N. Song, H. Hu, et al., A degradable triple temperature-, pH-, and redox-responsive drug system for cancer chemotherapy, J. Biomed. Mater. Res. A 106 (12) (2018) 3203–3210. [15] G.L. Zwicke, G. Ali Mansoori, C.J. Jeffery, Utilizing the folate receptor for active targeting of cancer nanotherapeutics, Nano Rev. 3 (1) (2012) 18496. [16] E.K. Efthimiadou, C. Tapeinos, P. Bilalis, et al., New approach in synthesis, characterization and release study of pH-sensitive polymeric micelles, based on PLA-Lys-b-PEGm, conjugated with doxorubicin, J. Nanopart. Res. 13 (12) (2011) 6725–6736. [17] G. Kordas, Nanotechnology in cancer treatment as a Trojan horse: from the workbench in pharmaceutical industry, United J. Pharmacol. Ther. 1 (1) (2018) 1–9. [18] C. Tapeinos, E.K. Efthimiadou, N. Boukos, et al., Sustained release profile of quatro stimuli nanocontainers as a multi sensitive vehicle exploiting cancer characteristics, Colloids Surf. B: Biointerfaces 148 (2016) 95–103. [19] A. Chatzipavlidis, P. Bilalis, E.K. Efthimiadou, et al., Sacrificial template-directed fabrication of superparamagnetic polymer microcontainers for pH-activated controlled release of Daunorubicin, Langmuir 27 (13) (2011) 8478–8485. [20] G. Kordas, Adjustable quarto stimuli (T, pH, redox, hyperthermia) targeted nanocontainers (Nano4Dox and Nano4Cis) for cancer therapy based on Trojan horse approach, Arch. Pharm. Pharmacol. Res. 1 (1) (2018) 1–7. [21] G. Kordas, Nanotechnology in cancer treatment as a Trojan horse: from the bench to preclinical studies, in: M.J. Sarat Kumar Swain (Ed.), Nanostructured Polymer Composites for Biomedical Applications, Elsevier Inc., London, 2019, pp. 323–365. [22] G. Kordas, WO2015074762A1.pdf. WO 2015/074762 A1, 2015. [23] T. Mosmann, Rapid colorimetric assay for cellular growth and survival: application to proliferation and cytotoxicity assays, J. lmmunol. Methods 65 (80) (1983) 55–63. [24] E.K. Efthimiadou, P. Lelovas, E. Fragogeorgi, et al., Folic acid mediated endocytosis enhanced by modified multi stimuli nanocontainers for cancer targeting and treatment: synthesis, characterization, in-vitro and in-vivo evaluation of therapeutic efficacy, J. Drug Delivery Sci. Technol. 55 (January) (2020), 101481. [25] B. Sahoo, K.S.P. Devi, R. Banerjee, et al., Thermal and pH responsive polymer-tethered multifunctional magnetic nanoparticles for targeted delivery of anticancer drug, ACS Appl. Mater. Interfaces 5 (9) (2013) 3884–3893. [26] H. Yang, C. Lou, M. Xu, et al., Investigation of folate-conjugated fluorescent silica nanoparticles for targeting delivery to folate receptor-positive tumors and their internalization mechanism, Int. J. Nanomedicine 6 (2011) 2023–2032. [27] E. Roger, S. Kalscheuer, A. Kirtane, et al., Folic acid functionalized nanoparticles for enhanced oral drug delivery, Mol. Pharm. 9 (7) (2012) 2103–2110. [28] A. Rollett, T. Reiter, P. Nogueira, et al., Folic acid-functionalized human serum albumin nanocapsules for targeted drug delivery to chronically activated macrophages, Int. J. Pharm. 427 (2) (2012) 460–466. [29] G. Kordas, E. Efthimiadou, Comparison of therapeutic efficacy of quadrupole stimuli-targeted nanocontainers loaded with doxorubicin (Nano4Dox platform ) and cisplatin (Nano4Cis platform) to Doxil and Lipoplatin, respectively, Ann. Clin. Pharmacol. Toxicol. 1 (March) (2018) 1–5.

497

CHAPTER 19

Convergence of artificial intelligence and nanotechnology in the development of novel formulations for cancer treatment Abid Naeema,b, Muhammad Suhailc, Abdul Basite, Liu Yalia,d, Zhang Ming Xiaa,b, Zheng Qina,b, and Yang Minga,b a

Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China Key Laboratory of Modern Preparation of Traditional Chinese Medicine, Ministry of Education, State Key Lab of Innovation Drug and Efficient Energy-Saving Pharmaceutical Equipment, Jiangxi University of Traditional Chinese Medicine, Nanchang, Jiangxi, People’s Republic of China c School of Pharmacy, Kaohsiung Medical University, Kaohsiung City, Taiwan, ROC d Nanchang Medical College, Nanchang University, Nanchang, Jiangxi, People’s Republic of China e Department of Pharmaceutical Chemistry, Faculty of Pharmacy, The Islamia University of Bahawalpur, Bahawalpur, Punjab, Pakistan b

19.1 Introduction Drug discovery and drug development are among the most significant translational activities of science, which contribute the most to human survival and quality of life by providing novel drug candidates. However, the process of development of such new entities is highly complex, time-consuming, and expensive. Despite recent advances in disease biology and dramatic technological innovations, the process of bringing new medicines to the market has remained difficult and expensive, mainly because of the substantial costs and higher failure rates associated with clinical trials [1,2]. It is estimated that the process from discovery to development of a new drug takes 12 years and 2.6 billion dollars on average [3]. This problem is more likely to occur during the development and design of anticancer drugs. It is due to the drug’s nature, complexity, and high failure rate in the clinical trial phase, and the urgent need for the patients. The United States is expected to see a significant increase in the cost of cancer treatment in the next decade, when conventional research methods are used to discover new drugs. The cost of cancer treatment increased from $124,57 billion in 2010 to $157,77 billion in 2020 [4]. Therefore, the development of new and innovative methods of understanding drug discovery and methods of delivering these drugs to the body is crucial, as is improving access to medicines at lower prices for a wider population. The pharmaceutical industry faces challenging issues like how to decrease cost and gear up the discovery of new drugs to control cancer. Researchers must extend their thanks to the 1956 Dartmouth Conference, where A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00019-8

Copyright © 2023 Elsevier Inc. All rights reserved.

499

500

A handbook of artificial intelligence in drug delivery

the idea of human intelligence-simulating technology, i.e., “artificial intelligence,” was born. The term “artificial intelligence” has evolved over the past several decades and is now used to describe a variety of technologies, including artificial neural networks, deep learning, machine learning (ML), and others [5–7]. AI, or artificial intelligence (AI), refers to a computer system’s simulation of human intelligence. The process comprises collecting information, establishing rules for the use of that information, forming estimates or conclusions, and self-correction. Each new development in the field of AI is highly valued due to the expectation that it will greatly contribute to the improvement of society. AI is used in many different fields, from the development of educational methods to the automated management of business processes. Recently, the idea of incorporating AI into the drug development process has evolved into a real possibility. The potential benefits of AI in the pharmaceutical industry include its effect on drug development strategies, attrition, and efficiency and can be explored more through the formation of partnerships using AI to supplement pharmaceutical research. Recently, AI has been expanding its application to various areas of society, with the pharmaceutical industry being one of the leading beneficiaries. This chapter highlights the impactful use of AI in the design and development of cancer drugs, ML methods for modeling cancer risk, AI in the preparation and optimization of nanotechnology-based formulations for cancer, and cancer nanomedicines.

19.2 Areas where AI is potentially implied in drug discovery and development Prior to the widely accepted application of AI and MLT, most drug design and discovery processes were based on trial and error or hand fitting through the modification of a known compound and a subsequent experiment with rodents to evaluate its toxicity and effectiveness. Nowadays, the drug industry makes use of AI and computational methods in order to simulate the drug process and the manner in which a drug is formulated using software packages [8]. The process of drug discovery and development depicted in Fig. 19.1 shows the potential areas where AI is significantly used.

19.2.1 Target identification and validation Biological targets can be identified in several ways, including gene expression, genomics, proteomics, and phenotypic screening. The conventional approaches used for target identification and validation are expensive in terms of time, labor, and cost. AI has enhanced target identification and validation. Genomics together with biochemical and histopathological data make it possible. Five novel RNA-binding proteins discovered by IBM Watson have potential roles in the pathogenesis of amyotrophic lateral sclerosis, which is still incurable. Moreover, next-generation sequencing (NGS) is a platform

Convergence of artificial intelligence and nanotechnology in cancer

-II

se

a Ph e-I

as

Ph

V

e-I

as

Ph

Target identification

II

e-I

s ha

Pr ed Fu ictio nct n o ion f p an rote di nte in fol rac din tio g ns

P

Target validation

Clinical

AI in Drug Discovery and Development Hit discovery

Pre-clinical

Hit to lead

ET

M AD of s on tie ati per alu pro Ev

Lead optimization

g sin po ning r g pu ee in re scr cor g l s u a Dr irtu vitty V cti A

QSAR De novo drug design

Fig. 19.1 AI in drug discovery and development.

used by scientists to decode the genetic patterns of cancer patients. The precise and accurate data obtained from NGS lead to the identification of a large set of genetic variations that are considered an important cause of cancer. Genetic variation is looked at as a potential area for target identification in drug discovery and development. Advancements in sequencing technology have made it quite easy to generate a large set of data explored using computational techniques to identify de novo mutations. The various computational methods used to identify mutations or genetic variations from complicated DNA sequences include NovaSeq 5000/6000, HiSEq. (2500), HiSeq 3000/HiSeq 4000, MiSeq, NextSeq, and Illumina [9,10].

501

502

A handbook of artificial intelligence in drug delivery

19.2.1.1 Prediction of the target protein structure Most diseases are caused by malfunctioning proteins. Studying their structures is a structure-based approach, which can be used to identify small molecules capable of binding to protein targets in a highly effective manner. However, measuring the threedimensional structures of proteins would require significant resources in terms of time and money, and it would be more beneficial to develop algorithms that can predict the 3D structure of proteins. Despite the fact that protein sequences are available for most proteins, accurate predictions of their 3D structures remain a challenge. Recent advances in deep learning have been made possible by the ability to extract features from a protein’s secondary structure [11], backbone torsion angle [12], and residue contacts [13]. CASP12 (12th round of Critical Assessment of Structure Prediction), a deep learning method combining one-dimensional (1D) and two-dimensional (2D) CNNs, was found to be superior in predicting residue contacts compared to others. A deep learning architecture enables the accurate determination of the relationship between sequences and structures through the extraction of features. The prediction of the 3D structure of proteins is still a distant objective, but deep learning techniques have shown great potential in this area [14]. 19.2.1.2 Predicting drug-protein interactions Drugs must interact with the receptor protein in order to exert their therapeutic effects. For example, computational approaches based on AI are useful in the prediction of target protein structures, allowing the assessment of drug-target protein interactions. For obtaining the most accurate outputs, a combination of molecular mechanics, quantum mechanics, and deep ML is needed. In addition to the analysis of atomic simulations and prediction of electrical properties, ML can be used to quantify the energetic potential of small molecules through deep learning [14].

19.2.2 Hit discovery Following the identification and validation of a target, the next step is to identify a hit [3]. There are various meanings for the term “hit” compound; within the scope of this chapter, we refer to it as a compound that exhibits the desired screening effect, which has been substantiated through further testing. The identification of a hit from various molecular libraries is accomplished through high-throughput screening, combinatorial chemistry, virtual screening, and drug repurposing, which can be referred to as drug repositioning. There are several physico-chemical properties of drugs that need to be taken into consideration while screening drugs, such as aqueous solubility, lipophilicity, and intrinsic permeability. These properties have a significant impact on properties such as selectivity, potency, and ADME [14]. Recently, an AI-based approach has been used to determine the relationship between physico-chemical parameters and molecular attributes, such as surface areas, molecular volumes, and hydrogen bonds. The prediction of lipophilicity

Convergence of artificial intelligence and nanotechnology in cancer

involves computational methods such as group-contribution (GC) models, equations of state, and conductor-like screening models (COSMOs)/conductor-like screening models for real solvents (COSMO-RSs), molecular simulation, and linear/nonlinear QSPR based on quantum chemistry [15]. The in silico methods used for predicting the solubility of aqueous solutions are divided into three main categories based on their principles: (1) molecular dynamics simulations, (2) quantum chemical techniques, and (3) descriptor-based methods. Structure-based methods are capable of predicting solubility by utilizing molecular topology and fragment information [16]. There are several names for drug repurposing, such as drug repositioning, drug rescuing, drug retasking, drug redirection, drug recycling, and drug switching. These terms are often used to describe a process that defines the process of discovering new, pharmacologically meaningful applications for already available/existing/failed/experimental/ already commercialized/FDA-approved drugs/prodrugs and utilizes the newly developed drugs to treat conditions other than their original or intended therapeutic use. The purpose of this effort is to find new therapeutic applications for existing drugs, which may include those that have been discontinued, approved, abandoned, and experimentally developed [17,18]. This novel approach to drug repositioning has the potential to replace conventional drug discovery processes by reducing the high cost, longer duration, and increased risk of failure associated with traditional drug discovery programs [19]. Repositioning of drugs involves two complementary methodologies: one is based on experimentation and the other is based on in silico analysis. Experimental repositioning is an approach that is focused on testing original drugs within new therapeutic areas based on scientific evidence. This is also known as activity-based repositioning. This technique consists of a series of protein-targeted (cell-based) and organism-based (organism-based) screenings in in vitro and/or in vivo disease models without considering the structure of the proteins involved. Experimental repositioning can be accomplished in several ways, including target screening, animal models, cell assays, and clinical trials [20,21]. Comparatively, in silico repositioning relies on the virtual screening of large drug or chemical libraries compiled in public databases through the use of computational biology and bioinformatics or cheminformatic tools. This approach identifies potentially bioactive molecules based on the molecular interactions between drug molecules and their targets [22]. The methodologies used in drug research can be divided into three broad categories based on the quality and quantity of information available on pharmacology, toxicology, and bioactivity. Generally, these studies are (i) drug-oriented, (ii) target-oriented, or (iii) disease- or therapy-oriented. Drug-oriented methods focus on the structure of drug molecules, their biological activities, adverse effects, and their toxicities. This strategy is designed to identify molecules that have biological effects by employing cell/animal-based assays. Typically, repositioning methods are based on traditional principles of pharmacology and drug discovery, in which studies are conducted to investigate the biological action of molecules without taking into account their biological targets.

503

504

A handbook of artificial intelligence in drug delivery

DR success has been largely achieved using this orientation profile by accident or by observation of phenomena within a clinical setting, like the discovery of sildenafil [23]. A target-based methodology is a biochemical approach that involves the in silico or virtual screening of drugs or compounds from drug/compound library databases. Drug candidates are tested against a selected protein molecule and/or biomarker of interest through database-based screening (ligand-based screening or molecular docking), and, then, in vitro and in vivo high-throughput and/or high-content screenings (HTS/ HCS) are performed. Drug discovery strategies based upon this method have a high rate of success in comparison to those relying upon drug-oriented strategies, since most biological targets have a direct relationship with disease mechanisms [24]. In the past, drug repositioning tremendously contributed in terms of drugs with new indications (Fig. 19.2). For example, aspirin (1), a nonsteroidal antiinflammatory drug, is currently under trial for use in prostate cancer. Another drug of the same class, celecoxib (2), is under trial for breast and colon cancers. Cimetidine (3), indicated for gastric ulcer, is under trial for breast, lung, and prostate cancers. Daunorubicin (4), an antibiotic, has already been developed by drug repositioning for its new indication in breast cancer. Similarly, digoxin (5), a cardiotonic drug, is under development for prostate cancer, and disulfiram (6), which was originally used in chronic alcoholism, is now in the trial phase for use in cancer. The immunosuppressant drug everolimus (7) is already developed for its new indication in pancreatic neuroendocrine tumors. The commonly used antidiabetic metformin (8) is in the trial phase for use in breast and colon cancers, simvastatin (9) has already successfully passed clinical trials for use in lung cancer, retinoic acid (10) can be now used for acute leukemia besides its basic use for treating acne, and the antipsychotic drugs penfluridol (11) and pimozide (12) are in the trial phase for use in breast cancer. Similarly, nitroxoline (13), an antibacterial drug, is under development for use in breast, bladder, and pancreatic cancers, and nelfinavir (14), an antiviral, is in the trial stage for use in breast cancer [25].

19.2.3 Hit-to-lead optimization and lead optimization The objective of this intermediate phase is to design and synthesize compounds with improved properties and with pharmacokinetics that can be used for a variety of in vivo applications. During this step, each hit compound undergoes a series of structureactivity-relationship (SAR) studies in order to measure its activity and selectivity. During the final lead discovery phase, the primary goal is to discover compounds that have excellent pharmacokinetic, metabolic, and structural characteristics. In addition, several in vitro and in vivo tests may also be required. In recent years, the use of AI algorithms for de novo drug design has gained popularity as a means of identifying new active compounds for hit-to-lead optimization. Medicinal

Convergence of artificial intelligence and nanotechnology in cancer

O

NH2

S O

F O

N

N

F F

NH

N

O N

S O OH Aspirin (1)

N H

Celecoxib (2)

N H

N

Cimetidine (3)

NH2 OH

HO

O

O O

O

O

O

O

OH

O

OH O

O

OH

Daunorubicin (4)

Simvastatin (9)

F

O

HO N

H N

N

NH2

N F

NH NH metformin (8)

NH O

pimozide (12)

retinoic acid (10)

O–

O N+ F Cl OH N

F

F F

N F

penfluridol (11)

nitroxoline (13) OH

Fig. 19.2 Structures of some repurposed drugs, which are developed or are under trial for indication in cancers.

505

506

A handbook of artificial intelligence in drug delivery

chemistry has greatly benefitted from the introduction of AI into de novo drug design, as it allows a more efficient screening of compound libraries containing more than 1060 compounds and establishing a scoring function that takes into account a variety of factors, including the drugs’ activity, synthesis tractability, and pharmacokinetic/dynamic effects [26]. Hartenfeller et al. developed an algorithm that facilitates in silico synthesis by taking templates and combining them with predefined synthesis plans, which incorporate predictable building blocks and reaction procedures [26,27]. This system is a stepping stone to accelerated progress in the discovery of new medicines through implementation of knowledge-based directives and rules. QSAR analysis can be used in hit-to-lead optimization to identify effective leading compounds based on the predicted bioactivity of their analogs. QSAR refers to the application of mathematical methods to study the quantitative relationships between the chemical and physical properties of compounds and their biological activities [28]. QSAR analysis primarily involves gathering molecular information, selecting it, and generating molecular descriptions, along with the establishment, assessment and interpretation, and application of mathematical models [29]. One of the main issues that need to be addressed pertains to both the structural representation of a chemical compound and its mathematical representation. The structure-activity relationship needs to be modeled using a suitable mathematical model, following the selection of descriptors.

19.2.4 Lead optimization and preclinical testing 19.2.4.1 Prediction of bioactivity AI-driven bioactivity prediction has seen many significant innovations over the past few years, including the development of matched molecular pair analysis, which is used to identify the influence of a single alteration within a molecule on its properties and bioactivity [30]. For the purpose of chemically defining a candidate molecule, a static core and two fragments are used, both of which are encoded. Different ML techniques are used to extrapolate the outcomes of transforming, fragmenting, or altering the static core. Moreover, the MMP analysis of public drug databases can also predict a drug’s biological activity, including its oral bioavailability, distribution coefficient, clearance rate, and ADME profile [31]. 19.2.4.2 Prediction of toxicity The accurate determination of the toxic properties of compounds and their optimization during the development of a drug can save valuable time and resources. DeepTox’s algorithm, which relies on ML, provides accurate predictions of the toxicological profiles of new compounds (tested with Tox21 data). There have been several studies that demonstrate that DeepTox is capable of providing toxicity estimates by analyzing the presence or absence of more than 2500 toxicophore features [32].

Convergence of artificial intelligence and nanotechnology in cancer

19.2.4.3 Clinical trials An ideal AI system for clinical trials would be capable of recognizing diseases in patients, identifying gene targets, predicting the effects of the molecule developed, and identifying any side effects that it may encounter. Similarly, a novel AI platform called AiCure was developed for measuring medication adherence in a phase II trial for patients suffering from schizophrenia, and it was reported that AiCure increased adherence by 25% over that of the traditional method of modified directly observed therapy [33]. The selection of patients for clinical trials is an important process. The investigation of how humanrelevant biomarkers relate to in vitro phenotypes serves to offer a more predictable and quantifiable assessment of a specific patient’s therapeutic response uncertainty. By developing AI techniques for identifying and predicting biomarkers relevant to human disease, phase II and III clinical trials can be tailored to a specific patient population. Clinical trials could be improved using AI predictive models to select the patient population [34,35].

19.3 Statistical and ML methods for modeling cancer risk Continuous advancements in the field of oncology have brought about improvisation in the protocols of cancer prevention and treatment. Early diagnosis and detection of tumors are inevitable for reducing the disastrous impact of cancers. Moreover, the risk of a cancer reoccurring after significant treatment and management cannot be ignored. Therefore, predictive models can be established using previous data of patients to simulate the features of patients who have suffered from a cancer or relapse in order to reduce the tendency of cancer reoccurrence. Such a model can then be subjected to clinical settings to predict and determine whether the new individuals are at risk of cancer development or reoccurrence. For building models on a large scale, well-structured data for a vast range of diverse patients are required for better utilization [36].

19.3.1 Common features and differences in statistical and ML models The majority of scientists establish a model to predict and determine a patient at risk of cancer development or reoccurrence in the future. However, different techniques are used in different studies. Usually, a study uses classical statistical methods like survival analysis and regression or ML methods like super vector machines (SVMs), artificial neural networks (ANNs), or tree models. Some studies prefer conjugate systems comprised of both statistical and ML techniques. Before discussing the models in detail, understanding the similarities and differences between statistical and ML models is necessary [37]. The identification of the use of these types of protocol sets is possible through consideration of the purpose for which they were originally designed. Most statistical

507

508

A handbook of artificial intelligence in drug delivery

approaches are oriented toward inference, the process of drawing conclusions about populations or demonstrating scientific evidence from representative samples of the population. The majority of statistical approaches such as logistic and linear regression can be used to make predictions concerning new data; however, their primary function is to set up inferences regarding the relationship between variables. In order to identify the factors that differentiate individuals at low mortality risk from those at higher mortality risk, as an example, if we were to develop a model that describes the relationship between clinical variables and outcomes in transplant recipients, then we would have to identify those factors so that interventions could be developed, which would improve outcomes and decrease mortality in the future. Therefore, the objective of statistical inference is to elucidate the relationships between variables [38]. On the other hand, the primary objective of ML is to make predictions with a higher accuracy, which is more of a “what,” rather than a “how.” For example, in image recognition, the linkage between individual properties like pixels and yields is relatively insignificant if the prediction is accurate. Essentially, it is a crucial aspect of ML because the linkage between many inputs, like pixels in a video or the location of a geopositioning image, is highly complicated and often nonlinear. Due to these factors, it is challenging to describe the relationship between outcomes and predictors when there are nonlinear relationships and when a large number of predictors contribute little information to a model of prediction [39]. Fortunately, many relationships are highly beneficial for the medical field, such as the relationship between diabetes patients and body mass index and the one between lung cancer and tobacco use. Due to this, relationships can be reasonably utilized in a welldefined manner using extremely simple models. There are many common applications of ML, such as translating documents, optimizing navigation, and locating objects in video clips, in which understanding the relationship between outcomes and features is of little significance. This allows the implementation of complicated nonlinear algorithms. Considering these fundamental differences, scientists might be interested in considering logarithms that vary along a continuum between simpler algorithms such as auditable algorithms and more complex ones such as black boxes [40].

19.3.2 Commonly used models A number of predictive and prognostic models are employed in clinical settings. Some of the models are not based on ML or statistical models but on clinical guidelines or rulebased approaches, e.g., nomograms and cancer staging [41,42].

19.3.3 Statistical models Modeling of the risk of disease development or recurrence has been widely established as a survival analysis issue, and many investigations employ the technique of survival analysis

Convergence of artificial intelligence and nanotechnology in cancer

to build their models for prediction. For example, a typical choice model, such as Cox proportional hazards, allows for multivariate analysis and time censoring [43]. Essentially, it is a regression model with the ability to create a function of time, based on baseline covariate values, in order to simulate the chances of a disease occurring in the near future. Usually, in studies aimed at predicting risks, the event is the detection of cancer and time zero is either the initiation of an investigation or enrollment in a study. For studies that predict cancer recurrence, the event is the return of cancer or a relapse and time zero is the date of a significant treatment, usually the removal of the tumor through surgery. The patient is censored if follow-up with them is lost before the censored event, which is usually the end of the follow-up period, although other events may also occur, for instance, the patient dying or dropping out of the study. Cahlon et al. developed a model capable of evaluating the risk of survival for a large group of patients who died without experiencing a recurrence of sarcoma and treated nonrecurring deaths as a competing risk factor [44]. The purpose of their study was to perform survival analysis on a single profile using a model that was different from the Cox proportional hazards model. Usually, a Kaplan-Meir curve is generated in order to visualize and elaborate the results of the model. According to this curve, the survival function is estimated for various cohorts of individuals and the chances of survival are plotted along the axis of time. This allows for comparisons to be made between cohorts of patients with different characteristics and treatment regimens in order to identify which treatment regimen is appropriate for the new patient. Kaplan-Meier analysis does not merely rely on statistical survival models but has the ability to predict survival outcomes with the help of ML algorithms. Kim et al. applied a Kaplan-Meier curve in order to compare the survival rates of low- and high-risk breast cancer patients as estimated by a ML model together with statistical analysis [45]. Another commonly used statistical model is the logistic regression model. Multivariate models can be adapted to analyze and model binary dependent variables. Importantly, LR models are developed for covariates, and, then, logistic functions are used to analyze both output categories. Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, was predicted by El-Serag et al. using an LR model after 6 months following an α-fetoprotein (AFP) screening [46]. Similarly, Cirkovic et al. established an LR model to determine the likelihood of breast cancer recurrence after surgery [47]. In a recent study, Bayati et al. have compared a traditional logistic regression model with their own improvised logistic regression model utilizing multitask learning, and the results showed that the latter should be capable of accurately forecasting the risk of cancer, a debilitating disease [48]. An RL model is the most commonly employed model in medical science because of its simplicity and performance. The practitioner is able to observe which feature is responsible for making the prediction. However, RL models are not considered ideal because they are difficult to fit into linear models. In addition, models with a large number of variables are difficult to interpret.

509

510

A handbook of artificial intelligence in drug delivery

19.3.4 ML models ML methods can be classified into two main categories of learning techniques: supervised and unsupervised. Both categories are elaborated in the upcoming sections [49]. 19.3.4.1 Supervised learning Supervised ML includes techniques in which a predictive model is established and trained on a wide range of features or inputs that are associated with a specific outcome. From a medical perspective, this might mean attempting to predict a specific outcome, such as diabetes within 5 years, based on a person’s characteristics, such as their weight, height, and smoking habits [50]. Once the model is established and trained, it will be able to predict outcomes from new data. Making predictions with the help of supervised ML can be discrete, e.g., negative or positive, and malignant, benign, or continuous, e.g., a score in the range of 0–100. Generally, a model that creates discrete classes is referred to as a classification algorithm. For example, a classification includes those that predict whether a tumor is malignant or benign or determine whether a patient’s comments convey a negative or positive message. A classification algorithm in routine has the feature of returning a probability of category 1 for definite and 0 for impossible. Generally, we would categorize probabilities greater than 50 as 1, but this value may be modified as necessary to improve algorithm performance. This study presents an example of a classification algorithm that identifies potential diagnoses. A regression algorithm is one that returns a prediction of a continuous value. Statistical use of the term “aggression” differs from its use in ML. The use of a regression algorithm may be implied in ML applications for predicting a patient’s lifespan or the amount of chemotherapy that will be tolerable to the patient [51]. Supervised ML algorithms are generally established using a dataset with several relevant outcomes and variables. For certain tasks like language processing or recognition of images, the variables, e.g., words or pixels, must be selected by the feature selector. The feature selector identifies unique characteristics of readily identifiable data, which are then transformed into an algorithm-friendly numerical matrix. For example, a feature may be the color of pixels in an image or the number of words in a given text. For instance, in the same example, outcomes might include an image depicting a benign or malignant tumor or transcriptions of interview responses that predict the likelihood of developing a mental health condition [52]. After the dataset is arranged into outcomes and features, a ML algorithm may be employed. The iteratively improved algorithm reduces the prediction error using an optimized technique. During the process of developing and training ML algorithms, there is the possibility of overfitting the algorithm to the characteristics of a specific set of data, resulting in a predictive model that is incapable of generalizing to new data. This overfitting risk can be diminished using different techniques. Probably the most important technique used in this work is to divide our dataset into two portions, one portion of training and the other of testing, so that the model can generalize beyond

Convergence of artificial intelligence and nanotechnology in cancer

the data used for training. Each portion contains randomly selected portions of the feature and the corresponding results. It enables the algorithm to link some characteristics to specific outcomes and is appropriately referred to as a training algorithm. Once the training is completed, the algorithm is applied to the features of the dataset without regarding their related results. Afterward, the prediction is compared with the already known outcomes of the dataset to establish the model’s performance. For the algorithm to perform well on the new dataset, it is critical to improve its generalization abilities [53]. 19.3.4.2 Unsupervised ML Unlike supervised ML, unsupervised ML does not associate with predefined outcomes. In this category of ML, patterns are searched for or found by an algorithm with no input by the user. Therefore, unsupervised learning methods are exploratory in nature and are designed to determine patterns within a dataset, which may be inexplicable but are nevertheless likely to occur. These approaches are generally called dimension reduction approaches or techniques and consist of processes like latent Dirichlet analysis, principal component analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE) [54]. Unsupervised learning methods can often be used in conjunction with the approaches employed in this project to reduce the number of features included in the analysis, thereby contributing to the project’s efficiency. Embedding the information contained in a set of data into fewer features or dimensions is a convenient method to reduce issues such as multiple collinearity and high computational costs. Fig. 19.3 is a visual representation of an unsupervised dimension reduction algorithm. These raw data are represented by various shapes in the left panel and are processed by an algorithm, which categorizes the data into similar data point clusters in the right panel. Typically, datasets with no sufficient similarity to the clustered data will be omitted, leading to a decrease in the number of features present in the dataset [37]. Regularized logistic regression There are many similarities between this method and multivariate logistic regression. The use of the function of regularization makes it somehow prominent and distinguished, which reduces the number of model features and attenuates the coefficients’ magnitude. Therefore, regularization is suitable for a set of data consisting of a number of variables and missing data called high-sparsity sets of data like the term “document matrices” that is employed to show text in text-missing studies [55]. Support vector machine An support vector machine (SVM) algorithm is one of the best known ML methods, owing to its high performance with regard to providing complete accuracy under conditions in which the relationship between outcomes and features is not linear. The kernel trick is used to perform mathematical transformation [56].

511

512

A handbook of artificial intelligence in drug delivery

(A)

a

Label Features Training dataset

Feature selector

Individual algorithm in training

b Label Features Testing dataset

Feature selector

Trained algorithm

Statistical comparison (accuracy, sensitivity, specificity)

Machinegenerated prediction

c Features New data

Feature selector

Trained algorithm

Machine generated prediction

(B)

Unsupervised algorithm

Fig. 19.3 Supervised machine learning method. (a) Training, (b) validating, and (c) applying algorithm to fresh data (A). Unsupervised dimension reducing method (B). (Adopted from the study by J.A.M. SideyGibbons, C.J. Sidey-Gibbons, Machine learning in medicine: a practical introduction, BMC Med. Res. Methodol. 9(1) (2019) 64.)

Artificial neural network In recent years, an artificial neural network (ANN) with a complex architecture and intuitively changing parameters has become the most widely used algorithm for a wide range of difficult tasks, including video and image recognition. ANNs can be enhanced by adding neural networks’ specialty, such as convolutional and recurrent networks, to improve

Convergence of artificial intelligence and nanotechnology in cancer

their performance and accuracy. Since ANNs are highly parameterized, they are susceptible to overfitting. Regularization techniques such as DropConnect can enhance their performance [57].

19.4 Preparation and optimization of nanomedicines 19.4.1 Nanoparticles There are numerous advantages associated with nanoparticles (NPs) compared to conventional preparations. NPs can be filled with tiny molecules or biopharmaceuticals to achieve protection from harmful environmental conditions, continuous and controlled release of APIs for an extended period, ensuring that API concentrations are within therapeutic limits, and the ability to transport multiple APIs in a synergistic manner in order to carry out a therapeutic function [58]. Even so, relative to the thousands of sustainedrelease oral delivery systems that have received widespread approval during the past 30 years, the number of NP formulations is rather low—i.e., only 20–50 formulations. Due to the complexity of these delivery systems, it is difficult to bring them to the market, since many parameters must be assessed in order to develop the most suitable formulation. The problems associated with preparing these systems present an additional opportunity for the application of ML.

19.4.2 Targeted drug delivery Delivering drugs directly to the target site is a novel concept that aims to avoid harming normal tissues, especially in cancer treatment. However, there have been some limitations, especially in its designation, such as improving our ability to predict the relationship between the various formulation parameters and treatment outcomes as well as the speed with which unpredicted phenomena appear. The simple addition of a targeting moiety to drug-loaded nanoparticles is insufficient to provide efficient drug delivery and release. Additionally, the incorporation of computational models into the preparation of nanoparticles can also contribute to a higher success rate of targeted treatments since the effect of the nanomedicine produced as a consequence of interactions with the plasma, endothelium, and cellular membranes is difficult to predict and could be dramatically improved through computations [59].

19.4.3 ML and AI in formulation designing A variety of data are required for designing ML methods than traditional computational models. Developing a computational model that takes into account the prevailing mechanisms in the process requires a profound understanding of the physico-chemical and biological principles. Hence, in ML methods, process understanding is not required; instead, it is necessary to obtain high numbers of experimental data concerning the investigated subject and to determine relationships within the data. In the meantime, data

513

514

A handbook of artificial intelligence in drug delivery

understanding may provide additional insights when selecting ML models and algorithms to ensure the highest degree of accuracy [60]. The types of data (numbers, text, images, etc.), mathematical properties of the processes involved, and dependent associations among data topographies can be some of the variables that should be taken into account when constructing a ML model. For example, an ANN is used for predicting the burst rate and size of poly(lactic-co-glycolic acid) nanoparticles (PLGA NPs). ANNs are used to predict the expected nanoparticle size and initial burst rate based on the molecular weights of PLGA and poly(vinyl alcohol) and the sonication rate employed during preparation. In spite of the results provided by an algorithm that had a 0.9). Hence, overfitting may be an issue in this situation. Despite some studies using early stopping protocols, additional measures should have been employed to reduce overfitting (e.g., regularization, cross-validation, etc.). The authors developed a total of 32 variations of 5-fluorouracil lipid nanoparticles (which are comprised of 16 solid lipid NPs (SLNs) and 16 nanostructured lipid carriers (NLCs)) with the goal of designing the optimal formulation. The obtained data were then used to train NNs that were able to predict NP properties. The trained NNs were evaluated, and the results were used to simulate experiments with the aim of finding the experimental settings that produce optimal properties for transdermal delivery of 5-fluorouracil-loaded lipid nanoparticles. Furthermore, ML models have been developed to predict the experimental conditions necessary to organize nanocrystals. He et al. developed light gradient-boosting machine models, which

Convergence of artificial intelligence and nanotechnology in cancer

can be used for predicting the particle size and particle density of drug nanoparticles obtained using three different procedures (namely, wet ball milling (WBM), highpressure homogenization (HPH), and antisolvent precipitation (ASP)) [63]. Data training for these models was performed using previously published data (i.e., 523, 197, and 190 data samples for WBM, HPH, and ASP, respectively). During this study, the authors used both 10-fold cross-validation and different kinds of model regularization methods to avoid overfitting throughout the training procedure. Additionally, the top 20 features for each improved size prediction model (WBM, HPH, and ASP) were ranked based on feature importance analysis. The trained models utilized the pharmaceutical knowledge available at the time as a basis for ranking features that were appropriate for the situation.

19.4.5 AI in nanoformulations Designing more effective drug delivery systems depends on the evaluation and examination of the parameters that affect the loading efficiency, size, or cytotoxicity of nanoparticles. The applied artificial neural networks have been used to calculate the physical and chemical characteristics of nanoparticles that have therapeutic potential against a variety of diseases, to analyze nonlinear relationships and features affecting the size and constancy of nanoparticles, or to build models for determining the relation between factors influencing controlled release drug delivery systems. Furthermore, as ANNs represent input-output interactions, they can be applied to recognize and model the main limits that limit the size of nanoparticles in a multidimensional environment [61]. In order to accelerate and enhance the nanofabrication process, AI programs could be employed. For data mining, gathering, ordering, or extrapolation and different methods, including decision trees and Bayesian networks, have been used. Several scientific fields are affected by integrating AI and nanotechnology, which might produce smarter technologies. AI has been used in nanotechnology to solve problems related to the design of nanosystems, nanocomputing, and nanoscale simulation. The use of intelligent methods allows for the development of novel designs, condensed computation times, accurate parameter estimates, simulation of systems, and interpretation of experimental results. AI models offer the opportunity to overcome the limitations of nanotechnology and produce nanoarchitectures with enhanced computing capabilities [62]. AI has not only been used in the development of smarter sensors but also in the classification of nanomaterial properties and in the evaluation of their influence on biological systems. Nanomedicines use ANNs to analyze or model the preparation of nanoparticles, resulting in notable reductions in efforts, time, and costs. Several QSPR modeling approaches have been applied to the analysis of the phase behavior of amphiphilic nanoparticles. ANNs are used to develop mathematical models for estimating the polydispersity index (PDI) and particle size of polymeric nanoparticles produced from emulsification solvent evaporation of polymer solution. An ANN model was constructed

515

516

A handbook of artificial intelligence in drug delivery

by employing experimental datasets to cover all of the polymer properties that influence the PDI and particle size of nanoparticles. Five factors are involved in the input layer of an ANN model: water viscosity, polymer film contact angle, water-polymer solution interfacial tension, poly(vinyl alcohol) concentration, and solvent-to-water ratio. The output layer contains two responses, namely, the polydispersity index and nanoparticle size. The trained model uses a forward neural network with a back propagation learning algorithm. The most significant factor was the concentration of poly(vinyl acetate), which was reflected by the interfacial tension between the particles, the solvent-to-water ratio, and the viscosity of the solution. Additionally, the concentration of poly(vinyl acetate) had the greatest impact on the PDI, as indicated by the viscosity, interfacial tension, contact angle, and solvent-to-water ratio. ANNs were used to construct models to predict the particle size and entrapment efficiency of polyethylene glycol/polylactide nanoparticles loaded with noscapine, taking into account numerous factors, such as the polymer-to-drug ratio, number of blocks, and polymer’s weight. Accordingly, the polymer-to-drug ratio and polymer molecular weight were the two factors that had the maximum influence on particle size and entrapment efficiency, respectively. Developing trained artificial neural networks could significantly contribute to developing nanotherapeutics that are more effective due to their smaller size and increased entrapment ability [60]. The combination of factorial design, artificial neural networks (ANNs), and continuous generalized additive computation (GAC) appears to be the most promising strategy for multiobjective optimization and modeling nanoformulations. The formulation parameters of chitosan-tripolyphosphate nanoparticles have been improved using artificial neural networks (ANN) to improve the yield of the process and control the nanoparticle size [64].

19.4.6 ML in cancer nanomedicines Recent advances in nanomedicines, which combine nanotechnology with medicine and is aimed at diagnosing, monitoring, and treating diseases, have improved the outcome of treatment for some of the most complex and fatal conditions by precisely targeting the therapeutic dosage. The term “cancer nanomedicine” has evolved far beyond the concept of chemotherapy drugs being delivered to tumor cells using nanosized vehicles. Nanomedicines may facilitate earlier cancer diagnosis, stimulate the immune system against tumors, disrupt the microenvironment that supports tumor growth, and perform other functions that may prove to be preferable to conventional drug delivery systems [65]. The use of ML algorithms has become increasingly important in this data age, contributing to a more accurate understanding of biological, chemical, physical, and toxicological states. Due to the increase in the size of information, AI techniques are of great importance to the analysis of such a large amount of information. Practical applications

Convergence of artificial intelligence and nanotechnology in cancer

include virtual throughput screening, and, in the case of nanomaterials, various characteristics, such as size, shape, surface modifications, composition, agglomeration tendency, interaction with other molecules, and solubility in water, can be obtained, which have an effect on their properties and biological effects [66]. 19.4.6.1 Application of AI and related technologies in cancer treatment using nanomedicines The emergence of ML has a profound impact on the diagnosis and management of cancer [67]. For example, Google’s DeepMind, a ML algorithm, was used for mammography screening for the early detection of breast cancer. The data evaluated through DeepMind considerably outclassed human oncology experts in precision by an absolute margin of 11.5%, organized with extremely small false-positive and false-negative rates [68]. Because the prodigious nanomedicine progresses depend on manual synthesis and trial-and-error models of learning, there is substantial space for connecting ML to enhance nanomedicine formulations. ML has been employed for high-throughput screening nanomedicine formulations and analytical analysis of nanostructure-activity relationships [69–72]. A quantitative structure nanoparticle assembly prediction (QSNAP) algorithm was established in 2018 to detect and authenticate electrotopological molecular descriptors as extremely prognostic indicators of indocyanine nanoparticle-forming drugs and nanoparticle size with an ordinary precision of 15 nm [72]. About 295 drugs have been identified by this QSNAP algorithm, which could develop nanoparticles of indocyanine and are stable at pH 7.4 from a library of 5653 tiny drug molecules in the DrugBank database. The loading capability of indocyanine nanoparticles to encapsulate drugs was 90% (by mass). A ML-assisted high-throughput screening method was reported in 2019 to assess and analyze the structure-activity relationship of spherical nucleic acids (SNAs) as cancer vaccines [69]. In order to quantitatively model the whole SNA immune activation activity and recognize the smallest number of SNAs successfully needed to capture optimum structure-activity relationships, a ML algorithm was employed in a large library of 960 SNAs. Hence, the number of tests in the preparation of SNA nanoparticles was considerably reduced by this approach and assessment by order of magnitude, indicating a favorable tool of high-throughput screening for development of nanomedicines. ML has been applied to illuminate the complex interactions between tumor microenvironments and nanomedicines [73,74]. Recently, a novel ML analysis method based on 3D imaging has been designed to quantitatively analyze the interaction between a gold nanoparticle and a tumor cell in local and liver-metastasized tumors [74]. The physiological and nanoparticle delivery conditions of 1301 liver micrometastases were summarized, and it was revealed that a greater number of cells in micrometastases (50% nanoparticle-positive cells) were accessed by nanoparticles than in primary tumors

517

518

A handbook of artificial intelligence in drug delivery

(17% nanoparticle-positive cells), relying on their diffusion distance from blood vessels. Approaches like these are visualized to predict the worth of treatment of personalized nanomedicines by considering the micrometastasis characteristics of a patient. The clinical efficacy of cancer treatments can be enhanced by properly combining drugs in order to maximize their efficacy. However, optimization can be challenging because it involves developing the best drug combinations, doses, and frequency of administration to maximize efficacy and minimize unwanted side effects. Furthermore, some drug combinations may cause undesirable side effects due to the complex nature of biological systems. However, despite the improved therapeutic efficacy of multifunctional nanomedicines, they also face similar optimization challenges. Nanomedicines and AI can thus be used together to address cancer treatment challenges and improve efficacy [75]. A recent study conducted by Wang et al. has demonstrated the use of feedback system control as a tool for optimizing the performance of both conventional and nanotechnology-based therapeutics. Using AI, the authors were able to standardize combinations of drug doses that would allow them to achieve maximum cytotoxicity. Four different combinations were tested: nanodiamond doxorubicin, nanodiamond bleomycin, nanodiamond mitoxantrone, and unmodified paclitaxel. Tests were conducted on multiple breast cancer cell lines to determine the effectiveness of these combinations. Based on the results, AI-optimized nanomedicine drug combinations performed better than did randomly selected nanomedicine combinations and AI-optimized nonmodified drug combinations as well as nanomedicine-modified and nonmodified drugs [76]. In addition, although ML techniques are not commonly utilized in nanomedicines, they have already been applied in the characterization of drug-loaded nanoparticles. It has been demonstrated that artificial neural networks can predict the size and polydispersity of drug-loaded nanoparticles, both of which have significant effects on nanoparticlebased therapies. Moreover, ML is proving to be useful in improving nanoparticle uptake, reducing cytotoxicity, and predicting protein corona formation around nanoparticles [77]. The integration of these techniques into in silico models can further enhance their benefits, since this can speed up the process and reduce costs, as in silico models can be used as inputs for ML methods. The concept of algorithmic, learning-specific mathematical models has already been demonstrated in the area of materials science, where the exploration of the state space of systems has been accelerated using algorithms. ML can be used in conjunction with experimental design to yield additional advantages in addition to the characterization and design of nanoparticles. The active learning strategy involves guiding the experimental design using ML methods (e.g., reinforcement learning and surrogate learning) to choose only the most promising candidates for evaluation. This approach has already been proven to be successful in areas such as cancer clinical trials, medical materials, and drug discovery, as opposed to traditional trial-and-error methods [78].

Convergence of artificial intelligence and nanotechnology in cancer

19.4.6.2 Big data libraries for nanomedicines The advent of AI, big data, and the collection of extensive nanoparticle libraries has enabled advanced multivariate analyses of these nanoparticles, which reveal the relationship between their characteristics and their biological effects. The application of data-driven computing techniques in nanomedicines (nanoinformatics) can provide predictive models for the biochemical effects of nanoparticles such as their absorption, distribution, uptake, and other physiological effects [79]. In recent years, nanocarrier technology has led to fundamental advances in the fabrication of large samples of polymer- and lipid-based nanocarriers for gene and drug delivery purposes, including lipid nanoparticles for RNA delivery, a number of coreshell nanoparticles, and polyesters functionalized with lipids. Recently, nanocarrier library screens have been combined with ML algorithms to identify materials with properties that are critical for cellular uptake, gene silencing, and safety. Reineke and coworkers examined the physico-chemical properties of 43 chemically different copolymers to identify the properties required for delivering nucleoproteins (RNPs) and for carrying out gene editing efficiently[80]. These researchers concluded that a successful transfection relies on three main factors: editing efficiency (hydrophilicity), uptake of RNPs (protonation state), and cytotoxicity (polyplex diameter) [80]. These libraries provide a wide range of parameter space and statistical power, which has enabled detailed investigations of nanocarrier features that have led to more effective and efficient delivery technology. However, most of these screening approaches are focused on identifying materials with properties that facilitate successful delivery rather than testing these combinatorial libraries in their limited in vitro environments. Considering the large number and variety of nanocarriers that can be developed, extending these studies or studying in vivo combinations of nanocarriers can be extremely expensive and difficult [81]. Data curation is essential for the development of robust nanomedicine platforms, which require assembling and standardizing nanomaterial data. In 2007, the National Cancer Institute (NCI) launched a nanotechnology database to identify potential treatments for cancers. The caNanoLab website is divided into three main sections: samples, methods, and published results (2150). There are methods of preparing and synthesizing samples as well as in vitro and physico-chemical assays [82]. Nano (https://nano.nature. com) was founded by Springer Nature to support researchers in multidisciplinary fields. The Nano database contains approximately 350,000 nanomaterials and 970,000 nanotechnology-related publications. After Nano’s planned retirement in June 30, 2022, the underlying data were integrated into other systems [83]. The PubVINAS database provides access to more than 700 nanocomposites composed of 11 different components. The company was founded by a Korean research group whose objective is to produce nanotechnology that is safe for both humans and the environment [84]. S2Nano provides a curated dataset, predictive models, and general assistance with chemical safety assessments via its web portal (http://portal.s2nano.org) [85]. The Nanoparticle

519

520

A handbook of artificial intelligence in drug delivery

Information Library (NIL) was created by the National Institute for Occupational Safety and Health (NIOSH) to facilitate the sharing of nanomaterial characterization data. In addition to facilitating collaboration, the NIL provides researchers with curated data and ensures the safety of workers. The OSU partners manage the website and characterize nanomaterials for use in the database [86]. The Zinc (https://zinc.docking.org) website contains a database of excipients and small-molecule nanomaterials. It was invented by UCSF researchers for the purpose of designing new nanoparticles. Currently, the market contains more than 120 million “drug-like” molecules. Similarly, the same group developed the Excipients Browser (https://excipients.ucsf.bkslab.org/index) with the intention of improving drug delivery formulations [87]. eNanoMapper (http://www. enanomapper.net/) is an online database containing data from cross-laboratory studies on nanomaterials. Several key partners have been involved in the development and administration of this platform, with expertise ranging from data science through nanoscale engineering and community development to computational modeling [88]. The eNanoMapper database was designed to offer a flexible model with a user-friendly interface that can be tailored to meet the needs of any researcher. The eNanoMapper data curation and analysis tool will be enhanced with integrated QSAR modeling software. All these databases will improve and fast-track nanomedicines for various diseases, especially cancer nanomedicines that will benefit a lot from it. The generation of large datasets of physiological significance is also a challenge, and cancer nanomedicine may learn from other disciplines, such as molecular biology, which have established methods for untangling biological complexity through large-scale data analysis. For example, DNA barcoding and sequencing are used to enable simultaneous examination of multiple nanoformulations within the same experimental animals. Preclinical studies have utilized this approach to assess the efficacy of comparing 5 liposomal formulations in addition to comparing the biodistribution of 100 nanoparticles at the cellular level [89]. 19.4.6.3 AI and ML for determining the in vivo fate of nanomedicines Several factors contribute to the complexity of the nano-bio interface, including the high complexity of synthesized nanoparticles, the diversity of biochemical barriers with which they interact, and the highly heterogeneous nature of biological systems and diseases. The results of in vitro and in vivo experiments are not frequently correlated, due to the additional complexity posed by the human body. Therefore, novel strategies for evaluating the performance of nanocarriers will need to be considered in diverse biological contexts. Besides the biological complexity resulting from tumor heterogeneity and patient-topatient differences, other factors, such as protein-serum interactions, reticuloendothelial system detection and clearance, and transport and penetration barriers, must be properly taken into consideration [90]. Therefore, the development of high-throughput pooling techniques allowing simultaneous testing of multiple agents under the same experimental

Convergence of artificial intelligence and nanotechnology in cancer

conditions becomes critically important. Traditionally, engineering nanomaterial systems for drug delivery have been centered on understanding and optimizing the properties of the material. The most effective means of overcoming biological delivery barriers is to understand cellular-nanocarrier interactions. This can be achieved through the use of libraries of nanoparticles, pooled screening methods, and omics analyses, among other techniques [91]. Candidate formulations have traditionally been tested on one or two models at a time, in an iterative process that focuses on the material properties. Pooled cell screening enables hundreds of different cell lines to be simultaneously tested against the same formulation in the same manner as can be accomplished through single-cell screening. This procedure will provide physicians with some insight into the properties that can be associated with effective targeting and uptake of nanocarriers. Furthermore, nanocarriers can be pooled using barcode strategies, thereby reducing biological screening time. Additionally, using in silico models in conjunction with active learning allows the exploration and design of nanoparticles to be automated, as well as the testing of the designs, a necessary step for active learning. The development of clinically relevant in silico methods has now become possible due to recent developments in multiscale models of tumors and nanomedicines combined with ML methods [92]. Prediction of the in vivo behavior of nanomaterials Moreover, ML algorithms are being developed to predict how NMs interact with biological processes, which will subsequently optimize NMs’ roles in diagnosis and therapy. The human body consists of proteins that rapidly bind to the surface of NMs and form protein coronas that play an important role in the recognition of NMs by cells. Protein coronas affect the transport, release, and pharmacological action of therapeutic agents. Understanding the formation of protein coronas is essential for the development of optimal and safe nanoparticles for use in biosensing, nanomedicines, targeting of organs, and other applications. Models of linear regression are unable to predict the characteristics of protein coronas. An RF can assist in solving this issue since it is capable of handling heterogeneous datasets. The use of an RF model and metaanalysis together is highly effective in predicting the function of protein coronas. This method was successfully used to predict cellular recognition [93]. NM accumulation was predicted using a supervised deep neural network. Consequently, the network was able to accurately predict nanoparticle accumulation in the spleen and liver, which will facilitate predicting nanoparticle behavior and developing surface chemistries that are more suitable for the body [94]. The brain microenvironment and NM properties were predicted by an artificial neural network. It may be possible to use these data to build predictive models of nanomaterial transport, thus enabling the development of nanotherapeutic platforms that overcome biological barriers and facilitate local delivery. The surface chemistry and uptake relationship between nanoparticles has been predicted with ML, in order to investigate their

521

522

A handbook of artificial intelligence in drug delivery

capability to target normal carcinoma cells. The capability of precisely targeting NMs to specific cells is critical for the successful application of NM-based theranostics and personalized medicine [95]. 19.4.6.4 Nanomaterials and biotoxicity prediction NMs are increasingly being utilized in the industry, which requires continuous research and monitoring to understand their toxicity and risks to humans and the environment. ML techniques have been successfully applied to the study of biological toxicity of NMs, yielding promising results [96]. A tissue-specific classification model was developed based on RF techniques to determine the neurotoxic potential of nanoparticles in in vitro experiments. The datasets extracted from 36 articles included the properties of nanoparticles, conditions of exposure, and results of in vitro tests. Performance of the external 10-fold cross-validation model showed an accuracy of 98.3%, a precision of 98.3%, a sensitivity of 98.3%, and a specificity of 98.2% [97]. Singh et al. developed standard operating procedures in nanotoxicology based on the cell shape index (CSI) and nuclear area factors (NAFs). An array of seven ML algorithms, like linear discriminant analysis (LDA), neural networks, RFs, and AdaBoost, were employed to estimate the toxic effects of metal oxide nanoparticles on Escherichia coli. The results provide a scientific basis for designing and creating safe nanomaterials [98]. The tree-based RF features analysis (TBRFA) method has been a recently developed analysis method for RF feature network interactions based on tree-based RF feature analysis. The major advantage of this strategy is that it is capable of not only accurately predicting the pulmonary immune responses and lung burden associated with nanoparticles but also constructing feature interaction networks to assist in interpreting or explaining their effects. In addition, perturbation theory machine learning (PTML) and quantitative structure-toxicity relationship (QSTR) models have been used to predict genetic responses to NMs under various assay conditions, and more than 96% of the predictions are accurate. Therefore, the PTML-QSTR model may be utilized for quickly and efficiently analyzing changes in gene expression in response to NMs. A multinano-readacross modeling technique was used to examine whether metal oxide nanoparticles are toxic to diverse groups of organisms, including algae, protozoa, bacteria, and animal cells [99,100]. 19.4.6.5 Data on the interplay between nanotechnology and biology Data-driven ML algorithms rely on large amounts of data in order to maximize their performance. ML algorithms are largely dependent on the quality and quantity of data. In nanobiological studies, researchers frequently encounter incomplete data and poor data quality. Metaanalysis and data mining can be used to address these issues. Metaanalyses represent one of the most effective methods of assessing the diversity of materials and

Convergence of artificial intelligence and nanotechnology in cancer

the heterogeneity of research data. Data concerning biological responses to NMs are derived from published studies using DT and RF algorithms and then analyzed using ML algorithms to ascertain the relationship between NM properties and physiological responses. Based on a metaanalysis conducted by Oh et al. from the published literature, 1741 samples of semiconductor quantum dots with cadmium were identified and their toxicity was evaluated [101]. There is considerable information readily available on the interactions between model organisms (such as Daphnia magna, Caenorhabditis elegans, rats, zebrafish, and mice) and NMs. Using metaanalysis to create databases and then applying ML algorithms to investigate model animal-NM interactions present great advantages. ML, for example, has been used to phenotype C. elegans embryos. The use of metaanalysis coupled with ML allows for high predictive accuracy and reveals hidden relationships, assisting future studies in developing NMs that are highly efficacious, reliable, and efficient by design [90]. ML algorithms are highly susceptible to errors due to poor data quality. The development of open and high-quality databases allows for the generation of large quantities of standard data. Yan et al. established a universal NM database comprising 705 different NMs with a wide range of biological functions [84]. For low-quality data, data cleaning techniques, such as the locally weighted regression method (LOWESS), are useful for cleaning errors, removing outliers, filling in missing values, and normalizing a dataset [102]. 19.4.6.6 ML promotes bioapplications of NMs NMs have attracted much attention over the past decade because of their antimicrobial properties. Graphene-based materials and copper-based nanomaterials, for example, have demonstrated excellent antibacterial properties by trapping bacteria and damaging their membranes [103]. The high biocompatibility of graphene-based nanomaterials significantly accelerates differentiation of human neural stem cells, suggesting tissue engineering applications [104]. NMs are capable of detecting, targeting, and treating cancers in an early stage of development [105]. There is a high probability of failure when NMs are designed for bioapplications as they are based on scientific theories and practical experience, and their selection is limited by these principles. To ensure that NMs are used in the most advantageous manner, data-driven ML allows predicting the ideal properties. The only drawback is their toxic effects. The size and morphology of NMs influence their cytotoxicity and effects on animals for a long time. Nanotoxicity is an important aspect of the bioapplications of nanomaterials [106]. We must understand how ML can be used to manage it. In general, chemicals pose the greatest barrier to NMs’ safe use, since they can have adverse effects on human health. Several studies have been conducted on the green synthesis of NMs using biocompatible, biodegradable substances such as green tea, bacteriorhodopsin, melatonin, and glucose. The use of ML in future research on the green synthesis of NMs may be promising.

523

524

A handbook of artificial intelligence in drug delivery

19.4.6.7 Cancer nanomedicine: The future The field of cancer nanomedicine is becoming increasingly interdisciplinary, incorporating technologies from various fields, such as modeling diseases (including in vitro 3D disease models and tumor or tissue chips), omics (such as nanoparticle barcoding), computation, modeling, and AI (such as managing big data, developing models to predict nanomedicine design, and targeting). The study of the fundamental and quantitative aspects of nanobiointeractions is likely to become more relevant as a result of these developments. Moreover, we may be able to tackle the complex, multivariate problem of designing highly modular nanomedicines in ways that were not possible when using previous analytical strategies and methodologies.

19.5 Conclusions and future outlook Increased costs and an enormous amount of time necessitate the use of advanced techniques and technologies for designing and developing better diagnostics and therapeutics for the management of diseases. AI has significant advantages such as the capacity to analyze large amounts of data, solve complex issues related to the design and development of novel drug carriers, provide accuracy in decisions, classification, and disease modeling, fast-track drug discovery, identify potential biomarkers, specify drug targets, drug candidates based on the pharmacological profile, and new indicators for current therapeutics, establish a connection between process variables and formulations, manage functional and pathophysiological processes, optimize the drug-dose ratio, predict interactions between bioactivities and drugs, and determine molecular behavior, status of the disease, cellular responses, compatibility of drug combinations, and therapeutic outcomes. AI may flourish and expedite the digitalization of health care and pharmaceutical sciences with effective, rapid, and cost-effective options. One of the challenges that must be addressed when applying AI and ML in health care and pharmaceutical sciences is data quality. The term “quality” refers to data’s consistency, dependability, accuracy, availability, and accessibility. The size of the dataset should also be considered. Simple ML techniques can model small datasets; if the dataset is too large and complex to model via simple ML approaches, then advanced ANN models with DL strategies can provide a better alternative. Other difficulties to consider include training or learning time, overfitting, and underfitting. As a result, if these challenges were properly considered and well-trained and well-validated ML models were carefully designed, then the risk of using unreliable ML models can be eliminated. As a result, employing AI to digitize pharmaceutical data may necessitate domain knowledge and capability of training algorithms; each ML approach should be applied “task by task.” In the coming years, the application of ML in health care and pharmaceutical sciences will necessitate sustained integrated interdisciplinary collaboration between data science, pharmaceutical sciences, and computer scientists. More importantly, the possibility

Convergence of artificial intelligence and nanotechnology in cancer

provided by ML integration into drug formulation development and, more specifically into pharmaceutical sciences, should be viewed not only as a way to expedite efforts but also as a way to discover new materials, novel formulations, and new information. Therefore, we believe that ML has an excellent chance of revolutionizing the pharmaceutical industry’s approach to drug discovery and development.

References [1] K. Smietana, M. Siatkowski, M. Møller, Trends in Clinical Success Rates, Nature Publishing Group, 2016. [2] A.J. Mullard, 2018 FDA drug approvals, Nat. Rev. Drug Discov. 18 (2) (2019) 85–89. [3] H.S. Chan, et al., Advancing drug discovery via artificial intelligence, Trends Pharmacol. Sci. 40 (8) (2019) 592–604. [4] A.B. Mariotto, et al., Projections of the cost of cancer care in the United States: 2010–2020, J. Natl. Cancer Inst. 103 (2) (2011) 117–128. [5] G. Liang, et al., The emerging roles of artificial intelligence in cancer drug development and precision therapy, Biomed. Pharmacother. 128 (2020) 110255. [6] J. Abbasi, Artificial intelligence tools for sepsis and cancer, JAMA 320 (22) (2018) 2303. [7] W.L. Bi, et al., Artificial intelligence in cancer imaging: clinical challenges and applications, CA Cancer J. Clin. 69 (2) (2019) 127–157. [8] H. Bohr, Drug discovery and molecular modeling using artificial intelligence, in: Artificial Intelligence in Healthcare, Elsevier, 2020, pp. 61–83. [9] N. Nagarajan, et al., Application of computational biology and artificial intelligence technologies in cancer precision drug discovery, Biomed. Res. Int. 2019 (2019) 8427042. [10] R.K. Patel, M. Jain, NGS QC toolkit: a toolkit for quality control of next generation sequencing data, PLoS One 7 (2) (2012) e30619. [11] M. Spencer, et al., A deep learning network approach to ab initio protein secondary structure prediction, IEEE/ACM Trans. Comput. Biol. Bioinform. 12 (1) (2014) 103–112. [12] H. Li, et al., Deep learning methods for protein torsion angle prediction, BMC Bioinformatics 18 (1) (2017) 417. [13] C. Wang, Y.J. Zhang, Improving scoring-docking-screening powers of protein–ligand scoring functions using random forest, J. Comput. Chem. 38 (3) (2017) 169–177. [14] F. Zhong, et al., Artificial intelligence in drug design, Sci. China Life Sci. 61 (10) (2018) 1191–1204. [15] Q.C. Zhang, et al., Protein interface conservation across structure space, Proc. Natl. Acad. Sci. USA 107 (24) (2010) 10896–10901. [16] R. Todeschini, V. Consonni, Molecular Descriptors for Chemoinformatics: Volume I: Alphabetical Listing/Volume II: Appendices, References, vol. 41, John Wiley & Sons, 2009. [17] T.T. Ashburn, K.B. Thor, Drug repositioning: identifying and developing new uses for existing drugs, Nat. Rev. Drug Discov. 3 (8) (2004) 673–683. [18] H.-T. Yang, et al., Literature-based discovery of new candidates for drug repurposing, Brief. Bioinform. 18 (3) (2017) 488–497. [19] H. Xue, et al., Review of drug repositioning approaches and resources, Int. J. Biol. Sci. 14 (10) (2018) 1232. [20] T.I. Oprea, J.P. Overington, Computational and practical aspects of drug repositioning, Assay Drug Dev. Technol. 13 (6) (2015) 299–306. [21] E. Lionta, et al., Structure-based virtual screening for drug discovery: principles, applications and recent advances, Curr. Top. Med. Chem. 14 (16) (2014) 1923–1938. [22] A. Talevi, Drug repositioning: current approaches and their implications in the precision medicine era, Expert Rev. Precis. Med. Drug Dev. 3 (1) (2018) 49–61. [23] U. Koch, et al., Cheminformatics at the interface of medicinal chemistry and proteomics, Biochim. Biophys. Acta, Proteins Proteomics 1844 (1) (2014) 156–161.

525

526

A handbook of artificial intelligence in drug delivery

[24] F. Napolitano, et al., Drug repositioning: a machine-learning approach through data integration, J. Cheminform. 5 (1) (2013) 30. [25] M. Rudrapal, S.J. Khairnar, A.G. Jadhav, Drug repurposing (DR): an emerging approach in drug discovery, in: Drug Repurposing-Hypothesis, Molecular Aspects and Therapeutic Applications, IntechOpen, 2020. [26] D. Reker, et al., Identifying the macromolecular targets of de novo-designed chemical entities through self-organizing map consensus, PNAS 111 (11) (2014) 4067–4072. [27] M. Hartenfeller, H. Zettl, M. Walter, M. Rupp, F. Reisen, E. Proschak, S. Weggen, H. Stark, G. Schneider, DOGS: reaction-driven de novo design of bioactive compounds, PLoS Comput. Biol. 8 (2) (2012) e1002380, https://doi.org/10.1371/journal.pcbi.1002380. [28] E.X. Esposito, A.J. Hopfinger, J.D. Madura, Methods for applying the quantitative structure-activity relationship paradigm, in: Chemoinformatics, Springer, 2004, pp. 131–213. [29] K.Z. Myint, X.-Q. Xie, Recent advances in fragment-based QSAR and multi-dimensional QSAR methods, Int. J. Mol. Sci. 11 (10) (2010) 3846–3866. [30] C. Tyrchan, E.J.C. Evertsson, Matched molecular pair analysis in short: algorithms, applications and limitations, Comput. Struct. Biotechnol. J. 15 (2017) 86–90. [31] A.G. Leach, et al., Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure, J. Med. Chem. 49 (23) (2006) 6672–6682. [32] A. Mayr, et al., DeepTox: toxicity prediction using deep learning, Front. Environ. Sci. 3 (2016) 80. [33] E.E. Bain, et al., Use of a novel artificial intelligence platform on mobile devices to assess dosing compliance in a phase 2 clinical trial in subjects with schizophrenia, JMIR mHealth uHealth 5 (2) (2017) e18. [34] J.L. Perez-Gracia, et al., Strategies to design clinical studies to identify predictive biomarkers in cancer research, Cancer Treat. Rev. 53 (2017) 79–97. [35] R.O. Deliberato, L.A. Celi, D.J. Stone, Clinical note creation, binning, and artificial intelligence, JMIR Med. Inform. 5 (3) (2017) e24. [36] A.N. Richter, T.M. Khoshgoftaar, A review of statistical and machine learning methods for modeling cancer risk using structured clinical data, Artif. Intell. Med. 90 (2018) 1–14. [37] J.A. Sidey-Gibbons, C.J. Sidey-Gibbons, Machine learning in medicine: a practical introduction, BMC Med. Res. Methodol. 19 (1) (2019) 1–18. [38] H.S.R. Rajula, et al., Comparison of conventional statistical methods with machine learning in medicine: diagnosis, drug development, and treatment, Medicina 56 (9) (2020) 455. [39] J.M. Helm, et al., Machine learning and artificial intelligence: definitions, applications, and future directions, Curr. Rev. Musculoskelet. Med. 13 (1) (2020) 69–76. [40] C.J. Harrison, C.J. Sidey-Gibbons, Machine learning in medicine: a practical introduction to natural language processing, BMC Med. Res. Methodol. 21 (1) (2021) 1–11. [41] S.B. Edge, C.C. Compton, The American joint committee on cancer: the 7th edition of the AJCC cancer staging manual and the future of TNM, Ann. Surg. Oncol. 17 (6) (2010) 1471–1474. [42] V.P. Balachandran, et al., Nomograms in oncology: more than meets the eye, Lancet Oncol. 16 (4) (2015) e173–e180. [43] D.R. Cox, D. Oakes, Analysis of Survival Data, vol. 21, CRC Press, 1984. [44] O. Cahlon, et al., A postoperative nomogram for local recurrence risk in extremity soft tissue sarcomas after limb-sparing surgery without adjuvant radiation, Ann. Surg. 255 (2) (2012) 343. [45] W. Kim, et al., Development of novel breast cancer recurrence prediction model using support vector machine, J. Breast Cancer 15 (2) (2012) 230–238. [46] H.B. El-Serag, et al., A new laboratory-based algorithm to predict development of hepatocellular carcinoma in patients with hepatitis C and cirrhosis, Gastroenterology 146 (5) (2014) 1249–1255.e1. [47] B.R.A. Cirkovic, et al., Prediction models for estimation of survival rate and relapse for breast cancer patients, in: 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), IEEE, 2015. [48] M. Bayati, S. Bhaskar, A. Montanari, A low-cost method for multiple disease prediction, in: AMIA Annual Symposium Proceedings, American Medical Informatics Association, 2015. [49] N.H. Shah, A. Milstein, S.C. Bagley, Making machine learning models clinically useful, JAMA 322 (14) (2019) 1351–1352.

Convergence of artificial intelligence and nanotechnology in cancer

[50] A. Estava, et al., Dermatologist level classification of skin cancer with deep neural networks, Nature 542 (7639) (2017) 115–118. [51] A. Rajkomar, J. Dean, I. Kohane, Machine learning in medicine, N. Engl. J. Med. 380 (14) (2019) 1347–1358. [52] T. Jiang, J.L. Gradus, A.J. Rosellini, Supervised machine learning: a brief primer, Behav. Ther. 51 (5) (2020) 675–687. [53] R.C. Deo, Machine learning in medicine, Circulation 132 (20) (2015) 1920–1930. [54] L. Van der Maaten, G. Hinton, Visualizing data using t-SNE, J. Mach. Learn. Res. 9 (11) (2008) 2579–2605. [55] F. Salehi, E. Abbasi, B. Hassibi, The impact of regularization on high-dimensional logistic regression, arXiv (2019). preprint arXiv:1906.03761. [56] S. Huang, et al., Applications of support vector machine (SVM) learning in cancer genomics, Cancer Genomics Proteomics 15 (1) (2018) 41–51. [57] M. Salah, et al., Predicting medical expenses using artificial neural network, Int. J. Eng. Inform. Syst. 2 (20) (2018) 11–17. [58] A. Aghebati-Maleki, et al., Nanoparticles and cancer therapy: perspectives for application of nanoparticles in the treatment of cancers, J. Cell. Physiol. 235 (3) (2020) 1962–1972. [59] B. Bahrami, et al., Nanoparticles and targeted drug delivery in cancer therapy, Immunol. Lett. 190 (2017) 64–83. [60] O. Adir, et al., Integrating artificial intelligence and nanotechnology for precision cancer medicine, Adv. Mater. 32 (13) (2020) 1901989. [61] P. Hassanzadeh, F. Atyabi, R. Dinarvand, The significance of artificial intelligence in drug delivery system design, Adv. Drug Deliv. Rev. 151 (2019) 169–190. [62] P. Bannigan, et al., Machine learning directed drug formulation development, Adv. Drug Deliv. Rev. 175 (2021) 113806. [63] Y. He, et al., Can machine learning predict drug nanocrystals? J. Control. Release 322 (2020) 274–285. [64] P.K. Deb, et al., Applications of computers in pharmaceutical product formulation, in: Dosage Form Design Parameters, Elsevier, 2018, pp. 665–703. [65] I. de La´zaro, D.J. Mooney, Obstacles and opportunities in a forward vision for cancer nanomedicine, Nat. Mater. 20 (11) (2021) 1469–1479. [66] S. Goswami, et al., Computer vision and machine-learning techniques for quantification and predictive modeling of intracellular anticancer drug delivery by nanocarriers, Appl. AI Lett. 3 (2021) e50. [67] E.J. Topol, High-performance medicine: the convergence of human and artificial intelligence, Nat. Med. 25 (1) (2019) 44–56. [68] H.M. Khorasani, H. Usefi, L. Pen˜a-Castillo, Detecting ulcerative colitis from colon samples using efficient feature selection and machine learning, Sci. Rep. 10 (1) (2020) 1–9. [69] G. Yamanluirt, et al., Exploration of the nanomedicine-design space with high-throughput screening and machine learning, in: Spherical Nucleic Acids, Jenny Stanford Publishing, 2020, pp. 1687–1716. [70] F. Li, et al., Design of self-assembly dipeptide hydrogels and machine learning via their chemical features, Proc. Natl. Acad. Sci. 116 (23) (2019) 11259–11264. [71] R. Han, et al., Predicting physical stability of solid dispersions by machine learning techniques, J. Control. Release 311 (2019) 16–25. [72] Y. Shamay, et al., Quantitative self-assembly prediction yields targeted nanomedicines, Nat. Mater. 17 (4) (2018) 361–368. [73] E. Price, A.J. Gesquiere, An in vitro assay and artificial intelligence approach to determine rate constants of nanomaterial-cell interactions, Sci. Rep. 9 (1) (2019) 1–14. [74] B.R. Kingston, et al., Assessing micrometastases as a target for nanoparticles using 3D microscopy and machine learning, Proc. Natl. Acad. Sci. 116 (30) (2019) 14937–14946. [75] J. Khong, et al., The role of artificial intelligence in scaling nanomedicine toward broad clinical impact, in: Nanoparticles for Biomedical Applications, Elsevier, 2020, pp. 385–407. [76] H. Wang, et al., Mechanism-independent optimization of combinatorial nanodiamond and unmodified drug delivery using a phenotypically driven platform technology, ACS Nano 9 (3) (2015) 3332–3344. [77] M.R. Findlay, et al., Machine learning provides predictive analysis into silver nanoparticle protein corona formation from physicochemical properties, Environ. Sci. Nano 5 (1) (2018) 64–71.

527

528

A handbook of artificial intelligence in drug delivery

[78] N.R. Stillman, et al., In silico modelling of cancer nanomedicine, across scales and transport barriers, npj Comput. Mater. 6 (1) (2020) 1–10. [79] M. Alber, et al., Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences, npj Digit. Med. 2 (1) (2019) 1–11. [80] R. Kumar, et al., Efficient polymer-mediated delivery of gene-editing ribonucleoprotein payloads through combinatorial design, parallelized experimentation, and machine learning, ACS Nano 14 (12) (2020) 17626–17639. [81] H. Zhu, Big data and artificial intelligence modeling for drug discovery, Annu. Rev. Pharmacol. Toxicol. 60 (2020) 573–589. [82] S. Gaheen, et al., caNanoLab: data sharing to expedite the use of nanotechnology in biomedicine, Comput. Sci. Discovery 6 (1) (2013) 014010. [83] C. Chen, et al., Merging data curation and machine learning to improve nanomedicines, Adv. Drug Deliv. Rev. (2022) 114172. [84] X. Yan, et al., Construction of a web-based nanomaterial database by big data curation and modeling friendly nanostructure annotations, Nat. Commun. 11 (1) (2020) 1–10. [85] I. Furxhi, et al., Machine learning prediction of nanoparticle in vitro toxicity: a comparative study of classifiers and ensemble-classifiers using the Copeland index, Toxicol. Lett. 312 (2019) 157–166. [86] A.L. Miller, et al., The nanoparticle information library (NIL): a prototype for linking and sharing emerging data, J. Occup. Environ. Hyg. 4 (12) (2007) D131–D134. [87] T. Sterling, J.J. Irwin, ZINC 15-ligand discovery for everyone, J. Chem. Inf. Model. 55 (11) (2015) 2324–2337. [88] N. Jeliazkova, et al., The eNanoMapper database for nanomaterial safety information, Beilstein J. Nanotechnol. 6 (1) (2015) 1609–1634. [89] K. Paunovska, et al., A direct comparison of in vitro and in vivo nucleic acid delivery mediated by hundreds of nanoparticles reveals a weak correlation, Nano Lett. 18 (3) (2018) 2148–2157. [90] Y. Jia, et al., Machine learning boosts the design and discovery of nanomaterials, ACS Sustain. Chem. Eng. 9 (18) (2021) 6130–6147. [91] D. Fourches, et al., Quantitative nanostructure-activity relationship modeling, ACS Nano 4 (10) (2010) 5703–5712. [92] N. Boehnke, P.T. Hammond, Power in numbers: harnessing combinatorial and integrated screens to advance nanomedicine, JACS Au 2 (1) (2021) 12–21. [93] Z. Ban, et al., Machine learning predicts the functional composition of the protein corona and the cellular recognition of nanoparticles, Proc. Natl. Acad. Sci. 117 (19) (2020) 10492–10499. [94] J. Lazarovits, et al., Supervised learning and mass spectrometry predicts the in vivo fate of nanomaterials, ACS Nano 13 (7) (2019) 8023–8034. [95] C. Curtis, et al., Predicting in situ nanoparticle behavior using multiple particle tracking and artificial neural networks, Nanoscale 11 (46) (2019) 22515–22530. [96] M. Souri, et al., Towards principled design of cancer nanomedicine to accelerate clinical translation, Mater. Today Bio (2022) 100208. [97] I. Furxhi, F. Murphy, Predicting in vitro neurotoxicity induced by nanoparticles using machine learning, Int. J. Mol. Sci. 21 (15) (2020) 5280. [98] A.V. Singh, et al., Machine-learning-based approach to decode the influence of nanomaterial properties on their interaction with cells, ACS Appl. Mater. Interfaces 13 (1) (2020) 1943–1955. [99] S. Kar, et al., Evaluating the cytotoxicity of a large pool of metal oxide nanoparticles to Escherichia coli: mechanistic understanding through in vitro and in silico studies, Chemosphere 264 (2021) 128428. [100] N. Sizochenko, et al., How the toxicity of nanomaterials towards different species could be simultaneously evaluated: a novel multi-nano-read-across approach, Nanoscale 10 (2) (2018) 582–591. [101] E. Oh, et al., Meta-analysis of cellular toxicity for cadmium-containing quantum dots, Nat. Nanotechnol. 11 (5) (2016) 479–486. [102] J. Nikota, et al., Meta-analysis of transcriptomic responses as a means to identify pulmonary disease outcomes for engineered nanomaterials, Part. Fibre Toxicol. 13 (1) (2015) 1–18.

Convergence of artificial intelligence and nanotechnology in cancer

[103] O. Akhavan, E. Ghaderi, Escherichia coli bacteria reduce graphene oxide to bactericidal graphene in a self-limiting manner, Carbon 50 (5) (2012) 1853–1860. [104] O. Akhavan, Graphene scaffolds in progressive nanotechnology/stem cell-based tissue engineering of the nervous system, J. Mater. Chem. B 4 (19) (2016) 3169–3190. [105] O. Akhavan, E. Ghaderi, Graphene nanomesh promises extremely efficient in vivo photothermal therapy, Small 9 (21) (2013) 3593–3601. [106] E. Hashemi, et al., Cyto and genotoxicities of graphene oxide and reduced graphene oxide sheets on spermatozoa, RSC Adv. 4 (52) (2014) 27213–27223.

529

CHAPTER 20

Artificial intelligence in precision medicine Shahid S. Siddiquia,b,c, Sivakumar Loganathand, Venkateswaran R. Elangovane, and M. Yusuf Alif a McGenome LLC, Glenview, IL, United States Abbott, Abbott Park, IL, United States c Department of Medicine, University of Chicago, Chicago, IL, United States d Department of Environmental Science, Periyar University, Salem, Tamil Nadu, India e Department of Pediatrics-Endocrinology, University of Michigan, Ann Arbor, MI, United States f Department of Molecular Physiology and Biophysics, University of Vermont, Burlington, VT, United States b

20.1 Artificial intelligence and precision medicine There are several ways to define artificial intelligence. AI encompasses multiple disciplines, including computer science, linguistics, psychology, and philosophy. Simply put, AI is intelligence demonstrated by machines, which may mimic the cognitive features and functions that are commonly associated with the working of the human brain. Such activities may include problem-solving skills and learning a new task. Since this volume has several chapters dedicated to the field of artificial intelligence, we will not dwell much on it but will provide a brief introduction for those interested in the use of AI in solving problems of precision medicine (PM). The main approach of artificial intelligence (AI) is based upon a combination of algorithms in a computer program, which recognize patterns in a variety of different datasets, i.e., multidimensional datasets that are harnessed to optimize similar data obtained from individual patients, thereby predicting a given outcome. Thus, the AI strategy uses learning modalities on pattern recognition that is based on the classification of multidimensional dataset input to arrive at predictions from dataset(s) provided for analysis in the future. For instance, in a clinical diagnosis, one may perform screening and staging of a pathological specimen dataset to predict the diagnosis and thereafter suggest an intervention for a new pathology case. Such AI algorithms include random forest algorithms, evolutionary algorithms (EAs), and backpropagation-based neural network algorithms. The broad difference in such algorithm types lies in whether these are “supervised” or “unsupervised.” Supervised, semisupervised, and unsupervised learning algorithms in machine learning (ML) learn from experience to improve the validity of outcomes, whereas deep learning (DL), a subset of machine learning, allows computation of multilayer neural networks for better outcomes. Supervised learning uses the approaches of classification and regression, and unsupervised learning use the clustering A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00020-4

Copyright © 2023 Elsevier Inc. All rights reserved.

531

532

A handbook of artificial intelligence in drug delivery

approach. At the core, both ML and DL cover a subset of approaches in artificial intelligence in which algorithms capture (learn) hidden patterns and relationships in the available dataset. ML algorithms are based on building a mathematical model, also known as a mapping function, derived from example data that allow predictions of “unobserved” or future data points (Fig. 20.1). Thus, the power of ML method lies in that this approach automatically learns the “rules” that are required to predict a future event or data point. Because of this, ML algorithms are more powerful, effective, and could be scaled up as compared to other rulebased algorithms. Deep learning (DL) is a special subset of AI and ML, which can learn patterns in data using multiple layers of nonlinear functions, in place of just one function as is the case of classical machine learning (ML). Consequently, deep learning is capable of estimating highly complex functions in cases in which traditional machine learning models may not exhibit so robust a performance [1].

20.1.1 Precision medicine In PM (precision medicine) patients are classified on the basis of their different genetic backgrounds and other characteristics like growth environment, disease risk index, prognosis, and possible response to a given treatment option. The premise is that datasets with

Unsupervised Learning

GEN ERALI Z AT I ON

Data Visualization

CLUSTERING

Biological Hypothesis Generation

Supervised Learning

CLASSIFICATION

Medical Images X-ray, Ultrasound

Risk Prediction Survival Prediction

Variable Selection

Novel Phenotype Identification

Phenotyping

Data Compression

New Patient Diagnosis And Therapies

Cohort Identification

Advantage: Easy to Program Challenge: Genera ng Performance Metric is Difficult

REGRESSION

Outcome Prediction

Advantage: Easy to Apply and Program Challenge: Labels are needed to be Generated

Fig. 20.1 Supervised and unsupervised learning algorithms in precision medicine: supervised and unsupervised learning algorithms in machine learning (ML) learn from experience to improve the validity of outcomes, whereas deep learning (DL), a subset of machine learning, allows computation of multilayer neural networks for better outcomes. Supervised learning uses the approaches of classification and regression, and unsupervised learning use the clustering approach. At the core, both ML and DL cover a subset of approaches in artificial intelligence, in which algorithms capture (learn) hidden patterns and relationships in the available dataset.

Artificial intelligence in precision medicine

different dimensions can be used to identify such variations and can be trained, using artificial intelligence (AI) algorithms, to discover the unusual genotype or phenotype of an individual or a subpopulation for the assessment of the risk of a certain disease, treatment response, and prognosis. Similarly, patients’ data can be analyzed based on their individual features and characteristics (Fig. 20.2). Table 20.1 provides some examples of how artificial intelligence facilitates the understanding of different disease types in precision medicine. The key requirement of precision medicine (PM) is the availability of multidimensional datasets, which may be put to use in high-performance computing algorithms using artificial intelligence (AI). There has been great progress on both fronts in recent years as the computing power has grown in a logarithmic manner and clinical and biological datasets are increasingly becoming available due to the spirit of collaboration and openness in the scientific community. Several positive examples are available in cancer diagnosis and treatment [14–17] and in the analysis of cardiovascular ailments using AI in precision medicine [18–22].

Fig. 20.2 An artificial intelligence (AI) algorithm-based approach to extract data from various “omics” to facilitate precision medicine in individual diagnostic and treatment options. The AI strategy uses learning modalities on pattern recognition that is based on classification of multidimensional dataset input to arrive at predictions from dataset(s) provided for analysis in future.

533

534

A handbook of artificial intelligence in drug delivery

Table 20.1 Artificial intelligence applications in precision medicine and their utility in various diseases. Area of biology

Pulmonary biology

Neurological disorders Cardiology

Oncology

Imaging

Organ/system

AI method

Utility

Cystic fibrosis [2] COPD [3] Sepsis [4]

Machine learning

IPF [5] Pulmonary artery hypertension

Machine learning

Automate prognosis for lung transplantation Identifying COPD subtypes, genetic variants Identifying and classifying sepsis subtypes Classification of IPF

Autism spectrum disorder [6] Atrial fibrillation [7]

Machine learning

CAD [8] Lung cancer [9,10]

Memetic patternbased algorithm Deep learning, convolutional neural networks Machine learning

Lung disease [11,12] Oncology [13]

Machine learning Machine learning

Machine learning

Convolutional neural networks

Machine learning

Identifying immune clusters to help identify PAH endophenotypes Image-based analysis to detect fibrosis Developing point-of-care application for atrial fibrillation detection Noninvasive diagnostic prediction of CAD Identifying and connecting phenotypes to mutations Connecting imaging phenotypes to mutation status Predicting cancerous growth by observing cervical abnormalities

Different AI algorithms used to quickly diagnose diseases obtained from the literature for machine learning, neural networks, and deep learning are depicted in the table along with the disease condition in which they were employed and the end function.

The fundamental requirements of PM are based on accessibility to different datasets, including genetic background and genomic analyses (such as biomarker genes, transcription profiles, etc.), genetic penetrance (indicating what percentage of individuals with a specific genetic trait show the given phenotype), expressivity (how much an individual expresses a given phenotype for a specific genetic trait, the degree of phenotypic expression), accurate biological phenotype data, and reliable quantification of the key environmental attributes (Fig. 20.3) [14,23,24]. An AI-based analysis is comparatively simple for well-defined diseases like sickle cell anemia but is extremely challenging for neural and social disorders, such as autism

Artificial intelligence in precision medicine

Fig. 20.3 Different cancer treatment options, including the use of specific inhibitors targeting cell surface signaling receptors, such as EGFR, VEGFR, and HER2 (cellular signaling pathway inhibitors, such as specific inhibitors for PI3K/Akt/mTOR, RAS/RAF/MEK/ERK, and JAK/STAT3 signaling); genetic manipulation therapeutic strategies, including gene alteration therapy, gene addition therapy, and epigenetic modification therapy (cytotoxic chemotherapy with specific inhibitors, such as cisplatin, methotrexate, 5-fluoruracil, bleomycin, and docetaxel); and precision targeted radionuclide therapy.

spectrum disorder (ASD), epilepsy, attention-deficit hyperactivity disorder (ADHD), etc. A serious impediment to analyzing neural disorders is the apparent degree of overlap between various neural disorders and the significant differences found in individuals with an identical diagnosis [20,25,26], resulting in a missed or incorrect diagnosis, thus placing a heavy burden on both the health-care system and individuals. Therefore, AI may expedite early and accurate diagnosis and recommend specific treatment options for a given subpopulation or an individual using the precision medicine paradigm; this approach may boost proper health care and reduce cost on the health-care system and patient populations [14,19,20,25,27,28].

535

536

A handbook of artificial intelligence in drug delivery

20.2 AI in cancer classification and subtype determination Cancer is a complex and multifactorial disease. It is a result of perturbation in biological factors, including genes, proteins, mRNAs, miRNAs, metabolites, etc. [29,30]. In cancer treatment, the outcome of patients differs in pathological findings and it is important to evaluate pathological results to optimize individual treatment options. Precision medicine is an emerging medical approach, based on symptom-driven practice of medicine, for improved diagnostics and provides the best individualized treatment that harnesses artificial intelligence (AI), a rapidly growing branch of computer science. AI provides advanced analytical methods with prognostic capabilities [19,30–34]. To provide better treatment options for cancer, there is a need to find out the similar symptoms of patients and these symptoms are most related to the nature and abundant in individual patients. AI is used to find out the subtypes of various cancers by detecting clusters of different genes, mRNAs, and miRNAs. Cancer subtypes can be determined in breast, glioblastoma, and ovarian cancers by multiomics integration into single omics data using expressions of mRNA and miRNA and DNA methylation data. In a study, a stacked autoencoder was used on each omics data. The extracted representations were integrated into another autoencoder. Finally, the complex representation was used in a deep flexible neural forest network model for subclassification of cancers [35]. Using both supervised and unsupervised learning methods, hepatocellular carcinoma (HCC) was identified in two subgroups of patients with significant survival differences through expression of RNA transcriptomics, miRNA transcriptomics, and DNA methylation data [33]. These two learning methods, further used in a number of cohort studies comprising patients of varying ethnicities, identified 10 consensus driver genes, which were significantly associated with patients’ survival [36].

20.2.1 Major AI-based methodologies For analysis of the multiomics data in cancer diagnosis, support vector machines (SVMs) are used, which are based on supervised learning methods such as classification and regression. An SVM belongs to a family of generalized linear classifiers. An SVM algorithm makes a linear hyperplane and maintains the largest possible distance between different types of data points. In cancer study, an SVM is used for detecting cancer subtypes, predicting various markers of biomolecules, and classifying cancer-based molecular features rather than tissue types [37–39]. A random forest (RF) is another major frequently using algorithm for big-sized multiomics data classification, which is composed of many decision trees. An RF is applied to classification of cancer microarrays and nextgeneration sequencing (NGS) data. An RF contains autoencoders and decoders, which extract features from large input data and try to generate an output highly similar to the input using only the extracted characters, respectively; in this mechanism, the redundant data are excluded [40].

Artificial intelligence in precision medicine

20.2.2 Importance of structural variant detection in cancer Among the genomic alterations in cancer, at least one-third have a known pathogenic structural variant (SV) that can help in diagnosis or treatment stratification. The challenges to characterizing an SV include polyploidy, tumor tissue heterogeneity, and determination of germline and somatic SVs in healthy cells. Other issues in the categorization of cancer-specific SVs are that the break points in a nucleotide sequence are inconsistent and often complex. Thus, improved algorithms and integration of multiple strategies into computation are required to analyze variants that are difficult to classify using an individual algorithm, to make progress in precision oncology. Cancer genomic alterations encompass a wide spectrum of types and sizes, ranging from single nucleotide variants (SNVs) to larger structural variants (SVs), which impact genome organization and cellular structural modification. Among these SVs are most occur genomic variation in cancer, they alter most base pairs in the genome [41,42]. First SVs find by gene fusion that repeatedly observed in pediatric cancer [43,44], then in 30% cancers are affected by this pathogenic SVs. Later the SVs are used for the diagnosis and treatment stratification [31,43–45] additionally, identifying any new SV-driven oncogenic alteration is precious for understanding cancer etiology. However, in research, SV detection is extremely difficult, which is mostly performed by coopting technology that is specifically developed for SNV identification. However, advanced sequencing technologies have increased the quantity of structural variant (SV) detection per genome from 2, through 1–2, to 5k [42,46,47]. The findings of multiplatform analyses also underline the existing SV blind spots in cancer databases like cBioportal, COSMIC, etc. Despite the many technological advances in cancer genomics, SV identification remains a challenge owing to biological factors like contagion from normal tissues, intratumoral heterogeneity, and polyploidy. In tumor cells, the identification of acquired variants necessitates selective tumor-specific somatic SVs (TSSVs) from germline and (somatic) variants present in healthy normal cells [48], and this is carried out by analysis of paired tumor and normal samples [49]. Detection of SVs and consequent classification of different variants (germline, tumor-specific, or mosaic variations in healthy tissues) are not only important for diagnostics and cancer etiology but also for their genetic interactions and understanding the underlying molecular mechanism.

20.2.3 Detection of somatic SVs in short-read WGS data Short-read and aligned data are used to detect SVs, and, these reads are sequenced as paired ends of 150–250 bp. Changes in read depth are used to find copy number variants (CNVs). The discordant read pair (DP) method, used to align abnormal distance and/or orientation compared to a reference genome sequence, is apt for detecting large SVs. The split or soft-clipped read (SR) method predicts partially mapped reads and indicates break

537

538

A handbook of artificial intelligence in drug delivery

points with base-pair resolution [50]. Both these methods are used to perform SV detection algorithms [50,51]. BWA-MEM is a predominantly used algorithm to detect SVs; it furnishes secondary alignments to map reads to multiple positions [52,53].

20.2.4 Combinatorial algorithms integrate multiple read alignment patterns Currently, in SV detection, algorithms combine multiple read alignment patterns, which can detect a wide range of types and sizes. These combinatorial algorithms have the ability to detect specific variant classes [54,55]. Comparatively, multiple algorithms are the most beneficial for the full spectrum of SV detection compared to a single algorithm [54]. In recent studies, SV algorithms have mostly focused on germline SV detection [56]. Both somatic and germline SVs were highly detected by five SV callers, namely, DELLY, LUMPY, Manta, SvABA, and GRIDSS [54,56,57].

20.2.5 New PCWAG and TCGA approach to classify structural variants Li et al. [58] described methods to classify and group somatic SVs using data from the PanCancer Analysis of Whole Genomes (PCAWG) of the International Cancer Genome Consortium (ICGC). They also used data from The Cancer Genome Atlas (TCGA), using whole-genome sequencing (WGS) data from 2658 cancer cases encompassing 38 tumor types, which yielded 16 signatures of SVs. Among these, the deletion variants showed a multimodal size distribution, were enriched in late replicating genomic regions, showed correlation with inversions, and were unevenly assorted across cancer tissue type. In case of tandem duplication that shows multimodal size distribution, prevalence in genomic regions that replicate early and unbalanced translocations. Thus, in cancer, multiple chromosomal rearrangement processes are manifested, which generate complex configurations of the genomic sequence that is amenable to a wide variety of selection processes.

20.2.6 Histology and imaging in cancer diagnosis For cancer diagnosis, histological analysis of the tumor sample is the key diagnostic tool. With the advent of genomic analysis of tumor samples, patient stratification and clinical diagnostics have helped histopathology in treating cancer, but genomics and histopathological analysis are costly and time-consuming and often produce equivocal results across measurements. This has been partially addressed by the advent of AI algorithms in cancer tissue analyses. Biomedical image analysis, electronic health records, and genomic analyses (whole-genome sequencing (WGS), RNA sequencing (RNASeq), structural variants (SVs), and copy number variants (CNVs)) have provided vast amounts of data, but, among these approaches, medical image analysis has made significant progress in recent years. Digital image analyses have provided the most striking clues that are often missed by human perception [19,59].

Artificial intelligence in precision medicine

Studies on skin cancer images using neural networks could discriminate between malignant and benign tumors with an accuracy close to that of trained clinicians [27], stimulating ideas that such an AI-mediated image analysis may go beyond the stratification of normal versus cancer tissues. This has led to the prediction of driver mutations and classification of cancer types by deep learning algorithms on tumor histology slides [60], and further work showed that for a fairly large set of data, such algorithms can be harnessed to identify molecular correlates and prognosis across diverse tumor types [61,62].

20.2.7 Solid tumors and radiographic images Radiographic imaging is extensively used in cancer diagnosis and treatment for response evaluation such as in a clinical trial, commonly by determining tumor size change before and after the treatment, but these response patterns are often complex and heterogeneous, defying an accurate evaluation of the underlying biological changes. Thus, tumor response prediction remains a diagnostic challenge. Jin et al. [63] reported the use of multitask deep learning in predicting treatment responses from longitudinal images, allowing simultaneous tumor segmentation and response prediction. The researchers designed two Siamese subnetworks joined at multiple layers, allowing integration of multiscale aspects and feature representations and detailed comparison of pre- and posttreatment images. Using 2568 magnetic resonance imaging scans of 321 rectal cancer patients, the network is trained for predicting the complete pathological response after neoadjuvant chemoradiotherapy. The imaging-based algorithm strategy AUC of 0.95 and 0.92 are used in two independent cohorts of 160 and 141 solid tumor cases, respectively. Furthermore, if blood-based tumor marker data are also combined, then the integrated model increases prediction accuracy with an AUC of 0.97 (0.93–0.99). Thus, the approach to capturing dynamic information in longitudinal images may be utilized for screening, treatment response evaluation, disease monitoring, and surveillance in a broad sense [64]. Bejnordi et al. [65] demonstrated that deep learning models can achieve accurate detection of lymph node metastases in breast cancer, at the level of human clinical teams; similarly, Hollon et al. [66] showed that AI-based algorithms can provide accurate diagnoses in patients required to undergo brain cancer surgery, thereby facilitating a speedier diagnosis. It is necessary that AI systems be evaluated in controlled prospective investigations to assess their clinical value, and this requires specific guidelines [64,67]. A welcome development in AI imaging research is that the strategy may be used by clinics and institutions that may not be able to afford expensive software and medical hardware, as regular cell-phone cameras may be able to make use of the advanced imaging algorithms [27]. Through the augmented reality strategy, which combines overlaying an AI system with a microscopic field of vision, automated histological annotation can be carried out in regions and places that lack access to histopathologists. These AI-based approaches, which have been tried for prostate cancer diagnosis, may find application in diverse tumor types and pathologies that are often dependent on histological examination by highly trained personnel.

539

540

A handbook of artificial intelligence in drug delivery

20.3 AI in the characterization of biomarkers Artificial intelligence (AI) is an important computational method of analyzing clinical data for timely diagnosis, planning, and treatment. Here, we focus on how AI algorithms are applied in a clinical setting to unveil the role of mechanochemical enzyme motor proteins in neurodegenerative diseases and model treatment strategies for these diseases. Neurodegenerative disorders result from a gradual, progressive loss of neuronal functions. The microtubule-based molecular motors kinesin and dynein are essential for axonal transport in neurons, as demonstrated by the fact that mutations in these and in related proteins cause a variety of human diseases, including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and other motor neuron diseases. However, there are currently no effective drugs available for the treatment of these diseases. Given the underlying complexities of these diseases, AI may allow identification of novel biomarkers that serve as both disease indicators and potential therapeutic targets, offering the possibility of combined diagnosis and treatment (theragnostic) of neural diseases (Fig. 20.4).

20.3.1 Motor proteins The microtubule-based motor proteins kinesin and dynein are the driving force behind delivering various cellular cargoes, such as vesicles, organelles, mRNAs, and proteins, along the axons of neurons; accordingly, they are essential for normal cellular function [68–70]. These motor proteins harness the energy from ATP hydrolysis to deliver cargoes Bidireconal Moon Dendrite

Cargo Dynein

Axon Terminal Kinesin

-

+

Nucleus Axon neurofilament

Cell Body of Neuron

Dynein -

Kinesin Microtubule

microtubule neurofilament cargo

+

Fig. 20.4 Both kinesin and dynein transport intracellular cargoes (light green) and neurofilaments (red) along microtubular tracks. While kinesin transports cargo from the cell body to the axonal terminal along the axons of neurons, dynein transports them in the opposite direction. In cells, the opposite polarity motors kinesin and dynein are attached to a common cargo and deliver cargo to its destination using bidirectional motion.

Artificial intelligence in precision medicine

along microtubular tracks in a stepwise manner. One of the major functions of kinesins is to transport cellular cargo from the center of the cell to the periphery. Conventional kinesin (kinesin-1) is a dimeric protein consisting of two heavy chains and two light chains. Each heavy chain contains an N-terminal motor domain, followed by a coiled-coil structure for dimerization and a cargo-binding tail domain at its C-terminal domain [71]. Mammalian cytoplasmic dynein is a dimeric molecular motor complex of the AAA + ATPase family, whose members utilize ATP as an energy source to deliver cargo to the center of the cells along the microtubular minus end [72]. Using single-molecule imaging techniques, researchers have characterized the force-generating capacity, stepping behavior, travel distance and velocity, and structural features of the motor domain as well as its ability to maneuver through dense cytoskeletal networks of cells [73–75]. By applying classical and molecular genetics techniques, researchers have revealed a role of kinesin motor proteins in metazoan cell division, axonal transport, and development [76–81]. The crucial importance of axonal transport is evidenced by the fact that mutations in motor proteins severely compromise neuronal function and cause neurodegenerative diseases in humans [68,82].

20.3.2 Motor proteins and neurofilaments in neural diseases Numerous studies suggest that the disruption of intracellular transport causes neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS), Alzheimer’s disease (AD), Parkinson’s disease (PD), spastic paraplegia, and Huntington’s disease [82,83]. Abnormal axonal transport caused by mutations in microtubule-based motor proteins results in protein aggregation in neuronal nerve processes and leads to neural damage [84,85]. The damage to neuronal function caused by protein misfolding and aggregation is a key contributor to neurodegenerative disorders in humans [86]. For example, aggregation of the microtubule-binding protein tau may induce AD, aggregation of the α-synuclein protein is believed to contribute to the pathogenesis of PD, and mutations in superoxide dismutase 1 (SOD1) lead to ALS [87–89]. In addition to molecular motors, neurofilaments, which are involved in brain diseases, play a crucial role in axonal transport [90]. Neurofilaments are neuron-specific intermediate filaments, expressed abundantly in axons, which move bidirectionally along axons driven by microtubule-based motor proteins [91–94]. Abnormal assembly or accumulation of neurofilaments in axons contributes to many diseases, including ALS, PD, and AD, and, as such, neurofilaments are used as biomarkers for the clinical assessment of neurological disorders [95].

20.3.3 Engineered motor proteins Both kinesin and dynein are tiny biological motors that perform many tasks, including axonal transport, muscle contraction, and cell division, among others. These motors are

541

542

A handbook of artificial intelligence in drug delivery

highly efficient biological machines that operate on principles distinct from those of manmade machines [96]. One of the elegant properties of biological motors is their ability to convert chemical energy into mechanical motion, which enables them to travel long distances along their tracks. Using a similar energy conversion mechanism and the collective properties of these biological motors, scientists have made significant strides in designing functional artificial molecular motors [93,97]. Using synthesized small molecules, researchers have engineered synthetic motors that efficiently “walk” along molecular tracks using both diffusional processes and ratchet mechanisms [98]. Different varieties of chimeric motor proteins have been engineered to alter the directionality and enhance the speed and travel distances of individual motors [99–101]. For example, the kinesinrelated motor, Ncd, moves toward minus-end microtubules. Interestingly, the direction of motion of Ncd can be reversed by changing a single amino acid in the neck domain [102]. In the case of dynein, the direction of movement can be controlled by changing the length of the coiled-coil stalk domain [99]. Molecular motors are involved in insulin granule transport to the plasma membrane, as evidenced by the fact that mutations in motor proteins or their adapters cause insufficient insulin secretion and lead to type 2 diabetes [103]. Insulin granule delivery can be enhanced by engineering motor proteins, potentially providing a pathway to the effective treatment of diabetes. Importantly, AI approaches may prove to be invaluable in identifying mutations linked to motor protein-related diseases (Section 20.3.2) and in designing appropriate chimeric motors, thus opening new avenues for future drug discovery efforts.

20.3.4 Artificial intelligence in the structure-function analysis of kinesins Microtubule-based motor proteins in the kinesin and dynein families interact with cellular cargoes via adapter proteins to recruit diverse cargoes and perform cellular functions. In the absence of bound cargoes, many of these motor proteins exist in autoinhibited conformations. For example, human bicaudal-D2 (BicD2), an adapter protein that links dynein to its cargo, relieves the autoinhibitory conformation of dynein, converting it to an active motor [104]. Many BicD2-like adapter proteins expressed in cells can interact with dynein and select cargoes for diverse functions. Using artificial intelligence (AI), Kunar and colleagues [105] developed an automated, multiplexed, super-resolution imaging method for determining the nanoarchitectures of multiple protein distributions in neuronal cells. Their automated system, which employs 3D STORM (dSTORM) multiplex imaging and sequential staining procedures, identified at least 15 targets in single cells, detected 16 targets specifically in presynaptic nerve terminals, and determined the structures of the identified proteins. Thus, AI algorithms may aid in characterizing adapter proteins, cargoes, or other binding proteins that associate motor proteins with therapeutic targets.

Artificial intelligence in precision medicine

“Motor neurons” are a specialized type of brain cell responsible for communicating from the brain to the spinal cord and muscles. ALS is a neurodegenerative disease characterized by the death of motor neurons located in the central nervous system. Mutations in the SOD1 gene cause about 20% of ALS familial disease in humans [106]. Interestingly, the motion of dynein on microtubules is altered in ALS transgenic model mice harboring the SOD1 mutant G693A, suggesting a link between dynein and SOD1 protein. Although dynein-mediated transport is slower in SOD1 mutant mice than in WT mice at an early presymptomatic stage, the speed of kinesin-1-driven transport along the plus end of microtubules is unaffected [107–109]. Mutations in the dynein-dynactin complex lead to abnormal axonal transport, which, in turn, causes pathologies in transgenic SOD1 mice [110,111]. Mutant SOD1 in ALS patients forms a misfolded, aggregated enzyme that is toxic to neurons [112], thus highlighting the importance of SOD1 protein stability to neuronal function. Mutations in RNA-binding proteins also play a crucial role in ALS in humans. Among the 1542 known RNA-binding proteins encoded in the human genome, 11 may contribute to human ALS disease and 6 show an abnormal distribution in ALS [113–115]. AD, in which neural degeneration leads to memory loss, is a condition that afflicts 5.4 million people in the United States and for which no precise treatment exists. Since the average life span of Americans is increasing, the number of AD cases in the United States is expected to increase to 14 million by 2050. This is alarming because the failure rate of drug development for AD is 99%, although research continues to eliminate or slow down the progression of “symptoms” [116]. It has been shown that overexpression of several kinesin genes, including those encoding KIF5A, KIF1B, and KIF21B, affects axonal transport in AD in humans, suggesting a link between motor proteins and this disease [117,118]. Using an AI approach, Liu et al. [119] analyzed different multimodality images for early diagnosis of AD. A recent work of Rudolph Tanzi’s group using whole-genome sequencing (WGS) has identified rare variants in several genes associated with AD, providing new information for future drug development [120]. Specifically, this analysis revealed 13 rare genetic variants associated with the loss of neuronal function and development: FNBP1L, SEL1L, LINC00298, PRKCH, C15ORF41, C2CD3, KIF2A, APC, LHX9, NALCN, CTNNA2, SYTL3, and CLSTN2. Among these new AD candidate genes was the kinesin KIF2A, encoding a kinesin motor protein; other members of the kinesin family that appeared in WGS analyses include KIF2A, KIF1C, and KIF27. The WGS approach, in which the entire nucleotide sequence of the genome is scanned, is a clear improvement over genome-wide association studies (WGASs), as the latter approach misses rare gene variants (i.e., those that occur in less than 1% of the population). In this context, it is notable that individuals harbor 50–60 million gene variants, of which more than 70% are rare. The current challenge is to explore the functional significance of these newly discovered

543

544

A handbook of artificial intelligence in drug delivery

rare gene variants. One possible approach to examining the role of such gene variants is the use of 3D organoid cultures [121]. Early detection of neurodegenerative diseases—a key to therapeutic success— requires reliable diagnosis techniques. AI-based machine learning tools that utilize advanced-level algorithms are being developed for the treatment of PD [122]. PD, like ALS and AD, is also a neurodegenerative disease caused by abnormal axonal transport. Interestingly, axonal transport is compromised in PD patients due to the abnormal or reduced expression of kinesin and dynein, suggesting a strong link between the impaired axonal transport attributable to motor protein deficiencies and the aggregation of α-synuclein—a hallmark of PD [123]. Bazgir et al. [124] designed a classification system capable of assessing PD tremor with high accuracy using a smartphone accelerometer. Tremor is a common symptom of PD that causes uncontrollable muscle contraction and abnormal hand movements or shaking. AI-based tools may play essential roles in identifying target proteins that severely affect axonal transport and protein aggregation, thus bringing about the possibility of early diagnosis and treatment of neurodegenerative diseases.

20.3.5 AI and precision medicine in a clinical setting “Precision medicine” is also a term coined during efforts to describe approaches to building a knowledge network that sought to classify disease susceptibility into subpopulations using “omics”-based approaches. Precision medicine attempts to quantify an individual’s disease risk to tailor therapeutic interventions based on their genetic makeup and environment. This approach relies on methodologies generating high-dimensional data obtained from large-scale sequencing experiments, proteomics, and imaging studies. Extracting valuable actionable insights from such large-scale datasets requires intensive data mining and interpretation, which is often enhanced by the use of artificial intelligence (AI). Generally classified into machine learning (ML), deep learning (DL), and artificial neural networks, artificial intelligence (AI), powered by high-performance computing clusters, has helped advance precision medicine. Machine learning approaches tend to use learning approaches on existing datasets to analyze them for disease-related patterns called biomarkers that help predict how patients may respond to treatment. For example, in the field of pulmonology, ML algorithms have been employed to classify pulmonary artery hypertension (PAH) patients based on observed immune clusters [64,125,126]. PAH is a complex disorder characterized by clinical heterogeneity that is hard to group and classify, leading to challenges in treatment in which a one-size-fits-all approach is not effective. To solve this, Sweatt et al. [125] used a consensus clustering algorithm to classify PAH patients into specific subgroups based on their immune profiles into distinct immune clusters using a set of 48 cytokines. Similarly, other ML algorithms have been used to identify sepsis subtypes [4,127],

Artificial intelligence in precision medicine

to predict reintubation times for extubated patients on mechanical ventilation [127–129], and to diagnose and treat neuro-developmental disorders [130]. Similarly, AI plays an important role in the disease diagnosis [19,64] of cardiovascular disorders [8,131], in oncology [9,10], and in drug development [132–134].

20.4 Commercial companies focused on AI and precision medicine In recent studies, AI algorithms have been developed and trained on 60,000 images from the NIH cervical cancer trial, which was then used to predict cervical precancer. The trial itself was conducted over a period of 18 years, capturing the course of cancer development in 9400 women from Costa Rica, and served as an excellent resource to train algorithms. During testing, it was found that the trained AI algorithms were effective in detecting precancerous cervical abnormalities that would eventually lead to cancer and thus helped identify patients who may have needed to be on medical surveillance for cancer growth in the cervix [13]. In another instance, deep learning methodologies have been developed to identify and classify Alzheimer’s disease using magnetic resonance imaging (MRI) of the brain [135]. Using convolutional neural networks on MRI images, the study was able to use AI algorithms to efficiently build neuroimaging signatures to predict Alzheimer’s risk and therefore provided a reasonable clinical decision support strategy to aid physicians in quickly diagnosing disease risk. Table 20.2 shows a number of companies that have harnessed the power of AI and ML in precision medicine, using innovative ideas in disease diagnosis, drug discovery, and digital health. Furthermore, an emerging field of work termed “radiogenomics” attempts to link imaging-based phenotypes to specific mutations. A good example is connecting lung imaging phenotypes to the EGFR mutation status for assessing lung diseases like chronic obstructive pulmonary disease [12,136,137]. Thus, the implementation of AI methods drives precision medicine in clinical decision-making and in discerning diagnosis for diseases for which traditional methods are inadequate and are not sensitive enough. There is an increasing role of AI in different aspects of medical practice, which has shown varying degrees of success and use, e.g., imaging and surgical methods have a relatively higher AI and machine learning interface, whereas other areas such as robotics, drug discovery, decision support, etc. are emerging AI-based methods. On the other hand, biomedical diagnostics, altered reality, digital health, wearable technologies, visual assistant, and similar areas receive relatively lower contributions from machine learning algorithms.

20.5 Molecular structure prediction: AlphaFold Most critical to a functional life is the role of proteins, which serve as both structural and functional units such as the cytoskeleton, and that of catalytic enzymes, and elucidating their structures can help reveal a mechanistic understanding of their physiological and

545

546

A handbook of artificial intelligence in drug delivery

Table 20.2 Commercial companies focused on AI and precision medicine. S. no. Name of the company Focus of the company

1 2 3

4

5 6

7

8 9 10

11

12 13

14 15

16 17

18

Cambridge Cancer Genomics Deep Gene

Using blood tests to guide cancer therapy

Providing cancer-type classifiers based on deep learning and somatic point mutations Deep Genomics Developing genetic medicines using artificial intelligence technology, with a focus on the preclinical development of oligonucleotide therapies Watson for Genomics Providing in-depth clinical interpretation of the genetic alterations in the sample automatically, enabling clinical decisionmaking for personalized cancer care Deep Variant Analyzing pipeline using a deep neural network to call genetic variants from NGS DNA data 23 & me; Mountain Providing ancestry data and possibly linking together long-lost View, CA family members even if an individual is susceptible to some genetic diseases Genomenon Using a genomic search engine and database to provide diseasegene variant relationships from the full text of the scientific literature for gene and variant interpretation Genoox Providing a fully customized platform for genetic applications, including primary, secondary, and tertiary analyses Zephyr Health Identifying therapies Literome Providing an automatic curation system to extract genomic knowledge from PubMed articles to facilitate browsing, searching, and reasoning Perthera Managing processes from tumor testing through Perthera reports to provide cancer patients and physicians with therapeutic options ranked by the probability of outcomes Blueprint Genetics Offering single-gene test, targeted variant testing, or whole exome sequencing services along with interpretation Sophia Genetics Providing NGS data analysis to detect, annotate, and preclassify genomic variants associated with multiple disorder areas with five major applications: oncology, hereditary disorders, cardiology, metabolism, COVID-19, and pediatrics RDMD Providing rare disease research and drug development WuXi NextCODE Using genomics to identify the underlying biology and advance the scientific understanding of disease and propel the next generation of transformative therapies Atomwise Small-molecule drug development McGenome LLC, IL Drug delivery, design, and discovery Providing innovative ideas in drug discovery and delivery processes Fabric Genomics, CA Providing clinical decision support software that enable clinical (software) laboratories, hospital systems, and country-sequencing programs to gain actionable genomic insights, resulting in faster and more accurate diagnoses and reduced turnaround time

Artificial intelligence in precision medicine

Table 20.2 Commercial companies focused on AI and precision medicine—cont’d S. no. Name of the company Focus of the company

19

Syapse, CA (software)

20

LifeOmic, Indiana (software)

21

Recursion Pharmaceuticals

Offering cloud-based platforms and data-sharing networks for delivering care through PM for cancer patients. The company is engaged in gathering various health-care clinical treatments, data outcome systems, and streamlining into a unified ecosystem to serve cancer patients Offering disruptive solutions through cloud learning, ML, and mobile devices and providing cloud- and mobile-based solutions to clinicians, pharmaceutical researchers, and health and IT companies, with applications in oncology, cardiology, functional medicine, employer wellness, and health coaching Recursion algorithm can reveal new drug candidates, mechanisms of action, and potential toxicity, which can lead to novel treatments for patients

structural functions. Traditionally, several experimental techniques, such as X-ray crystallography, NMR, circular dichroism, cryo-electron microscopy, etc., have helped in deducing the structures of several thousand proteins, yet this represents a tiny fraction of the millions and more known protein sequences. One of the major impediments to drug discovery is the complexity of protein structures as simple folding rules cannot be easily applied to structure prediction, given that the folding of a peptide is governed by quantum mechanics to predict the optimum or most favored structure. Currently, quantum analysis of a protein structure is still beyond the realm of available computational programs, as predicting possible protein conformations is a Herculean task. New computational programs in structure predictions provide a glimmer of hope to approach protein structure prediction. Structural analysis of a protein requires time and effort, and not all proteins are amenable to purification and structural analyses; thus, computational strategies are required to fill this gap and to allow large-scale protein structural analyses using artificial intelligence and machine learning. Thus, the “protein folding” problem, i.e., predicting the 3D structure that a protein may assume based entirely on its primary amino acid sequence, has captivated biologists in different disciplines for the last half century [138,139]. Several approaches have been reported in using AI and machine learning and deep learning algorithms to predict protein structures [44,140–142], but their ability to correctly predict at an atomic level has met with limited success, particularly in those cases in which no homologous protein structure is known in the databases. In 2021, Jumper et al. described AlphaFold (more accurately, AlphaFold2), a computational method that can reliably predict a three-dimensional protein structure with atomic resolution, even when a similar or homologous protein structure is known.

547

548

A handbook of artificial intelligence in drug delivery

This AlphaFold algorithm, which is based on a neural network model, was the top winner in CASP14 (14th Critical Assessment of Structure Prediction), demonstrating structural accuracy that can be compared with experimental structures in a majority of comparisons, and showed a much higher success rate when compared to other structure prediction algorithms [143]. The key development in the latest version of AlphaFold is an improved machine learning approach that combines physical and biological attributes of the protein structure, leveraging multisequence alignments, into a neural network deep learning algorithm to achieve atomic accuracy in protein structure prediction. A recent development from Google’s AI subsidiary, DeepMind, has been that its AlphaFold program could enable computational predictions of a protein structure, which approach the predictability and quality of those provided by gold-standard experimental techniques such as X-ray crystallography. AlphaFold exhibits unprecedented progress in its computational abilities [144–147]. It can predict the shape of proteins within the width of an atom [145,146]. The 3D models of proteins that AlphaFold generates are of a level of accuracy reported to be comparable to experimental techniques like X-ray crystallography [144–147]. The newest version of DeepMind was released in 2020 [145–147]. The journey to AlphaFold spans several decades: for instance, in 1956, AI was first described by John McCarthy as the science and engineering of making intelligent machines [148]. In the 1960s, artificial neural networks (ANNs) were inspired by biological processes as a basis for deep learning [149]. Owing to the need for continued performance growth, deep learning has become one of the most complex forms of machine learning and a form of AI technology applied in medicine [149–151]. Over the past decade, artificial intelligence (AI)-based models have successfully revolutionized drug discovery [152]. Although experimental techniques such as X-ray crystallography and cryo-electron microscopy (cryo-EM) are powerful tools in modern structural biology [44], each method depends on several trial-and-error methods and both come with numerous challenges when predicting the 3D structures of proteins [145,146,153]. Protein folding has seen great progress in recent years [138], summed up by AI solving the mysteries of life on a molecular scale [145,146,154,155].

20.5.1 Emergence of AlphaFold: Description of Google’s algorithm In 2016, Google’s DeepMind introduced AlphaFold, an artificial intelligence program that has recently succeeded in solving one of biology’s grandest challenges—determining a protein’s 3D shape from its amino acid sequence [138,140,145,146,156]. Jumper et al. [147] provided the code availability and other resources to access the AlphaFold algorithm. The basic methodology used in formulating AlphaFold is the twin use of physical approaches combined with bioinformatics: this uses a physical and geometric inductive bias to build components that learn from protein database (PDB) data with minimal imposition of handcrafted attributes; e.g., the AlphaFold algorithm, which effectively

Artificial intelligence in precision medicine

constructs hydrogen bonds without a hydrogen bond score function and provides a neural network that learns with much greater efficiency from the limited data provided in the PDB, can nevertheless manage the complexity and variety of the structural features of the protein. More specifically, this algorithm is able to manage the lack of physical context and predict highly accurate models in complex and sophisticated cases such as intertwined homomers and in cases in which proteins only fold in the presence of a heme group. This information to address the structural constraints of underspecified structural attributes is critical to learning from protein structures in the PDB as the PDB embodies the complete range of physical and biological conditions from which protein structures are deduced. Thus, the overriding strategy in the AlphaFold algorithm is to produce a 3D protein structure that most closely resembles a PDB protein structure. To illustrate, in examples in which a given ligand, an ion, or stoichiometry is predictable from the sequence alone, AlphaFold is programmed to produce a structure that implicitly resembles those constraints in the structure prediction outcome [147,157]. Code availability: The source code for the AlphaFold model, trained weights, and inference script are available under an open-source license at https://github.com/ deepmind/alphafold. Neural networks were developed using TensorFlow v.1 (https:// github.com/tensorflow/tensorflow), Sonnet v.1 (https://github.com/deepmind/sonnet), JAX v.0.1.69 (https://github.com/google/jax/), and Haiku v.0.0.4 (https://github.com/ deepmind/dm-haiku). The XLA compiler is bundled with JAX and does not have a separate version number. AlphaFold uses decades of prior research work and large genomic datasets to predict protein structures [145,146]. According to the DeepMind team, the system was trained on publicly available data consisting of 170,000 protein structures from the protein data bank, using a relatively modest amount of computing by modern machine learning standards—approximately 128 TPUv3 cores (roughly equivalent to 100–200 GPUs) run over a few weeks [145,146]. In 2020, the last release of the software, AlphaFold 2, successfully made the best prediction for 88 out of the 97 targets and won the 14th Critical Assessment of Structure Prediction (CASP14) [145,146,158]. According to the DeepMind team, in the future, increased accuracy will enable insights into the function and malfunction of proteins and the manner in which they interact [140]. In addition, more accurate protein structure predictions using potentials from deep learning will allow scientists to solve protein structures that were stuck on for close to a decade and discover more druggable proteins or those likely to be good drug targets, thus accelerating the drug discovery process. The team is exploring how protein structure predictions could contribute to our understanding of specific diseases with a small number of specialist groups. These insights could provide more precise work on future drug development, complementing the existing experimental methods to find faster diagnosis treatments. More importantly, AI will

549

550

A handbook of artificial intelligence in drug delivery

illuminate the function of unsolved proteins in the human genome [144–147,157] and potentially make sense of disease-causing gene variations that differ between people and pave the way for advances in genomic considerations that will solve the most difficult challenges facing precision medicine, consequently unleashing the power of precision medicine and revolutionizing health care on scale. Despite the astonishing success of DeepMind, there is still much to learn, starting by answering some questions on how multiple proteins form complexes, how they interact with DNAs, RNAs, or small molecules, and how we can determine the precise location of all amino acid side chains, including collaborations across disciplines. AlphaFold has proven its worth in experimental structural analyses, such as molecular replacement [159], and in better understanding cryogenic electron microscopy assessment maps [160]. In addition, since AlphaFold provides protein coordinates directly, such predictions are in GPU (graphics processing unit) minutes to GPU hours as this relates to the length of the polypeptide (encoded protein) sequence. This allows the tremendous opportunity of predicting protein structures on the proteome scale (comprising a total set of proteins) and beyond, as shown in the case of an entire human proteome [147,157].

20.5.2 AlphaFold: Challenges to tackle For many applications, the accuracy of AlphaFold is been optimized, e.g., how various small molecules such as drugs bind to the target proteins or how various enzymes perform ˚. the catalytic function. Both of these functions require a resolution in the range of 2–3 A Furthermore, AlphaFold is still slow to perform complex tasks and may take several days to arrive at a prediction. In real-time biological situations and in protein design requiring the structure of multiple protein sequences to be modeled and elucidated, the slow speed of AlphaFold is an obstacle. This is not to minimize the notion that AlphaFold can address these problems that have hitherto remained extremely difficult to solve. However, this ˚ resolution of field has taken a great stride with the introduction of AlphaFold. The 4-A most protein structures, which are comprised of a single folded domain such as an alpha helix or a beta-pleated sheet from structure prediction, continues to increase and will ramp up the structural conformation for many proteins, thus helping drug discovery and basic biology at a level that was hitherto difficult to imagine. Similarly, progress in cryo-electron microscopy could also add to protein structure analyses and allow a mechanistic approach to understanding the complex, structural, biological, and quantitative basis of a biological function. One of the major impediments to drug discovery is the complexity of protein structures as simple folding rules cannot be easily applied to structure prediction, given that the folding of a peptide is governed by quantum mechanics to predict the optimum or most favored structure. Currently, quantum analysis of a protein structure is still beyond the

Artificial intelligence in precision medicine

realm of available computational programs, as predicting possible protein conformations is a Herculean task. New computational programs in structure predictions provide a glimmer of hope to approach protein structure prediction.

20.5.3 AI and precision medicine in COVID-19 research COVID-19 has impacted the world in an unprecedented manner; the pandemic caused by severe acute respiratory syndrome corona virus 2 (SARS-COV2) was named COVID-19 (coronavirus-induced disease 2019), which began from a wet market in Wuhan, China, but soon engulfed the entire world. Globally, millions lost their lives and that number exceeds the total death toll of World War I, World War II, the Vietnam War, the Iraq War, and the Afghanistan conflict combined. There were travel restrictions, quarantines, mandates on the use of masks, social distancing, and instructions of hygiene such as washing hands and avoiding public gatherings. A number of approaches have explored artificial intelligence to help in the fight against the COVID-19 pandemic. These include molecular biology and genetics, elucidating the viral genome and encoded protein epidemiology, studying the spread of the disease, drug development, including designing vaccines and monoclonal antibodies, repurposing known drugs for the treatment of COVID-19, and the socioeconomic aspect of the crisis. Fig. 20.5 shows the use of AI in the rapid screening of COVID-19 patients such as using X-ray, ultrasound, and MRI (CT) images, clinical characteristics, electronic health records, and diagnosis using speech, cough, and other parameters. AI-based algorithms have been in the forefront of combatting the pandemic [161]. Table 20.3 shows how AI-based deep learning, machine learning, artificial neural networks, and novel algorithms have been used to study and diagnose COVID-19 [162–164].

20.5.4 New-generation sequencing and rapid identification of SARSCOV2 variants With the advent of new-generation sequencing and sequence alignment algorithms, vigorous efforts to design and produce an anti-SARS-COV2 vaccine could be realized in a matter of days. If not for the timely introduction of the mRNA-based BioNTech/Pfizer vaccine and later the Moderna vaccine and a host of other anti-SARS-COV2 vaccines, and their early emergency-use approvals by the respective government agencies, the death toll may still be rising. The triumph of modern medicine against such a powerful and invisible pathogen largely came from the multidisciplinary approach to counter the pandemic. These dedicated researchers included immunologists, molecular biologists, geneticists, virologists, computational scientists, pharmacologists, public health scientists, and clinical staff, from physicians, nurses, and paramedic workers to social workers and community activists, and government agencies. The success of mRNA and other vaccines can be attributed to the early availability of the genomic sequence of

551

552

A handbook of artificial intelligence in drug delivery

Artificial Intelligence Strategy in COVID-19 Diagnosis

Machine Learning

Clinical Characteristics CT Scans X-Ray Images

Diagnosis

Electronic Health Records Deep Learning

COVID-19 ?

Ultrasound Images Lab Indications Cough Pattern

Fig. 20.5 An AI strategy for COVID-19 diagnosis: a combination of machine learning (ML) and deep learning (DL) algorithms are used in the analyses of X-ray, CT, and ultrasound images, electronic medical records, laboratory indications, and clinical characteristics. These combined approaches help in a much improved and rapid diagnosis of COVID-19. Table 20.3 Use of machine learning and artificial intelligence in COVID-19 research. Title

Remarks

References

The role of good governance in the race for global vaccination during the COVID-19 pandemic

Data on six World Bank good governance indicators for 172 countries for 2019 and machine learning methods (k-means method and principal component analysis) were used to cluster countries based on these indicators and COVID-19 vaccination rates The authors developed a hybrid machine learning/deep learning model to classify patients into two outcome categories: non-ICU and ICU; death. A fully 3D patient-level CNN classifier on baseline CT images was used as a feature extractor. A classifier was built on the reduced feature space using the CatBoost gradient boosting algorithm and reached a probabilistic AUC of 0.949 on the holdout test set

Tatar et al. Sci. Rep. 11, 22440 (2021)

A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data

Chieregato et al. Sci. Rep. 12, 4329 (2022)

Artificial intelligence in precision medicine

Table 20.3 Use of machine learning and artificial intelligence in COVID-19 research—cont’d Title

Remarks

References

A clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography

The authors developed an AI system that can diagnose COVID-19 pneumonia using CT scans and allowed prediction of progression to critical illness. This AI approach can assist in the evaluation of drug treatment effects with CT quantification A quality-by-design (QbD) framework is a cost-yield optimized method that enhances the production of RNA vaccines against known and future viral pathogens Instead of using the current VEEV-based siRNA platform for vaccine development, this study focuses on developing synthetic self-replicating RNA vaccines. This new drug development approach is being evaluated for its effectiveness against infectious diseases and oncology The authors have developed a mortality risk prediction model for COVID-19 (MRPMC) based on multiple machine learning methods. This model used the clinical data of patients on admission and accurately predicted mortality risk up to 20 days in advance The authors established a machine learning model that predicted COVID-19 test results with high accuracy using parameters such as age (60 years), sex, contact with an infected individual, and initial COVID-19 symptoms. This model is useful when COVID-19 testing resources are limited

Kang et al. Cell 181(6), P1423–-1433.e11, 2020

Quality by design modeling to support rapid RNA vaccine production against emerging infectious diseases

Next-generation self-replicating RNA vectors for vaccines and immunotherapies

A machine learning-based early warning system enables accurate mortality risk prediction for COVID-19

Machine learning-based prediction of COVID-19 diagnosis based on symptoms

Van de et al. NPJ Vaccines 6, 65 (2021)

Aliahmad et al. Cancer Gene Ther. (2022). https://doi.org/10. 1038/s41417-02200435-8

Gao et al. Nat. Commun. 11, 5033 (2020)

Zoabi et al. npj Digital Med. 4, 3 (2021)

Continued

553

554

A handbook of artificial intelligence in drug delivery

Table 20.3 Use of machine learning and artificial intelligence in COVID-19 research—cont’d Title

Remarks

References

A proficient approach to forecast COVID-19 spread via optimized dynamic machine learning models

The authors developed a Gaussian process regression (GPR) model in which the optimized dynamic machine learning approach was taken into account to forecast the COVID-19 cases with 95% confidence intervals Based on clinical features such as patients’ COVID-19 symptoms, blood test results, demographics, and medical history, the authors have developed a machine learning program that provides early identification of patients at risk for clinical deterioration. Considering the deterioration of COVID patients within the next 7–30 h, their model achieved an area under the ROC curve of 0.84 and an area under the precision-recall curve of 0.74 The risks of COVID-19 vaccines still remain as some of the COVID variants are vaccineresistant. The artificial intelligence approach helps in selecting potential drug candidates, faster diagnosis of patients, and drug repurposing of therapeutics for the treatment of COVID-19 A tool called Thoracic VCAR software (GE Healthcare, Italy) has been used for diagnosis, which can provide a clear, fast, and concise report for physicians. Although CT is a highly sensitive tool, it does not recognize COVID-19. The (AI) software is extremely useful for facilitating COVID diagnosis

Alali et al. Sci. Rep. 12, 2467 (2022)

A machine learning model for predicting deterioration of COVID-19 inpatients

Fighting COVID-19 with artificial intelligence

Artificial intelligence to codify lung CT in Covid-19 patients

Noy et al. Sci. Rep. 12, 2630 (2022)

Monteleone et al. Methods Mol. Biol. 2390, 103–112 (2022)

Belfiore et al. Radiol. Med. 125(5), 500–504 (2020)

Artificial intelligence in precision medicine

Table 20.3 Use of machine learning and artificial intelligence in COVID-19 research—cont’d Title

Remarks

References

Artificial intelligence and machine learning to fight COVID-19

The White House, on March 16, 2020, issued a call to global artificial intelligence (AI) researchers to assist in COVID19-related research. As a result, Allen Institute and partner organizations used the large-scale data of COVID-19 patients and also used advanced machine learning algorithms to better understand the pattern of COVID-19 spread, diagnose with high accuracy and speed, develop potential drug candidates, and identify patients at a high risk, based on their genetic factors and immunity

Alimadadi et al. Physiol. Genomics 52(4), 200–202 (2020)

SARS-COV2 and the identity of the encoded proteins, including the spike protein that has been shown to provide attachment of the virus to the host cell surface. This infectious disease brings into focus the necessity of identifying factors that impact disease susceptibility. For instance, the emergence of SARS-COV2, which has differential effects on susceptibility based on age, sex, and comorbidities, highlights the importance of precision medicine in identifying and mitigating risk factors [159]. In addition, studies have also demonstrated that the severity of an infection is correlated with the variant status and APOE genotype [165]. A number of genetic variants have been identified since the first reports of the Wuhan SARS-COV2 virus, namely, the Alpha variant (B.1.1.7), identified in the United Kingdom in December 2020, followed by the Beta variant (B.1.351), first discovered in South Africa in late 2020. These variants were followed by the Gamma (P.1) variant, first identified in Brazil in January of 2021, and the Delta variant (B.1.617.2) first reported in India in December 2020. Among these, another South African SARS-COV2 variant Omicron (B.1.1.529) was first discovered in November 2021. It is remarkable that both Beta and Omicron variants were identified in South Africa, highlighting the significance of having disease monitoring spread uniformly around the globe. In the absence of disease monitoring, i.e., where disease surveillance is poor, there is a greater risk of new variants of COVID-19 showing up and spreading with no limits and the possibility of entirely new diseases cropping up. The success in the rapid identification of these viral

555

556

A handbook of artificial intelligence in drug delivery

variants is due to the great advances made in genomic sequencing technologies and artificial intelligence-based sequence alignment tools in helping genome analysis at a rapid pace. These approaches have tackled other viral outbreaks such as Ebola, Dengue, and Zika and also more familiar diseases such as tuberculosis and AIDS. The World Health Organization (WHO) had declared Delta and Omicron as variants of concern (VOC) due to their potential to cause rapid COVID-19. The Omicron variant is highly unusual due to a large number of genetic changes in the sequence of the spike protein (>30 mutations), including T91 in the envelope protein, P13L, E31del, R32del, S33del, R203K, and G204R in the nucleocapsid protein, D3G, Q19E, and A63T in the matrix, and N211del/L212I, Y145del, Y144del, Y143del, G142D, T95I, V70del, H69del, and A67V in the N-terminal domain of the spike. In addition, there are significant mutations in the receptor-binding domain of the spike protein of the virus (Y505H, N501Y, Q498R, G496S, Q493R, E484A, T478K, S477N, G446S, N440K, K417N, S375F, S373P, S371L, G339D). Mutation D796Y is identified in the fusion peptide of the spike, and the heptad repeat 1 of the spike protein also harbors L981F, N969K, and Q954H mutations. There are numerous other mutations in nonstructural proteins and spike proteins. The Omicron variant has been found to show more than 10-fold viral infectivity than the original Wuhan virus and has also shown breakthrough infections in individuals previously vaccinated against the original SARS-COV2 spike protein [166].

20.5.5 Epidemiological models and COVID-19 severity The recent COVID-19 variant “Omicron” has been spreading more rapidly from one person to another than the original SARS-COV2 virus, although Omicron-infected patients show less severe symptoms of the disease. However, it is still unknown how easily these viruses spread and infect people, even if they are vaccinated. In addition, another COVID variant Delta is more contagious than the original one, and patients with Delta are at a greater risk if not vaccinated. It is also predicted by scientists that more new variants of COVID-19 may occur at any time. Although current vaccines such as Pfizer and Moderna are working against COVID-19 variants, scientists are in the process of developing a universal vaccine called the “pan-coronavirus vaccine” to combat all present and hopefully future variants of COVID-19. Besides vaccines, COVID-19 diagnostic testing plays a critical role in preventing person-to-person transmission by isolating patients when they test COVID-positive. Since the number of COVID-19 patients is growing worldwide with different variants and there is no effective drug to treat COVID-19 patients, an effective and rapid diagnosis strategy is needed to fight against COVID-19. In addition to the variants of concern, such as the Delta and Omicron, several other viral variants have been identified by genome sequencing, but, since these have not been associated with the incidence of severe disease, these variants are characterized as variants of interest (VOI), which may cause enhanced transmission of the virus, a decrease in the ability of antibodies to neutralize the variants (antibodies from natural infection or due to

Artificial intelligence in precision medicine

vaccination), or a decrease in the ability to evade viral detection [166,167]. The CDC and the WHO have declared eight different variants as variants of interest, namely, Epsilon (B.1.427 and B.1.429), Zeta (P.2), Eta (B.1.525), Theta (P.3), Iota (B.1.526), Kappa (B.1.617.1), Lambda (C.37), and Mu (B.1.621). Artificial intelligence is an important tool to combat infectious diseases, including detecting the severity of disease stratified on age and sex and developing drugs and vaccines for combating the disease [168]. In a recent AI implementation in screening for SARS-COV2 infection, patient triage data, including vital signs and full blood counts, have been used to screen for infection and have successfully identified negative results under 30 min compared to PCR-based methods that would usually take longer from a nasal swab. These results strongly encourage developing and expanding such algorithms to quickly diagnose and treat SARS-COV2 and other infections [169]. Assaf et al. [170] developed an artificial neural network to screen the severity of COVID-19 patients with respiratory issues and lack of oxygen saturation. They used three different machine learning models and diagnosed patients at a high risk of deterioration. Using the APACHE II score, the authors evaluated 6995 patients with 92% accuracy and identified that white blood cell count, oxygen saturation level, etc. are the most contributory variables to their models. Although elderly people are at a higher risk of COVID-19, many young adults also suffered from severe symptoms and died due to their weak and compromised genetic and physiological conditions. Therefore, early assessment of severe prognosis is essential for treatment by providing intensive care, intubation, ventilation, etc. To address these issues, Kocadagli et al. [171] developed a hybrid machine learning (ML) approach based on genetic algorithms and information complexity. A total of 166 inpatients of different ages and genders were evaluated based on their demography, symptoms, blood test results, and health histories. Their hybrid ML models accurately predicted the potential risk factors in COVID-19 patients that allow their prompt treatment. Furthermore, to reduce the spread of COVID-19, the Food and Drug Administration (FDA) of the United States has recently approved molecular and antigen-based at-home rapid COVID-19 testing kits. An individual can test and obtain a result at home within 15–30 min. This rapid test would help individuals receive treatment in a timely manner, if the test result is positive. For individuals with a negative test, they may attend any indoor gathering, join offices, and perform other activities without any anxiety. Table 20.3 shows how COVID-19 has attracted a number of AI researchers in combatting the pandemic in diagnosis, drug discovery, epidemiology, public health, and vaccine research.

20.5.6 AI in innovative diagnosis approach and analysis of cough pattern An important lesson learned from the COVID-19 pandemic is to improve virus detection by collecting samples from SARS-COV2-infected individuals and make chest X-ray images, as discussed previously. As the virus is known to affect the respiratory and

557

558

A handbook of artificial intelligence in drug delivery

pulmonary systems, markers affecting the speech and cough pattern of COVID-19 have gained attention due to AI-based screening of speech and cough databases that have recently become available. Audio signals generated from human cough, speech, and ultrasound can be analyzed using pattern recognition to screen, monitor, and diagnose symptoms and spread awareness of the disease. These patterns can be well-characterized by machine learning techniques. Several efforts have been taken to develop automated systems, which may facilitate a noninvasive and simple mechanism to study bioinformation in human speech, cough, and breathing modalities [172–175] (Fig. 20.6). Sattar [176] proposed an interesting novel approach that depends on time domain using only phase 1 data that are derived from most cough events, generating plausible click sequences. These consist of clicks for various cough patterns obtained from COVID-19 cases. A click sequence, which is obtained from the phase slope function of an input signal and is utilized to compute interclick intervals (ICIs), resulting in a scoring index (SI), is derived depending on the CV (coefficient of variation) of the extracted interclick intervals (ICIs). Furthermore, the probability density function of the output clicks can be determined and does not require adjustment of parameters. Using the clinically annotated NoCoCoDa (Novel Coronavirus Cough Database), the experimental results obtained from real-recorded COVID-19 cough data show that the proposed

Audio Signal, Including Speech Analysis for Screening and Diagnosis of COVID-19

Capturing Mechanisms

Capturing and Analysis of Signals

Applicaon and Use

Cough

Capturing and Processing Audio Signals from Diverse Sources to Screen and Diagnose COVID-19

Phone Conversaon

Uploads on Web-Interface

Breathing

Developing Labelled Databases For Research

“Hello, Hi”

Speech

Fig. 20.6 An AI strategy for COVID-19 diagnosis: a combination of machine learning (ML) and deep learning (DL) algorithms have been used in the analyses of X-ray, CT, and ultrasound images, electronic medical records, laboratory indications, and clinical characteristics. These combined approaches help in a much improved and rapid diagnosis of COVID-19.

Artificial intelligence in precision medicine

time-domain technique has great potential in automatic cough pattern processing to monitor the disease progression of COVID-19 patients. Similarly, Pahar et al. [177] showed the power of transfer learning and bottleneck feature extraction in diagnosing COVID-19 based on recordings of speech, breath, and cough, using a contact-free smartphone-based method. They used databases of these patterns (cough, speech, and sneeze but free from COVID-19 labels) to retrain DNNs (deep neural networks), namely, CNN, an LSTM, and a Resnet50, and these pretrained networks were eventually refined using smaller datasets of cough patterns from COVID-19 labels either for transfer learning or as a bottleneck feature extractor. Their data suggest that a Resnet50 classifier trained with this approach provides near-optimal performances across all databases under the receiver operating chrematistics (ROC AUC) of 0.98 for coughs, 0.94 for breaths, and 0.92 for speech labels, suggesting that cough provides the strongest COVID-19 signature, with a reduced standard deviation of the classifier AUCs, monitored over the outer folds during nested cross-validation, and a platform for improved generalization, providing automatic disease detection with a consistent performance. Whether an AI system can assess COVID-19 by simply listening to the cough or breathing pattern is nontrivial, and one must consider several supplemental requirements, such as a clinically sound diagnosis, as cough is known to have many other respiratory diseases modulating it, including asthma, chronic obstructive pulmonary disorder (COPD), cold, influenza, and whooping cough, which may be confounding factors. Thus, ML and DL approaches may become empirical. With improved AI techniques and application of feature selection in deep architectures, by combining diverse classifiers such as cough, breath, speech, enhancement, and adaptation to allow the use of smartphone and similar mobile modules/platforms, a simple diagnosis paradigm may emerge.

20.6 Challenges in AI One example of the challenges in AI is IBM’s Supercomputer Watson for Oncology. It is significant in the AI field since it can identify cancer treatment options that human clinicians may fail to recognize. It can store and analyze vast amounts of data, such as the history of clinical trials, both big and small, patient’s histories, and genetic alterations in human genome such as single-nucleotide polymorphisms (SNPs), variations of gene copy number, deletions, insertions, chromosomal translocations, and gender-specific variations. Additionally, complex algorithms in neural network-based learning, such as the “error backpropagation” model, help Watson “learn” from such analytical challenges. A significant handicap for Watson for Oncology is that it is not widely used in cancer treatment clinics. Most physicians prescribe a cancer treatment based on their own experience and training, and WFO is merely used to confirm the decision of that treatment. Countries such as China, India, and South Korea have utilized the Watson for Oncology,

559

560

A handbook of artificial intelligence in drug delivery

but since the program’s training is largely based on the clinical practices in the United States, the usefulness of Watson has been limited, as the genetic background, environmental factors, and the choice of diet of Asian populations are significantly different from those of the US population. A related problem is the lack of FDA oversight for WFO diagnosis recommendations. As WFO deals with real cancer patients, it is vital that its performance is critically appraised and compared with human experts to determine whether WFO performs at the level of expertise and claims for which this has been advertised. Furthermore, such evaluations may still lack some aspects of ethical issues emanating from WFO’s treatment options, and, thus, we are still not sure whether WFO is free from causing any harm to patients.

20.7 Future prospects and issues in artificial intelligence and precision medicine In spite of the major breakthroughs in how AI algorithms may help in drug discovery, imaging, and treatment options, there is a huge gap in harnessing a large amount of omics data, as integrating complex networks of biological, chemical, and clinical information into one analyzable source is a complicated process for evidence-based medical practice. Translating genetic and other information into a clinically useful intervention to patients is still a distant goal since it is necessary to build such databases with a large number of patients, with diverse ethnic and cultural backgrounds, and other environmental and social factors. This may be partly mitigated through improvement in electronic health records (EHRs) concerning crucial medical information. For example, little is available to decipher drug resistance, which may be better understood by sequencing technology and identifying a shared basis for drug resistance [178–180]. Most of the biomedical information is buried in formats that are not structured and are not amenable to computing and AI analyses, such as patents, books, publications, histological data, and clinical records; however, natural language processing (NLP) algorithms may provide a remedy to this problem. NLP may utilize machine learning to extract relevant key elements such as names of genes, proteins, drugs, and descriptions of the relevant disease, providing links to pathology. There is also a “jargon gap” between biologists and AI engineers who use different concepts and languages. Merely feeding data without appropriate expertise on harnessing what is critical may lead to little or no progress. Nevertheless, these NLP interconnections can enable more sophisticated predictions on how to develop therapies that safely and effectively target a particular medical condition. There are thousands of diseases affecting millions of patients, but effective treatments are available for only a small percentage of these ailments. Big efforts have been taken for discovering new uses for old

Artificial intelligence in precision medicine

drugs, from a collection of more than 4000 FDA-approved drugs, as repurposing of registered drugs for new conditions may bring about new insights. For example, there is a possible cure for the intellectual developmental disorder fragile X syndrome, which may enter clinical trials in the near future [57,181]. Future opportunities are obvious, and AI-based techniques, such as “machine learning” play a big role in drug discovery and drug development workflows, which may provide efficiency and success in future endeavors. In the final analysis, AI can bring huge gains to the practice of medicine and the drug discovery process, as improved data access can allow better diagnosis and translational research [19]. Thus, merging medicine and AI promises to create specialized and high-performance health care, with superior personalized disease management and precision diagnosis; however, for the optimal use of these systems, there is an increasing demand for large numbers of electronic personal health records that could be accessed with the constrains of trust, privacy, and openness. For proper integration of various technological approaches into a new health-care paradigm, there is a need to identify the right tools for the desired goals. International collaborations such as those between premier institutions like MIT in the United States and CNRS in France are good initiatives, such as an origami robot, by Dr. Daniela Rus, which can move across a simulated stomach to remove batteries and may deliver specific drugs. Similarly, Dr. Ninon Burgos of CNRS, France, is using AI to study dementia. Nevertheless, basic questions to be addressed are: who will have access to the global view of a patient’s health data? and who will regulate the AI processing of these data? There is also a need for a dialogue between different experts from various disciplines, including engineers, researchers, physicians, academics of medical computations, and mathematicians. There is a big gap between universal electronic health record (EHRs) and the complexity of the US HIPPA law that controls patient health data, being both underprotective and overprotective, with data privacy and accessibility issues. Like the United States, the “General Data Protection Regulation” (GDPR) law regulates data access in the European Union. Whether AI and ethical issues can be adequately addressed remains an emerging challenge to precision medicine, and it should be ascertained whether there is a need for human supervision.

Acknowledgments Many interesting articles were not included in the list of references; we regret not discussing several important developments in the use of AI in PM, due to limitations of space. We are grateful to Dr. Moahmed-Ramzi Temanni for invaluable comments and critique of this chapter, Dr. Sivakumar Murugasen for help with the references, and Master Azeem O. Siddiqui for inspiring communications during the writing. Authors do not have any conflict of interest in the publication of this chapter.

561

562

A handbook of artificial intelligence in drug delivery

References [1] Y. LeCun, Y. Bengio, G. Hinton, Deep learning, Nature 521 (2015) 43644. [2] A.M. Alaa, M. van der Schaar, Prognostication and risk factors for cystic fibrosis via automated machine learning, Sci. Rep. 8 (1) (2018) 11242. [3] P.J. Castaldi, A. Boueiz, J. Yun, R.S.J. Estepar, J.C. Ross, G. Washko, M.H. Cho, C.P. Hersh, G.L. Kinney, K.A. Young, E.A. Regan, Machine learning characterization of COPD subtypes: insights from the COPD Gene study, Chest 157 (5) (2020) 1147–1157. [4] C.W. Seymour, et al., Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis, JAMA 321 (20) (2019) 2003–2017. [5] A. Christe, et al., Computer-aided diagnosis of pulmonary fibrosis using deep learning and CT images, Invest. Radiol. 54 (10) (2019) 627. [6] T. Chen, Y. Chen, M. Yuan, M. Gerstein, T. Li, H. Liang, T. Froehlich, L. Lu, The development of a practical artificial intelligence tool for diagnosing and evaluating autism spectrum disorder: multicenter study, JMIR Med. Inform. 8 (5) (2020), e15767. [7] Z.I. Attia, P.A. Noseworthy, F. Lopez-Jimenez, S.J. Asirvatham, A.J. Deshmukh, B.J. Gersh, R.E. Carter, X. Yao, A.A. Rabinstein, B.J. Erickson, S. Kapa, An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction, Lancet 394 (10201) (2019) 861–867. [8] M.J. Zellweger, A. Tsirkin, V. Vasilchenko, M. Failer, A. Dressel, M.E. Kleber, P. Ruff, W. M€arz, A new non-invasive diagnostic tool in coronary artery disease: artificial intelligence as an essential element of predictive, preventive, and personalized medicine, EPMA J. 9 (3) (2018) 235–247. [9] B.J. Ainscough, et al., Deep learning approach to automate refinement of somatic variant calling from cancer sequencing data, Nat. Genet. 50 (2018) 1735–1743. [10] P. Chang, et al., Deep-learning convolutional neural networks accurately classify genetic mutations in gliomas, AJNR Am. J. Neuroradiol. 39 (7) (2018) 1201–1207. [11] S.B. Ginsburg, D.A. Lynch, R.P. Bowler, J.D. Schroeder, Automated texture-based quantification of centrilobular nodularity and centrilobular emphysema in chest CT images, Acad. Radiol. 19 (10) (2012) 1241–1251. [12] G.A. Pinheiro, J. Mucelini, M.D. Soares, R.C. Prati, J.L. Da Silva, M.G. Quiles, Machine learning prediction of nine molecular properties based on the SMILES representation of the QM9 quantumchemistry dataset, J. Phys. Chem. A 124 (47) (2020) 9854–9866. [13] L. Hu, et al., An observational study of deep learning and automated evaluation of cervical images for cancer screening, J. Natl. Cancer Inst. 111 (9) (2019) 923–932. [14] S.A. Alsagaby, Omics-based insights into therapy failure of pediatric B-lineage acute lymphoblastic leukemia, Oncol. Rev. 13 (2) (2019) 435. [15] A. Buzdin, I.I. Skvortsova, X. Li, Y. Wang, Editorial: next generation sequencing based diagnostic approaches in clinical oncology, Front. Oncol. 10 (2021) 635555. [16] S. Supplitt, P. Karpinski, M. Sasiadek, I. Laczmanska, Current achievements and applications of transcriptomics in personalized cancer medicine, Int. J. Mol. Sci. 22 (3) (2021) 1422. [17] A.M. Tsimberidou, E. Fountzilas, M. Nikanjam, R. Kurzrock, Review of precision cancer medicine: evolution of the treatment paradigm, Cancer Treat. Rev. 86 (2020), 102019. [18] N. Trayanova, From genetics to smart watches: developments in precision cardiology, Nat. Rev. Cardiol. 16 (2) (2019) 72–73. [19] J.C. Denny, F.S. Collins, Precision medicine in 2030-seven ways to transform healthcare, Cell 184 (6) (2021) 1415–1419, https://doi.org/10.1016/j.cell.2021.01.015 (PMID: 33740447). [20] O. Strianese, F. Rizzo, M. Ciccarelli, G. Galasso, Y. D’Agostino, A. Salvati, C. Del Giudice, P. Tesorio, M.R. Rusciano, Precision and personalized medicine: how genomic approach improves the management of cardiovascular and neurodegenerative disease, Genes (Basel) 11 (7) (2020) 747. [21] E.M. Antman, J. Loscalzo, Precision medicine in cardiology, Nat. Rev. Cardiol. 13 (10) (2016) 591–602. [22] F. Sahli-Costabal, K. Seo, E. Ashley, E. Kuhl, Classifying drugs by their arrhythmogenic risk using machine learning, Biophys. J. 118 (5) (2020) 1165–1176.

Artificial intelligence in precision medicine

[23] M. Olivier, R. Asmis, G.A. Hawkins, T.D. Howard, L.A. Cox, The need for multi-omics biomarker signatures in precision medicine, Int. J. Mol. Sci. 20 (19) (2019) 4781. [24] K.H. Yu, M. Snyder, Omics profiling in precision oncology, Mol. Cell. Proteom. 15 (8) (2016) 2525–2536. [25] S.P. Raikwar, et al., Next generation precision medicine: CRISPR-mediated genome editing for the treatment of neurodegenerative disorders, J. NeuroImmune Pharmacol. 14 (4) (2019) 608–641. [26] C. Strafella, V. Caputo, M.R. Galota, S. Zampatti, G. Marella, S. Mauriello, R. Cascella, E. Giardina, Application of precision medicine in neurodegenerative diseases, Front. Neurol. 9 (2018) 701. [27] A. Esteva, B. Kuprel, R.A. Novoa, J. Ko, S.M. Swetter, H.M. Blau, S. Thrun, Dermatologist-level classification of skin cancer with deep neural networks, Nature 542 (7639) (2017) 115–118. [28] D. Traversi, et al., Precision medicine and public health: new challenges for effective and sustainable health, J. Perinat. Med. 11 (2) (2021) 135. [29] R. Fisher, L. Pusztai, C. Swanton, Cancer heterogeneity: implications for targeted therapeutics, Br. J. Cancer 108 (2013) 479–485. [30] P. Henstock, Artificial intelligence in pharma: positive trends but more investment needed to drive a transformation, Arch. Pharmacol. Ther. 2 (2) (2020) 24–28. [31] J.R. Dixon, et al., Integrative detection and analysis of structural variation in cancer genomes, Nat. Genet. 50 (2018) 1388–1398. [32] C. Dupain, et al., Discovery of new fusion transcripts in a cohort of pediatric solid cancers at relapse and relevance for personalized medicine, Mol. Ther. 27 (2019) 200–218. [33] K. Chaudhary, O.B. Poirion, L. Lu, L.X. Garmire, Deep learning-based multiomics integration robustly predicts survival in liver cancer, Clin. Cancer Res. 24 (2018) 1248–1259. [34] N. Biswas, S. Chakrabarti, Artificial intelligence (AI)-based systems biology approaches in multiomics data analysis of cancer, Front. Oncol. 10 (2020) 588221. [35] J. Xu, P. Wu, Y. Chen, Q. Meng, H. Dawood, H. Dawood, A hierarchical integration deep flexible neural forest framework for cancer subtype classification by integrating multi-omics data, BMC Bioinf. 20 (2019) 527. [36] K. Chaudhary, O.B. Poirion, L. Lu, S. Huang, T. Ching, L.X. Garmire, Multimodal meta-analysis of 1,494 hepatocellular carcinoma samples reveals significant impact of consensus driver genes on phenotypes, Clin. Cancer Res. 25 (2019) 463–472. [37] D. Capper, et al., DNA methylation-based classification of central nervous system tumours, Nature 555 (2018) 469–474. [38] D. Wang, J. Gu, Integrative clustering methods of multi-omics data for molecule-based cancer classifications, Quant. Biol. 4 (2016) 58–67. [39] D. Wong, S. Yip, Machine learning classifies cancer, Nature 555 (2018) 446–447. [40] K.P. Murphy, Machine Learning: A Probabilistic Perspective, The MIT Press, Cambridge, MA, 2012. [41] C. Calabrese, et al., Genomic basis for RNA alterations in cancer, Nature 578 (2020) 129–136. [42] S.S. Ho, A.E. Urban, R.E. Mills, Structural variation in the sequencing era, Nat. Rev. Genet. 21 (2019) 1–19. [43] F. Mitelman, B. Johansson, F. Mertens, The impact of translocations and gene fusions on cancer causation, Nat. Rev. Cancer 7 (2007) 233–245. [44] Y. Wang, N. Wu, D. Liu, Y. Jin, Recurrent fusion genes in leukemia: an attractive target for diagnosis and treatment, Curr. Genomics 18 (2017) 378–384. [45] J. Li, W. Wang, J. Wang, Y. Cao, S. Wang, J. Zhao, Viral gene therapy for glioblastoma multiforme: a promising hope for the current dilemma, Front. Oncol. 11 (2021), 678226, https://doi.org/10.3389/ fonc.2021.678226. [46] M.J.P. Chaisson, et al., Multi-platform discovery of haplotype-resolved structural variation in human genomes, Nat. Commun. 10 (2019) 1784. [47] P.H. Sudmant, et al., An integrated map of structural variation in 2,504 human genomes, Nature 526 (2015) 75. [48] I.A. van Belzen, A. Sch€ onhuth, P. Kemmeren, J.Y. Hehir-Kwa, Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology, NPJ Precis, Oncol. 5 (1) (2021) 15.

563

564

A handbook of artificial intelligence in drug delivery

[49] D. Mandelker, O. Ceyhan-Birsoy, Evolving significance of tumor-normal sequencing in cancer care, Trends Cancer Res. 6 (2020) 31–39. [50] B. Liu, et al., Structural variation discovery in the cancer genome using next generation sequencing: computational solutions and perspectives, Oncotarget 6 (2015) 5477–5489. [51] M. Ruffalo, T. LaFramboise, M. Koyuturk, Comparative analysis of algorithms for next-generation sequencing read alignment, Bioinformatics 27 (2011) 2790–2796. [52] H. Li, R. Durbin, Fast and accurate short read alignment with Burrows-Wheeler transform, Bioinformatics 25 (2009) 1754–1760. [53] H. Li, Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM, arXiv (2013), arXiv:1303.3997. [54] S. Kosugi, et al., Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing, Genome Biol. 20 (2019) 117. [55] K. Lin, S. Smit, G. Bonnema, G. Sanchez-Perez, D. de Ridder, Making the difference: integrating structural variation detection tools, Brief. Bioinform. 16 (2015) 852–864. [56] T. Gong, V.M. Hayes, E.K.F. Chan, Detection of somatic structural variants from short-read nextgeneration sequencing data, Brief. Bioinform. (2020), bbaa056. [57] R. Liu, L. Wei, P. Zhang, A deep learning framework for drug repurposing via emulating clinical trials on real-world patient data, Nat. Mach. Intell. 3 (2021) 68–75, https://doi.org/10.1038/s42256-02000276-w. [58] Y. Li, et al., Patterns of somatic structural variation in human cancer genomes, Nature 578 (2020) 112–121. [59] J. Zhang, Y. Hong, P. Xie, Y. Chen, L. Jiang, Z. Yang, G. Cao, Z. Chen, X. Liu, Y. Chen, Y. Wu, Z. Cai, Spatial lipidomics reveals anticancer mechanisms of bufalin in combination with cinobufagin in tumor-bearing mice, Front. Pharmacol. 11 (2021), 593815, https://doi.org/10.3389/fphar.2020. 593815. [60] N. Coudray, P.S. Ocampo, T. Sakellaropoulos, N. Narula, M. Snuderl, D. Feny€ o, A.L. Moreira, N. Razavian, A. Tsirigos, Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning, Nat. Med. 24 (10) (2018) 1559–1567. [61] Y. Fu, et al., Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis, Nat. Cancer 1 (2020) 800–810. [62] J.N. Kather, et al., Pan-cancer image-based detection of clinically actionable genetic alterations, Nat. Cancer 1 (2020) 789–799. [63] C. Jin, et al., Predicting treatment response from longitudinal images using multi-task deep learning, Nat. Commun. 12 (2021) 1851. [64] D.F. Steiner, P.-H.C. Chen, C.H. Mermel, Closing the translation gap: AI applications in digital pathology, Biochim. Biophys. Acta. 1875(1) (2021), 188452. [65] B.E. Bejnordi, et al., Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer, JAMA 22 (2017) 2199–2210. [66] T.C. Hollon, et al., Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks, Nat. Med. 26 (2020) 52–58. [67] X. Liu, et al., Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension, Nat. Med. 26 (2020) 1364–1374. [68] W.O. Hancock, Bidirectional cargo transport: moving beyond tug of war, Nat. Rev. Mol. Cell Biol. 15 (2014) 615–628. [69] Y. Kanai, N. Dohmae, N. Hirokawa, Kinesin transports RNA: isolation and characterization of an RNA-transporting granule, Neuron 43 (4) (2004) 513–525. [70] S. Millecamps, J.P. Julien, Axonal transport deficits and neurodegenerative diseases, Nat. Rev. Neurosci. 14 (2013) 161–176. [71] J.T. Yang, R.A. Laymon, L.S.B. Goldstein, A three-domain structure of kinesin heavy chain revealed by DNA sequence and microtubule binding analyses, Cell 56 (1989) 879–889. [72] A.J. Roberts, T. Kon, P.J. Knight, K. Sutoh, S.A. Burgess, Functions and mechanics of dynein motor proteins, Nat. Rev. Mol. Cell Biol. 14 (2013) 713–726. [73] M.Y. Ali, S. Uemura, K. Adachi, H. Itoh, K. Kinosita Jr., S. Ishiwata, Myosin V is a left-handed spiral motor on the right-handed actin helix, Nat. Struct. Biol. 9 (2002) 464–467.

Artificial intelligence in precision medicine

[74] K. Svoboda, S.M. Block, Force and velocity measured for single kinesin molecule, Cell 77 (5) (2007) 773–784. [75] R.D. Vale, V Myosin, motor proteins: marching stepwise towards a mechanism, J. Cell Biol. 163 (3) (2003) 445–450. [76] M.L. Khan, C.B. Gogonea, Z.K. Siddiqui, M.Y. Ali, R. Kikuno, K. Nishikawa, S.S. Siddiqui, Molecular cloning and expression of the Caenorhabditis elegans klp-3, an ortholog of C terminus motor kinesins Kar3 and ncd, J. Mol. Biol. 270 (5) (1997) 627–639. [77] N. Hirokawa, mRNA transport in dendrites: RNA granules, motors, and tracks, J. Neurosci. 26 (27) (2006) 7139–7142. [78] N.F. Joseph, S. Swarnkar, S.V. Puthanveettil, Double duty: mitotic kinesins and their post-mitotic functions in neurons, Cell 10 (1) (2021) 136. [79] M.Y. Ali, Z.K. Siddiqui, A.B. Malik, S.S. Siddiqui, A novel C-terminal kinesin subfamily may be involved in chromosomal movement in Caenorhabditis elegans, FEBS Lett. 470 (1) (2000) 70–76. [80] M.Y. Ali, S.S. Siddiqui, cDNA cloning and expression of a C-terminus motor kinesin-like protein KLP-17, involved in chromosomal movement in Caenorhabditis elegans, Biochem. Biophys. Res. Commun. 267 (2) (2000) 643–650. [81] S.S. Siddiqui, Metazoan motor models: kinesin superfamily in C. elegans, Traffic 3 (1) (2002) 20–28. [82] S. Maday, A.E. Twelvetrees, A.J. Moughamian, E.L. Holzbaur, Axonal transport: cargo specific mechanisms of motility and regulation, Neuron 84 (2014) 292–309. [83] X.A. Liu, V. Rizzo, S.V. Puthanveettil, Pathologies of axonal transport in neurodegenerative diseases, Transl. Neurosci. 3 (2012) 355–372. [84] B. Carroll, G. Hewitt, V.I. Korolchuk, Autophagy and ageing: implications for age-related neurodegenerative diseases, Essays Biochem. 55 (2013) 119–131. [85] S.C. Correia, G. Perry, P.I. Moreira, Mitochondrial traffic jams in Alzheimer’s disease – pinpointing the roadblocks, Biochim. Biophys. Acta 1862 (2016) 1909–1917. [86] P.M. Douglas, A. Dillin, Protein homeostasis and aging in neurodegeneration, J. Cell Biol. 190 (5) (2010) 719–729. [87] L. Breydo, J.W. Wu, V.N. Uversky, Alpha-synuclein misfolding and Parkinson’s disease, Biochim. Biophys. Acta 1822 (2012) 261–285. [88] G.B. Irvine, O.M. El-Agnaf, G.M. Shankar, D.M. Walsh, Protein aggregation in the brain: the molecular basis for Alzheimer’s and Parkinson’s diseases, Mol. Med. 14 (2008) 451–464. [89] O. Pansarasa, et al., SOD1 in amyotrophic lateral sclerosis: “ambivalent” behavior connected to the disease, Int. J. Mol. Sci. 19 (5) (2018) 1345. [90] A. Didonna, P. Opal, The role of neurofilament aggregation in neurodegeneration: lessons from rare inherited neurological disorders, Mol. Neurodegener. 14 (2019) 19. [91] J.D. Glass, J.W. Griffin, Neurofilament redistribution in transected nerves: evidence for bidirectional transport of neurofilaments, J. Neurosci. 17 (1991) 3146–3154. [92] P.N. Hoffman, R.J. Lasek, The slow component of axonal transport. Identification of major structural polypeptides of the axon and their generality among mammalian neurons, J. Cell Biol. 66 (1975) 351–366. [93] S. Kassem, T.V. Leeuwen, A.S. Lubbe, M.R. Wilson, B.L. Feringa, D.A. Leigh, Artificial molecular motors, Chem. Soc. Rev. 46 (9) (2017) 2592–2621. [94] T.B. Shea, L.A. Flanagan, Kinesin, dynein and neurofilament transport, Trends Neurosci. 24 (2001) 644–648. [95] M. Khalil, et al., Neurofilaments as biomarkers in neurological disorders, Nat. Rev. Neurol. 14 (2018) 577–589. [96] C. Bustamante, Y.R. Chemla, N.R. Forde, D. Izhaky, Mechanical processes in biochemistry, Annu. Rev. Biochem. 73 (2004) 705–748. [97] H. Linke, B. H€ ocker, K. Furuta, N.R. Forde, P.M.G. Curmi, Synthetic biology approaches to dissecting linear motor protein function: towards the design and synthesis of artificial autonomous protein walkers, Biophys. Rev. 12 (2020) 1041–1054. [98] D.A. Leigh, U. Lewandowska, B. Lewandowski, M.R. Wilson, Synthetic molecular walkers, Top. Curr. Chem. 354 (2014) 111–138.

565

566

A handbook of artificial intelligence in drug delivery

[99] S. Can, S. Lacey, M. Gur, A.P. Carter, A. Yildiz, Directionality of dynein is controlled by the angle and length of its stalk, Nature 566 (2019) 407–410. [100] M. Yamagishi, H. Shigematsu, T. Yokoyama, M. Kikkawa, M. Sugawa, M. Aoki, M. Shirouzu, J. Yajima, R. Nitta, Structural basis of backwards motion in kinesin-1-kinesin-14 chimera: implication for kinesin-14 motility, Structure 24 (2016) 1322–1334. [101] M.Y. Ali, H. Lu, C.S. Bookwalter, D.M. Warshaw, K.M. Trybus, Myosin V and kinesin act as tethers to enhance each other’s’ processivity, Proc. Natl. Acad. Sci. U. S. A. 105 (2008) 4691–4696. [102] S.A. Endow, H. Higuchi, A mutant of the motor protein kinesin that moves in both directions on microtubules, Nature 406 (2000) 913–916. [103] A. Varadi, E.K. Ainscow, V.J. Allan, G.A. Rutter, Involvement of conventional kinesin in glucosestimulated secretory granule movements and exocytosis in clonal pancreatic beta-cells, J. Cell Sci. 115 (2002) 4177–4189. [104] T.E. Sladewski, N. Billington, M.Y. Ali, C.S. Bookwalter, H. Lu, E.B. Krementsova, T.A. Schroer, K.M. Trybus, Recruitment of two dyneins to an mRNA-dependent Bicaudal D transport complex, elife 7 (2018). [105] M. Klevanski, F. Herrmannsdoerfer, S. Sass, V. Venkataramani, M. Heilemann, T. Kuner, Automated highly multiplexed super-resolution imaging of protein nano-architecture in cells and tissues, Nat. Commun. 8 (2020) 1552. [106] D.R. Rosen, et al., Mutations in Cu/Zn superoxide dismutase gene are associated with familial amyotrophic lateral sclerosis, Nature 362 (6415) (1993) 59–62. Erratum in: Nature 364(6435), 362. [107] L.G. Bilsland, E. Sahai, G. Kelly, M. Golding, L. Greensmith, G. Schiavo, Deficits in axonal transport precede ALS symptoms in vivo, Proc. Natl. Acad. Sci. U. S. A. 107 (2010) 20523–20528. [108] G.A. Morfini, et al., Axonal transport defects in neurodegenerative diseases, J. Neurosci. 29 (2009) 12776–12786. [109] P. Shi, J. Gal, D.M. Kwinter, X. Liu, H. Zhu, Mitochondrial dysfunction in amyotrophic lateral sclerosis, Biochim. Biophys. Acta 1802 (2010) 45–51. [110] C. M€ unch, R. Sedlmeier, T. Meyer, V. Homberg, A.D. Sperfeld, A. Kurt, J. Prudlo, G. Peraus, C.O. Hanemann, G. Stumm, A.C. Ludolph, Point mutations of the p150 subunit of dynactin (DCTN1) gene in ALS, Neurology 63 (2004) 724–726. [111] K.Y. Soo, M. Farg, J.D. Atkin, Molecular motor proteins and amyotrophic lateral sclerosis, Int. J. Mol. Sci. 12 (2011) 9057–9082. [112] A.A. Brasil, M.D.C. de Carvalho, E. Gerhardt, D.D. Queiroz, M.D. Pereira, T.F. Outeiro, E.C.A. Eleutherio, Characterization of the activity, aggregation, and toxicity of heterodimers of WT and ALS-associated mutant Sod1, Proc. Natl. Acad. Sci. U. S. A. 116 (51) (2019) 25991–26000. [113] N. Bakkar, et al., Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis, Acta Neuropathol. 135 (2018) 227–247. [114] W. Kim, D.Y. Kim, K.H. Lee, RNA-binding proteins and the complex pathophysiology of ALS, Int. J. Mol. Sci. 22 (5) (2021) 2598. [115] M. Zhao, J.R. Kim, R. van Bruggen, J. Park, RNA-binding proteins in amyotrophic lateral sclerosis, Mol. Cells 41 (9) (2018) 818–829. [116] J. Cummings, C. Reiber, P. Kumar, The price of progress: funding and financing Alzheimer’s disease drug development, Alzheimer’s Dement.: Transl. Res. Clin. Interv. 4 (2018) 330–343. [117] K. Hares, J.S. Miners, A.J. Cook, C. Rice, N. Scolding, S. Love, A. Wilkins, Overexpression of kinesin superfamily motor proteins in Alzheimer’s disease, J. Alzheimers Dis. 60 (2017) 1511–1524. [118] G.B. Stokin, L.S. Goldstein, Axonal transport and Alzheimer’s disease, Annu. Rev. Biochem. 75 (2006) 607–627. [119] P.P. Liu, Y. Xie, X.Y. Meng, J.S. Kang, History and progress of hypotheses and clinical trials for Alzheimer’s disease, Signal Transduct. Target. Ther. 4 (2019) 29. [120] D. Prokopenko, J. Hecker, R. Kirchner, B.A. Chapman, O. Hoffman, K. Mullin, W. Hide, L. Bertram, N. Laird, D.L. DeMeo, C. Lange, Identification of novel Alzheimer’s disease loci using sex-specific family-based association analysis of whole-genome sequence data, Sci. Rep. 10 (1) (2020) 5029.

Artificial intelligence in precision medicine

[121] S.S. Siddiqui, K. Aboshamat, S. Loganathan, Z.K. Siddiqui, Differentiation of stem cells into neuronal lineage: in vitro cell culture and in vivo transplantation in animal models, in: Advances in Application of Stem Cells: From Bench to Clinics, vol. 69, Springer, 2021, pp. 73–102. [122] M. Belica, V. Bobic, M. Badzˇa, N. Sˇolaja, M. Đuric-Jovicica, V.S. Kostic, Artificial intelligence for assisting diagnostics and assessment of Parkinson’s disease—a review, Clin. Neurol. Neurosurg. 184 (2019), 105442. [123] Y. Chu, G.A. Morfini, L.B. Langhamer, Y. He, S.T. Brady, J.H. Kordower, Alterations in axonal transport motor proteins in sporadic and experimental Parkinson’s disease, Brain 135 (7) (2012) 2058–2073. [124] O. Bazgir, S.H. Habibi, L. Palma, P. Pierleoni, S. Nafees, A classification system for assessment and home monitoring of tremor in patients with Parkinson’s disease, J. Med. Signals Sens. 8 (2) (2018) 65–72. [125] A.J. Sweatt, et al., Discovery of distinct immune phenotypes using machine learning in pulmonary arterial hypertension, Circ. Res. 124 (6) (2019) 904–919. [126] J.A. Leopold, B.A. Maron, Precision medicine in pulmonary arterial hypertension: a first step, Circ. Res. 124 (6) (2019) 832–833. [127] E. Mlodzinski, D.J. Stone, L.A. Celi, Machine learning for pulmonary and critical care medicine: a narrative review, Pulm. Ther. 6 (1) (2020) 67–77. [128] M.H. Hsieh, M.J. Hsieh, C.M. Chen, C.C. Hsieh, C.M. Chao, C.C. Lai, An artificial neural network model for predicting successful extubation in intensive care units, J. Clin. Med. 7 (9) (2018) 240. [129] B. Rush, K. Wiskar, C. Fruhstorfer, L.A. Celi, K.R. Walley, The impact of chronic ozone and particulate air pollution on mortality in patients with sepsis across the United States, J. Intensive Care Med. 35 (10) (2020) 1002–1007. [130] M. Uddin, Y. Wang, M. Woodbury-Smith, Artificial intelligence for precision medicine in neurodevelopmental disorders, NPJ Digit. Med. 2 (2019) 112. [131] P. Mathur, S. Srivastava, X. Xu, J.L. Mehta, Artificial intelligence, machine learning, and cardiovascular disease, Clin. Med. Insights Cardiol. 14 (2020) 1–9. [132] P. Shah, F. Kendall, S. Khozin, R. Goosen, J. Hu, J. Laramie, M. Ringel, N. Schork, Artificial intelligence and machine learning in clinical development: a translational perspective, NPJ Digit. Med. 2 (2019) 69. [133] T.S. Toh, F. Dondelinger, D. Wang, Looking beyond the hype: applied AI and machine learning in translational medicine, EBioMedicine 47 (2019) 607–615. [134] F.V. Filipp, Opportunities for artificial intelligence in advancing precision medicine, Curr. Genet. Med. Rep. 7 (2019) 208–213. [135] C.R. Jack Jr., H.J. Wiste, S.D. Weigand, T.M. Therneau, V.J. Lowe, D.S. Knopman, H. Botha, J. Graff-Radford, D.T. Jones, T.J. Ferman, B.F. Boeve, Predicting future rates of tau accumulation on PET, Brain 143 (10) (2020) 3136–3150. [136] G. Campanella, et al., Clinical-grade computational pathology using weakly supervised deep learning on whole slide images, Nat. Med. 25 (2019) 1301–1309. [137] G. Pinheiro, T. Pereira, C. Dias, C. Freitas, V. Hespanhol, J.L. Costa, A. Cunha, H.P. Oliveira, Identifying relationships between imaging phenotypes and lung cancer-related mutation status: EGFR and KRAS, Sci. Rep. 10 (1) (2020) 1–9. [138] K.A. Dill, S.B. Ozkan, M.S. Shell, T.R. Weikl, The protein folding problem, Annu. Rev. Biophys. 37 (2008) 289–316. [139] C.B. Anfinsen, Principles that govern the folding of protein chains, Science 181 (4096) (1973) 223–230. [140] A.W. Senior, et al., Improved protein structure prediction using potentials from deep learning, Nature 577 (2020) 706–710. [141] W. Zheng, Y. Li, C. Zhang, R. Pearce, S.M. Mortuza, Y. Zhang, Deep-learning contact-map guided protein structure prediction in CASP13, Proteins: Struct. Funct. Genet. 87 (12) (2019) 1149–1164. [142] R. Pearce, Y. Zhang, Deep learning techniques have significantly impacted protein structure prediction and protein design, Curr. Opin. Struct. Biol. 68 (2021) 194–207.

567

568

A handbook of artificial intelligence in drug delivery

[143] A. Kryshtafovych, T. Schwede, M. Topf, K. Fidelis, J. Moult, Critical assessment of methods of protein structure prediction (CASP)—round XIV, Proteins: Struct. Funct. Genet. 89 (12) (2021) 1607–1617. [144] E. Callaway, ‘It will change everything’: DeepMind’s AI makes gigantic leap in solving protein structures, Nature 588 (2020) 203–204. [145] A.W. Senior, et al., Using AI for Scientific Discovery: A Solution to a 50-Year-Old Grand Challenge in Biology, 2021. https://deepmind.com/blog/article/AlphaFold-Using-AI-for-scientificdiscovery. [146] A. Senior, J. Jumper, D, Hassabis, AlphaFold. Using AI for Scientific Discovery, December 2018. https://www.deepmind.com/blog/alphafold-using-ai-for-scientific-discovery-2020. [147] J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Zˇ´ıdek, A. Potapenko, A. Bridgland, Highly accurate protein structure prediction with AlphaFold, Nature 596 (7873) (2021) 583–589. [148] Amisha, P. Malik, M. Pathania, V.K. Rathaur, Overview of artificial intelligence in medicine, J. Family Med. Prim. Care 8 (7) (2019) 2328–2331, https://doi.org/10.4103/jfmpc.jfmpc_440_19. [149] C. Cao, F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, Z. Xie, Deep learning and its applications in biomedicine, Genomics Proteomics Bioinformatics 16 (1) (2018) 17–32. [150] T. Davenport, R. Kalakota, The potential for artificial intelligence in healthcare, Future Healthc. J. 6 (2) (2019) 94–98. [151] A.A. Kalinin, G.A. Higgins, N. Reamaroon, S. Soroushmehr, A. Allyn-Feuer, I.D. Dinov, K. Najarian, B.D. Athey, Deep learning in pharmacogenomics: from gene regulation to patient stratification, Pharmacogenomics 19 (7) (2018) 629–650. [152] F. Zhong, et al., Artificial intelligence in drug design, Sci. China Life Sci. 61 (2018) 1191–1204. [153] D. Lyumkis, Challenges and opportunities in cryo-EM single-particle analysis, J. Biol. Chem. 294 (13) (2019) 5181–5197. [154] A. Breda, et al., Protein structure, modelling and applications, 2006 May 1 [Updated 2007 Sep 14], in: A. Gruber, et al. (Eds.), Bioinformatics in Tropical Disease Research: A Practical and Case-Study Approach [Internet], National Center for Biotechnology Information (US), Bethesda, MD, 2008. Chapter A06. Available from: https://www.ncbi.nlm.nih.gov/books/NBK6824/. [155] A.M. Lesk, Introduction to Protein Architecture, OUP, Oxford, 2001. [156] A.W. Senior, et al., Protein structure prediction using multiple deep neural networks in the 13th Critical Assessment of Protein Structure Prediction (CASP13), Proteins 87 (2019) 1141–1148. [157] K. Tunyasuvunakool, J. Adler, Z. Wu, T. Green, M. Zielinski, A. Zˇ´ıdek, A. Bridgland, A. Cowie, C. Meyer, A. Laydon, S. Velankar, Highly accurate protein structure prediction for the human proteome, Nature 596 (7873) (2021) 590–596. [158] M. Al-Quraishi, CASP14 Scores Just Came Out and They’re Astounding, Twitter, 2020. https:// twitter.com/MoAlQuraishi/status/1333383634649313280. [159] N.L. Pereira, F. Ahmad, M. Byku, N.W. Cummins, A.A. Morris, A. Owens, S. Tuteja, S. Cresci, COVID-19: understanding inter-individual variability and implications for precision medicine, Mayo Clin. Proc. 96 (2) (2021) 446–463. [160] M. Gupta, C.M. Azumaya, M. Moritz, S. Pourmal, A. Diallo, G.E. Merz, G. Jang, M. Bouhaddou, A. Fossati, A.F. Brilot, D. Diwanji, CryoEM and AI reveal a structure of SARS-CoV-2 Nsp2, a multifunctional protein involved in key host processes, Res. Sq. (2021). [161] A. Alharthy, F. Faqihi, M. Abuhamdah, A. Noor, N. Naseem, A. Balhamar, A.A.A.S.B.A.A. Saud, P.G. Brindley, Z.A. Memish, D. Karakitsos, et al., Prospective longitudinal evaluation of point-ofcare lung ultrasound in critically ill patients with severe COVID-19 pneumonia, J. Ultrasound Med. 40 (2021) 443–456. [162] S. Huang, J. Yang, S. Fong, Q. Zhao, Artificial intelligence in the diagnosis of COVID-19: challenges and perspectives, Int. J. Biol. Sci. 17 (6) (2021) 1581–1587. [163] M. Subramanian, K. Shanmuga Vadivel, W.A. Hatamleh, A.A. Alnuaim, M. Abdelhady, V.E. Sathishkumar, The role of contemporary digital tools and technologies in Covid-19 crisis: an exploratory analysis, Expert. Syst. (2021), https://doi.org/10.1111/exsy.12834.

Artificial intelligence in precision medicine

[164] J. Wang, X. Yang, B. Zhou, J.J. Sohn, J. Zhou, J.T. Jacob, K.A. Higgins, J.D. Bradley, T. Liu, Review of machine learning in lung ultrasound in COVID-19 pandemic, J. Imaging 8 (3) (2022) 65. [165] A. Zhou, M. Sabatello, G. Eyal, S.S. Lee, J.W. Rowe, D.F. Stiles, A. Swanson, P.S. Appelbaum, Is precision medicine relevant in the age of COVID-19? Genet. Med. 13 (2021) 1–2. [166] A. Aleem, A.B. Akbar Samad, A.K. Slenker, Emerging variants of SARS-CoV-2 and novel therapeutics against coronavirus (COVID-19), in: StatPearls, StatPearls Publishing, Treasure Island (FL), 2022. [167] G. Augusto, M.O. Mohsen, S. Zinkhan, X. Liu, M. Vogel, M.F. Bachmann, In vitro data suggest that Indian delta variant B.1.617 of SARS-CoV-2 escapes neutralization by both receptor affinity and immune evasion, Allergy 77 (1) (2022) 111–117. [168] B. Malone, et al., Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs, Sci. Rep. 10 (1) (2020) 22375. [169] A.A.S. Soltan, S. Kouchaki, T. Zhu, D. Kiyasseh, T. Taylor, Z.B. Hussain, T. Peto, A.J. Brent, D.W. Eyre, D.A. Clifton, Rapid triage for COVID-19 using routine clinical data for patients attending hospital: development and prospective validation of an artificial intelligence screening test, Lancet Digit. Health 3 (2) (2021) e78–e87. [170] D. Assaf, et al., Utilization of machine-learning models to accurately predict the risk for critical COVID-19, Intern. Emerg. Med. 15 (8) (2020) 1435–1443. [171] O. Kocadagli, E. Deniz, D. Ilter, Artificial Intelligence Techniques and Information Criteria in Statistical Modeling, 2020. [172] Y.Y. Broza, R. Vishinkin, O. Barash, M.K. Nakhleh, H. Haick, Synergy between Nanomaterials and Volatile Organic Compounds for Non-Invasive Medical Evaluation, vol. 47, Royal Society of Chemistry, 2018. [173] C.E. Davis, M. Schivo, N.J. Kenyon, A breath of fresh air – the potential for COVID-19 breath diagnostics, EBioMedicine 63 (2021) 2020–2021. [174] S. Grassin-Delyle, C. Roquencourt, P. Moine, G. Saffroy, S. Carn, N. Heming, J. Fleuriet, H. Salvator, E. Naline, L.-J. Couderc, et al., Metabolomics of exhaled breath in critically ill COVID19 patients: a pilot study, EBioMedicine 63 (2021), 103154, https://doi.org/10.1016/j. ebiom.2020.103154. [175] T. Drugman, J. Urbain, N. Bauwens, R. Chessini, C. Valderrama, P. Lebecque, T. Dutoit, Objective study of sensor relevance for automatic cough detection, IEEE J. Biomed. Health Inform. 17 (3) (2013) 699–707. [176] F. Sattar, A fully-automated method to evaluate coronavirus disease progression with COVID-19 cough sounds using minimal phase information, Ann. Biomed. Eng. 49 (9) (2021) 2481–2490. [177] M. Pahar, M. Klopper, R. Warren, T. Niesler, COVID-19 detection in cough, breath and speech using deep transfer learning and bottleneck features, Comput. Biol. Med. 141 (2022) 105153. [178] S. Madhavan, S. Subramaniam, T.D. Brown, J.L. Chen, Art and challenges of precision medicine: interpreting and integrating genomic data into clinical practice, Am. Soc. Clin. Oncol. Educ. Book 38 (2018) 546–553. [179] M. Prosperi, J.S. Min, J. Bian, F. Modave, Big data hurdles in precision medicine and precision public health, BMC Med. Inform. Decis. Mak. 18 (1) (2018) 139. [180] S.J.H. Vijverberg, P. Brinkman, N.W.P. Rutjes, Z.A.H. Maitland-Van Der, Precision medicine in severe pediatric asthma: opportunities and challenges, Curr. Opin. Pulm. Med. 26 (1) (2020) 77–83. [181] T.A. Manolio, P. Goodhand, G. Ginsburg, The international hundred thousand plus cohort consortium: integrating large-scale cohorts to address global scientific challenges, Lancet Digit. Health 2 (11) (2020) e567–e568.

569

CHAPTER 21

Artificial intelligence and machine learning in clinical trial design and application Robert J. Beetela, Uli Chettipallyb, Rajesh Dasha, and Sushant Shankara a HealthPals, Redwood City, CA, United States InnovatorMD, San Francisco, CA, United States

b

21.1 Introduction: Clinical trials in this new world In 2020, COVID-19 changed how health care was approached both in the United States and globally. In the early phases, the vast majority of energy and attention was devoted to containing the pandemic and treating the infected. Toward the end of 2020, that attention expanded to vaccinating people across the globe. What was not being considered at the time were challenges to future health and clinical trials that power new treatments for COVID-19 and non-COVID-19 treatments. Clinical trials had already been increasing in terms of cost, duration, and complexity. Despite the vast resources invested in trial execution, there remained significant risk of not hitting study end points. As a result of COVID-19, new challenges arose. Toward the end of 2020, more than 80% of non-COVID-related clinical trials were indefinitely paused or canceled outright and the ability to recruit patients and conduct studies that were still ongoing was severely hampered. However, many of those challenges could be mitigated through the use of real-world data and modern analytics capabilities. In this chapter, an overview of clinical trial evolution and the current challenges in a postpandemic era will be detailed. In addition, innovation in clinical trial design that leverages learnings from real-world data will be summarized, along with the unique sets of challenges that real-world evidence presents.

21.1.1 History of clinical trials and innovation Although medical experimentation has been well-documented by civilizations for several thousand years, the concept of clinical trials was not clearly described until the 1700s, when observations of sailors with scurvy, with and without orange and lemon “treatment,” were made by James Lind in 1747. Nearly 200 years later, the first randomized controlled trial of an antibiotic was published, and, from there, the current era of A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00021-6

Copyright © 2023 Elsevier Inc. All rights reserved.

571

572

A handbook of artificial intelligence in drug delivery

controlled clinical trials was born, leading to the eventual pervasive exploration of disease progression and management that we have today (Fig. 21.1). Improvements in trial methodology have continually been made in order to better isolate the impact of a particular treatment or clinical intervention. These improvements have often centered around study recruitment, consent, statistical design, inclusion/ exclusion criteria, and patient safety/beneficence. Most of these methodology improvements were instituted to remove investigator or subject bias, such that an objective comparison between groups can be made and logically tied to the singular intervention that the trial aimed to test. Accounting for as many relevant patient variables as possible, and requiring that these remain essentially constant between groups, is at once the greatest strength and weakness of current clinical trial design. These shortcomings are described in the next section.

21.2 Gaps in clinical trials today 21.2.1 Limited applicability A randomized clinical trial design often requires that both the experimental and control groups are homogeneous, such that they differ only in the specific treatment being tested in the study. Although this strives to create near-ideal scientific experimental conditions, the resulting conclusions from the study will undoubtedly have limited applicability to an individual patient or patient cohorts who deviate, even slightly, from the studied population from that trial. For example, a typical cardiovascular clinical trial may have anywhere from 5 to 12 “inclusion criteria” for patients PLUS an additional 15–30 “exclusion criteria,” just to be eligible for enrollment in the trial. As a result, every enrolled subject is part of a highly selective cohort that was designed for the sole purpose of seeing a difference between the control and experimental arms. By definition, although the positive results from such a trial may have a statistically significant impact on a disease state or outcome, they will hardly ever perfectly fit patients who are treated by clinicians in the “real world.” Patients will usually lack many of the “inclusion criteria” of the trial, and they will often possess many of the “exclusion criteria” of the trial as well (Fig. 21.2). What impact does the limited applicability of RCTs have on patient care? Clinicians are forced to go through a “best-fit” mental model of the trial results and do the best they can to incorporate the latest clinical trial data into their practice decisions. Alternatively, clinicians will simply ignore the trial data until such time that a medical society incorporates that study’s results into a new guideline for disease management. However, incorporation of new clinical trial data into society guidelines may take 3–5 years or longer. How can we better understand the applicability of clinical trial data to real-world patients? How can we demonstrate that the results of clinical trials do indeed apply to the general population and not only to this narrow cohort that was selected for the study? We address these questions and others in the following.

AI and ML in clinical trial design

Fig. 21.1 Timeline of the history of artificial intelligence, clinical trials, and computing.

573

574

A handbook of artificial intelligence in drug delivery

Fig. 21.2 Strategic recruitment planning.

21.2.2 Time Clinical trials are often extremely long endeavors. From study design and IRB approval to consent and full study enrollment, most studies are already falling behind. More than 70% of all clinical trials fail to achieve their enrollment targets on time, often because of the stringent enrollment criteria described earlier. Then, the study must be conducted within the required observation period, and, lastly, study closeout may also be lengthy due to requiring in-person patient visits and assessments. Once again using a typical cardiovascular outcomes trial, the study size is usually many thousands of participants and could take 2–3 years to fully enroll them. For cardiovascular outcomes like stroke, heart attack, or death, the observation period usually lasts for at least 3 years. So, the trial itself is at least 6 years of execution, not including the many months of study design and IRB approval.

21.2.3 Cost The Tufts Center for the Study of Drug Development (Tufts CSDD) is an academic nonprofit think tank at Tufts University in Boston, dedicated to researching drug

AI and ML in clinical trial design

development. In 2016, Tufts CSDD published an analysis of 106 randomly selected new drugs and found that the total capitalized costs of drug development were increasing at a rate of 8.5% above general inflation. In addition, it was found that 57% of all clinical trial protocols in all phases had at least one substantial amendment with the most frequent changes, including modifications and revisions, to study volunteer demographics and eligibility criteria. The total median direct cost to implement a substantial amendment for phase II and III protocols is $141,000 and $535,000, respectively. [Amendments reduce number of patients, but at high cost, longer study times. Tufts Center for the Study of Drug Development Impact Report. 1 Jan 2016.] In 2018, Tufts CSDD also reported that rising protocol complexities were hindering study performance, cost, and efficiency. They found that phase III clinical trials have seen the highest increase in complexity in the past 10 years, with the total number of end points rising to 86%. As protocols have grown more complex, site initiation and data management cycle times have increased. [Rising Protocol Complexity Is Hindering Performance while Driving Up Cost of Clinical Trials. Tufts Center for the Study of Drug Development Impact Report. 17 Jul 2018.] In 2020, Tufts CSDD reported that, despite faster new drug approval phases, clinical trial times are taking longer. Data were analyzed spanning from 2014 to 2018, and it was found that although the mean approval phase decreased by 1.9 months, the overall trial times increased by 6.7 months. [Faster New Drug Approval Phases Are More Than Offset by Longer Clinical Times in U.S. Tufts Center for the Study of Drug Development Impact Report. 16 Jul 2020.] RCTs also carry the risk of not hitting the end points with regard to safety or efficacy and not receiving regulatory approval. The Tufts 2016 study also showed that among drugs that enter clinical testing, the probability of a drug being approved is 11.83%. [Amendments reduce number of patients, but at high cost, longer study times. Tufts Center for the Study of Drug Development Impact Report. 1 Jan 2016.] In 1950, approximately 30 drugs were developed for every $1 billion spent on research and development. Today, we get about one-third of a drug for the same price—or to put it another way, it costs close to $3 billion to bring a new medicine to the market. Eroom’s law is the observation that drug discovery is becoming slower and more expensive over time, despite improvements in technology (such as high-throughput screening, biotechnology, combinatorial chemistry, and computational drug design). There are many explanations given for why drug discovery follows Eroom’s law, from cautious regulators to increasing overall R&D costs. However, one of the biggest areas holding back progress is inefficiency in the preclinical, animal testing phase of the drug discovery process. Only 1 in 10 drugs that enter human clinical trials reach the market after preclinical success.

575

576

A handbook of artificial intelligence in drug delivery

Historically, pharmaceutical companies have achieved great success in generating “blockbuster” drugs, which generate annual sales of at least $1 billion in revenue. Common examples of blockbuster drugs include Vioxx, Lipitor, and Zoloft. The return on individual drugs has been decreasing over the years as the cost of development has been increasing.

21.3 Next generation of clinical trials powered by deep technology and AI 21.3.1 External control arm Real-world evidence (RWE) is defined as clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD. RWE can be generated by different study designs or analyses, including, but not limited to, randomized trials, including large simple trials, pragmatic trials, and observational studies (prospective and/or retrospective). Examples of RWD include: Electronic health records (EHRs) Claims and billing activities Product and disease registries Patient-generated data, including those generated in home-use settings Data gathered from other sources that can inform on health status, such as mobile devices External control arms (ECAs) are a specific use for RWD/RWE in which patient cohorts are derived from external, real-world data to provide a comparison control arm for an experimental arm in a clinical trial. ECAs are matched to experimental arms in such a way so as to simulate the effects of randomization by: Reducing the bias associated with confounding factors by distributing those factors equally across experimental and control groups Facilitating causal inference Providing the basis for statistical inference External controls may be used in cases in which conducting a study using a traditional control arm may be unethical or unfeasible, including circumstances in which: The effect size is expected to be large The control treatment is known to be inferior (i.e., providers are unlikely to enroll patients as controls), and when the benefit of experimental treatment becomes clear and patients withdraw from the control arm or cross over into the experimental arm When the experimental drug is being successfully used off-label to treat the condition under investigation The disease is rare There are challenges to patient involvement and retention as in the case of Alzheimer’s

AI and ML in clinical trial design

External control arms have a specific use that has been popularized but rarely achieved. Although the initial methodology to generate an external control arm is common to retrospective views of a dataset, the ultimate goal is to be able to use ECAs as prospective control arms that replace traditional control arm cohorts. ECAs can be used to reduce the necessary sample size for the study control arm and thereby reduce the duration and cost of trials associated with patient recruitment. In addition, ECAs can be used to supplement submission to regulatory bodies and help mitigate the risks associated with regulatory approvals. The FDA uses RWD and RWE to monitor postmarket safety and adverse events and to make regulatory decisions, whereas medical product developers use RWD and RWE to support clinical trial designs (e.g., large simple trials, pragmatic clinical trials) and generate observational studies to produce innovative, new treatment approaches. Due to the additional constraints on clinical trial initiation and conduction imposed by COVID-19 and based on conversations with the FDA, we believe that the FDA will be open to using ECAs in a broader context.

21.3.2 Risk models to optimize cohorts Cohort selection and optimization Clinical trial design involves determining the number of events that need to take place to generate statistically significant results. Researchers then need to estimate the event rates within the target population and thereafter calculate the number of patients that need to be recruited as well as the estimated follow-up period to arrive at the desired number of events. In executing the study, patients are followed up until the predetermined number of events have occurred. For populations with lower event rates, greater numbers of patients must be included in the study and the follow-up duration is longer. For populations with higher event rates, fewer patients are required and the follow-up period is decreased. ML-based risk models can be used to estimate a patient’s risk of experiencing a particular event. These models can be used to identify the subset of patients within a population of interest, who are likely to have higher event rates. By identifying these patients and including them in a study, researchers can generate an enriched study cohort that generates statistically significant results using a smaller number of patients in a shorter period of time. Patient recruitment/site recruitment Clinical trial recruitment is a daunting process. There is an elaborate process that goes into planning a clinical trial. Clinical requirements, cost, and time are the most critical elements of this process. Regulations add another layer to this complexity. There are several challenges to the traditional patient recruitment process, which can ultimately affect the success of a clinical trial.

577

578

A handbook of artificial intelligence in drug delivery

Challenges in patient recruitment are major hurdles that clinical trialists have to overcome. Delays and difficulties in this process account for the increased duration and thus the increased cost and potential failure of the trial. In all, 19% of clinical trials had to be closed because they were not able to recruit enough participants [1]. About 80% of clinical trials are delayed due to challenges in patient recruitment [2]. These delays have caused a near doubling of the clinical trial duration [3]. Delays can affect not only the duration but also the overall cost of the clinical trial. The consequences of these delays are not only cost and time but also the delay in developing science, with a subsequent delay in providing new treatments to people. This poses some ethical challenges [1,4,5]. Here is an example of how social media campaigns can improve this process [6–10] (Fig. 21.3).

21.4 Discussion The ability to isolate and define the contribution of a specific aspect or element of a patient’s care to an overall clinical outcome has always been challenging in medicine. There are a multitude of factors that can contribute to an outcome. Our current, traditional clinical trial approach has produced tried-and-true methodologies to determine whether a specific intervention is superior to, or at least equivalent to, the current standard of care. However, for the many reasons detailed in this chapter, the traditional model of recruitment, enrollment, and study conduction often fails to produce timely and meaningful results in a large percentage of trials. In addition, the resources needed to prove the effectiveness of a treatment has exponentially grown over the years. New approaches that leverage the existing real-world data offer a valuable opportunity to allow

Fig. 21.3 Example of the use of social media campaigns to accelerate recruitment to a fully decentralized trial.

AI and ML in clinical trial design

clinical trials to adapt and become more sustainable, more reflective, and representative of the actual target population and dramatically more efficient and cost-effective to run.

21.4.1 Prospective ECA and retrospective comparative arms As described in this review, virtually every phase of a clinical trial, from planning to execution, can benefit from the application of intelligent analytics to real-world data. The most dramatic example of saving both cost and time in clinical trials, as well as the most robust use for an analytics engine sitting on top of an RWE dataset, is a retrospective comparative arm analysis or the prospective external control arm support of a pivotal trial. In each case, the analytics engine must deeply dive into trial eligibility criteria for each individual in an RWE dataset and then monitor those qualified individuals at every clinical touchpoint to ensure durable eligibility as well as to detect every primary and secondary end point achieved. This is in some ways a pinnacle of RWE analytics, and, yet, the FDA and other regulatory bodies have already begun accepting submissions for pivotal trials that leverage these techniques.

21.4.2 Cohort optimization It is important to highlight the fact that there are many intermediate points of value that can be extracted from a rich RWE dataset paired with an integrated analytics platform. The deep engagement of external control or comparative arm analytics from RWE often includes some of these elements along the way, but, on their own, they can be highly useful for RCT planning and execution. One example of this has been detailed earlier, i.e., cohort optimization, in which the end points of interest can be enriched by leveraging accurate risk models of the candidate population. More frequent and earlier clinical events may result in smaller, cheaper, and faster trials that get a product to market much faster provided that the results are positive. This is an upstream planning application of analytics models on top of retrospective real world data. However, they can be extended to a layer of analysis at the recruitment stage as well, if enough feature information is collected early in the recruitment process.

21.4.3 Decentralized RCTs to improve health equity and representation As described earlier, the combination of RWE datasets, advanced analytics capabilities, and a decentralized approach to recruitment of patients adds an entirely new dimension to the future of RCT design and execution. Already there is at least one fully decentralized cardiovascular medical intervention trial that will inform a 15,000 patient phase III study. However, through the use of targeted social media advertisements, robust clinical trial operation teams, and a sophisticated online trial presence, recruitment can be dramatically accelerated to meet the deadlines of any trial for ambulatory (outpatient) subjects. Irrespective of whether the clinical trial is then ultimately conducted in a traditional in-person format or in a decentralized format, there are significant opportunities to apply

579

580

A handbook of artificial intelligence in drug delivery

analytics to a potential recruitment pool (via a registry or a de novo recruitment approach) to create a truly representative study population from a health equity standpoint while still retaining the powerful insights that can drive drug development. Using an analytics platform like HealthPals’ CLINT to apply next-generation risk modeling for study end points will unlock the ability to recruit an enriched cohort for these study end points, which leads to smaller, shorter, and faster trials. However, the same analytics platform can also balance cohorts for race/ethnicity, gender, socioeconomic status, education, income level, etc. The overwhelming degree of health disparities and bias in clinical trial enrollment is a critical reason why analytics must be applied to patient recruitment— to achieve real health equity in clinical trial inclusion and, as a result, health equity in drug approvals from regulatory agencies. In summary, real-world evidence can achieve previously unimaginable efficiency in clinical trial design and conduction, slash the cost of study execution, and dramatically lower the time to market a drug or device development. The industry must adapt to the pressures of pandemics and the economic pressures of therapeutic development costs. Use of a sufficiently advanced and intelligent analytics platform, along with capable RWE datasets, can help achieve these efficiencies and meet the challenges of our time.

References [1] U.S. Department of Health and Human Services, Office of the Assistant Secretary for Planning and Evaluation, Examination of Clinical Trial Costs and Barriers for Drug Development, 2014. https:// aspe.hhs.gov/report/examination-clinical-trial-costs-and-barriers-drug-development. [2] S. Ross, A. Grant, C. Counsell, W. Gillespie, I. Russell, R. Prescott, Barriers to participation in randomised controlled trials: a systematic review, J. Clin. Epidemiol. 52 (1999) 1143–1156. 10580777. [3] M.J. Lamberti, A. Mathias, J.E. Myles, D. Howe, K. Getz, Evaluating the impact of patient recruitment and retention practices, Drug Inf. J. 46 (2012) 573–580, https://doi.org/10.1177/0092861512453040. [4] B. Carlisle, J. Kimmelman, T. Ramsay, N. MacKinnon, Unsuccessful trial accrual and human subjects protections: an empirical analysis of recently closed trials, Clin. Trials 12 (2015) 77–83, https://doi.org/ 10.1177/1740774514558307. 25475878. [5] Institute of Medicine The National Academies Collection: Reports Funded by National Institutes of Health, Forum on Drug Discovery, Development Translation. Transforming Clinical Research in the United States: Challenges and Opportunities: Workshop Summary, 2010. [6] S. Treweek, P. Lockhart, M. Pitkethly, J.A. Cook, M. Kjeldstrom, M. Johansen, et al., Methods to improve recruitment to randomised controlled trials: Cochrane systematic review and meta-analysis, BMJ Open 3 (2013), 23396504, https://doi.org/10.1136/bmjopen-2012-002360. [7] A.M. McDonald, R.C. Knight, M.K. Campbell, V.A. Entwistle, A.M. Grant, J.A. Cook, et al., What influences recruitment to randomised controlled trials? A review of trials funded by two UK funding agencies, Trials 7 (2006) 9, https://doi.org/10.1186/1745-6215-7-9 (PMID). [8] P. Bower, V. Brueton, C. Gamble, S. Treweek, C.T. Smith, B. Young, et al., Interventions to improve recruitment and retention in clinical trials: a survey and workshop to assess current practice and future priorities, Trials 15 (2014) 399, https://doi.org/10.1186/1745-6215-15-399. 25322807. [9] K.D. Barnard, L. Dent, A. Cook, A systematic review of models to predict recruitment to multicentre clinical trials, BMC Med. Res. Methodol. 10 (2010) 63, https://doi.org/10.1186/1471-2288-10-63. 20604946. [10] Clinical Trials Recruitment and Retention: Best Practices and Promising Approaches, Meeting Report, 2006.

CHAPTER 22

Artificial intelligence from a regulatory perspective: Drug delivery and devices Nirav Chokshi and Subhodeep Chakraborty Zydus Lifesciences Limited, Gandhinagar, India

22.1 Introduction This chapter provides insights into regulatory perspectives on drug delivery and devices, divided into three subparts. The first part introduces different regulatory agencies, regulatory pathways, and the prevailing challenges for the regulatory agencies. The second part discusses the current progress of artificial intelligence (AI) and machine learning in drug discovery and development. The third part discusses the future perspective of AI and gives a glimpse into the approaches of the regulatory agencies.

22.1.1 Regulatory agencies and regulatory pathways 22.1.1.1 USFDA regulatory regime [1] The Food and Drug Administration of the United States (USFDA) is the regulatory agency of the Department of Health and Human Services. Though the roots of regulation in the United States date back to the 18th century, the current framework of regulatory oversight started in 1906 with the Pure Food and Drug Act. The focus of regulatory vigilance started with purity and expanded to encompass the wider horizon of today, which covers quality, safety, and efficacy. The USFDA regulates a range of products including human and veterinary drugs, biological products, medical devices and radiation-emitting products, human and animal food, and cosmetics. While the Center for Drug Evaluation and Research (CDER) regulates the drug products, the Center for Devices and Radiological Health (CDRH) regulates medical devices. There are examples of products that are combinations of drugs and devices wherein both CDER and CDRH collaborate on the review process. CDER regulates prescription drugs and biological products, as well as over-thecounter products. While a majority of the products are medicines, there are products that do not fall under the typical definition of medicines, like dandruff shampoos and fluoride toothpastes. CDRH primarily regulates medical devices like X-ray and ultrasound equipment. However, the scope covers many radiation-emitting products that do not fall under the typical definition of medical devices, like television sets and microwave ovens. A Handbook of Artificial Intelligence in Drug Delivery https://doi.org/10.1016/B978-0-323-89925-3.00022-8

Copyright © 2023 Elsevier Inc. All rights reserved.

581

582

A handbook of artificial intelligence in drug delivery

22.1.1.2 European regulatory regime [2] Drug product regulation in Europe is unique, as the entire system works on two layers. At the central level the European Union acts as a unified body to frame policies and develop regulatory frameworks. However, the legal enforcement occurs at the national level, where all European policies take the form of a national law. The overall regulatory governance involves about 50 regulatory authorities from 30+ countries. The European Commission acts as the overarching organization for the rest of the institutions in the regulatory ecosystem. The primary responsibility of the EC is proposing legislation based on forward-looking policies. This must be approved first in the European Parliament before it becomes binding for all member states. To make these directives legally enforceable, each country will pass them at the national level to provide a form of national law. There are multiple regulatory pathways to get approval for drug products in European countries. The centralized procedure yields single authorization throughout Europe through the European Medicines Agency. Every country has a national procedure to register drug products at the national level, which is governed by national laws and the jurisdiction is restricted to that country only. However, in many cases pharmaceutical companies want to register a product in more than one country within Europe. A decentralized procedure route is used to obtain simultaneous authorizations in multiple countries. If a drug product is approved by one country, market authorization in any other European countries can be granted through a mutual recognition procedure. Similarly, there are a number of agencies involved in the medical device regulatory framework, which includes the European Commission, competent authorities, notification bodies, and authorized representatives. The competent authorities are the national regulatory agencies (NRAs), which are involved in regulation and enforcement at a national level. Notified bodies are nongovernmental agencies involved in conformance assessment through audits of manufacturing and testing facilities. They may perform testing of devices as appropriate to ensure regulatory conformance. For marketing authorization of devices manufactured outside Europe, the application shall be made by a unique authorized representative within Europe. The representative shall act as a contact point and primary responsible entity. Currently the devices are regulated broadly through three directives: Council Directive 90/385/EEC on Active Implantable Medical Devices (AIMDD) (1990), Council Directive 93/42/EEC on Medical Devices (MDD) (1993), and Directive 98/79/EC of the European Parliament and of the Council on in vitro Diagnostic Medical Devices (IVDMD). However, these are being progressively replaced with two new directives, Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002, and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC, and Regulation (EU) 2017/746 of the European Parliament and of the Council of 5 April 2017 on

Artificial intelligence from a regulatory perspective

in vitro diagnostic medical devices and repealing Directive 98/79/EC and Commission Decision 2010/227/EU. For the medical devices with an ancillary medicinal substance that are under the scope of centralized procedure, typically the notified bodies seek an opinion from the European Medicines Agency for the CE certificate is issued only after positive scientific opinion from the European Medicines Agency. For complex products involving both drug and a device aspect, the European Commission, in consultation with the European Medicines Agency, decides on the application regulatory framework and the procedure for granting authorization to market. For products that are a combination of drug and devices (“combination products”) the evaluation of the quality, safety, and efficacy of marketing authorization applications are assessed by the European Medicines Agency through the centralized procedure. 22.1.1.3 The regulatory alliances [3,4] The regional and global alliances play vital roles in regulating emerging markets. ASEAN [1–4] and GCC are the examples of regional cooperation groups that influence the regulatory frameworks at a regional level. The Association of Southeast Asian Nations, or ASEAN, comprises 10 Southeast Asian nations. The ASEAN was established in Thailand by Indonesia, Malaysia, Philippines, Singapore, and Thailand. Later Brunei, Vietnam, Laos, Myanmar, and Cambodia joined to make it a 10-country association. The ASEAN published a series of guidelines that are implemented by the national regulatory agencies. While member countries have disparity in the level of infrastructure and human resources to develop regulatory frameworks, ASEAN guidelines act as guiding notes for each individual national regulatory agency for regulatory compliance and enforcement. The Cooperation Council for the Arab States of the Gulf, popularly known as Gulf Cooperation Council, is composed of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and UAE. The guidelines published on regulatory sciences by the GCC are benchmarks for the member states to devise a regional regulatory compliance strategy. There are many regional cooperation groups working worldwide to navigate regulatory compliance harmonization and interorganizational reliance. While the Pan American Health Organization (PAHO) guides reliance and cooperation across the regulatory agencies of South America, there are many similar associations in Africa doing the same on the African continent. ZaZiBoNa is a recent example of regulatory coordination, cooperation, and reliance between Zambia, Zimbabwe, Botswana, and Namibia. While regional groups have done considerable work in bringing harmonization and interreliance to the regional level, the global associations still remain most relevant and influential in the era of globalization. ICH and WHO are the leading global establishments that bring an international consensus of regulatory science.

583

584

A handbook of artificial intelligence in drug delivery

The International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH) has emerged as the leading international establishment in the last three decades, growing from three founding members to a truly international association with 17 members and 32 observers. ICH published a series of regulations encompassing all dimensions of drug development. On October 23, 2015, ICH established itself as an international association that is a legal entity under Swiss law. While the ICH has been growing its member base and establishing itself as a future leader in global regulatory benchmarks, the WHO has still remained a truly global establishment with its strong global foothold and strong relationships with national regulatory agencies. Any member of the United Nations can be a party to WHO, accepting its constitution. Currently, WHO has 194 members across the globe and it has been an undisputed reference standard for the vast majority of the global community of regulators for design and enforcement of regulations at a national level.

22.2 Artificial intelligence and machine learning synergies with mission of regulatory agencies The USFDA’s Office of Regulatory Affair’s vision statement is “All food is safe; all medical products are safe and effective; and the public health is advanced and protected.” The USFDA activities are built around its mission of protecting consumers and enhancing public health, which they aim to achieve by maximization of compliance and minimization of risk. The USFDA vision and mission statements are forward looking and agile, allowing the agency to adapt and innovate. Over the last century, the USFDA has evolved through adoption of newer ideas, concepts, and scientific and technological advancements. In 1906, with the Pure Food and Drugs Act, the regulators strived to stop adulteration and misbranding. Purity was the idea of safeguarding public healthcare. Later, in the 1930s, the regulators raised the bar from purity to safety. Multiple campaigns citing gaps in the prevailing act enforcement since 1906 and their impact on public health and safety led to passing the new law. President Franklin D. Roosevelt signed the new Food, Drug, and Cosmetic Act (popularly known as the FD&C Act) into law on June 24, 1938, mandating a regulatory review related to safety before granting marketing authorization. The law was further amended multiple times to keep pace with the scientific and technological advancements. The 1962 Kefauver-Harris Amendment to the FD&C Act led to a paradigm shift in the way drug products were regulated in the country. This led to assessment of efficacy in addition to safety. Continuing in the regulatory agency’s quest to match the advancements of human civilization in the field of science and technology, two major adoptions, the FDA Food Safety Modernization Act and the Food and Drug Administration Safety and Innovation Act, occurred around 2011–12.

Artificial intelligence from a regulatory perspective

In 2011, the USFDA developed a strategic plan for regulatory science that exhibits eight priority areas where adopting newer technologies and scientific advancements is required to ensure the regulatory agency continues to progress in its mission. Later, in 2013, a ninth important area was added in the priority list. These include the following: 1. Modernize Toxicology to Enhance Product Safety. 2. Stimulate Innovation in Clinical Evaluations and Personalized Medicine to Improve Product Development and Patient Outcomes. 3. Support New Approaches to Improve Product Manufacturing and Quality. 4. Ensure FDA Readiness to Evaluate Innovative Emerging Technologies. 5. Harness Diverse Data Through Information Sciences to Improve Health Outcomes. 6. Implement a New Prevention-Focused Food Safety System to Protect Public Health. 7. Facilitate Development of Medical Countermeasures to Protect Against Threats to US and Global Health and Security. 8. Strengthen Social and Behavioral Science to Help Consumers and Professionals Make Informed Decisions on Regulated Products. 9. Strengthen the Global Product Safety Net. In 2018, the USFDA published a strategic plan for regulatory science exhibiting its commitment to speed innovation through progress in regulatory science, improvising regulatory decision-making, and ensuring quality, safety, and efficacy of marketed products. As the USFDA progresses in collaborative efforts, more progress in advancement of adoption of newer technologies will become visible. The European Medicines Agency is the flagship regulator for medicinal production in the union. The European Medicines Agency has as its mission to foster scientific excellence in the evaluation and supervision of medicines, for the benefit of public health. The entire mission of the European Medicines Agency focuses on forward-looking adoption of scientific and technological developments. The EU-Innovation Network has started a pilot program for simultaneous national scientific advice (SNSA) from national competent authorities (NCAs) to provide early regulatory support for innovation. Currently nanomedicines, biomaterials, pharmacogenomics, synthetic biology, modeling and simulation, and mobile devices to support health management are some of the topics being discussed. The European Medicines Agency’s (EMA) Regulatory Science to 2025 strategy is a plan for advancing EMA’s engagement with regulatory science over the next decades. The strategy aims to build a more adaptive regulatory system that will encourage innovation in drug discovery and development. The European Medicines Agency consulted with stakeholders on the draft strategy via a public consultation and dedicated workshops. The regulatory agencies’ mission and applications of artificial intelligence and machine learning are complementing each other. Rapid advancements in medical science, engineering, and technology have provided unprecedented opportunities in the

585

586

A handbook of artificial intelligence in drug delivery

area of public healthcare. The USFDA’s Strategic Plan for Regulatory Science as well as the 2025 action plan of European agencies reflect the commitment to be agile and adopt newer technologies. The only way to make this mission successful is to adapt and incorporate artificial intelligence and machine learning to ensure the agency remains forward looking and ready for the future.

22.3 Prevailing challenges for regulatory agencies The FDA’s software precertification program requires the manufacturer to demonstrate safeguards against cyberthreats. Excellent cybersecurity protection through active and collaborative engagement across stakeholders is the major concern. Privacy and security are intimately related. Devices that lack adequate privacy safeguards are less secure and more susceptible to exploitation, including exploitation that compromises safety and effectiveness. The FDA’s guidance on SaMD and AI therefore should include a recommendation that manufacturers employ “privacy by design” principles. Other recommendations state: “The implementation of AI systems is expanding rapidly, without adequate governance, oversight, or accountability regimes,” and “We need a sector-specific approach that does not prioritize the technology but focuses on its application within a given domain” [5]. As the regulator with the most immediate oversight over drugs and medical devices, the FDA should not simply defer privacy regulation to the Department of Health and Human Services or the Federal Trade Commission. As these technologies mature and converge, the FDA may need to consider new regulatory categories, or Congress may need to create such categories. In addition to new regulatory pathways, the acceleration of research and the convergence of drugs, devices, and AI will require even more careful thought about privacy, accountability, and access beyond the FDA’s remit. In future AI may bring development costs down and increase the accuracy of public health outcome predictions. Further, it would be interesting in future to see how regulatory and intellectual property policy regarding AI could lead to a more equitable and sustainable future for global health. Since AI systems that will be deployed in the healthcare setting are constrained to learn from available observational health data, high fidelity and reliably measured outcomes are not always achievable. Although data from EHRs and other health information systems provide a rich longitudinal, multidimensional set of details about an individual’s health, these data are often both noisy and biased, as they are produced for different purposes in the process of documenting care. Poorly constructed or inaccurately interpreted models can lead to serious consequences for the patients. Healthcare data scientists must be careful to apply the right types of modeling approaches based on the characteristics and limitations of the underlying data.

Artificial intelligence from a regulatory perspective

Human aspects like organizational behavior and culture have their own impact on excellence. Merely implementing AI will not yield the best outcome if it is not governed efficiently. The key risk management element for organizations is to remain in compliance with the government regulations, which are rapidly evolving. Making sense of the sheer volumes of data being generated today, which is primarily unstructured, is not an easy thing to do. We need data scientists, trained in the latest technologies and techniques in the healthcare industry. A supervised learning algorithm is designed to identify statistical patterns in a training dataset. If this training dataset reflects existing biases against a minority class when the dataset is unbalanced, the algorithm is likely to incorporate these biases. This can lead to less advantageous decisions for classes of these minority groups. The USFDA has approved AI algorithms in cardiac imaging and many other segments. However, regulations for AI are still evolving. Applications of AI raise many ethical questions, particularly in the event of an error in the AI diagnosis and the role of each stakeholder in the construction of safe AI devices. Moreover, it is complicated to ascertain continuous compliance, while dealing with dynamic algorithms, unlike locked ones. The implementation of AI will have to balance rapid technical advances with ethical and regulatory concerns, in order to leave time to map the potential risks and drawbacks with greater clarity. Bioethical discussions, led by biologists during the design step and doctors during the validation step, should be placed on the front line to monitor these processes closely and to establish long-term research standards on how to use AI in drug discovery. While leaving a complicated drug development pipeline to a sophisticated computer algorithm sounds like a brilliant idea to circumvent human error, there are critical ethical issues that must be addressed. AI essentially depends on data-driven intelligence, and its capability and precision in drug targets for rare diseases or conditions needs to be established. The FDA’s current approach is on validation and qualification, where AI may have a highly dynamic environment that is beyond a fixed algorithm. Accountability for errors in final treatment needs to be established. The biggest hindrance in using AI to predict drug targets remains translating traditional basic research conducted in labs around the world into a language that a computer can understand. Machine learning programs rely on data presented in a format where patterns can be identified, and the machine can be trained. This often requires a sophisticated experimental design where human error is kept at a minimum and multiple different iterations of an experiment can be performed in early identical conditions. Machine learning algorithms convert data into pathway detection, 3D protein structure, metabolite mass measure, etc. These AI transformations can occur with incredible speed. However, in many cases, the data being used are not of optimal quality (e.g., resolution of images) or are not balanced (i.e., samples from rare diseases are underrepresented in the dataset). While data augmentation techniques have been extensively used to balance data and thus reduce prediction bias, defining quantitative metrics is still an open problem.

587

588

A handbook of artificial intelligence in drug delivery

While anxiety over job losses due to AI and automation are likely exaggerated, advancing technology will almost certainly change roles as certain tasks are automated, as seen in other industries. A conceivable future could see AI eliminating a clinician’s need to perform manual tasks like checking patient vital signs (especially with selfmonitoring devices), collecting laboratory specimens, filling prior authorization forms, appointments, collecting standard history elements, and making routine diagnoses. While much of the popular discussion of AI focuses on how AI tools will replace human workers, realistically, in the foreseeable future, AI will function in an augmenting role, collaboratively synergizing with the capabilities of the technology’s human partners. As the volume of data and information available to inform patient care grows exponentially, AI tools will naturally become part of the clinical care team in much the same way a doctor is supported by a team of intelligent agents that includes specialists, nurses, physician assistants, pharmacists, social workers, and other health professionals. The technologies will be able to provide task-specific expertise in the data and information space, augmenting the capabilities of the physician and the entire team, making their jobs easier and more effective, and ultimately improving patient care. One of the recent cases of postmarketing vigilance is nitrosamine impurities. In 2019 to 2020, worldwide recalls were observed, led by the USFDA and European agencies. On one side, the recall resulted in a huge cost to the pharmaceutical companies, while on the other side regulatory agencies ended up investing resources heavily in managing the entire issue, while leaving the ordinary citizen facing shortages of medicines for chronic conditions like hypertension. The agencies proposed a transition period of about 2 years to allow pharmaceutical companies time to understand the root cause and establish control, as well as process improvements to reduce the risk. The impurities initially found in sartan drugs (angiotensin receptor blockers, also known as angiotensin II receptor antagonists, that are used to treat high blood pressure and heart failure) that contain a tetrazole ring. N-Nitrosodimethylamine, or NDMA, was the first impurity to be detected. The regulatory scientific community assumed that the tetrazole ring was the main culprit whose synthesis could potentially lead to the formation of nitrosamine impurities. Initially, molecules like azilsartan, eprosartan, and telmisartan were excluded from the risk management action plan on the basis of the absence of the chemical ring in focus. Later, several other nitrosamine impurities were discovered and other classes of drugs, including ranitidine (H2 receptor blocker) and metformin (antidiabetic), were also recalled. If we look at the past data, we can conclude that nitrosamine impurity was not new. In 1956 Magee and Barnes showed the carcinogenic potential of nitrosodimethylamine (NDMA) in rats. Since then, about 90% of the 300 tested nitrosamines have shown potent carcinogenic effects in experimental studies. The scientific literature indicates that NDMA can be found in drinking water due to degradation of industry waste and pesticide contaminations. It can be formed as a

Artificial intelligence from a regulatory perspective

by-product of the chloramination process. It can also be generated in anion exchange treatment of water. In such a scenario, if AI had been implemented it could have been a game changer; however, the challenge in implementing it in this area is not only the available data, but also prediction of the probable impurities that may arise during the manufacturing process, which may be debatable.

22.4 Current state of regulatory affairs and drug regulations Regulatory agencies are always in pursuit of adapting and evolving to be relevant to industry and markets. With scientific advancements in the field of deep learning and artificial intelligence, stringent regulatory agencies like the USFDA and European National Regulatory Agencies initiated a mission to upgrade relevant guidelines and redesign existing regulatory pathways and frameworks to meet the need to address emerging technological advancement. Fostering knowledge creation and leveraging the collective knowledge of people to develop a high-quality knowledge base may be the fundamental requirement for successful AI implementation. This requires pragmatic and dynamic regulatory settings to ensure balance between timely market availability and risk management.

22.4.1 Artificial intelligence and machine learning in drug discovery and development—Current regulatory perspective [6–8] Artificial intelligence (computer-based intelligence) has been recognized as having a ground-breaking impact on drug development. Even though there is no internationally acknowledged definition for AI, it is ordinarily utilized as an overall term to incorporate machine learning (including deep learning), natural language processing, and computer or machine vision. Artificial intelligence is ordinarily utilized as an overall term to incorporate all techniques that the drug business is actualizing, for example, AI (counting deep learning), characteristic language preparation, and PC or machine vision. Every one of these developments, regardless of being called artificial intelligence, reflect contrasting logical methodologies. Drugs and biotechnology companies are using machine learning in various ways for discovery and biomarker identification. Machine learning is used in pharmacovigilance exercises, including adverse event case processing, or gathering regulatory intelligence. The learning utilizes real-world data (RWD), which involves the use of large datasets or electronic health data. AI has been utilized in various disease areas within drug discovery. One territory is cancer research, where it has been used to distinguish normal cells from tumor cells and to decode cancer pathology images. AI has additionally achieved major efficiencies when

589

590

A handbook of artificial intelligence in drug delivery

compared with traditional drug discovery methods in repurposing high-throughput imaging assays to predict biological activity of compounds in other assays. During our search we came across AI use in drug design, with AI techniques used to classify candidate compounds in terms of their activity and other properties. AI methods are also useful in identification of biomarkers that correlate with tested outcomes. For monitoring the effectiveness of drugs, the USFDA allows using such biomarkers. For example, changes in cholesterol levels may be used to measure the effectiveness of some drugs, because low cholesterol level correlates well with a healthy cardiovascular system. If we focus on the area of structural bioinformatics, prediction of secondary structure of an amino acid residue in a protein was one of the first successful applications of machine learning techniques. AI and machine learning have a promising approach in QSAR modeling. QSAR approaches involve using the known responses (activities) of simple compounds (structures) to predict the responses of complex compounds, made from different combinations of basic modules. Only compounds predicted to have desired properties would then be tested. QSAR methods have been used to model numerous drug safety endpoints, including drug lethal dose 50% (LD50) values, skin/eye irritation, and tissue-specific toxicity. Specifically, a QSAR model can analyze the relationship between several molecular properties and a response such as binding affinity. Toxicity is a major challenge in drug development. Preclinical assessments are needed for keeping toxic drugs from arriving at clinical preliminaries. Several computational, in silico approaches have been exhibited in assessing the toxicity of drug candidates. AI-based data mining systems can be helpful in predicting safety based on the toxicity data. The Distributed Structure-Searchable Toxicity (DSSTox) Database Network created by the US Environmental Protection Agency’s Computational Toxicology Program has created a public data foundation for predictive toxicology research. Preclinical assessments are required to keep harmful medications from arriving at clinical preliminaries. Another database initiative, the Vitic toxicity database, supported by a number of pharmaceutical and chemical companies, was begun by the Health and Environmental Sciences Institute (HESI) as part of the International Life Sciences Institute (ILSI), and is being managed now by Lhasa Limited. These databases store various in-house toxicology data that may be reanalyzed using different techniques. Accuracy of computational approaches in some cases is in the range of 80%–95%, comparable to in vivo assessments. Not only in the preclinical stage but for pharmacovigilance, different clinical data generated during trials utilize the approach of AI and machine learning. The USFDA maintains FAERS, a database containing adverse event reports, medication error reports, and product quality complaints resulting in AEs. The postmarketing drug safety mining

Artificial intelligence from a regulatory perspective

significantly relies on the self-reported Individual Case Safety Reports (ICSRs). The methodologies and information sources that can uphold postmarketing PV alongside the related artificial intelligence-driven strategies are expected to extricate data and gain from it. The manufacturing process in pharmaceuticals, which is considered tedious, involving multiple process parameters, uses a novel technique for improving existing pharmaceutical production processes by interaction between human and artificial intelligence (AI) that is mainly an interface between AI programs and a human operator. The method is optimized to work on initial data collected from the production batches, considering the huge cost of test trials. The information is utilized to develop choices, which are then transformed into human understandable standards that will guide the production process. The outcomes are presented to the human operator through perception procedures, which permit operators to test a few potential future settings of parameters and assess the subsequent simulated AI predictions. AI jobs will be vital for execution of USFDA’s Process Analytical Technology (PAT), an initiative to understand and control the manufacturing process flexibly in real time, and the Quality by Design (QbD) initiative, intended to achieve superior quality with as little testing as possible, by focusing the testing on a few critical parameters and attributes that affect the product and the process the most, can be achieved by introduction of sensors and an automatic logging system into the production line and new methodologies into the process design, and improving existing processes by data analysis. The USFDA’s Information Exchange and Data Transformation (INFORMED) initiative was designed to tap into the power of big data and advanced analytics to improve disease outcomes. At its inception in 2016, INFORMED was anchored in the agency’s Oncology Center of Excellence (OCE). In one of the projects to examine the impact of a recent labeling change for two approved products from weight-based dosing to flat-dosing of immune checkpoint inhibitors, and to examine how community practices are adopting the flat dose after the labeling change and factors that may affect adoption, INFORMED utilized RWD. One of the objectives of the INFORMED is to develop agency curricula on machine learning and artificial intelligence in partnership with external academic partners and internal medical product centers. The program is designed to improve the ability of reviewers and managers of USFDA to evaluate products that incorporate advanced algorithms, thus enhancing the capabilities to develop novel regulatory science tools to harness the future. A competitive fellowship program under INFORMED in Artificial Intelligence and Machine Learning is planned where postdoctoral fellows can be associated with the agency for 2 years, helping them in developing high-impact AI-based regulatory science tools. OCE, the Oncology Center of Excellence, is also working with many institutions for the harmonization of

591

592

A handbook of artificial intelligence in drug delivery

reference standards for assessing tumor mutational burden (TMB), to help identify cancer patients who are more likely to respond to immunotherapy. Harmonization of the measurement of tumor mutational burden across commercial assays used in routine oncology care will lead to reduction in the variability of treatments. It will also improve the utility of TMB as a potential biomarker for enriching clinical trials testing immunotherapies. OCE is working on the project Switch. Here they are investigating possibilities of using contemporaneous synthetic control arms based on prior clinical trials for inferring the effect of a new drug. They are also assessing the possibilities of using real-world endpoints, like time to treatment discontinuation (TTD), as a potential real-world endpoint for pragmatic randomized clinical trials for FDA-approved therapies in the postmarket setting. Agility and efficiency of clinical trials can be achieved by optimizing administrative burdens, which is possible by incorporating real-world evidence in decentralized trials. This will enable community providers to give treatment to the patients while ensuring data integrity and overall quality of the clinical trial. USFDA’s use of RWD and RWE, derived from the Sentinel system, eliminated the need for postmarketing studies on nine potential safety issues involving five products, making their postmarket evaluation of safety timelier and more effective. In the oncology setting, the USFDA currently has new drug applications under review where RWD and RWE are helping to inform their ongoing evaluation. When related to the treatment of very rare tumors, this becomes most relevant. In appropriate cases of oncology and rare diseases, USFDA accepted RWE to support the evaluation of efficacy in product approvals, using data from registries, natural history studies, and chart reviews, to establish a comparison. RWE captured for the entire lifecycle has been a significant contribution to new drug development and change management. USFDA is also exploring ways to better harness patient-reported information. This includes their recently announced “My Studies mobile application.” As part of the agency’s work to foster greater opportunities around real-world evidence, the USFDA partnered with Kaiser Permanente on a pilot study to measure the functionality and engagement of the MyStudies App. Based on the successful outcome of the study, the USFDA is now releasing the open source code and technical documents that will allow researchers and developers to customize and use the USFDA’s newly created MyStudies App to expand the diversity of health information available for clinical trials and studies, while directly capturing the perspective of patients. The USFDA has provided a tool, in the form of an open-source code, that can support in advancement of the specific clinical trial and real-world evidence needs, while also remaining compliant with the USFDA’s regulations and guidance for data authenticity, integrity, and confidentiality.

Artificial intelligence from a regulatory perspective

22.4.2 Artificial intelligence and machine learning in medical devices— Current regulatory perspective [9–11] The CDRH is the primary division involved in regulating medical devices. One of its key objectives is to partner with stakeholders to be digital-future ready. Since 2013, the USFDA has published a series of guidelines to bring more clarity into the public domain, specifically researchers and industry, about the expectations of regulators. From RF wireless guidelines to postmarketing requirements and interoperability, the USFDA created a base of founding guidance for the industry to build their research and development plans. The entire approach of the CDRH to guidelines hovers around three key factors: alignment with product development timelines, alignment with real world experience, and practices and synergies with global regulatory agencies. The way the USFDA regulates medical devices is based on the three-tier classification. Class 1 are the low-risk devices for which generally no active regulation is required. Class 2 are those with moderate risk and mostly regulated through 510 (k) route. Class 3 mostly requires premarket approval (PMA). The CDRH has three approaches to deal with artificial intelligence, while approving and regulating throughout the life cycle: (1) Artificial intelligence used in manufacturing medical devices. (2) Artificial intelligence used in medical devices. (3) Artificial intelligence as the medical device itself. The CDRH adopted a concept of smart regulations, as presented in Fig. 22.1.

Fig. 22.1 CDRH smart regulation concept.

593

594

A handbook of artificial intelligence in drug delivery

Based on risk profiling, AI can be regulated by a combination of general controls and special controls. General controls include prohibition of adulterated and misbranded devices, GXP, labeling and instructions, registration of facilities, recordkeeping, etc., while the special controls include performance standards, postmarketing surveillance, design controls, tracking requirements, and patient registries. The USFDA’s traditional approach to the regulation of hardware-based medical devices is not well suited for the faster and more iterative design, development, and validation techniques used to develop high-quality, safe, and effective software, including Software as a Medical Device (SaMD). Rather, today’s software development techniques and lifecycles offer unique opportunities and benefits that are not fully realized under the USFDA’s traditional pathways for regulating medical devices. Software developers can address malfunctions to reduce adverse events, capture the product performance information outside clinical trial areas, and improve patient engagements, using modern technologies. The USFDA has proposed a new framework in the working model of the software precertification program known as WOMO 1.0. The USFDA initiated the test phase in 2019 and published two policies, the Test Plan and the Regulatory Framework, for the pilot program. The Test Plan described how the agency intends to test the proposed framework to assess whether equivalent basis is established to determine reasonable safety and effectiveness assurance in comparison to traditional methodologies. The Regulatory Framework explained how the FDA planned to implement the WOMO1.0 through the De Novo classification process (section 513(f )(2) of the FD&C Act). The USFDA has cleared or approved several medical devices; however, those were based on “locked” algorithms. These are the algorithms that provide the same result each time the same input is applied to them. This static-ness and repeatability are complementary with the agency’s current philosophy of validation. However, many recent medical devices, especially when AI/ML based, use algorithms that change and can adapt over time; these are described by the USFDA as adaptive algorithms, for which current regulatory frameworks were not designed. The USFDA published a discussion paper titled “Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD)—Discussion Paper and Request for Feedback” in April 2019. This publication explains the agency’s foundation for the regulatory approach to the premarket review for AI and ML driven software modifications. This provides guidance on when to consider a 510(k) for a Software Change to an existing device. Here the agency anticipates algorithm architecture modifications and retraining with new datasets, when involvement in SaMD would lead to premarket review. There are three broad categories of such modifications: performance (analytical and clinical validation),

Artificial intelligence from a regulatory perspective

inputs (which will be used by the algorithm along with their clinical association with the SaMD output), and intended use of the SaMD. A total product lifecycle regulatory approach for AI/ML-based SaMD has been described that enables the evaluation and monitoring of a software product from its premarket development to postmarket performance. The USFDA proposed the TPLC approach, which is based on principles of risk-benefit balance. Significant progress in policy development for SaMD has been made by the USFDA, fostering its mission to ensure safety and effectiveness of approved marketed products while simultaneously achieving timely availability of the latest technologies to patients and healthcare professionals. The USFDA describes Quality Systems and Good Machine Learning Practices (GMLPs). Demonstration of analytical and clinical validation is required for the AI/ML processes. GMLP encompasses activities like data management, feature extraction, training, and evaluation, in line with the agency’s GXP philosophy, which also is in line with good software engineering practices. A description of SaMD including possible data sources from which inputs are derived and that may be used for one or more medical purposes is presented in Fig. 22.2. The requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration, etc. are covered under the general controls provisions of the Act. Existing major regulations can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898.

SaMD Algorithm SaMD Inputs Data Sources

Laboratory, medical devices, medical imaging devices, physiological monitors, IVD test instruments, patientreported outcome, medical purpose sensors, computer peripherals and others sensors, personal records, patient records, etc.

Data Type Lab results, medical images, symptoms, genomic data, environmental signals, Pictures, activity data, phenotype data, IVD instrument results, patient demographic information, progress notes, vital signs, medications diagnoses, immunization dates, allergies, etc.

SaMD Output

Algorithm, inference engine, Equations, Analysis engine Model based logic, AI/ML, etc.

Intended Use for Medical Purpose (Inform, Drive, Diagnoses, Treat)

Reference data, Knowledge data, Rules, Criteria, etc.

Fig. 22.2 Description of SaMD including possible data sources from which inputs are derived and that may be used for one or more medical purposes [12].

595

596

A handbook of artificial intelligence in drug delivery

Radiology 8%

Cardiology

4% 3% 6% 7%

Neurology 49% Opthalmology

23%

Endocrinology

Fig. 22.3 USFDA-approved medical devices based on AI [13].

The USFDA has cleared many applications involving artificial intelligence and deep learning so far. Radiology and cardiology are the two major fields receiving benefits from the regulatory approvals. A pie chart is presented in Fig. 22.3 indicating the USFDA’s approved medical devices based on AI [13]. A significant number of AI-based products have already been cleared by the USFDA. Some examples are discussed here to gain perspective. USFDA Authorized Marketing of First Cardiac Ultrasound Software That Uses Artificial Intelligence to Guide User. This software uses machine learning to decide whether the images are acceptable or unacceptable. This can be used in specific devices approved by the USFDA. While the artificial intelligence makes the decision on selection of images and video clips to be considered for recording, the cardiologist still reviews the images for a final assessment of the images and videos for patient evaluation. The approval was granted using the De Novo premarket review pathway. However, subsequent similar products can be registered and regulated through the 510 (k) route. Here, the prima facie factor for consideration of approval is establishing similarities with the first approved software—the Caption Guidance software. The USFDA approved the Arterys Cardio DL from Arterys Inc. indicated for analysis of cardiovascular imaging from MRI. This technology leverages cloud computing and deep learning. This is among the initial products to be approved by the USFDA. The accuracy of the outcome was validated against the segmentation conducted by physicians. Arterys had two more approved products, the Arterys Oncology DL and Arterys MICA. The oncology artificial intelligence suite is AI cloud-based medical imaging software with enhanced evaluation and quantification of nodules and lesions of lung and liver. This enables easy diagnosis by radiologists. It runs on MICA, a medical imaging and cloud AI

Artificial intelligence from a regulatory perspective

platform. The clinician can edit the segmentation done by the software and reach appropriate conclusions. All three products are approved by the USFDA through the 510 (k) premarket notification route. USFDA has cleared Deep Learning Image Reconstruction from GE Healthcare, which is an image reconstruction engine based on deep learning. This is used in CT devices to enable DNN-generated imaging. This was approved through the 510 (k) route. Two Edison applications, Bone VCAR and SnapShot Freeze, were also cleared by the agency using the same route. Bone VCAR uses deep learning algorithms to achieve improved quality and speed of spin assessment by auto labeling vertebrae. SnapShot Freeze is based on a motion correction algorithm used primarily for motion correction of the heart. OsteoDetect from Imagen Technologies is an AI-empowered diagnosis software for detection of wrist fractures. This product was approved by the USFDA via the De Novo clearance route. The approval was based on a retrospective validation using about 1000 radiograph images wherein the accuracy of fracture localization was compared with diagnosis of three qualified orthopedics. Other data considered by the USFDA was a retrospective study using a database of 200 patients. The studies establish superior sensitivity and specificity as compared to a controlled data outcome based on conventional manual interpretation. The De Novo premarket review pathway is designed for low to moderate risk devices of a new type. USFDA cleared multiple solutions, AI RAD Brain MR, AI RAD Chest CT, AI RAD Organs RT, and AI RAD Prostate MR, from Siemens Medical Imaging, creating a family of AI RAD products. These are well integrated with clinical workflow. The products are equipped with deep learning algorithms, which can detect abnormalities, segment anatomies, and make comparisons with the reference standards. The products are Digital Imaging and Communications in Medicine (DICOM) compliant. While this range of products offers to free the radiologist from routine activities, the final decisionmaking is still manual. The USFDA cleared the applications through the 510 (k) premarket notification route. The software has been validated for noncardiac chest CT data with filtered back projection reconstruction from Siemens Healthineers, GE Healthcare, Philips, and Toshiba/Canon. The performance of the AI-Rad Companion (Cardiovascular) device has been validated in retrospective performance studies on noncardiac chest CT data from multiple clinical sites across the United States. With respect to the cardiac function, the logarithmic correlation coefficient of total coronary calcium volume between subject and predicate device was 0.96. With respect to the aorta function, the average absolute error in aorta diameters was 1.6 mm across all nine measurement locations. Performance was consistent for all critical subgroups, such as vendors or slice thickness. The USFDA noted that the products also conform to other international standards like NEMA, AAMI, and ISO.

597

598

A handbook of artificial intelligence in drug delivery

Zebra Medical had secured USFDA clearance for seven products by the end of 2020. One of them is HealthPNX. It is a radiological computer-assisted notification software system used for analysis of chest X-rays. It detects and signals any suspicions of pneumothorax. The performance of the HealthPNX device has been validated in a pivotal performance study that was carried out in the United States with a simulated synthetic work-flow consisting of a truthed validation dataset. The data included a retrospective data evaluation of 588 anonymized chest X-ray cases from the United States and Israel, including 146 pneumothorax positive and 442 negative cases, as well as confounding imaging factors. Three US board-certified radiologists (truthers) truthed the validation dataset. The stand-alone detection accuracy was measured. The triage effectiveness was evaluated by three different US board-certified radiologists (readers) that read these cases prospectively in real time with the HealthPNX device and without (standard of care), with a washout period separating between the two read periods, with and without the HealthPNX device. The detection accuracy met the a priori performance goal (more than 80% level of accuracy compared with ground truth). AUC at 95% confidence interval was found to be 98.3% confirming equivalence with 93% of sensitivity and specificity. Moreover, the triage time was found to be around 8 min against about 69 min in the case of the standard of care. These observations established that the application should be cleared, as it meets the intended use statement and equivalency to the predicate device. The USFDA cleared Conta CT, a product that is a type of clinical decision support software designed to analyze computed tomography (CT) results that may notify providers of a potential stroke in their patients. USFDA approval is based on a retrospective study of 300 CT images, which assessed the independent performance of the image analysis algorithm and notification functionality of the Viz.AI Contact application against the performance of two trained neuro-radiologists. The application was subjected to De Novo premarket review regulatory pathways. This pathway is for new types of medical devices with moderate risk. As the device is a new type, there is no legally marketed product to create a reference base. This approval created a base for subsequent products to get USFDA clearance under the 510 (k) route. In the European Union, medical devices are regulated through CE marking. Unlike the USFDA, in Europe the clearance is decentralized and governed by notified private bodies. CE encompasses a variety of products and not just medical devices. To obtain CE marking, we need to meet the European regulations as applicable. For AI-based medical devices, the compliance starts with EU medical device regulation 2017/745 by the European Commission, while in Switzerland, they are regulated through the Therapeutical Products Act. The European directives categorize AI-based medical devices based on the following four intended utilities: 1. Diagnosis, prevention, monitoring, prediction, prognosis, treatment, or alleviation of disease.

Artificial intelligence from a regulatory perspective

2. Diagnosis, monitoring, treatment, alleviation of, or compensation for an injury or disability. 3. Investigation, replacement, or modification of the anatomy or of a physiological or pathological process or state. 4. Providing information by means of in vitro examination of specimens derived from the human body, including organ, blood, and tissue donations. Compatibility and interoperability are the two important elements in regulatory review of medical devices. While dealing with AI-based medical devices, the European regulators consider the risks associated with the possible negative interaction between software and the IT environment within which it operates and interacts. In addition to risk, the efficacy of the AI-based medical devices is evaluated by validating the repeatability, reliability, and performance. The product is expected to consider the principles of the development life cycle and risk management, including information security, verification, and validation. Most of the software products are classified as Class 1 as per the existing European regulatory framework. When the decisions for diagnosis or therapy are made based on the information provided by the software, it is generally classified as Class IIa. However, if the decision error may lead to surgical intervention or immediate danger to the patients, it is considered as Class IIb. Those cases where the decision may cause a serious patient safety impact, like death or irreversible damage to health, the software is classified as III. The European pathway considers general requirements like safety, performance, and reliability, and clinical studies, which varies across notified bodies. Compared to the USFDA, very limited information is available in the public domain and hence it is difficult to estimate the requirements precisely at the design stage. However, getting CE is generally considered less stringent than USFDA approval. This hypothesis is further strengthened with an observation that in a majority of cases the product gets the CE mark first, before USFDA clearance. Through mutual recognition, the CE can get the products access to European Free Trade Association states (Liechtenstein, Iceland, Norway, and Switzerland) and other markets like Turkey. The emerging markets are not yet fully evolved with completely matured regulatory frameworks to approve AI-based medical devices. Currently, most of the national regulatory agencies are considering the view of stringent regulatory agencies (SRAs), like USFDA clearance and CE, in addition to local regulatory compliances, for consideration to grant permission to commercially distribute the products in the countries.

22.5 The future regulatory perspective The pharmaceutical industry is very cautious about handing over their highly regimented R&D processes to hard-to-understand algorithms. A full industry transformation may

599

600

A handbook of artificial intelligence in drug delivery

take more than a decade. However, organizations need to understand AI and how it can benefit their business. Identification of candidate molecules to prediction of a synthesis process are possible with artificial intelligence using a vast data directory. We are already seeing examples of policies and approaches of different regulatory agencies on AI helping drug and device research, thereby reducing discovery as well as approval time. AI could conceivably save a year off the development of many drugs and devices, which would be worth billions. To benefit from AI, companies are looking at how it can deliver across different parts of the discovery process.

22.5.1 Artificial intelligence and machine learning in drug discovery and development—Future perspective [14,15] In the previous chapter, we have seen how AI gained prominence due to the success of machine learning and data science techniques. Various ideas have laid the groundwork for artificial intelligence, which has shown significant performance improvements over prior generations of algorithms. Different regulatory agencies are rapidly exhibiting their action plans, which will continue to evolve as we pursue these technologies and have more clarity in this space. In future, combining computational de novo design with AI could allow a “computer chemist” to learn from known useful compounds and enable the production of chemically correct and synthesizable structures with a planned biological activity [16]. Efficiency and accuracy in the drug discovery process have long been demanding the use of analytics and statistical models. A streamlined and automated approach across these various stages is now possible in future with the extensive use of AI. A future AI may hold within its databases the sum of all knowledge of biology, genes, and chemical interactions. Even for biological entities, AI can enable scientists to identify the target and conduct validation. Rapid molecular development, through identification of new entity candidates and performing a virtual cycle, is possible with the use of AI. This requires ADMET predictions as well, which cover absorption, distribution, metabolism, elimination, and toxicity. AI can conduct prediction in silico properties, allow early elimination of poor candidates, and improve throughput quality. Silicon modeling is not novel, but the existing frame can be significantly improved with more data and the latest algorithms. Neural networks can also be used for predicting retrosynthesis routes, assessing the synthesizability of a candidate, and so helping to understand how easy the drug is to make. This will improve the success probability, as we can remove the avenues that will not scale viably. Newer techniques in molecular analysis, called “machine-vision” image analysis, allow AI systems to predict which molecules might be effective for which biological

Artificial intelligence from a regulatory perspective

targets, and thus accelerate the process of drug discovery. Similarly, simulations of chemical interactions can be performed to assess a drug’s efficacy in disease treatment. Rapid and accurate identification of vaccines is another domain where this technology can be experimented with. Oncology is another major area in which AI is being leveraged. Treatment plans are often complex and must take into consideration many factors, such as available treatment modalities and the preservation of healthy tissue when targeting cancerous cells. These processes can be extremely time consuming and pose significant challenges. There are thousands of clinical trials being conducted around the world in all subspecialties of oncology. Reading and assimilating all of the current information would be a monumental task for a single oncology practice, much less one physician, but a computer with ML and NLP can interpret thousands of pages of study results and compare the data to a specific patient case for the physician. Another difficult and time-consuming task in oncology is determining the best way to deliver the treatment, such that it targets cancerous cells and avoids damage to surrounding healthy tissues. Radiation oncologists and their staff can spend hours mapping the anatomical structures and calculating radiation absorption at various angles and beam strengths, in order to plan the best way to deliver the therapy. Here again, ML coupled with NLP has consistently demonstrated value. ML systems are able to create treatment plans in minutes or even seconds, as compared to the hours it might take a human oncology team. AI-enabled pharmacogenetics will predict personalized drug response. This will further optimize the dose selection to the individual level. This will be made possible based on knowledge derived from the large pool of complex molecular and demographic clinical datasets.

22.5.2 Artificial intelligence and machine learning in medical devices—Future perspective [17–19] The USFDA is on top of things in providing guidance relating to AI and medical devices, including Software as Medical Device (SaMD), but it seems to be behind concerning drugs, biologics, and genetic therapies, regardless of calls for help for in silico trials for drugs. This is largely because wearable and implantable devices with sensors that provide information to users and their doctors are already here, while the high-throughput screening methods used by pharmaceutical companies today operate prior to filing for regulatory approvals and the technology for in silico drug trials remains nascent. Further, the regulatory category of “devices” is much broader than drugs or biologics, as the different classes of devices in the regulations suggest. The public health consequences of device malfunctions can be easier to predict and constrain with many kinds of devices than many kinds of drugs. For biologics and genetic therapies, the public health risks can be even broader, particularly if a genetic change becomes inheritable.

601

602

A handbook of artificial intelligence in drug delivery

The USFDA’s thinking about in silico trials seems more advanced in the area of medical devices. This is because of several factors. First, the FDA already defines certain kinds of software used in disease detection, diagnosis, or other medical applications as a regulated “medical device”—SaMD. The recent AI/ML SaMD action plan of the USFDA summarizes the intended actions and goals, which are as follows: • Develop an update to the proposed regulatory framework presented in the AI/MLbased SaMD discussion paper, including through the issuance of a Draft Guidance on the Predetermined Change Control Plan. • Strengthen FDA’s encouragement of the harmonized development of Good Machine Learning Practice (GMLP) through additional FDA participation in collaborative communities and consensus standards development efforts. • Support a patient-centered approach by continuing to host discussions on the role of transparency to users of AI/ML-based devices. Building upon the October 2020 Patient Engagement Advisory Committee (PEAC) meeting focused on patient trust in AI/ML technologies, hold a public workshop on medical device labeling to support transparency to users of AI/ML-based devices. • Support regulatory science efforts on the development of methodology for the evaluation and improvement of machine learning algorithms, including for the identification and elimination of bias, and on the robustness and resilience of these algorithms to withstand changing clinical inputs and conditions. • Advance real-world performance pilots in coordination with stakeholders and other FDA programs, to provide additional clarity on what a real-world evidence generation program could look like for AI/ML-based SaMD [19]. The US Government Accountability Office (GAO) and the National Academy of Medicine (NAM), individually and in collaboration, have taken up the charge to explore AI/ ML in healthcare, assess its implications, and identify ways to maximize its utility. In recognition of mutual interests and obligations, and to reinforce and complement each other’s work, NAM and GAO are cooperating. A report jointly published by the Government Accountability Office (GAO) and the National Academy of Medicine (NAM) highlights the potential importance of AI/ML to progress in health and healthcare. The report suggests that the use of AI and ML tools will soon be essential to assist with the growing field of precision medicine with much of health and healthcare moving onto digital platforms. On transparency and AI, it suggests that AI developers, implementers, users, and regulators should collaboratively define guidelines for clarifying the level of transparency needed across a spectrum [20]. The report “Focus Areas of Regulatory Science” (FARS) is intended to identify and communicate priority areas where new or enhanced investments in regulatory science research capacity are essential to support the FDA’s regulatory and public health mission.

Artificial intelligence from a regulatory perspective

It also reflects important changes to the science and technology underpinning FDAregulated products. FARS is organized across three strategic initiatives: • Unleashing the power of data • Increasing choice and competition through innovation • Empowering patients and consumers Unleashing the power of data refers to identifying and using reliable data sources, some of which may represent large, complex datasets requiring improved analytics, and in some cases, harnessing high-performance computing environments and new computational tools based on machine learning (ML) and artificial intelligence (AI). The FDA uses healthcare data and analytics to improve the quality and integrity of FDA-regulated products throughout the product lifecycle. Health data include but are not limited to data from electronic health records (EHRs), medical imaging, genomic sequencing, pharmaceutical research, digital health technologies (DHTs), and medical devices. The FDA is interested in developing new approaches to harnessing this power to improve regulatory decision-making and more effectively connect today’s groundbreaking scientific discoveries with the rapid development and approval of new FDAregulated products. The FDA is working to improve ways to identify new safety signals with increased precision in decreased time. Some of the approaches being evaluated for feasibility rely on incorporating artificial intelligence (AI), real-world evidence (RWE), and leveraging data from a combination of active and passive safety surveillance systems. Some of the active and passive safety surveillance systems are presented; please refer to Fig. 22.4. To achieve and promote efficiencies within the FDA and in industry, the FDA aims to improve its understanding of AI’s potential and limitations. Considerations include the technical and practical application of AI and ML, new regulatory questions introduced by using AI applications, and the impact of AI solutions across the lifecycle of FDAregulated products. A few approaches have been represented in Fig. 22.5. The capability of true information or real-world data (RWD) should be used in information mining. RWD are data relating to a patient’s health status and/or the delivery of healthcare routinely collected from a variety of sources. Examples of RWD include data derived from electronic health records (EHRs), administrative claims, registry, and patient-generated data, and data gathered from mobile devices and other digital health technologies (DHTs). Real-world evidence (RWE) refers to clinical evidence of the usage and potential benefits or risks of an A regulated product derived from analysis of RWD. Big data analytics enables researchers to analyze diverse data sources. Recognizing the potential value of RWD, the USFDA is committed to exploring the use of RWE in regulatory decision-making, including its ability to provide fit-forpurpose clinically meaningful information about the safety and effectiveness of medical

603

604

A handbook of artificial intelligence in drug delivery

An acve surveillance system that uses roune querying tools and pre-exisng electronic healthcare data from a distributed data network to detect safety signals and evaluate the safety of FDA-regulated medical products

FDA Sennel System

An acve surveillance system which builds and expands upon acvies undertaken as part of previous FDA collaborave studies for biologic product safety and effecveness

Biologics, Effecveness and Safety (BEST)

FDA’s medical product safety reporng program for health professionals, paents and consumers

MedWatch

A passive reporng database for healthcare professionals, paents, consumers, manufacturers, and others to report adverse events, medicaon errors, and product quality complaints

A passive reporng database administered by FDA and Centers for Disease Control and Prevenon (CDC), that contains adverse event reports associated with licensed vaccines

A passive reporng database contains reports of adverse events involving medical devices

FDA Adverse Event Reporng System

Vaccine Adverse Events Reporng System

FDA Manufacturer and User Facility

Fig. 22.4 Active and passive safety surveillance systems of USFDA.

products. As part of its efforts under the 21st Century Cures Act (Public Law 114-225), the FDA established the RWE program to explore the use of RWE in regulatory decision-making. As a result, the FDA is developing and supporting projects that will provide insight into how RWD and RWE can play a role in supporting the evaluation of a product’s safety and effectiveness.

Artificial intelligence from a regulatory perspective

Detect adverse events in different data sets, including postmarket data. Future aspects of AI use by US FDA

ML-Algorithms

Study the effects of synthesized data sets for training and testing in both pre-market testing and the FDA-regulated product lifecycle. Predict the time to first submissions for abbreviated new drug applications (ANDA) referencing new chemical entities to inform the Agency’s ANDA workload and prioritize research.

To retrieve and synthesize drug-related AE information from FDA Adverse Event Reporting System and FDA Vaccine Adverse Events Reporting System reports and to dynamically present the data in an information visualization platform equipped with deduplication and case classification models to inform postmarket safety evaluations.

ML-Algorithms & Natural Language Processing (NLP)

To identify how to code adverse events (AE) in the International Conference for Harmonisation Medical Dictionary for Regulatory Activities (MedDRA).

To investigate the potential of AI to improve the efficiency of reviewing regulatory submissions.

To Study how AI can combine diverse data so clinical trial results can be analyzed in a more comprehensive and expeditious way.

Fig. 22.5 FDA’s approach to future use of AI and ML.

While we cannot predict the future with accuracy, we can consider future scenarios that are likely to happen. ML has the potential to be used for a variety of healthcare goals, including the development of smarter electronic health record (EHR) and health information exchange (HIE) systems, and the prediction of epidemic outbreaks. In the imaging space, for instance, AI is currently being used in computer-aided detection (CAD) systems to retrospectively learn about how different clinical abnormalities appear on imaging studies by analyzing both image data and associated clinical information. CAD systems are able to use the knowledge they have learned to prospectively identify areas of abnormality on imaging studies and generate a differential diagnosis for the finding. CAD is currently being reviewed for use in a variety of imaging studies, including mammography. In the future, advances in AI will likely create additional capabilities for CAD solutions. Over time, it would not be surprising to see the accuracy of CAD systems for some imaging studies exceed that of radiologists, with CAD systems potentially becoming the primary interpreters of imaging studies and radiologists only reviewing imaging studies that exceed a certain level of uncertainty of the CAD/AI system.

605

606

A handbook of artificial intelligence in drug delivery

In addition to applications in radiology, CAD is also used in dermatology to help diagnose skin lesions. CAD dermatology systems learn about the various appearances of dermatological lesions by reviewing large numbers of photographs of lesions along with the dermatological diagnoses associated with them. Mirroring how CAD functions in radiology, these dermatologic CAD systems can then use the knowledge they have acquired to prospectively identify dermatological lesions that are at high risk for being malignant. In the future, the patient is likely to have access to wearable devices to track blood glucose levels, heart rates and rhythms, and exercise levels over time. That information may be synchronized to a central monitoring system that uses ML to recognize abnormal or undesired pattern changes. When an abnormal pattern change is recognized, the monitoring system can automatically notify the patient’s provider and instruct the patient to schedule an appointment or tell the patient to call the paramedics, or seek emergency care or urgent care, should the identified pattern change be more critical. One of the most promising developments in predictive analytics for drugs and devices is in the use of “virtual patient” models for in silico trials, and the FDA has already issued guidance on employing such models in device approvals. A “virtual patient” model uses data analytics to simulate an organ or system in the human body, such as the heart [21]. A researcher can analyze, and with some models even visualize in three dimensions, the predicted effects of an action such as the introduction of a drug or medical device to the body. Sophisticated models use forms of AI to predict how the system will change over time. The FDA guidance recommends that applicants submit information that validates the computer model, such as an in vivo, ex vivo, or in vitro comparator or test data. There is no other guidance on the source, ownership, or use of model data. This is another area in which the FDA should provide further guidance. Currently the FDA maintains a repository of data, models, and software developed by government employees and therefore not protected by intellectual property rights [22].

References [1] US Food and Drug Administration. https://www.fda.gov/. [2] European Medicines Agency. https://www.ema.europa.eu/en. [3] The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH). https://www.ich.org/. [4] Association of Southeast Asian Nations. https://asean.org/. [5] M. Whittaker, et al., AI now Inst., AI now report 2018, at 4, 2018. https://ainowinstitute.org/AI_ Now_2018_Report.pdf. https://perma.cc/HQJ6-7KKR. [6] D.W. Opderbeck, Artificial intelligence in pharmaceuticals, biologics, and medical devices: present and future regulatory models, Fordham L. Rev. 88 (2019) 553. Available at: https://ir.lawnet.fordham. edu/flr/vol88/iss2/7. [7] https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-andmachine-learning-software-medical-device. [8] https://www.fda.gov/media/122535/download.

Artificial intelligence from a regulatory perspective

[9] Advancing Regulatory Science at FDA: A Strategic Plan-August, 2011. www.fda.gov/ regulatoryscience. [10] How Artificial Intelligence Is Transforming Drug Design—Drug Discovery World (DDW). https:// www.ddw-online.com/how-artificial-intelligence-is-transforming-drug-design-1530-201910/. [11] T.J. Struble, Current and future roles of artificial intelligence in medicinal chemistry synthesis, J. Med. Chem. 63 (2020) 8667–8682, https://doi.org/10.1021/acs.jmedchem.9b02120. [12] Guidance for Industry and Food and Drug Administration Staff, Software as a Medical Device (SAMD): Clinical Evaluation. https://www.fda.gov/media/100714/download. [13] S. Benjamens, P. Dhunnoo, B. Mesko´, The state of artificial intelligence-based FDA-approved medical devices and algorithms, An Online Database (2020). [14] F. Lake, Artificial Intelligence in Drug Discovery: What Is New, and What Is Next? Newlands Press, Unitec House, Albert Place, London, UK, 2019. [15] A. Zhavoronkov, Q. Vanhaelen, T.I. Oprea, Will artificial intelligence for drug discovery impact clinical pharmacology? Clin. Pharm. Therap. 107 (4) (2020). [16] D. Merk, L. Friedrich, F. Grisoni, G. Schneider, De Novo Design of Bioactive Small Molecules by Artificial Intelligence, 2018. https://onlinelibrary.wiley.com/doi/full/10.1002/minf.201700153. [17] Advancing Regulatory Science at FDA: Focus Areas of Regulatory Science, 2021. https://www.fda. gov/science-research/advancing-regulatory-science/focus-areas-regulatory-science. [18] https://www.fda.gov/news-events/press-announcements/fda-releases-artificial-intelligencemachinelearning-action-plan. [19] AIML_SaMD_Action_Plan (fda.gov), Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device [SaMD] Action Plan-January, 2021. [20] GAO Technology Assessment GAO-20-215SP, Artificial Intelligence in Health Care Benefits and Challenges of Machine Learning in Drug Development, December 2019. [21] M. Nicholls, A 3D virtual heart tool, Eur. Heart J. 37 (2016) 2813. The living heart project: a translational research initiative to revolutionize cardiovascular science through realistic simulation, Dassault Syste`mes https://www.3ds.com/products-services/simulia/solutions/life-sciences/the-living-heartproject/. https://perma.cc/6W34-4VX8. [22] US Food & Drug Admin, Public Domain Data, Modeling, and Software. https://www.fda.gov/aboutfda/cdrh-offices/public-domain-data-modeling-and-software. https://perma.cc/5AVF-8V5V.

607

Index Note: Page numbers followed by f indicate figures and t indicate tables.

A Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET), 99–102 artificial intelligence tools and software, 324, 325–326t clinical pharmacology, 322–323 in silico modeling, 304–316 machine learning algorithms, 324–330 pharmacodynamics, 324 toxicokinetics, 322 Absorption modeling bioavailability, 305 Caco-2 cells, 308–309 chemical/biochemical transportation, 305–307 intestinal absorption in humans, 309 lipophilicity, 309 permeability, 307–308 protein and tissue binding, 309–310 solubility, 308 state of drug, 305–307 systemic administration, 307 Active diffusion, 445 Active pharmaceutical ingredients (APIs), 12–13, 423 risk assessment, 401 stability, 106–107 Active targeting, 243 Active transport, 308 Actuators, 351 Acute toxicity, 314–315, 317 Additive manufacturing (AM). See 3D printing ADMET. See Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) Advanced deep Q learning network with a fragment-based drug design (ADQN-FBDD), 483 Adverse drug reaction (ADR), 103–104, 104–105t, 115–116 AI-integrated smart biosensors, 223–228 conducting polymers, 225–226 fabrication, 226–227 microneedles, 224

nanobots, 227–228 responsive polymers, 224–225 Allergic reactions, 321 AlphaFold, 545–559 challenges, 550–551 emergence, 548–550 Alzheimer’s disease (AD), 245–248, 247–248f, 248t Ames toxicity, 315–316 4-Aminosalicylic acid, 267–268 Amitriptyline hydrochloride, 278 Amphipathic molecules (AM), 178 Analytical Quality by Design (AQbD), 43–44, 44f Anesthesia, 360–361 Angiotensin II receptor antagonists, 588 ANN. See Artificial neural networks (ANNs) ANOVA, 79–80 Antibiotics, 358 Antibody-drug conjugates (ADCs), 487 Anticancer drug discovery, 14–15 Artificial DNA nanostructures, 206–208 Artificial General Intelligence (AGI), 148 Artificial intelligence (AI), 1 definition, 55, 147 drug product commercial manufacturing and analysis, 430–438 drug product research and development, 433–435 in drug repurposing, 3–4 human interaction, in production, 55 impact of, 2–3, 398–403 implementation, 435–438 inception, 149f in silico physicochemical property prediction, 452–454 levels, 145 machine learning, 397 mathematical modeling, 455–456 molecular modeling, 454–455 nanoformulations, 515–516 pharmacokinetics, 451, 452f policy making, 4–5 product research and development, 435 research outlook, 447–450

609

610

Index

Artificial intelligence (AI) (Continued) risk assessment, 398–403 role, 150–151 significance, 446–447 Artificial Narrow Intelligence (ANI), 147 Artificial neural networks (ANNs), 13–14, 50, 222–223, 433–434, 512–513 adverse drug reactions, prediction of, 103–104, 104–105t application, 125, 125f CAD/Chem software, 86 control of micro-/nanorobots, 110–112 control of nanomaterials, 112 in drug administration, 107–110 drug delivery modeling using carriers, 110–112 drug delivery system design, 84–93 drug efficiency modeling, 97–99 in drug formulation, 105–107 drug properties, prediction of, 99–102 drug-target interactions, 109–110, 111t effectiveness of drug dosing, 106 formulation using, 126–139 hidden layer, 123 input layer, 123 loading efficacy, 107–108 membrane interaction and cellular uptake prediction, 108 neuronal architecture, 84 neurons, 84 output layer, 123 in personalized medicine delivery, 114–115, 115f in pharmaceutics, 83 prediction of drug release profiles, 85–87, 88–89t, 113–114, 113t stability of active pharmaceutical ingredients, 106–107 in targeted drug delivery, 110 toxicity, prediction of, 102–103 Artificial pancreas, drug delivery application to, 218–219 Artificial pancreas systems (APS), 218 Artificial Super Intelligence (ASI), 148 ASD-005, 200, 200f Aspect ratio (AR), 90 Aspirin, 504, 505f Association for Quality Control (ASQC), 35 Atomic force microscope (AFM), 155

Attributable, legible, contemporaneous, original, and accurate (ALCOA), 65 Autoencoders, 97–98 Automatic chemical design, 16 Autonomous nanorobots, 387–388 Azithromycin, 3–4

B Backpropagation ANN (BP-ANN), 106–107 Bagging trees, 48 Bag-of-Words (BOW), 108 Batch manufacturing process, 432 Bayesian Neural Networks (BNNs), 102 Bayesian Optimization (BO), 16 Bayesian regularization model, 87, 101 Bayesian regularized artificial neural networks (BRANN), 101, 136 Beam speed, 281 Beta Bionics, 356 β-lactam antibacterials, 249 Big data (BD), 79–80, 145, 150, 152, 420, 519–520 Binder jetting/material jetting, 272–279, 278f Bingham plastic, 291 Bio/chemoinformatics, nose-to-brain delivery cefotaxime and ceftriaxone in meningitis treatment, 249–251, 250f, 251t curcuminoids in Alzheimer’s disease treatment, 245–248, 247–248f, 248t in evaluating efficiency of targeting moieties, 251–254 Biocompatible polymers, 289–290 Biodegradable polymers, 215 Biofluids, 350–351 Biohybrid magnetic robots, 228 Bioinformatics, 244 genomics, 468–469 pattern recognition, 469 proteomics, 468 Bio inks, 271 Biologically inspired nanorobots, 388–389 Biological membranes, 444–445 Biological targets, 500–501 Biomarkers, 540–545 Biomedical engineering, 22–23, 115–116 Bionanorobots. See Organic nanorobots Bio printability, 290 Bioprinting defined, 271 droplet-based, 271–272

Index

extrusion-based, 271–272 laser-based, 271 materials used in, 289–290 Biosensors, 223–228, 380–382, 380f Biotoxicity, 522 Biotransformation reactions, 312 Blocking, 411 Blood-brain barrier (BBB), 100, 241, 311 Blood-brain-barrier-on-a-chip, 232 Blood-cerebrospinal fluid barrier (BCSFB), 241 Bola-surfactant containing niosomes, 183 Brain Initiative, 27 Brain-machine interface (BMI), 27, 158 Brownian motion, 374 Business Resilience System (BRS), 145, 152, 153f

C C4.5 (successor of ID3), 86 Caco-2 cells, 308–309 CAD/Chem Modeling and Optimization System version 5.1, 87 Calcium phosphate cement (CPC), 270–271 CamAPS FX DanaRS, 218 Cancer classification, 536–539 diagnosis, 538–539 intercalation, 492–493 properties, 489–490 structural variant detection, 537 Cancer nanomedicines, 516–524 Cancer-on-a-chip systems, 232 Cancer therapy chemotherapy, 487 FA-targeted nanocontainers, 493 nanomedicines, 488–489 Nano4XX platform, 490–491 pharmaceuticals, 488 switching effect, 494–495, 495f Candurin gold sheen, 280–282 Carbamazepine (CBZ), 138–139, 270–271 Catalytic biosensors, 223–224 Cefotaxime, nose-to-brain delivery of, 249–251, 250f, 251t Ceftriaxone, nose-to-brain delivery of, 249–251, 250f, 251t Celecoxib, 504, 505f Cellular uptake, prediction of, 108

Center for Devices and Radiological Health (CDRH), 581, 593, 593f Center for Drug Evaluation and Research (CDER), 581 Central nervous system (CNS), 100, 241 Centroid-based clustering method, 470 Chemical mechanical polishing (CMP), 168 Chemical vapor deposition (CVD), 168 Chemoinformatics, 17–18, 244 Chemotherapy, 487 Chi-Square, 79–80 Chloroquine, 3–4 Cholesterol, 177 Chronic toxicity, 317–318 Cimetidine, 504, 505f Ciprofloxacin, 269 Citric acid effervescent (EFF), 138–139 Classification and regression tree (CART), 48, 86 Clinical pharmacology, 322–323 Clinical Research Associates, 160 Clinical trials (CT), 160–163, 161f, 571–572 cost, 574–576 history, 571–572, 573f limited applicability, 572–573 recruitment, 577–578 risk models, 577 time, 574 Closed-loop-controlled anesthesia monitors, 360 Clottocytes, nanorobots and nanomachines, 379 Cloud computing, 79–80 Clustering, 470 k-means clustering algorithms, 471 logistic regression, 472 requirements, 471 CNN. See Convolutional neural networks (CNN) Coated microneedles, 230 Codelivery, 167 Cohort optimization, 579 Colloidal quantum dots, 205, 206f Commercial manufacturing and analysis, 430–438 batch manufacturing process, 432 continuous manufacturing process, 433 Complementary metal oxide semiconductor (CMOS), 168 Comprehensive R Archive Network (CRAN) network, 75, 77 Computational methods, 324

611

612

Index

Computational pharmacokinetic modeling, 451–454 Computational predictive models, 92–93 Computational protein design, 477–478 Computer-aided detection (CAD) systems, 605 Computer-assisted drug formulation design (CADFD), 244–245, 245f Conducting polymers, 225–226 Conjugation reactions, 312 Contact lenses, 221 Continued Process Verification (CPV), 57, 59–62 Continuous glucose monitor (CGM) systems, 354–356 Continuous manufacturing process, 429, 433 Controlled drug release, 219–220 Controlled release formulations, 85 Controller, 351 Conventional size reduction equipment, 187 Convolutional neural networks (CNN), 14, 21–22, 84, 99–100, 136–137 Cornea-on-a-chip, 232 Correctional offender management profiling for alternative sanctions (COMPAS) algorithm, 71 COVID-19, 46, 158, 162–163, 362–363 azithromycin, 3–4 chloroquine, 3–4 research, 551, 552–555t severity, 556–557 Criticality analysis (CA), 42 Critical material attributes (CMA), 39–41, 395–397 control space implementation, 413–417 design space development, 403–417 selection of factors, 405–406 Critical method parameters (CMP), 44 Critical process parameters (CPPs), 39–41, 83, 395–397 control space implementation, 413–417 control strategies, 415–416t design of experiments, 398, 404f selection of factors, 405–406 Critical quality attributes (CQAs), 39–40, 43, 83, 259–260, 395–398 CURATE.AI, 26–27 Curcuminoids, nose-to-brain delivery of, 245–248, 247–248f, 248t Cyclosporine A (CyA), 106 Cytotoxicity, 493, 494f

D Data curation, 519–520 Data-driven modeling, 427–428 Daunorubicin, 504, 505f DaunoXome, 488 DCNN. See Deep convolutional neural networks (DCNN) Decellularized extracellular matrix (dECM), 271 Decision support system (DSS), 133 Decision tree learning, 86 Deep belief network (DBN), 21–22 Deep brain stimulation (DBS), 362 Deep convolutional neural networks (DCNN), 477 DeepDTnet, 3–4 DeepFL-LogP, 101–102 Deep learning (DL), 9–10, 145, 151, 329–330, 459 Deep neural network (DNN), 10, 12–13, 97–98, 123, 124f feed-forward, 21 recurrent neural networks, 21–22 in virtual screening (VS), 21–22 Deep Reinforcement Learning (DRL) algorithm, 110–112 DeepTox pipeline, 102–103 Dendrimers, 196–198 chemical and physical stability, 198 electrostatic interactions, 198 monodispersity, 197 polyvalency, 197 self-assembly, 198 solubility, 198 unimolecular micelles, 198 Density-based clustering method, 471 Design of experiment (DoE), 83, 138, 259–260, 268–269, 398, 404f Design Space (DS), 39, 40f, 73, 412–413 Dexamethasone (DEX), 90 Diabetes continuous glucose monitor, 354–356 dual-hormone devices, 356 machine learning approaches, 358 MiniMed 670G, 356 self-regulated drug delivery devices, 354, 356–357, 357f Diethoxy bisdemethoxycurcumin, 246 Differential scanning calorimetry (DSC), 287 Diffusion-based drug delivery, 192, 192f Digitalization, 51

Index

Digital transformation (DX), 152 Digital twins (DT), 422–423, 422f Digoxin, 504, 505f Di-iron oxide nanoparticles (DIONPs), 220 Dimethyl bisdemethoxycurcumin, 246 Dipalmitoyl phosphatidylcholine, 178 Discrete wavelet transform (DWT), 108 Discriminator neural network, 15 Dissolvable or biodegradable microneedles, 230 Distance-aware graph attention algorithm, 109–110 Distribution modeling BBB permeability, 311 fraction unbound, 311 mechanism, 310 P-glycoprotein, 311 volume, 310–311 Disulfiram, 504, 505f DL. See Deep learning (DL) DNA, 381 DNN. See Deep neural network (DNN) Docking tools, 17–18 Dose-dependent reactions, 321 Doxil, 488 Doxorubicin, 128–129, 220 Droplet-based bioprinting, 271–272 Drop-on-powder (DoP) deposition, 272 Drug administration, artificial neural networks (ANNs) in, 107–110 Drug-containing reservoirs, 107–108 Drug delivery systems implants, 90–91 inhalers, 91–92 microparticles, 87–90 microspheres, 87–90 nanomedicines, 92–93 transdermal products, 90–91 Drug design, 14–18 Drug development, 2–3 Drug dosing, effectiveness of, 106 Drug-drug interactions, 322 Drug efficiency, 97–99 Drug formulation, 105–107 Drug-induced liver injury (DILI). See Hepatotoxicity Drug-membrane interaction, prediction of, 108 Drug permeability, 100

Drug property existing filters, 100t prediction of, 99–102 Drug-related death, 318 Drug release profiles, prediction of, 85–87, 88–89t Drug repositioning, 5 Drug repurposing, 3–4, 503 Drug-target interactions (DTIs), 109–110, 111t Dry powder inhalers (DPIs), 91–92 DUD-E (Directory of Useful Decoys), 18–20 Dynamic neural networks, 113–114

E Edge, 123–125 Electroactive biomaterials, 225 Electrochemical polymerization, 225 Electrospinning, 203 Electrospun-medicated nanofibers, 202–206 Elman neural network (ENN), 86 End-to-End freight management, 165 Engineered motor proteins, 541–542 Enhanced permeability and retention (EPR) effect, 372f Ensemble of ANNs (EANN), 106–107 Enterion capsule, 221 Enzymatic biosensors, 223–224 Enzyme-linked immunosorbent assay (ELISA), 223–224 Epidemic graph convolutional networks, 475–476 Epoxy macromers, 282–284 Eroom’s law, 575 EU-Innovation Network, 585 European Bioinformatics Institute (EMBL-EBI), 45–46 European Medicines Agency (EMA), 585 European regulatory regime, 582–583 Everolimus, 504, 505f Excipients, 401–402 Excretion modeling fraction of drug, 314 hepatic clearance, 314 renal clearance, 313 total clearance, 313 Expert-Guided Optimization (EGO), 24–26 External control arms (ECAs), 576 cohort optimization, 579 decentralized, 579–580

613

614

Index

Extract, transform, load (ETL) process, 67–68 Extreme gradient boosting (XGBoost), 49 Extrusion-based bioprinting, 271–272 material properties, 288 mechanical properties, 288 miscibility/solubilization capacity, 287 physical properties, 286–288 thermal properties, 287 Eye drops, 221

F Fabrication AI-integrated smart biosensors, 226–227 MEMS devices, 214–215 organ-on-a-chip (OOC) devices, 231–232 Factorial analysis (FA), 127 Factorial design-response surface methodology (FD-RSM), 131–132 Failure mode and effects analysis (FMEA), 42, 399–401 Failure modes and effects criticality analysis (FMECA), 42 Fault tree analysis (FTA), 42 Feedback GAN, 15 Feed-forward neural network (FFNN), 13–14, 97–98 Ferroelectricity, 384 File transfer protocols (FTP), 77 Fine particle fraction (FPF), 92 Flowsheet modeling, 429 Focus Areas of Regulatory Science (FARS), 602–603 Folic acid (FA), 492–493 Food and Drug Administration (FDA), 137–139, 162, 166–167, 581, 596–597, 603 Forced expiratory volume in 1s (FEV1), 91–92 Formulations artificial DNA nanostructures, 206–208 electrospun-medicated nanofibers, 202–206 inorganic, metallic, and magnetic nanoparticles, 195–196 nanocrystals, 202–206 nanoemulsion, 185–191 nanogels, 185–191 nanopowder, 202–206 nanosized biomaterials, 198–201 nanosuspension, 185–191, 190t polymeric and lipid nanoparticles, 191–195

polymeric micelles and dendrimers, 196–198 protein nanoparticles, 206–208 quantum dots, 202–206 types, 165–208 vesicular nanosystems, 169–185 Forward deep neural networks (FFDNNs), 21, 100, 106–107 Fourier transform near-infrared (FT-NIR) spectroscopy, 106–107 Fraction unbound, 311 Freeform reversible embedding of suspended hydrogels (FRESH), 291 Free radical polymerized polyethylene glycolpolycaprolactone (PEG-PCL-PEG), 272 Functional recognition imaging approach (FR-SPM), 27–28 Fused deposition modeling (FDM), 267–270, 268f Fused filament fabrication, 267–270

G Galvano mirrors, 279–280 GCNNs. See Graph convolutional neural networks (GCNNs) Generalized regression neural networks (GRNN), 128 General Regression-Ensemble ANN (GR-EANN), 106–107 Generative Adversarial Networks (GAN), 15, 16f Generator neural network, 15 Genetic algorithm (GA), 136 Genetic programming (GP), 128–129 Genetic toxicity, 319 Genetic variation, 500–501 Genomics, 468–469 Genomics of Drug Sensitivity in Cancer (GDSC), 109–110 Genotoxicology, 319 GitHub supply, 76–77 Glass transition temperature (Tg), 287 Glucose oxidase (GOx), 349 Gold-standard datasets, 18–21 Good Clinical Practice (GCP), 160 Good Machine Learning Practices (GMLPs), 595 Good Manufacturing Practices (GMP), 35–36, 65 G-protein-coupled receptors (GPCRs), 3, 108 GPS sensors, 22–23 Gradient Boosting Decision Tree (GBDT), 108 Gradient boosting machine (GBM), 49

Index

Graph convolutional neural networks (GCNNs), 21–22, 474–475 Graphene-based materials, 523 Graph neural network (GNN), 101–102 Greedy Equivalent Search algorithm (GES), 70 Grid-based clustering method, 470

H Hatch distance, 282 Hazard analysis and critical control points (HACCP), 42 Hazard and operability analysis (HAZOP), 42 Healthcare data, semantic annotation of, 23–24 Hearing aids, 282–284 Hepatic clearance (CLh), 314 Hepatotoxicity, 316 hERG prediction models, 315 Heterogeneous Network (HN), 109–110 Hierarchical machine learning (HML), 24–26 Hierarchy-based clustering method, 470 High-pressure homogenization, 185–186, 186f High shear granulation (HSG), 125–126 High throughput screening (HTS), 17 Hit discovery, 502–504 drug repurposing, 503 optimization, 504–506 Hollow microneedles, 230 Hot melt extrusion, 267 Human Brain Project, 27 Human parathyroid hormone (hPTH), 215–218 Human physiology, 444 Hybrid closed-loop systems, 218 Hydrogel based drug delivery system, 192, 193f Hydrogels, 271–272, 291 Hydrogen bonding, 452 Hydroxypropyl methylcellulose (HPMC), 138–139, 280–282 HyperChem 8.0.8 software, 91

Infections, 358–360, 359f Influenza vaccine, 472 Inhalers, optimization of, 91–92 Inline/online analysis, 417–423 Innovative diagnosis approach, 557–559 Inorganic nanoparticles, 195–196 In silico optimization, 85 Insulin administration methods, 443–444 Integrated theranostic nanoparticles, 356–357 InteliSite, 221 IntelliCap, 221 International Council for Harmonization of Technical Requirements for Pharmaceuticals for Human Use (ICH), 160 Q6 guide, 36–37 Q8 guide, 38, 43 Q9 guide, 38, 41 Q10 guide, 37–38, 38f International System Units (ISU), 155 Internet of Things (IoT), 22–23, 152, 422–423, 422f Intestinal absorption in humans (HIA), 309 Intestinal absorption of drugs, 101 In vitro assays, 324 In vitro-in vivo correlations (IVIVCs), 85, 303–304 In vivo drug delivery, 101–102 Iontophoresis-on-chip (IOC) devices, 231 Ishikawa fishbone diagram, 399–401, 400f Iterative Dichotomiser 3 (ID3), 64, 86 Itraconazole, solid dispersion of, 126

J Janus particles, 229 Japanese Standards Association (JSA), 35 Java vs. Python, 50–51 Jupyter Notebook, 76

K I Idiosyncratic reactions, 321 Implantable microchips, 215–220 controlled drug release, 219–220 defined, 215 drug delivery application to artificial pancreas, 218–219 Implants, 90–91 Impregnation, 267

Kaggle-Merck activity dataset, 18–20 Keras API for TensorFlow, 77 Ketoprofen (Ket), 91, 138 K-means clustering algorithms, 471 Kneading, 137–138 K nearest neighbor, 47, 329 K-nearest neighbor decision support system (KNN-DSS), 358 Kollidon VA64, 280–282

615

616

Index

L

M

Lab-on-a-chip (LOC) devices, 230–231 Lactoferrin (LfR) receptors, 253 Large unilamellar vesicles (LUV), 178, 182 Laser-assisted bioprinting (LAB), 285–286, 286f Laser beam, 281 Laser direct-write (LDW), 285–286 Laser scanning speed, 281 Laser sintering (LS), 279–280 Lead optimization bioactivity prediction, 506 clinical trials, 507 toxicity prediction, 506 Lethal toxicity, 320 Levetiracetam, 259–260 Ligand based screening, 17 Light-based photochemical cross-linking, 282–285, 285f Light emitting diodes (LEDs), 206 Linear neural network (LNN), 109–110 Lipid-based carriers, 129–130, 129f Lipid microparticles, 130–131 Lipid nanoparticles (LNPs), 193 Lipinski’s rule, 99, 101–102 Lipophilicity, 101–102, 309, 452–453 Liposomes, 129–132, 129f, 166, 169–185 absorption, 173 active ingredients, protection of, 172 advantages, 178 bioavailability, 174, 175f cholesterol, 177 disadvantages, 178 encapsulation, 175–176 masking, 173 mechanism of intracellular delivery, 177 medical applications, 175 phospholipids, 177 properties, 178 in treating diseases, 177 types, 177–178 Liposphere, 129–130 Liquid crystals (LCs), 134–137, 358–359 Loading efficacy, 107–108 Logistic of drug delivery, 165 Logistic regression, 123–125, 472–473, 509 LogP/LogD, 101–102 Long short-term memory (LSTM) networks, 103, 476 Lyotropic liquid crystals, 135

Machine learning (ML), 9–10, 145, 151, 269 algorithms for diagnosis of diseases, 25t artificial neural network, 50 bagging trees, 48 in biomedical and tissue engineering, 22–26 in cancer nanomedicines, 516–524 CART algorithm, 48 challenges, 26 classification, 469–474 clinical trials, 571–572 data analysis, 324–326 databases for virtual screening, 18–22 deep learning, 329–330 diagnostic tests, 326–327 in drug design, 14–18 in drug discovery and development, 589–592, 600–601 extreme gradient boosting, 49 formulation designing, 513–514 gradient boosting machine, 49 K nearest neighbor, 47, 329 in medical devices, 593–599, 601–606 Naı¨ve Bayes, 329 nanoformulations, 514–515 neural network, 328–329 random forest, 49, 327 reinforcement/sequential learning, 11, 13f statistical methods, 507–513 supervised learning, 10, 11f, 510–511 support vector machine, 327–328 support vector regression, 47–48 synergies with, 584–586 unsupervised learning, 10, 12f, 511–513 for vaccine development, 483–484 Machine-vision, 600–601 Magnetic nanoparticles, 195–196 Magnetic robots, 228 Magnetite (Fe3O4) coating, 228 Magnetococcus marinus, 386 Magnetotactic bacteria (MTB), 382 Manufacturing Intelligence, 80 Manufacturing process, 430–438 batch manufacturing, 432 continuous manufacturing, 433 in pharmaceuticals, 591 simulation tools, 423–430 Marqibo, 488

Index

Material bed based 3D printing binder jetting/material jetting, 272–279, 278f laser-assisted bioprinting, 285–286, 286f light-based photochemical cross-linking, 282–285, 285f processing variables, 273–277t selective laser sintering/selective laser melting, 279–282, 280f Material deposition based 3D printing, 260–272, 261–266t fused deposition modeling, 267–270 fused filament fabrication, 267–270 pressure mediated deposition of shear-thinning inks, 271–272 semisolid extrusion, 270–271, 271f Mathematical modeling, 455–456 Mathematical regression model graphical analysis, 412 numerical analysis, 412 Matrix tablets, 85 Maxillary nerve, 241 M3DISEEN, 268–269 Mean dissolution time (MDT), 90 Medical data science, 326–327 Medical Implant Communications Service (MICS), 215–218 Melting, 137–138 Melting solvent, 137–138 Melting temperature (Tm), 287 Meningitis, 249–251, 250f, 251t Mesoporous silica nanoparticles (MSNs), 384–385 Messenger RNA (mRNA), 194–195 Metabolism modeling biotransformation reactions, 312 conjugation reactions, 312 mechanism, 311–312 Metallic nanoparticles, 195–196 Metformin, 504, 505f Method Operable Design Region (MODR), 43–44 Micelle, 129f Microbased drug delivery system design considerations, 125–126, 126f lipid-based carriers, 129–130, 129f lipid microparticles, 130–131 liposome, 131–132 liquid crystals, 134–137 microemulsion, 132–134 microspheres, 126–129 solid dispersion (SD), 137–139

Microbivores, 379 Microcapsules, 126–127 Microchip reservoir systems, 215, 216–217t Microdevices, 350–353, 352f Microelectromechanical systems (MEMS), fabrication of, 214–215 Microemulsion, 130, 132–134 Microfabricated external drug reservoirs artificial neural networks, 222–223 contact lenses, 221 nanoemulsions, 221–222 oral tablets, 220–221 vaginal delivery, 222 Microfabrication, 107–108, 228–229, 375 Microfluidic platforms, 228–231 microfabrication of particles, 228–229 and smart drug delivery, 229 Micromatrices, 126–127 Micro-/nano-electromechanical systems (MEMS and NEMS), 113–114 Micro-/nanorobots, 110–112 Microneedles, 224, 229–230 Microparticles, 87–90 Microphysiological systems (MPS). See Organ-on-a-chip (OOC) devices Microsoft Windows-based NN software NeuroSolutions version 4.24, 87–90 Microspheres, 87–90, 126–129 MiniMed 670G System, 218–219, 356 MiniMed 770G System, 218–219 MIT’s OptiVax, 484 ML. See Machine learning (ML) Model-based clustering method, 470 Model predictive controller (MPC), 112, 132 Modulated temperature DSC (mDSC), 287 Molecular modeling, 454–455 ADME modeling, 455 molecular and pharmacophore modeling, 455 QSAR modeling, 454–455 Molecular structure prediction, 545–559 MoleculeNet, 18–20 Motor neurons, 543 Motor proteins, 540–542 mRNA vaccine, 194–195 Multiarmed bandit (MAB) algorithm, 11, 13f Multilamellar vesicles (MLV), 177, 182 Multilayer neural network (MNN), 102–103 Multilayer perceptron (MLP), 84–86, 98–99, 128 Multi linear regression (MLR), 138–139

617

618

Index

Multiobjective Optimization (MOO), 24–26 Multiple drug resistance (MDR), 113–114 Multiple linear regression with expectation maximization (MLREM), 136 Multiple regression analysis (MGA), 131–132 Multivariate adaptive regression splines (MARS), 86 Multivariate analysis (MVA), data and, 62–68 biopharmaceuticals, data quality, 64–65 data governance, 65–68 transformation, 67–68 Multivariate statistical process control (MSPC), 59 Multiwalled carbon nanotubes, 290 MUV (Maximum Unbiased Validation), 18–20

N Naı¨ve Bayesian (NB), 329 applications, 473 classification, 473 Nanobiotechnology, 371–372 Nanobots, 1, 227–228, 377f, 378–380, 378t. See also Nanorobots and nanomachines Nanocarriers, 112 Nanocrystals, 202–206 Nanodevices, 348–350 Nanoelectromechanical systems (NEMSs), 213–215, 374 Nanoemulsions, 185–191, 221–222 Nanofibers, 203–204 Nanogels, 185–191 Nanolithography, 376, 376t Nanomachines, 373–382 Nanomaterials (NM), 522 bioapplications, 523 control of, 112 Nanomedicines, 26–27, 92–93, 444, 488–489 applications, 517–518 big data libraries, 519–520 formulation designing, 513–514 in vivo fate of, 520–522 nanoparticles, 513 in nose-to-brain delivery, 243–244 prediction, 521–522 targeted drug delivery, 513 Nanoparticles (NPs), 92–93, 107–108 Nanopharmaceuticals, 488 Nanopowder (NP), 202–206

Nanorobots and nanomachines, 373–382, 374f actuation mechanisms, 376–377, 377f, 378t biosensors, 380–382, 380f challenges, 389 clottocytes, 379 design and fabrication, 373–376, 375f in vitro applications, 382–385 in vivo applications, 385–387, 385f microbivores, 379 respirocytes, 379 self-driven and bioinspired, 387–389 types, 379f Nanoscale tertiary phosphine-stabilized Ag-S cluster, 202, 202f Nanoscience, 154–158 Nanosized biomaterials, 198–201 Nanosuspension, 185–191, 189f, 190t Nano systems, for drug delivery, 158–165 Nanotechnology, 26–27, 154–158, 373, 444 Nanowires, 27–28 Nano4XX platform, 490–491, 491f Narrow Therapeutic Index Drugs (NTIDs), 107 Nasal cavity, 241 National Academy of Medicine (NAM), 602 National Nanotechnology Initiative (NNI), 159–160 Natural eggshell membrane (NEM), 173–174 Natural Language (NL), 152 Natural Language Processing (NLP), 150 Natural polymers, 289–290 Nelfinavir, 504, 505f Nervous system diseases, 361–362 Neural diseases, neurofilaments in, 541 Neural integration of neighbor information for DTI prediction (NeoDTI), 109–110 Neural network (NN), 328–329, 473–478, 600 computational protein design, 477–478 deep convolutional neural networks, 477 epidemic graph convolutional networks, 475–476 graph convolutional neural networks, 474–475 long short-term memory, 476 recurrent neural networks, 476 Neurofilaments, in neural diseases, 541 Neuron, 123–125 Neuronal architecture, 84 Neurons, 84

Index

Neuroprosthetics, 167–168, 168f Next-generation sequencing (NGS), 500–501, 551–556 Nimodipine, 138–139 Niosomes, 169–185 Nitroxoline, 504, 505f N-methyl pyrrolidone (NMP), 282–284 N-nitrosodimethylamine (NMDA), 588 Nonspecific toxicity, 320 Nose-to-brain drug delivery advantages, 242 bio/chemoinformatics tools, 245–254 challenges, 242–243 computer-assisted drug formulation design (CADFD), 244–245, 245f intranasal structures, 241, 242f nanomedicine in, 243–244 rationale of, 241 NuvaRing, 222

O Olfactory epithelium, 241 Olfactory sensory neurons (OSN), 241 Omicron variant, 556 Oncology Center of Excellence (OCE), 592 Ophthalmic OOC, 232 Oral tablets, 220–221 Organic nanorobots, 374 Organ-on-a-chip (OOC) devices, 231–233 drug delivery via, 232–233 fabrication, 231–232 Orthogonal arrays (OAs), 92

P Pain relief, 360–361 Pan-Cancer Analysis of Whole Genomes (PCAWG), 538 Partial least squares (PLS), 21 Particles from Gas-Saturated Solution (PGSS), 130–131 Partition-based clustering method, 470 Passive diffusion, 445 Passive targeting, 243 Passive transport, 307–308 Pathological toxicity, 319 Patisiran, 194 PBPs (penicillin-binding proteins), 249

PCA. See Principle component analysis (PCA) PEDOT:PSS film, 225 Pelican, 232–233 Peltarion software, 85–86 Penetration enhancers, 91 Penfluridol, 504, 505f Penicillin G sodium, “on-off” release of, 220 Peptide drugs, 15 Permeability, 307–308, 453 P-glycoprotein (P-gp) substrate, 311 Pharmaceutical 3D printing, materials in, 286–291 extrusion based, 286–288 powder-based, 288–289 Pharmaceutical industry, 431 Pharmaceutical manufacturing, AI, 45 chemometric approach, 59 continued process verification and drug manufacturing control, 61–62 critical process parameters, 58 data-driven process control strategy, 56–57 end-product release testing, 56–57 European Pharmacopoeia, 59 human interaction, in production, 55 ICH Q8 guidelines, 56–57 multivariate analysis (MVA), data and, 62–68 biopharmaceuticals, data quality, 64–65 data governance, 65–68 multivariate statistical process control (MSPC), 59 process validation and multivariate control, 59–61, 60f Process Validation Guidance, 57, 61 proteochemometrics (PCM), 59 real-time quality control, 56–57 regulations, AI and, 73–78 resources, 75–78 SPC techniques, 58 use cases, in drug manufacturing, 69–73 Pharmaceutical products, 45–46, 372 Pharmaceutics Informatics. See Computer-assisted drug formulation design (CADFD) Pharmacodynamics, 324, 457 Pharmacokinetics, 304–305, 451, 452f current status, 457–459 simulations, 456–457 Pharmacological toxicity, 318–319 Phosphatidylcholine (PC), 178 Phospholipids, 172, 177–178

619

620

Index

Photolithography, 213–214 Photomask, 213–214 Physical vapor deposition (PVD), 168 Physiologically based pharmacokinetic (PBPK) modeling, 306f Pimozide, 504, 505f PLGA. See Poly (lactic-co-glycolic acid) (PLGA) Policy making, 4–5 Polyacrylate, 282–284 Poly (acrylic acid) (PAA), 91 Polycaprolactone (PCL), 269 Polydimethylsiloxane (PDMS), 214 Polydispersity index (PDI), 93 Polyethylene glycol (PEG), 138–139, 269 Poly (3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV), 112 Poly (lactic-co-glycolic acid) (PLGA), 128–129, 246, 253, 289–290 Polymer drug conjugates, 192 Polymeric and lipid nanoparticles, 191–195 Polymeric micelles, 196–198 Polymethylmethacrylate (PMMA), 214 Polypropylene fumarate (PPF), 272 Polyvinyl alcohol (PVA), 112 Polyvinyl-pyrrolidone (PVP), 138–139 Population balance modeling (PBM), 426–427 Powder-based bioprinting, 288–289 optical properties, 289 particle shape and size, 288 rheological properties, 289 thermal properties, 289 Precision medicine (PM), 531–535 AI-based analysis, 534–535 applications, 534t in clinical setting, 544–545 commercial companies, 545, 546–547t in COVID-19 research, 551 prospects, 560–561 requirement, 533 supervised and unsupervised learning algorithms, 532f Prednisolone, 214–215 Preliminary hazard analysis (PHA), 43 Pressure-assisted micro syringe (PAM), 270 Pressure mediated deposition, of shear-thinning inks, 271–272

Principal component analysis (PCA), 10, 27–28, 70, 106–107, 246 Print bed, 270 Print head, 269 Printing chamber temperature, 281 Printing/powder bed temperature, 281 Process analytical technology (PAT), 12–13, 58, 60–61, 417–423 application, 421f classification, 417 and data science, 420–422 digital twins, 422–423, 422f sampling strategies, 418–420 tools, 418, 419t Product life cycle, 46 Proniosomes, 183 Proportional-integral-derivative (PID)-control, 225–226 Protein engineering, 482 Protein nanoparticles, 206–208 Proteochemometric (PCM) approach, 17, 21, 59 Proteomics, 468–469 Pulmonary artery hypertension (PAH), 544–545 Pulsatile delivery systems, 219–220 Pulsed electron beam (PEB) evaporation, 202 Pulsincap, 221 Python programming language, 76 Python vs. Java, 50–51

Q QbD. See Quality by Design (QbD) Quadratic phenotype optimization platform (QPOP), 26–27 Quality definitions, 35, 36f in drug formulation development, 35–44 and quality control, 36–38 Quality assurance system, 37 Quality attributes (QAs), 398 Quality by design (QbD), 13–14, 73–75, 83, 395, 396f analytical, 43–44, 44f for biotechnological drugs, 40–41 concept and benefits, 38–41 steps, 38–40, 39f Quality by testing (QbT), 36–37, 395 Quality control, 36–38

Index

“Quality Gurus”, 35 Quality risk management, 41 Quality target product profile (QTPP), 39, 259–260, 395–397 Quality target profile (QTP), 83 Quantitative nanostructure-activity relationship (QNAR) model, 92–93 Quantitative structure-activity relationship (QSAR) models, 59, 99, 101, 454–455, 506, 590 Quantitative structure-property relationship (QSPR), 92–93 Quantum computing, 215 Quantum dots (QDs), 202–206

R Radial Basis Function (RBF), 101 Radiographic images, 539 Random forest (RF), 21, 49, 327, 447–448, 536 Randomization of runs, 406–411 Raster scanning, 279 Real-world evidence (RWE), 576 Receptor-mediated endocytosis, 163–165 Recurrent neural network (RNN), 16, 21–22, 84, 86, 97–98, 476 Recursive feature elimination (RFE), 480–481 Reduced BP ANN, 138 Regenerative medicine, 371 Region-based CNNs (RCNN), 102–103 Regularized logistic regression, 511 Regulatory agencies current state, 589–599 European regulatory regime, 582–583 future perspective, 599–606 machine learning synergies with, 584–586 prevailing challenges, 586–589 regulatory alliances, 583–584 USFDA regulatory regime, 581 Reinforcement learning, 11, 13f, 468 Renal drug clearance (CLR), 313 Replication, 411 Research and Development (R&D), 159–160, 162 Reservoir-based microchip systems, 215, 216–217t Respirocytes, 379 Response surface methodology (RSM), 83, 90, 127 Responsive polymers, 224–225 Restricted Boltzmann Machine (RBM), 21–22 Retinoic acid, 504, 505f

Reverse vaccinology, 479 Reynolds number, 374 Riluzole-loaded chitosan nanoparticles, 253 Risk assessment, 41–43 Risk management, 41 Risk management facilitation methods, 42 Risk Priority Number (RPN), 42 Risk ranking and filtering (RR&F), 43 Ritonavir, 280–282 RNA vaccine, 194–195 RNN. See Recurrent neural network (RNN) Rotational central composite design-response surface methodology (RCCD-RSM), 131–132 R programming language, 76–77

S Salbutamol, 91–92 SARS-COV2 variants, 551–556 Scaffold-based bioprinting, 271 Scaffold-free bioprinting, 271 Scanning tunneling microscope (STM), 155 Scan spacing, 282 Selective laser melting (SLM), 279–280 Selective laser sintering (SLS), 279–282, 280f Self-Attention-based Message Passing Neural Network (SAMPN), 101–102 Self-emulsifying drug delivery systems (SEDDs), 448 Self-Organizing Maps (SOM), 106–107 Self-regulated drug delivery devices AI-based devices, 353–354, 355f applications, 354–362 challenges, 362–364 feedback-controlled regulatory, 347 medical emergencies, 347 microdevices, 350–353, 352f nanodevices, 348–350 working principles, design, and technologies, 348, 348f Semantic annotation, of healthcare data, 23–24 Semicrystalline polymers, 287 Semisolid extrusion (SSE), 270–271, 271f Semisupervised learning, 468 Sensors, 22–23, 223–224 Sequential iterative modeling OverNight (SIMON), 483–484

621

622

Index

Service Level Agreement (SLA), 151 Shallow neural network, 123, 124f Side Effect Resource (SIDER), 103 Silicone elastomer rings, 222 Silicon modeling, 600 Simplified Molecular Input Line Entry System (SMILEs), 102 Simulation tools classification, 425t data-driven modeling, 427–428, 428f data science-based approaches, 427 digital pharmaceutical development, 424–426 flowsheet modeling, 429 limitations, 429 machine learning, 429–430, 430f raw materials, 423 Simvastatin, 504, 505f Singularity, 149 Siri, 150 Small interfering RNA (siRNA), 193–194 Small unilamellar vesicles (SUVs), 177, 182–183 Smart biosensors, fabrication of, 226–227 Smart drug delivery, microfluidic platforms and, 229 Soft lithography, 214 Software as a Medical Device (SaMD), 594–595, 595f Softwares, 324, 325–326t Solid dispersion (SD), 137–139 Solid lipid microparticles (SLMPs), 130–131 Solid lipid nanoparticles (SLNs), 193 Solid tumors, 539 Solubility, 308, 453–454 Solvent evaporation, 137–138 SPACE, 21 Spiritam, 272 Statistical models, 508–509 Statistical process control (SPC), 58 Statistical tools, 43 Stereolithography (SLA), 282–285 “Strong” AI. See Artificial General Intelligence (AGI) Structural variant (SV) detection, 537 somatic, 537–538 Structure-based virtual screening, 17 Structure-function analysis, 542–544 Sublethal toxicity, 320 Sucrose acrylate isobutyrate (SAIB), 219

Super Artificial Intelligence (SAI), 152, 153f, 168–169 Super microneedles, 224 Supervised learning, 10, 11f, 467 Supply chain (SC), 165 Supply chain management (SCM), 165 Support vector machines (SVMs), 10, 21, 71, 327–328, 480, 511, 536 Support vector regression (SVR), 47–48 Surface machining/deposition, 213–214 SVM. See Support vector machines (SVMs) Switching effect, 494–495, 495f Synapses, 124f Synthetic Minority Oversampling Technique (SMOTE), 108

T Tablet coating, evaluation of, 14 Taguchi method, 92 Tandem Diabetes Care Control-IQ Technology, 219 Targeted drug delivery, 110, 163 Target product profile (TPP), 39 Taxol, 488 TensorFlow, 76–77 The Cancer Genome Atlas (TCGA), 538 Therapeutic toxicity, 321–322 allergic reactions, 321 dose-dependent reactions, 321 drug-drug interactions, 322 idiosyncratic reactions, 321 Thermal gravimetric analysis (TGA), 287 ThermiForm process, 272 Thermoplastic polymers, 267 Thermotropic liquid crystals, 135 3D printing, 24–26, 214, 259–260 bio printability, 290 material bed based, 272–286 material deposition based, 260–272 materials used in bioprinting, 289–290 pharmaceutical, 286–291 shape fidelity, 291 Tissue engineering, 24–26, 371 Total clearance (CLt), 313 Total quality system/management, 37 Tox21, 18–20 Toxicity, 590

Index

acute, 314–315, 317 Ames toxicity, 315–316 causes, 316–317 chronic, 317–318 classifications, 317–318 drug-related death, 318 effects, 316 genetic, 319 genotoxicity, 315 hepatotoxicity, 316 hERG inhibition, 315 in silico methods, 314–316 mechanisms, 320 modern medicine, 316 nonspecific, 320 pathological toxicity, 319 pharmacological toxicity, 318–319 prediction, 506 sublethal toxicity, 320 systems toxicology, 315 therapeutic, 321–322 Toxicokinetics, 322 Transdermal products, 90–91 Transferrin (Tf), 253–254 Transferrin receptors (TfR), 253–254 Transfersome, 169–185, 185f T-Student, 79–80 Tufts Center for the Study of Drug Development (Tufts CSDD), 574–575 Tumor neoantigens, 14–15

U Ultraviolet (UV) light, 205, 282–284 United States Food and Drugs Administration (USFDA), 259–260 Unsupervised learning, 10, 12f, 468 US Government Accountability Office (GAO), 602

V Vaccine Adverse Event Reporting System (VAERS), 481 Vaccine development challenges, 483–484 data bank, 481–482 deep learning, 478 implementation, 474 machine learning, 467–469

mRNA, 482 neural networks, 474–478 random forest analysis, 479–480 recursive feature elimination, 480–481 reverse vaccinology, 479 support vector machine, 480 Vaginal delivery, 222 Vaginal rings, 222 Variational autoencoder (VA), 16 VAT polymerization. See Stereolithography (SLA) VAXIGEN-ML, 479 Vector scanning, 279 Vector trace with raster fill, 279 Venlafaxine, 253 Verapamil hydrochloride (VRP), 127–128 Vesicular nanosystems, 169–185 “Virtual patient” model, 606 Virtual reality (VR), 2 Virtual screening (VS) databases for, 18–22 deep neural networks, 21–22 gold-standard datasets, 18–21 ligand based, 17 proteochemometric approach, 17 structure based, 17 toolkits and libraries, 19t Volume of drug distribution, 310–311 VS. See Virtual screening (VS)

W Warfarin, 106 “Weak” AI. See Artificial Narrow Intelligence (ANI) Wearable biosensors, 350–351 Wearables, in clinical trials, 23 Whole-genome sequencing (WGS), 543–544 Wireless endoscopic capsules, 221, 223 World Health Organization (WHO), 35, 556

X XGBoost. See Extreme gradient boosting (XGBoost)

Z Zealand Pharma, 356 Zidovudine-loaded intravaginal bioadhesive polymeric device (IBPD), 91

623