This detailed volume explores the frontiers of tumor targeting approaches in the field of tumor-targeted drug delivery a
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Table of contents :
Cover
Half Title
Methods in Pharmacology and Toxicology Series
Cancer-Targeted Drug Delivery
Copyright
Preface
References
Contents
Contributors
1. Application of Graphene Oxide in Tumor Targeting and Tumor Therapy
1. Introduction
2. Fabrication of GO-Based Nanomaterials and Their Functional Derivatives
3. Applications of GO in Cancer Therapy
3.1 Drug Delivery
3.2 Gene Delivery
3.3 Phototherapy
3.3.1 Photothermal Therapy (PTT)
3.3.2 Photodynamic Therapy (PDT)
3.3.3 Combined Therapy of PDT and PTT
3.4 Bioimaging
3.4.1 Magnetic Resonance Imaging
3.4.2 Fluorescence Imaging
3.4.3 Photoacoustic Imaging
3.4.4 Raman Imaging
4. Key Challenges and Future Outlook
5. Conclusion
References
2. Antitumoral Effect on Liver Cancer of a Tumor-Penetrating and Interfering Peptide
1. Introduction
1.1 Hepatocellular Carcinoma
1.2 Tumor-Penetrating and Interfering Peptides
1.3 Serine/Threonine Phosphatase PP2A
1.4 Oncoprotein SET
1.5 Molecular Aspects of iRGD-IP Internalization
1.6 Other Targetable Protein/Protein Interactions in Liver Cancer
2. Materials
2.1 Isolation of Proteins from Liver Tissues
2.2 Western Blot Analysis
2.3 Immunohistochemistry
2.4 Quantification of Proteins
2.5 NRP1/iRGD-IP Sequences
3. Methods
3.1 Peptides
3.2 Isolation of Proteins
3.3 Visualization of Protein Expression by Western Blot
3.4 Histological Characterization of Patient Samples
3.5 Generation of Xenograft Models
3.6 Complex Structure Modeling
3.7 Molecular Dynamics Simulation
4. Notes
References
3. Computer-Aided Design for Cancer-Targeted Peptide Drugs
1. Introduction
2. Structural Modeling
2.1 Peptide Structure Prediction
2.1.1 Energy-Based Peptide Structure Prediction
2.1.2 Deep?Learning-Based Peptide Structure Prediction
2.1.3 Structure Prediction for Synthetic Peptides: Combining Energy-Based and Deep Learning-Based Methods
2.2 Docking of Peptide and Target Protein
2.2.1 Docking Based on Traditional Methods
2.2.2 Docking Based on Deep Learning Methods
2.3 Affinity Prediction of Peptides and Target Proteins
3. Virtual Screening of Peptides
3.1 Peptide Library Design
3.1.1 Natural and Non-natural Amino Acids
3.1.2 Combinatorial Approaches
3.1.3 Structure-Based Design
3.2 Virtual Screening Workflow
3.2.1 Target Preparation
3.2.2 Peptide Library Preparation
3.2.3 Scoring
3.2.4 Post-docking Analysis
3.3 Scoring Functions for Peptides
3.3.1 Computer-Aided Drug Design (CADD)
3.3.2 Machine Learning-Based Scoring
3.4 Conclusion
4. Membrane Permeation
4.1 Challenges and Advances in Passive Membrane Permeation
4.2 Mechanisms and Computational Modeling of Active Membrane Penetration
4.3 Oral Bioavailability and the Challenges of Peptide Therapeutics
5. Peptide Optimization with AI-Based Methods
5.1 Peptide Optimization from Known Start Points
5.2 De Novo Peptide Design with Generative Models
5.3 Multi-objective Optimization
6. Closed-Loop Systems
References
4. Advances in the Manufacturing of CAR-NK Cells for Cancer Immunotherapy
1. Introduction
2. Materials
2.1 Equipment
2.1.1 Research Scale
2.1.2 CAR-NK Cell Production: CliniMACS Prodigy Combined with G-Rex Bioreactor Expansion
2.2 Reagents and Solutions
2.2.1 Research Scale
2.2.2 CAR-NK Cell Production: CliniMACS Prodigy Combined with G-Rex Bioreactors
2.3 Antibodies Flow Cytometry
3. Methods
3.1 Research Scale CAR-NK Cell Production
3.2 Feeder-Free CAR-NK Cell Production: CliniMACS Prodigy Combined with G-Rex Bioreactors
4. Notes
4.1 Expected NK Cell Numbers After Isolation from Peripheral Blood and Alternative Sources of NK Cells
4.2 Alternative Engineering Approaches for NK Cells
4.3 Freezing and Storage of CAR-NK Cells
4.4 Quality Control and Characterization
References
5. Addressing the Blood-Brain Barrier: Overcoming Glioblastoma Drug Delivery
1. Introduction: Healthy Brain and Glioblastoma
1.1 Components of the BBB
1.2 Transport Mechanism Across the BBB
1.3 Glioblastoma
1.3.1 GBM Variants
1.3.2 Cells in GBM
2. Current Challenges and Treatment Options
2.1 Main Challenges
2.2 Current Treatment and Perspectives
3. Nanoparticles
3.1 Nanoparticle-Aided Therapeutic Approaches
3.1.1 Viral Vectors
3.1.2 Peptide-Mediated Transport and Peptide-Aided Approaches
3.1.3 Exosomes
3.1.4 Liposomes and Lipid-Based Nanoparticles
3.1.5 Polymers
3.1.6 Metallic and Magnetic NP
3.1.7 Other Approaches
3.2 Advantages of NPs
3.3 Applications of NPs
3.4 Current State
References
6. Advances in Cancer Gene Therapy: Strategies, Delivery Methods, and Challenges
1. Introduction
2. Mechanisms and Strategies of Cancer Gene Therapy
2.1 Zinc-Finger Nucleases and TALENs as Pioneers of Targeted Gene Editing
2.2 CRISPR-Cas9 Advancing Precision Gene Editing in Cancer Therapy
2.3 Ex Vivo Genome Editing in CAR T-Cell-Based Cancer Therapies
2.4 Targeted Cell Destruction Via Suicide Gene Therapy
2.5 Introduction of Cytokine Genes to Enhance Immune Response
3. Gene Delivery Strategies in Cancer Therapy
3.1 Viral Vectors for Targeted Gene Therapy
3.2 Nonviral Approaches with Enhanced Tumor Targeting
4. Challenges and Future Directions
5. Conclusions
References
7. Targeted Tumor Delivery Using Extracellular Vesicles
1. Introduction
1.1 Surface Features of EVs
1.1.1 EV Surface Proteins
1.1.2 EV Surface Glycans
1.1.3 EV Surface Lipids
2. Targeted Delivery of EVs
2.1 Passive Targeting
2.2 Active Targeting
2.2.1 Engineering EV Surface
2.2.2 Other Engineering Strategies
2.2.3 Exogenous Engineering of EV
Engineering EVs by Physical Methods
Engineering EVs by Chemical Methods
2.2.4 Other Target Molecules for EV Engineering
Aptamers for EV Targeting
Small Molecule-Based EV Targeting
3. Challenges and Possibilities with EV-Based Targeting
3.1 Pharmacokinetics
3.2 Immunogenicity
3.3 Route of Administration
3.4 Uptake and Endosomal Escape
4. Conclusion
References
8. Peptide-Assisted CRISPR/Cas9 Delivery to Tumors
1. CRISPR/Cas9 Applications in Cancer Treatment and Research
2. Delivery of CRISPR/Cas9 Can Be Performed with DNA, RNA, and RNP Modalities
3. Peptides as Targeting Moieties for Tumor Delivery
4. Peptides as Delivery Vectors of CRISPR/Cas9
5. CPP-Mediated CRISPR/Cas9 Delivery to Tumors
6. Activatable Cell-Penetrating Peptides (ACPPs) Applied in Tumor Treatment for a Safer and More Effective Delivery
7. Near Future Clinical Applications of Peptide-Mediated Cas9 RNPs for Cancer Indications
8. Future Perspective
References
9. Identification of Organ- and Disease-Specific Homing Peptides Using In Vivo Peptide-Phage Display
1. Introduction
2. Materials
2.1 In Vivo Phage Display
2.1.1 General Reagents and Materials
2.1.2 Basic Manipulation of T7 Phages: Cloning, Titering, and Sequencing
Cloning T7 Peptide-Phage Libraries or Single Clones
Titering T7 Phage
Sequencing Peptide-Coding Region of Single T7 Phage Clones
2.1.3 Phage Amplification and Purification
2.1.4 Ex Vivo Phage Display
2.1.5 In Vivo Phage Display
2.2 Application of High-Throughput Sequencing and Bioinformatic Tools for Mapping of Vascular ZIP Codes
2.3 In Vivo Auditioning of Homing Peptides Using Phage Playoff
3. Methods
3.1 In Vivo Phage Display
3.1.1 Basic Manipulation of T7 Phages: Cloning, Titering, and Sequencing
Cloning T7 Peptide-Phage Libraries or Single Clones
Titering T7 Phage
Sequencing Peptide-Coding Region of Single T7 Phage Clones
3.1.2 Phage Amplification and Purification
3.1.3 Ex Vivo Phage Display
3.1.4 In Vivo Phage Display
3.2 Application of High-Throughput Sequencing and Bioinformatic Tools for Identification of Homing Peptides
3.2.1 Guidelines and Protocol for HTS
3.2.2 Mining of HTS Data for Differential Binding Analysis
3.3 In Vivo Auditioning of Homing Peptides Using Phage Playoff
4. Notes
References
10. Cancer Vaccines
1. Introduction
2. Preventive Cancer Vaccines
2.1 Vaccines Against HPV
2.2 Vaccine Against Hepatitis B Virus (HBV)
2.3 BSG as General Immune Stimulant
3. Therapeutic Cancer Vaccine
3.1 FDA-Approved Therapeutic Cancer Vaccines
3.1.1 Sipuleucel-T (Commercial Name Provenge)
3.1.2 BSG as a Therapeutic Cancer Vaccine
3.1.3 Nadofaragene Firadenovec, Commercially Adstiladrin
3.1.4 Talimogene Laherparepvec (T-VEC, Imlygic)
4. Clinical Trials
5. Auxiliary Adjuvants and Approaches to Intensify Cancer Vaccine Efficiency
References
Index
Methods in Pharmacology and Toxicology
Ülo Langel Editor
CancerTargeted Drug Delivery
Cancer-Targeted Drug Delivery
METHODS
IN
PHARMACOLOGY
TOXICOLOGY
Series Editor Y. James Kang Department of Pharmacology and Toxicology University of Louisville Louisville, KY, USA
For further volumes: http://www.springer.com/series/7653
AND
Methods in Pharmacology and Toxicology publishes cutting-edge techniques, including methods, protocols, and other hands-on guidance and context, in all areas of pharmacological and toxicological research. Each book in the series offers time-tested laboratory protocols and expert navigation necessary to aid toxicologists and pharmaceutical scientists in laboratory testing and beyond. With an emphasis on details and practicality, Methods in Pharmacology and Toxicology focuses on topics with wide-ranging implications on human health in order to provide investigators with highly useful compendiums of key strategies and approaches to successful research in their respective areas of study and practice.
Cancer-Targeted Drug Delivery Edited by
€Ulo Langel Institute of Technology, University of Tartu, Tartu, Estonia; Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden
Editor € Langel Ulo Institute of Technology University of Tartu Tartu, Estonia Department of Biochemistry and Biophysics Stockholm University Stockholm, Sweden
ISSN 1557-2153 ISSN 1940-6053 (electronic) Methods in Pharmacology and Toxicology ISBN 978-1-0716-4373-0 ISBN 978-1-0716-4374-7 (eBook) https://doi.org/10.1007/978-1-0716-4374-7 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2025 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Cover Illustration Caption: Schematic representation of the generation of xenograft models. A cell line or a patientderived sample are grafted into immunodeficient mice. Upon the tumor is visible, mice are treated with the peptide for several weeks and the tumor size is measured. See Chapter 2 for more details. This Humana imprint is published by the registered company Springer Science+Business Media, LLC, part of Springer Nature. The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A. If disposing of this product, please recycle the paper.
Preface According to the World Health Organization, cancer is a leading cause of death worldwide, accounting for nearly 10 million deaths in 2020 (https://gco.iarc.fr/today). Despite extensive research throughout the years (over 5m publications in PubMed), cancer still represents a global health burden, looking forward to finding the breakthrough therapies. Currently used approaches by a combination of surgery, chemotherapy, and radiotherapy should be considerably improved in order to avoid the serious side effects and serious additional hurdles such as the development of drug resistance by cancer cells [1]. In general, cancer is the uncontrolled growth of abnormal cells in the body (cancertutor. com) with the additional impediment that cancer is not only one disease, rather than a collection of related diseases that can occur almost anywhere in the body (https://www. cancer.gov). Hence, precise tumor targeting approaches promise higher potential for cancer diagnosis and treatment. This collection of ten chapters (three research articles/methods and seven reviews) aims to summarize and update the current situation in the field of highly effective tumor targeted drug delivery and cancer therapy. Due to the ongoing comprehensive research in the field as well as the limited space, only a small selection of topics has been made, which, unfortunately, does not allow for the coverage many exciting topics in the field. However, this book is about the frontiers of tumor targeting approaches. Several topics here are about the improvement of cancer detection and treatment using future nanotechnologies, where the main challenge today is to reach from the laboratory to the clinic, with more effective, targeted, and less invasive treatments becoming a reality. To improve the therapeutic index of anticancer drugs, many multiple drug delivery systems have been applied, often representing different nanomaterials (Fig. 1). These drug delivery systems originate from many research areas such as chemical engineering, materials science, and pharmaceutical technologies, including tumor targeting for diagnostics and therapy. Often the standard chemotherapeutic drugs, e.g., doxorubicin, paclitaxel, or highly potent toxins, are applied in such formulations. In recent years, much attention has been paid to the delivery of oligonucleotides, e.g., siRNA, mRNA, plasmids, etc., yielding the target-specific cancer gene therapy and diagnostics [3]. Targeted delivery strategies can precisely and effectively deliver most drugs to tumor cells or tissues instead of normal cells or tissues [4]. Targeted therapeutics can be defined as drugs that are specifically recognized by tumor-specific or overexpressed markers on the tumor cellular surface [5]. Consequently, the cancer-targeted drug should contain a recognition moiety somehow linked to a drug with an antiproliferative effect [5]. To improve the antitumor effects and to minimize undesirable side effects of cancer-targeted drugs, mainly two mainstream strategies apply: passive targeting and active targeting [6]. In passive targeting, the enhanced permeability and retention (EPR) effect, an MW-dependent phenomenon due to an increase in vascular permeability, is applied to deliver drugs to the tumor site, e.g., by using liposomes, polymers, metal oxides, silicon dioxide, or magnetic particles. In active targeting, various types of cell surface receptor ligands, peptides, antibodies, nucleic acid aptamers, small molecules, or other molecules like nanoparticles are used to address specific tumor cells or tissues at the targeted site [7]. It is obvious that the careful optimization of these targeting strategies will enable a strong interaction of the drug delivery system with tumor cells and improve the delivery efficiency
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Fig. 1 Nanomaterials, 1–1000 nm particles, explored for tumor targeting and anticancer therapy. (Image from Lammers [2] with permission from John Wiley & Sons)
and specificity of drugs. This is still necessary to overcome the limitations of the modern cancer-targeted drug delivery systems, such as “the complexity of tumor structure, the ambiguous biosafety of delivery systems, as well as the rapid metabolism and clearance of drugs” [7]. Peptide-mediated systems receive massive attention in the modern development of active and passive tumor-targeting delivery systems, enabling “better tumor targeting, enhanced retention, and increased uptake by tumor cells” and even the “a superior ability to overcome tumor multidrug resistance” [8]. These beneficial properties of peptides certainly contrast with the challenges such as in vivo nonspecific distribution in the liver and spleen, or the presence of peptide degradation enzymes, affecting the efficiency and targeting performance [8]. This book attempts to summarize some of the current cutting-edge technologies available for tumor targeting of drugs. Due to the enormous size of this research field, the largest drawback, obviously, is that the editor has to make a choice and select the most exciting topics in the field. This is already a serious challenge for any editor. Additionally, the selection of outstanding authors within each area of the research is another challenge. Hence, the resulting version of this book reflects the personal choice of the editor with all drawbacks available. This book consists of ten chapters from recognized specialists in the field of cancer drug delivery or the areas tightly connected to it. Several chapters introduce the use of peptides, including cell-penetrating peptides (CPP), in tumor targeting and their potential use in cancer diagnostics and efficient therapy. Despite the availability of many peptide drugs on the market today (insulin, GLP1R agonists, etc.), the peptides for drug delivery still require development into the clinical practice. This equally holds for the CPP field where only one drug, the CPP/drug (daxibotulinumtoxinA-lanm) complex from Revance, has been approved by the FDA for temporary treatment of glabellar lines in adults [9] and for treatment of cervical dystonia [10]. One chapter (Chapter 9) is dedicated to the use of efficient phage display method, in order to establish novel homing peptides for addressing different tumors, e.g., in glioblastoma models in mice. Another peptide, mimicking the interaction in cancer growth (between caspase-9 and serine/threonine phosphatase PP2A) and reducing tumor growth in animal models, is summarized. One chapter is dedicated to computer-aided design of cancer targeted peptide drugs, likely presenting the future possibility for anti-cancer drug
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design. Computational methods, especially artificial intelligence-based approach, for peptide drug design are summarized in Chapter 3 for computer-aided design for cancertargeted peptide drugs. Active tumor targeting technology of CAR-NK cells is described by experienced researchers with an industrial background. Application of graphene oxide in tumor targeting and tumor therapy through modern technology is described in another chapter. Delivery of RNA and DNA therapeutics to treat tumors is included due to the increased impact in the field of therapeutics in general, summarized in Chapter 6. It is obvious that this issue is closely concerned with the applications of cancer vaccines. Peptide-mediated CRISPR/Cas9 delivery to tumors is exciting and promising; however, additional studies are required in the field to demonstrate the clinical benefit, summarized in Chapter 8. One of the most difficult challenges in cancer therapeutics is addressing the blood-brain barrier in order to overcome glioblastoma drug delivery, summarized in Chapter 6. Targeted tumor delivery using extracellular vesicles is an issue of Chapter 7, a topic that becomes more and more important in drug delivery in general. Possibilities of applying cancer vaccines to combat the disease are summarized in Chapter 10. Stockholm, Sweden
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References 1. Shirbhate E, Singh V, Kore R, Vishwakarma S, Veerasamy R, Tiwari AK, Rajak H (2024) The role of cytokines in activation of tumour-promoting pathways and emergence of cancer drug resistance. Curr Top Med Chem 24(6):523–540. https://doi.org/10.2174/0115680266284527240118041129 2. Lammers T (2024) Nanomedicine tumor targeting. Adv Mater 36(26):e2312169. https://doi.org/ 10.1002/adma.202312169 3. Rayati M, Mansouri V, Ahmadbeigi N (2024) Gene therapy in glioblastoma multiforme: can it be a role changer? Heliyon 10(5):e27087. https://doi.org/10.1016/j.heliyon.2024.e27087 4. Glasgow MD, Chougule MB (2015) Recent developments in active tumor targeted multifunctional nanoparticles for combination chemotherapy in cancer treatment and imaging. J Biomed Nanotechnol 11(11):1859–1898 5. Shikalov A, Koman I, Kogan NM (2024) Targeted glioma therapy-clinical trials and future directions. Pharmaceutics 16(1):100. https://doi.org/10.3390/pharmaceutics16010100 6. Yu X, Jia S, Yu S, Chen Y, Zhang C, Chen H, Dai Y (2023) Recent advances in melittin-based nanoparticles for antitumor treatment: from mechanisms to targeted delivery strategies. J Nanobiotechnology 21(1):454. https://doi.org/10.1186/s12951-023-02223-4 7. Li J, Wang Q, Xia G, Adilijiang N, Li Y, Hou Z, Fan Z, Li J (2023) Recent advances in targeted drug delivery strategy for enhancing oncotherapy. Pharmaceutics 15(9):2233. https://doi.org/10.3390/ pharmaceutics15092233 8. Wang Y, Zhang L, Liu C, Luo Y, Chen D (2024) Peptide-mediated nanocarriers for targeted drug delivery: developments and strategies. Pharmaceutics 16(2):240. https://doi.org/10.3390/ pharmaceutics16020240 9. Dowdy SF, Gallagher CJ, Vitarella D, Brown J (2023) A technology evaluation of the atypical use of a CPP-containing peptide in the formulation and performance of a clinical botulinum toxin product. Expert Opin Drug Deliv 20(9):1157–1166. https://doi.org/10.1080/17425247.2023.2251399 10. Comella CL, Jankovic J, Hauser RA, Patel AT, Banach MD, Ehler E, Vitarella D, Rubio RG, Gross TM (2024) Efficacy and safety of DaxibotulinumtoxinA for Injection in cervical dystonia: ASPEN-1 phase 3 randomized controlled trial. Neurology 102(4):e208091. https://doi.org/10.1212/wnl. 0000000000208091
Contents Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Contributors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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1 Application of Graphene Oxide in Tumor Targeting and Tumor Therapy . . . . . Asif Mohd Itoo, Balaram Ghosh, and Swati Biswas 2 Antitumoral Effect on Liver Cancer of a Tumor-Penetrating and Interfering Peptide . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Eric Savier, Pierre Tuffery, Heriberto Bruzoni-Giovannelli, Rachid Boudjelloul, and Angelita Rebollo 3 Computer-Aided Design for Cancer-Targeted Peptide Drugs . . . . . . . . . . . . . . . Yan Degenhardt, Michael Poss, and Xin Gao 4 Advances in the Manufacturing of CAR-NK Cells for Cancer Immunotherapy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ¨ nkel Julia Uhlig, Dominik Schmiedel, U. Sandy Tretbar, and Anna Du 5 Addressing the Blood-Brain Barrier: Overcoming Glioblastoma Drug Delivery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . € lo Langel Ly Porosk and U 6 Advances in Cancer Gene Therapy: Strategies, Delivery Methods, and Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Anni Lepland and Kadi-Liis Veiman 7 Targeted Tumor Delivery Using Extracellular Vesicles . . . . . . . . . . . . . . . . . . . . . . Hema Saranya Ilamathi, Samir El Andaloussi, and Oscar P. B. Wiklander 8 Peptide-Assisted CRISPR/Cas9 Delivery to Tumors . . . . . . . . . . . . . . . . . . . . . . . Oskar Gustafsson, Samir EL Andaloussi, and Joel Z. Nordin 9 Identification of Organ- and Disease-Specific Homing Peptides Using In Vivo Peptide-Phage Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Kristina Po˜ˇsnograjeva, Karlis Pleiko, and Tambet Teesalu 10 Cancer Vaccines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . € lo Langel Matjazˇ Zorko and U
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Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
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Contributors SAMIR EL ANDALOUSSI • Department of Laboratory Medicine, Unit for Biomolecular and Cellular Medicine, Karolinska Institutet, Stockholm, Sweden; Karolinska ATMP Center, ANA Futura, Huddinge, Sweden; Department of Cellular Therapy and Allogeneic Stem Cell Transplantation (CAST), Karolinska University Hospital, Huddinge, Sweden SWATI BISWAS • Nanomedicine Research Laboratory, Department of Pharmacy, Birla Institute of Technology & Science-Pilani, Hyderabad Campus, Hyderabad, Telangana, India RACHID BOUDJELLOUL • Faculty of Pharmacy, UTCBS, Universite´ Paris Cite´, Inserm 1267, Paris, France HERIBERTO BRUZONI-GIOVANNELLI • Centre d’Investigation Clinique 1427 Inserm/AP-HP Hoˆpital Saint Louis, Paris, France YAN DEGENHARDT • Syneron Technology, Guangzhou, China ANNA DU¨NKEL • Department of Cell and Gene Therapy Development, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany XIN GAO • Syneron Technology, Guangzhou, China; Computer Science Program, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia; Center of Excellence for Smart Health (KCSH), King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia BALARAM GHOSH • Nanomedicine Research Laboratory, Department of Pharmacy, Birla Institute of Technology & Science-Pilani, Hyderabad Campus, Hyderabad, Telangana, India OSKAR GUSTAFSSON • Department of Laboratory Medicine, Unit for Biomolecular and Cellular Medicine, Karolinska Institutet, Stockholm, Sweden HEMA SARANYA ILAMATHI • Department of Laboratory Medicine, Unit for Biomolecular and Cellular Medicine, Karolinska Institutet, Huddinge, Sweden; Breast Center, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden; Karolinska ATMP Center, ANA Futura, Huddinge, Sweden ASIF MOHD ITOO • Nanomedicine Research Laboratory, Department of Pharmacy, Birla Institute of Technology & Science-Pilani, Hyderabad Campus, Hyderabad, Telangana, India € LO LANGEL • Institute of Technology, University of Tartu, Tartu, Estonia; Department of U Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden ANNI LEPLAND • Faculty of Medicine, University of Tartu, Estonian Cancer Center, Tartu, Estonia JOEL Z. NORDIN • Department of Laboratory Medicine, Unit for Biomolecular and Cellular Medicine, Karolinska Institutet, Stockholm, Sweden; Karolinska ATMP Center, ANA Futura, Huddinge, Sweden; Department of Clinical Immunology and Transfusion Medicine (KITM), Karolinska University Hospital, Stockholm, Sweden KARLIS PLEIKO • Laboratory of Precision- and Nanomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia LY POROSK • Institute of Technology, University of Tartu, Tartu, Estonia
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KRISTINA PO˜SˇNOGRAJEVA • Laboratory of Precision- and Nanomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia MICHAEL POSS • Syneron Technology, Guangzhou, China ANGELITA REBOLLO • Faculty of Pharmacy, UTCBS, Universite´ Paris Cite´, Inserm 1267, Paris, France ERIC SAVIER • Service de Chirurgie Digestive et He´pato-Bilio-Pancre´atique, Transplantation He´patique, CHU Pitie´-Salpeˆtrie`re, Assistance Publique-Hoˆpitaux de Paris (AP-HP), Sorbonne Universite´, Paris, France; Centre de Recherche Saint-Antoine (CRSA), Institute of Cardiometabolism and Nutrition (ICAN), Sorbonne Universite´, INSERM, Paris, France DOMINIK SCHMIEDEL • Department of Cell and Gene Therapy Development, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany TAMBET TEESALU • Laboratory of Precision- and Nanomedicine, Institute of Biomedicine and Translational Medicine, University of Tartu, Tartu, Estonia; Materials Research Laboratory, University of California, Santa Barbara, CA, USA U. SANDY TRETBAR • Department of Cell and Gene Therapy Development, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany PIERRE TUFFERY • BFA, Universite´ Paris Cite´, Inserm 1133, Paris, France JULIA UHLIG • Department of Cell and Gene Therapy Development, Fraunhofer Institute for Cell Therapy and Immunology, Leipzig, Germany KADI-LIIS VEIMAN • Faculty of Medicine, University of Tartu, Estonian Cancer Center, Tartu, Estonia OSCAR P. B. WIKLANDER • Department of Laboratory Medicine, Unit for Biomolecular and Cellular Medicine, Karolinska Institutet, Huddinge, Sweden; Breast Center, Karolinska Comprehensive Cancer Center, Karolinska University Hospital, Stockholm, Sweden; Karolinska ATMP Center, ANA Futura, Huddinge, Sweden MATJAZˇ ZORKO • Medical Faculty, Institute of Biochemistry and Molecular Genetics, University of Ljubljana, Ljubljana, Slovenia
Chapter 1 Application of Graphene Oxide in Tumor Targeting and Tumor Therapy Asif Mohd Itoo, Balaram Ghosh, and Swati Biswas Abstract Graphene oxide (GO) is a carbon-based nanoparticle that has garnered significant interest across various fields over the last decade. GO exhibits exceptional physical and chemical characteristics, making it highly promising in the realm of biomedicine. The application of GO nanostructures shows potential in the field of oncology, facilitating the accurate delivery of medicines and genetic material to specific locations. This chapter delves into the innovative application of GO nanomaterials in cancer therapy, focusing on their multifunctional capabilities. GO’s unique structure, characterized by a large surface area and functional groups, allows for effective drug and gene delivery, enhancing tumor targeting and treatment efficiency. The chapter also explores GO’s potential in phototherapy, its role in stimulating immune responses, and the challenges associated with its biocompatibility and toxicity. Key words Nanoparticles, Graphene oxide, Cancer, Drug delivery, Phototherapy
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Introduction Graphene, a two-dimensional carbon structure with hexagonal bonding and sp2 hybridization, possesses remarkable features. It has attracted significant multidisciplinary interest in several scientific and technical disciplines for the past 50 years. The honeycomb structure of graphene is single, multilayered (>10 layers), and flat. It possesses distinct characteristics such as limitless surface area, optical transmittance, thermal resistance, high hardness, and electrical conductivity, among others [1–4]. Graphene oxide (GO) is the result of oxidising graphene, typically using strong oxidation conditions. Graphene oxide (GO) features a range of oxygen-containing functional groups on its carbon surface, including epoxide, carboxyl, and hydroxyl groups. This makes GO more hydrophilic compared to graphene (G) [5–7]. Integrating G layers into nanocomposites is a technique used to regulate and enhance their thermal conductivity, mechanical/electrical properties, and
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surface area [8–10]. These nanocomposites, derived from G and GO, offer a large surface area, are highly functionalizable, and have a significant capacity for drug loading. Additionally, they can potentially generate reactive oxygen species (ROS), making them promising candidates for the targeted delivery of anticancer drugs, genes, and diagnostic agents [11–14]. Graphene (G) and graphene oxide (GO) derivatives, when combined with multifunctionalization techniques, can improve optical and electrical properties while also overcoming challenges related to poor solubility in aqueous solutions [15–18]. These nanosystems have garnered significant attention due to their ability to improve the biodistribution of pharmaceuticals, minimize harmful effects on healthy cells, increase selectivity and sensitivity, and enhance local therapeutic absorption [19, 20]. The key challenges associated with these substances include complex synthesis procedures, the risk of inflammatory responses, potential spleen accumulation, probable immunogenicity, cell disruption at elevated concentrations, and the need for extensive in vivo studies and protein folding analyses [21, 22]. G and GO, along with their respective nanocomposites, have demonstrated considerable potential for many applications, including biosensing, bioimaging [23], nano-detecting, labeling [24], gene and drug delivery [25], as well as regenerative medicine and tissue engineering [26]. Nanocarriers containing either G or GO have been utilized for the targeted delivery of anticancer drugs, demonstrating both great selectivity and drug-loading capacity [27, 28]. The G-based advanced functional structures possess significant surface areas, are easily functionalized/modified, and exhibit photothermal (PT) properties, making them highly desirable for cancer nanotherapy [29, 30]. For example, researchers have successfully created reduced-GO structures using Euphorbia heterophylla that have excellent biocompatibility. These structures were then tested for their cytotoxicity against HepG2 and A549 cancer cells. The results showed significant cytotoxic effects in a laboratory setting. However, further research is necessary to investigate their potential applications in other areas of biomedicine [31]. In addition, the use of Bacillus marisflavi as a stabilizer and reductant agent has resulted in the production of reduced-GO materials that exhibit dose-dependent cytotoxicity against MCF-7 cells. The bacterially reduced-GO materials, at a concentration of approximately 60 μg mL-1, could enhance the production of reactive oxygen species (ROS) and trigger the release of lactate dehydrogenase [32, 33]. conducted a review on the ways to enhance the functionality and efficiency of nanomaterials centered around GO for the transport of drugs and genes. Multiple approaches, such as non-covalent and covalent methods (such as addition, condensation, and nucleophilic/electrophilic substitution), have been extensively studied for the modification of G and GO. The
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functionalized materials have been found to exhibit notable advantages such as increased electrical conductivity, greater dispersibility, improved functionality, and superior biocompatibility. Nevertheless, certain functionalization procedures, like addition, may encounter challenges in terms of control. Therefore, researchers should explore controlled selection strategies [34, 35]. When developing sophisticated nanosystems based on graphene (G) for the diagnosis and treatment of malignancies, it is important to address several complex concerns. These include the capacity of the nanosystems to be flexible, compatible with living organisms, capable of breaking down naturally without causing harm to the body, able to have their surface properties modified, and having the capability to reduce fluorescence [36–41]. Various polymeric materials can be used to modify and functionalize G-based materials [18, 42]. Surface functionalization of G-based materials can be enhanced by using bioactive substances like gelatin, chitosan, and L-ascorbic acid. This improves the biocompatibility and targeting capabilities of these materials. The surface of these materials has been altered by introducing different functional groups, which helps to modify and control their surfaces and enhance their properties and activities as hybrid materials. Folic acid, hyaluronic acid, and galactose are notable compounds that have been reported to enhance the targeting and selectivity of anticancer delivery systems [43]. This chapter delves into the innovative application of GO nanomaterials in cancer therapy, focusing on their multifunctional capabilities, challenges, and future outcomes.
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Fabrication of GO-Based Nanomaterials and Their Functional Derivatives The remarkable inherent characteristics of G and GO make them highly sought-after materials for use in various research fields, including energy-related research [44], tissue engineering [45], drug delivery [19], medical and life science studies [46], cell imaging [47], bactericidal applications [48], air purification [49], and water treatment [50]. Although the potential of G’s and GOs has been promisingly established, their preparation is somewhat challenging and costly, which may limit their utilization on a broad industrial scale [51]. There are four primary ways for oxidizing the G to synthesis GO: Hummer’s processes [52], Brodie [53], Hoffmann [54], and Staudenmaier [55]. These methods have been modified to improve their efficiency, cost-effectiveness, and environmental friendliness [56–58]. However, significant hurdles remain; while alternative procedures, including chemical exfoliation [59, 60] and chemical vapor deposition [61], have been devised to produce G and GO, these approaches are costly and necessitate specialized equipment. In addition, the production of
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Table 1 Overview of the synthesis techniques for graphene oxide (GO) and its derivatives Material
Reaction condition
Method
Type
References
Graphene
H2SO4, HNO3, potassium chlorate, HCl
Covalent
Chlorination of graphene
[63]
Graphene
H2SO4, KMnO4, HNO3, Chemical GOQDs H2O2 oxidation
[64]
GO
Hydrazine hydrate, Ethanol
Chemical rGO reduction
[65]
GO
Ultrasonic stripping
Physical
GO
[66]
Graphene nanoplatelets
GO H2SO4, H3PO4, KMnO4, Chemical HCl oxidation
[65]
GO
3D-rGO
Chemical Hydrazine, hydrazine reduction hydrate, l-ascorbic acid
[67]
Graphene
GO
Chemical H2SO4, KMnO4, HCl oxidation
[68]
ClO2, N2O4, NO2, and other hazardous or combustible gases during these processes is a significant environmental concern that must be taken into careful consideration [62]. Due to the high cost of ingredients, the need for complex apparatus, and the negative environmental impact, there is a pressing need to develop simple and environmentally friendly methods for synthesizing G-based structures. One way to achieve sustainable manufacture of G-based products is by utilizing agricultural wastes such as walnut shells and husk. In addition, the necessity for elevated temperatures and the generation of potentially hazardous syngas may give rise to environmental concerns that should be investigated in future research to enhance the overall sustainability of the product. The techniques for producing nanomaterials based on GO and its derivatives are presented in Table 1.
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Applications of GO in Cancer Therapy Drug Delivery
The use of nanostructures for drug delivery is highly appealing due to the difficulty of establishing sufficient drug bioavailability at tumor locations. As a result, nanoparticles have become a method to significantly improve the way medications are distributed and absorbed in the body. GO has been utilized to transport chemotherapeutic medicines, leading to the development of nanomaterials that significantly boost the effectiveness of these drugs in eliminating tumors [69, 70]. Using GO nanostructures for
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delivering chemotherapeutic medications is highly suggested since they can effectively overcome chemoresistance and improve their efficacy in tumor treatment. A technique is used to modify GO with polyethylene glycol (PEG) and then functionalize it with folic acid to enhance its capacity to specifically target tumor cells. The nanocarriers have a remarkable ability to effectively transport paclitaxel into tumor cells, which enhances the effectiveness of paclitaxel in eliminating cancer cells [71]. Functionalizing GO nanoarchitectures with folic acid involves utilizing anchoring points on the surface of the nanocarriers. Initially, the GO nanostructures are linked with methyl acrylate, followed by modification with folic acid. This alteration improves the delivery of paclitaxel to breast tumor cells, resulting in a substantial increase in its cell-killing effectiveness [72]. An investigation was undertaken to evaluate the efficacy of three carbon carriers, namely, carbon nanotubes, fullerene, and GO, in the transportation of doxorubicin and paclitaxel for the purpose of cancer treatment. The nanostructures were specifically engineered to administer the medications in a pH-sensitive manner, with the goal of reducing tumor growth. According to the research, carbon nanotubes outperformed fullerene and GO as carriers for drug delivery, demonstrating greater performance [73]. GO nanostructures exhibit significant promise in combating chemoresistance in human malignancies. The GO nanostructures were functionalized with oxidized sodium alginate and then modified with PEG to enhance the administration of paclitaxel. The study has found that GO nanoparticles, when loaded with paclitaxel, cause an elevated formation of reactive oxygen species (ROS) and impede mitochondrial respiration, leading to a decrease in adenosine triphosphate (ATP) synthesis. Ultimately, this action hampers the functioning of P-gp, which in turn counteracts the insensitivity of stomach tumors to paclitaxel [74]. Furthermore, GO has the capacity to accept two medications concurrently, with the objective of reducing the concentration of chemotherapy treatment. The objective is to reduce the possible negative impacts while also improving the effectiveness of anticancer treatment [75]. Moreover, GO nanocomposites have the ability to convey organic molecules throughout the process of cancer therapy. Theranostic nanoplatforms have been developed by integrating GO with quantum dot/chitosan to facilitate the targeted delivery of curcumin. In order to improve their capacity to specifically attack cancer cells, these nanotheranostics have been altered with mucin-1 (MUC-1) aptamer, resulting in the inhibition of tumor formation [76]. In addition, A. M Diez-Pascual et al. [77] utilized a mixture of diminished GO with carboxymethyl cellulose and starch to facilitate the release of substances in response to changes in pH. According to the presented findings and comments, it can be inferred that GO nanostructures have great potential in aiding the
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transport of both synthetic and natural medications in cancer therapy. The present research highlights the difficulty of delivering drugs in cancer treatment, especially when providing them in living organisms. This process entails the initial circulation of medications in the bloodstream before they reach the tumor site. However, their accumulation at the tumor site tends to be minimal, and they are rapidly metabolized. Therefore, the use of GO nanoparticles provides a way to improve the pharmacokinetic properties of medications designed for cancer treatment. This enhancement includes advancements in the drug half-life, pharmacokinetics, reduction in metabolic breakdown, and increased accumulation within tumors. The combined effects of these factors can greatly enhance the effectiveness of cancer medicines supplied by GO nanoparticles. Furthermore, research has suggested that GO nanoparticles can combat drug resistance, making them a feasible approach for delivering numerous medications simultaneously. However, it is important to recognize that these studies have not fully investigated the complexities of cancer biology. The specific molecular pathways responsible for the cancer elimination facilitated by the GO nanostructure have not been thoroughly investigated. Moreover, it is essential to do more extensive research to fully understand the effects of these nanocarriers on cellular components like mitochondria and endoplasmic reticulum, as well as their role in regulating certain molecular pathways like autophagy, ferroptosis, and necroptosis. 3.2
Gene Delivery
Gene therapy is widely researched as a promising strategy for cancer treatment [78]. Gene therapy necessitates a vector capable of protecting genes from nuclease degradation and improving gene uptake with high transfection efficiency [79–81]. Nonviral gene carriers offer benefits over viral vectors by bypassing limitations such as high production costs, mutagenesis, chromosomal integration, toxicity, and immune responses [82, 83]. GO-based nanomaterials are considered superior gene carriers for gene delivery due to their exceptional biocompatibility, negative charge properties, high adsorption capacity, extensive surface area, and safety. C.H. Lu et al. [84] developed a gene delivery vector based on GO that can efficiently adsorb single-stranded DNA (ssDNA). Pristine GO exhibits the ability to adsorb single-stranded DNA via π-π stacking but faces challenges when it comes to loading double-stranded DNA (dsDNA). To address this problem, [78] developed nanoparticles consisting of plasmid DNA (pDNA) and GO/cationic lipid (GOCL) NPs for the purpose of introducing genetic material into human embryonic kidney (HEK-293) cells and human cervical cancer (HeLa) cells. In addition, the use of GO as a delivery mechanism for RNA molecules is extensively researched. In another study, the researchers [85] described the use of GO-FACO+ to deliver DOX and siRNA, with the aim of altering drug resistance.
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In addition, S. Du et al. [86] developed a targeted delivery system for siRNA using PEG, PEI, and FA-modified GO. This system effectively inhibits the proliferation of ovarian cancer cells, as demonstrated through a series of in vitro experiments. To enhance the effectiveness of gene delivery and achieve a greater transfection effect, researchers [36, 87, 88] developed a new gene delivery carrier called NGO-PEG-PEI. This carrier is made up of ultrasmall nano-GO that is co-modified with PEG and PEI, and it is sensitive to near-infrared light. It is used to load siRNA and pDNA. The researchers showed that the NGO-PEG-PEI complex could efficiently control the activation or suppression of the gene using controlled near-infrared (NIR) irradiation. GO-based nanomaterials have demonstrated significant benefits in terms of their ability to efficiently load drugs at high rates and achieve controlled release with targeted efficiency. These advantages are attributed to the exceptional properties of these materials, such as their lateral dimensions, surface area, layer number, and surface chemistry [21, 89, 90]. Developing the characteristics of GO-based nanomaterials to create multifunctional drug delivery systems is a viable approach for their application in clinical settings. 3.3
Phototherapy
3.3.1 Photothermal Therapy (PTT)
Phototherapies, such as photothermal therapy (PTT) and photodynamic therapy (PDT), are innovative methods for destroying tumors. They offer several benefits, including the ability to control them remotely, selectivity in terms of location and time, and the ability to repeat the treatment without causing cumulative toxicity [91–93]. PTT operates by transforming radiant light energy into localized heat through external near-infrared (NIR) laser irradiation. This method generates hyperthermia, increasing the temperature specifically at the tumor site and leading to the destruction and death of tumor cells [94, 95]. The targeted addition of a light source to a specific area enables the destruction of tumor cells in that area while sparing normal cells from harm [96]. GO-based nanomaterials have a unique electron configuration that allows them to strongly absorb near-infrared (NIR) light. This property makes them ideal candidates for photothermal sensitisers, as they can effectively convert light energy into heat energy [97]. Moreover, GO-based nanomaterials exhibit superior biocompatibility and are more cost-effective for cancer therapy [98]. Consequently, numerous researchers are dedicated to exploring GO as a potent PTT agent. In a study, C. H Lin et al. [99] developed a nanocomposite material called aptamer-gold nanoparticle-hybridized (Apt-AuNP-GO), which demonstrated exceptional targeting of breast cancer and photothermal characteristics. Apt-AuNP-GO can selectively target breast cancer cells that overexpress MUC1 due to the presence of Mucin 1 (MUC1) aptamers. AuNPs attached to
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GO can intensify the PTT effects by expediting the removal of oxygen from GO. Thus, this nanocomposite exhibited exceptional abilities in selectively targeting tumors and generating heat by PT effects. B. G Chung et al. [100] described the process of designing and using MnO2-FA-GO nanosheets in biological contexts. The MnO2-FA-GO nanosheet, as described, has the potential to serve as an effective carrier for cancer targeting and PTT applications. In addition, the PT effect of GO can be utilized to synthesize a gene vector that responds to light. Z. Liu et al. [101] developed a gene carrier called GO-PEG-PEI by chemically linking PEG and PEI with GO. This gene carrier demonstrated enhanced effectiveness in transferring genes into cells when exposed to laser light, and it did not aggregate in serum. Some studies have reported that PTT faces challenges in eliminating tumor cells because of the uneven distribution of heat within the cells and the limited ability of light to penetrate the tumor. These factors contribute to the recurrence of tumors [102]. Hence, the proposition of utilizing chemotherapy and PTT as a novel approach to augment the eradication of cancer cells and hinder the reappearance of tumors has been put up [103]. This combination can enhance the sensitivity of chemotherapy and also enhance the uptake of chemotherapy medications in tumor cells or regulate the release of therapeutic agent [104]. T. Chen et al. [105] designed a multifunctional nanocomposite called nrGO-PEG/PEI/DOX to enhance the effectiveness of tumor treatment. The results of both in vitro and in vivo experiments demonstrated that the produced nrGO-PEG/PEI/DOX nanocomposite displayed significant anticancer effects when exposed to NIR laser irradiation. Besides, our group developed a 2D carbon nanomaterial, GO, and transformed it into 3D colloidal spherical structures by adding the amphiphilic polymer mPEG-PLA and physically trapping doxorubicin (Dox). The resulting Dox@GO (mPP) (1/0.5) nanoparticles (NPs) had the smallest size (161 nm), highest Dox loading (6.3%), and greatest stability. The therapeutic effectiveness was tested in vitro and in vivo using triplenegative breast cancer models. Dox@GO(mPP) (1/0.5) NPs with laser treatment (808 nm) showed significant cytotoxicity, ROS generation, and tumor inhibition, highlighting their potential as a combined chemo-PTT for triple-negative breast cancer (Fig. 1) [20]. 3.3.2 Photodynamic Therapy (PDT)
The efficacy of PDT as a cancer treatment has been endorsed by the Food and Drug Administration (FDA) [106]. PDT has several advantages, including its specificity and ability to be repeated for treatment [79]. Photosensitizers (PSs) are required in PDT to produce reactive oxygen species (ROSs) when exposed to light irradiation, X-ray, or microwave radiation [107–110]. This process is crucial for the eradication of cancer cells. In addition, it has been
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Fig. 1 Polymeric graphene oxide nanoparticles loaded with doxorubicin for combined photothermal and chemotherapy in triple negative breast cancer. (Adapted with permission from Ref. [20])
confirmed by [111] and [112] that PDT has the potential to harm the blood vessels supplying the tumor and stimulate the immune system. Recently, PDT has been employed for the treatment of various cancer types, including breast cancer [113], superficial bladder cancer [114], lung cancer [115], and cervical cancer [116]. Several photosensitizers (PSs) have been used in clinical or preclinical settings for PDT, such as porphyrin, chlorin, or phthalocyanine derivatives, all of which possess tetrapyrrole structures [117]. Nevertheless, due to the typical insolubility of PSs in aqueous solutions, limited tumor selectivity, restricted absorption wavelength, and susceptibility to removal during blood circulation, PDT encounters a significant obstacle [118]. Hence, the efficient delivery of photosensitizers (PSs) to tumor cells or tumor sites is crucial for the successful implementation of PDT [119]. GO-based nanomaterials are optimal carriers for PSs due to their extensive specific surface area, highly efficient fluorescence quenching capabilities, and diverse surface functional groups [120]. In a study, D. Shi et al. [121] developed a novel methoxy-polyethylene glycol (mPEG) modified NGO-mPEG to transport PSs. The carrier may greatly improve the water solubility and biocompatibility of PSs, as well as facilitate the effective uptake by MCF-7 cells, which leads to
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Fig. 2 Schematic overview of the mutual sensitization, self-degradation, and anticancer activity of HA-TiO2-GO (a); pathways of reactive oxygen species (ROS) generation within the HA-TiO2-GO system upon irradiation. (1: absorption; 2: intersystem crossing; 3: energy transfer; 4: electron or hydrogen transfer; 5: electron transfer from excited HA to the TiO2 conduction band; 6: re-oxidation; 7: O2 - oxidation by h+; 8: reduction; 9: energy transfer from excited TiO2 to 3O2; 10: reduction of 3O2 by e-) (b); microscopic images of HeLa cells stained with trypan blue (c); schematic and TEM depiction of GO degradation by 1O2. The blue regions represent the HA-TiO2-GO complex and sp3 carbon clusters, where HA and TiO2 are localized (scale bar = 10 nm). (Adapted with permission from Ref. [122])
the suppression of MCF-7 cells through PDT. In addition, researchers [122] found that the use of GO-HA-TiO2 nanoparticles enhances the production of reactive oxygen species (ROSs) through mutual sensitization, as shown in Fig. 2. Titanium dioxide (TiO2) and hyaluronic acid (HA) can react to create a stable complex that is responsive to HA and produces additional reactive oxygen species (ROSs). Additionally, when TiO2 is combined with GO and exposed to visible light, it can also produce reactive
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oxygen species (ROSs). Moreover, cell damage from these drugs was assessed using a trypan blue exclusion assay, which distinguishes live (unstained) from dead (blue) cells, as trypan blue is impermeable to intact cell membranes. The results of the in vitro cell experiment demonstrate that the PDT effectiveness of GO-HA-TiO2 was significantly improved compared to that of HA-GO. Chemotherapy is the predominant approach employed for the management of cancer. Nevertheless, tumor cells employ many ways to diminish the buildup of the drug at its site of action within the cell. The primary cause of chemotherapy failure is drug resistance. P-glycoprotein (P-gp), a drug efflux transporter, plays a crucial role in tumor treatment resistance when it is overexpressed. Recent research suggests that PDT [123] could potentially prevent medication resistance caused by P-gp. Therefore, researchers have explored the use of combination therapy using PDT and chemotherapy to mitigate the effects of multidrug resistance and enhance the effectiveness of anticancer treatment [124]. As an illustration, X. Chen et al. [125] developed a polyvinylpyrrolidone (PVP)coated GO material that offers specific locations for the attachment of the ACDCRGDCFCG peptide (RGD4C). The aromatic photosensitizer chlorin e6 (Ce6) can be efficiently incorporated into the rGO-PVP-RGD system through hydrophobic interactions and π-π stacking. The nano-delivery technology enhances the accumulation of Ce6 in tumor cells and improves the efficacy of PDT in comparison to using Ce6 alone. Furthermore, A. Zhang et al. [126] documented the utilization of TiO2 nanoparticles combined with rGO-TiO2 composites for PDT. The study demonstrates that composites of reduced graphene oxide (rGO) and titanium dioxide (TiO2) can serve as a highly effective photosensitizer for PDT in tumor treatment. 3.3.3 Combined Therapy of PDT and PTT
PTT and PDT exhibit significant anticancer efficacy, yet their uses are still impeded by some constraints. For example, PDT exhibits minimal cellular uptake of photosensitizers (PSs) and has a restricted absorption range below 600 nm [127]. PTT often necessitates the use of laser irradiation with a high-power density [128]. The development of combination therapy is highly significant to address the limitations of PTT and PDT. According to reports, the combined therapy of PTT-PDT has demonstrated superior therapeutic efficacy in treating tumors compared to using PTT or PDT alone [79, 129]. Bianco et al. [130] designed a multifunctional nano-platform called mPEG-GO-C60 complex. Their study demonstrated that the combination of PDT/PTT resulted in a notable synergistic therapeutic effect. The mPEGGO-C60 complex not only broadens the absorption spectrum of the photosensitizer C60 by a conjugated effect but also does not
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affect the PTT impact of GO. When subjected to near-infrared (NIR) light at a wavelength of 808 nm and intensity of 4 W/cm2, GO-C60 exhibited a notable temperature rise (ΔT) of 13 °C over a 9-min period, surpassing the temperature increases observed in distilled water (1.6 °C) and an aqueous solution of C61(COONa) 2 (1.9 °C). This outcome demonstrates the exceptional PDT efficacy of GO-C60. Simultaneously, MTT was employed to assess the in vitro anticancer efficacy of the GO-C60 combination on Hela cells. It has been demonstrated that the dark cytotoxicity is not readily apparent in the absence of irradiation. The HeLa cells treated with GO-C60 and exposed to irradiation for 7 min had the lowest survival rate, measuring 58.52% ± 4.65%. The results demonstrate that GO-C60-based PDT and PTT exhibit a strong synergistic impact, making them significant therapeutic body for cancer treatment. In another study, a GO material was modified with both folic acid (FA) and Ce6, resulting in a doublefunctionalized GO. This modified GO was effectively manufactured and utilized for in vitro PTT and PDT combination treatment on MCF-7 cells and RAW 264.7 macrophages. The GO-FA/Ce6 composite exhibited robust photothermal characteristics, elevated reactive oxygen species (ROS) production, and efficient infiltration into cancer cells by FR-mediated endocytosis. The combined PTT and PDT treatment was more efficient in killing cancer cells compared to individual therapies. Specifically, the use of GO-FA/Ce6 achieved a 94% killing efficiency in macrophages, which were found to be more responsive to PTT. The findings emphasize the potential of multifunctional GO in the treatment of cancer and inflammation [131]. Bioimaging
Prompt identification and surveillance of carcinoma cells are crucial in order to impede the advancement of cancer. Biomedical imaging technologies serve as effective instruments for diagnosing tumors and offer vital recommendations for tumor treatments. GO-based nanomaterials have been widely studied in many imaging techniques, such as magnetic resonance imaging (MRI), fluorescence imaging (FLI), photoacoustic imaging (PAI), and computed tomography (CT) [132]. In the subsequent sections, we will examine the present utilization of GO-based nanomaterials for bioimaging.
3.4.1 Magnetic Resonance Imaging
MRI is a diagnostic technique that does not need cutting into the body or using ionizing radiation. It has extremely precise spatial and temporal resolution [133, 134]. Pathological regions, such as tumors, can be visually detected by observing their location, size, and boundaries [135]. To obtain precise and comprehensive imaging data, contrast agents (CAs) must be gathered in the specific area of the tumor. Examples of such contrast agents are Gd, Mn, and Fe [136]. The critical task at hand is to create a vector that possesses
3.4
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precise tumor-targeting capabilities and can extend the duration of blood circulation. GO-based nanomaterials functionalized with paramagnetic metals are appropriate for use in MRI. In a study, S. Yu et al. [136] utilized the high-temperature thermal decomposition approach to create Fe3O4 adorned poly(4-styrene sulfonate)GO for MRI application. The GO that was produced demonstrated high water solubility and exceptional MRI impact. Zhang et al. [137] described the synthesis of a two-dimensional nanomaterial called GO-based T1 MRI CA, which is used as a contrast agent in magnetic resonance imaging. Gadolinium-functionalized nanographene oxide (Gd-NGO) exhibited significantly greater T1 relaxivity (r1) and contrast in in vivo T1-weighted MRI compared to gadolinium diethylene triamine pentaacetate (Gd-DTPA). In addition, J. M. Xue et al. [138] designed a composite material consisting of MnFe2O4 nanoparticles coated with GO for T2-weighted magnetic resonance imaging (MRI). This composite material exhibited a significantly high T2 relaxivity value (r2) of 256.2 (mM)-1 s-1. Moreover, S. Yang et al. [139] developed amino-capped dendrimers (DEN) attached to GO nanosheets, which are then modified with gadolinium diethylene triamine pentaacetate (Gd-DTPA) and a monoclonal antibody (mAb) targeting prostate stem cell antigen (PSCA) shown in Fig. 3. The GO-DEN(Gd-DTPA)-mAb exhibits
Fig. 3 The diagram depicts the structure of GO-DEN(Gd-DTPA)-mAb. (Adapted with permission from Ref. [139])
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modest cytotoxicity, negligible hemolytic activity, and no discernible toxic effects in mice. It primarily focuses on cancer cells that have high levels of PSCA expression, which improves the contrast in T1-weighted MRI scans. GO-DEN(Gd-DTPA)-mAb could effectively incorporate doxorubicin (DOX), resulting in enhanced toxicity in PC-3 cells and successful suppression of tumor growth in a mouse model when administered intravenously. 3.4.2 Fluorescence Imaging
Fluorescence lifetime imaging (FLI) is a noninvasive method that utilizes photons emitted by fluorescent probes [140–143]. It is commonly employed to observe pathological tissue and track the distribution and metabolism of drugs during therapy [144]. This approach has several benefits, such as minimal harm to healthy tissue, quick reaction time, significant differentiation in imaging, and extremely high sensitivity. Therefore, it has the potential to be utilized as a very effective technique for both diagnosing and treating tumors [145]. FL agents with high-efficiency imaging performance are identified on the GO platform to offer FLI-guided theranostic, thanks to the remarkable payload capacity of GO [146]. In a study, M. Yang et al. [147] utilized the inherent photoluminescence (PL) of nGO in both the visible and infrared ranges to create a covalently conjugated B-cell-specific antibody called Rituxan (anti-CD20). This antibody was modified with PEG and designed to specifically identify and attach to B-cell lymphoma cells. Unfortunately, the measurement of the fluorescence quantum yield (QY) of the NGO-PEG-CD20 was challenging, therefore restricting its potential use. Hence, organic fluorescent dyes are employed to modify GO-based nanomaterials for fluorescence imaging purposes. As an illustration, researchers [97] developed a hybrid material called nGO-PEG-Cy7 by combining a modified nanographene oxide sheet (nGO) with a nearinfrared dye called Cy7. This hybrid material was used for in vivo fluorescence imaging of tumor xenografted mice. The study demonstrated that the hybrid material accumulated significantly in tumor tissues due to the increased permeability and retention (EPR) effect commonly observed in malignant tumors.
3.4.3 Photoacoustic Imaging
PAI is a robust diagnostic technique that utilizes the photoacoustic (PA) effect. This effect involves the conversion of absorbed brief pulses of non-ionizing laser energy into heat, which in turn leads to the emission of specific acoustic signals due to thermal expansion [148]. Due to its ability to offer optical absorption contrast and high resolution, PAI is better suited for imaging deep tissues and organs. PA agents typically require a high level of performance in terms of photothermal conversion efficiency [149]. Thus, PAI agents such as noble metal nanoparticles (e.g., Au NPs), inorganic nanoparticles (e.g., GO), semiconducting nanoparticles, and NIR
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dyes (e.g., indocyanine green) are commonly employed [150, 151]. GO-based nanomaterials utilize rGO as a contrast agent and carrier. This is because rGO, with a larger sp2 domain compared to GO, exhibits high optical absorbance in the nearinfrared (NIR) region [152]. In a study, D. K Lim et al. [153] have created a combination of rGO and gold nanorods (Au NRs) to improve the effectiveness of PTT and photoacoustic imaging (PA) in treating tumors. More precisely, the integration of gold nanorods (Au NRs) with GO may effectively transmit thermal energy and generate a magnified photoacoustic (PA) signal, reaching a maximum amplification of 40-fold. The in vivo experiment results showed that PA pictures have superior spatial resolution and less background noise, enabling the provision of crucial pathological information during subsequent therapy periods. Hence, the produced rGO-gold nanocomposite has shown significant potential in clinical settings for precise tumor therapy guided by photoacoustic imaging. 3.4.4
Raman Imaging
Surface-enhanced Raman scattering (SERS) is a commonly employed technique in biomolecular detection and illness diagnostics. It is favored for its noninvasive nature, exceptional sensitivity, and ability to provide high spatial resolution [134]. Gold, which is a noble metal, demonstrates exceptional surface-enhanced Raman scattering (SERS) activity due to its local surface plasmon resonance (LSPR) properties [154]. Hybrid GO nanoparticles have been created in recent years to enhance the optical characteristics of GO, hence enabling its use in SERS applications. The hybrid nanoparticles may greatly amplify the SERS signal by utilizing the electrostatic interaction between metals and the powerful electromagnetic field facilitated by plasma coupling in the cavity [154]. B. Yang et al. [87] conducted a study where they utilized a silver-GObased surface-enhanced Raman scattering (SERS) probe (FA-GOAgNPs) for the identification of cancer cells. The author employed FA-GO-AgNPs to attain a highly sensitive SERS signal in this work. In addition, Y. Li et al. [155] developed a versatile platform consisting of GO, gold nanoparticles (AuNP), and folic acid (FA) for the specific detection of HeLa cells using Raman imaging. This study utilized Raman imaging by harnessing the surface-enhanced Raman scattering (SERS) effect of the gold nanoparticles (AuNPs). To achieve a highly efficient surface-enhanced Raman scattering (SERS) imaging, D. K Lim et al. [153] additionally developed gold nanostars (GONS) specifically for the purpose of imaging cancer cells. Through the utilization of GONS, an advanced technology has successfully attained an exceptionally sensitive surfaceenhanced Raman scattering (SERS) signal.
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Key Challenges and Future Outlook The utilization of graphene and GO-based materials with enhanced stiffness, mechanical strength, and electrical conductivity in the development and production of anticancer nanosystems offers significant benefits and distinctive characteristics [156, 157]. Nevertheless, there are significant obstacles that must be thoroughly assessed to understand the bioaccumulation of these cells, intracellular uptake, clearance mechanism, multidrug resistance, bio-persistency, immunogenicity, and long-term cytotoxicity/histopathology. Additionally, the impact of particle size on cell viability has not received much attention from researchers [21, 106]. H. Lei et al. [158] have examined the potential for lung cancer to spread or worsen following extended exposure to G and carbon black particles in the lungs. Consequently, the death of cells and release of damage-associated molecular patterns (such as mitochondrial DNA) may have occurred. The mitochondrial DNA can strongly trigger the secretion of Wnt ligands in alveolar macrophages [158]. A significant concern is cellular membranes, which serve as barriers and limit the diffusion of different substances. Hence, it is necessary to create advanced nanosystems for drug administration that has enhanced membrane permeability characteristics. This is because previous studies have shown that Tegafur drug loaded onto GO nanosheets had advantageous traits in terms of cell membrane permeability [159]. Moreover, the insufficient stability of the bio-medium can impede the effectiveness of cancer PTT when utilizing G-based materials. Therefore, it is necessary to investigate different polymers to functionalize these materials. This has been demonstrated in the case of functionalized GO, which was modified by an amphiphilic polymer. The modified GO exhibited enhanced colloidal stability, sufficient cytocompatibility, appropriate size distribution, and a neutral surface charge [160]. In addition, researchers have investigated hybrid functional G-based nanocomposites to enhance their biocompatibility and cellular absorption characteristics [161]. For example, the combination of GO and GQDs demonstrated outstanding photothermal properties, enhanced biocompatibility, and strong cytotoxic effects against cancer cells. This suggests that hybrid G-based nanocomposites have great potential as candidates for cancer theranostics and cell imaging. Nevertheless, further investigation is required to thoroughly analyze these hybrid nanostructures, which possess synergistic and optimized capabilities. Additionally, more analytical studies are necessary to enhance specificity and minimize potential toxicity. It is crucial to evaluate essential factors such as pharmacokinetics, pharmacodynamic biomarkers, and tumor responses, particularly for targeted anticancer nano delivery [162–164]. To produce G-based nanomaterials for industrial manufacturing with
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anticancer properties, it is necessary to have simple, cost-effective, and environmentally friendly methods that yield high productivity. Both practical and theoretical studies should focus on developing optimized synthesis techniques that can smoothly transition from laboratory-scale to industrial production while still utilizing conventional laboratory techniques [165]. Postproduction, it is crucial to conduct clinical and long-term evaluations, which have been seldom conducted. It is important to determine the long-term cytotoxicity of G-based materials and their impact on cell signaling. Crucially, comprehending the mechanisms behind harmful effects can aid in identifying ways to minimize them, resulting in G-based products that have enhanced biocompatibility. Choosing sensible criteria is crucial for the successful creation of clinically effective and translatable nanomedicines. An approach focused on illness management can be used to create intelligent nanosystems for drug administration. This approach considers important factors connected to the drug-delivery system and the specific patient group being targeted. By carefully balancing various variables, the therapeutic effectiveness can be improved [166]. Moreover, the crucial factors for the use of G-based materials in anticancer applications are immunogenicity, inflammatory responses, and hemocompatibility [167]. Studies have shown that G-based nanocomposites can cause DNA or mitochondrial damage, inflammatory reactions, autophagy, necrosis, and apoptotic effects. Furthermore, these materials have demonstrated toxicity that increases with the dose [168]. One study found that the hemolytic effects of GO structures were usually caused by an electrostatic connection between these materials and the membrane of red blood cells. This interaction can be avoided by modifying the surface of the GO structures to enhance their compatibility with blood [169]. It was discovered that GO induced a strong immune response, as evidenced by a large increase in tumor necrosis factor-α, interleukin6, and interleukin-1. However, the modified GO structures showed enhanced compatibility with the immune system [170]. GO structures significantly increased the levels of interleukin-6, interleukin-12, tumor necrosis factor-α, interferon γ, and monocyte chemotactic protein 1, leading to notable inflammatory effects [171]. The contact between GO and toll-like receptors triggers the activation of the NF-κB pathway, leading to the production of inflammatory cytokines. However, when graphene-based nanomaterials are functionalized, they can avoid inducing inflammation in macrophages by reducing the link between opsonins and proteins [169]. Considering the extensive utilization of G-based materials, particularly in the field of biomedicine, it is crucial to carefully assess their toxicity [172]. Multiple studies have demonstrated the toxicity of G-based materials to animals and human cells in a dosedependent manner. This includes the formation of lung granulomas, damage to the liver and kidneys, decreased cell viability, and
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apoptosis. The toxicological profile of these materials is influenced by several key characteristics, including functionalization, surface structure, aggregations, lateral size, corona effect, charge, and impurities. However, it is important to thoroughly investigate the potential events and mechanisms associated with the toxicity of Gand GO-centered entities. These may include apoptosis, DNA damage, oxidative stress, necrosis, physical destruction, inflammatory reactions, and autophagy [172].
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Conclusion GO nanoparticles have become a potent tool in cancer therapy, providing novel strategies to address significant obstacles such as drug resistance and insufficient treatment responses. Their versatile characteristics enable enhanced administration of drugs, improved distribution inside the body, and efficient encapsulation of genes, all of which collectively contribute to more effective inhibition of tumor growth. The promise of GO in cancer treatment is highlighted by its capacity to enhance phototherapy and stimulate immunological responses. Although GO nanoparticles show great potential in enhancing cancer treatment, further investigation and refinement are required to fully exploit their capabilities and ensure their successful incorporation into clinical practice. Furthermore, it is imperative for future studies to prioritize the enhancement of the structure of GO-based nanocarriers to optimize their ability to target tumors, minimize toxicity, and guarantee long-term safety.
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Chapter 2 Antitumoral Effect on Liver Cancer of a Tumor-Penetrating and Interfering Peptide Eric Savier, Pierre Tuffery, Heriberto Bruzoni-Giovannelli, Rachid Boudjelloul, and Angelita Rebollo Abstract Hepatocellular carcinoma is one of the most frequent cancers in the world. Treatments such as immune checkpoint inhibitors or tyrosine kinase inhibitors (TKI) have some efficacy but have many adverse effects. So, more specifically targeted therapies are needed. In this study, we investigated the antitumoral effect of a tumor-penetrating and interfering peptide blocking the interaction between the proteins PP2A and SET. We analyzed the expression of phosphatase PP2A and oncoprotein SET in a group of samples from liver cancer patients with different aggressiveness scores. Expression of both proteins was found to correlate with aggressiveness of the tumor. We observed in xenograft models of hepatocellular carcinoma an antitumoral effect of iRGD-IP, a tumor-penetrating and interfering peptide blocking PP2A/SET interaction, suggesting that this peptide could be a strong candidate for development as therapeutic peptide for liver tumor treatment. Key words Tumor-penetrating peptides, Interfering peptides, Liver cancer, Hepatocellular carcinoma, PP2A, SET
1
Introduction
1.1 Hepatocellular Carcinoma
Hepatocellular carcinoma (HCC) is a primary liver cancer that originates from hepatocytes [1]. Now a day is recognized that HCC is the sixth most frequent cancer and the fourth cause of cancer mortality. Risk factors for HCC include viral infections, alcohol drink, fatty liver disease, certain toxins, and genetic diseases. These factors are responsible of liver inflammation, fibrosis, and finally, leading to liver cancer [2, 3]. Human HCC is classified in two major groups in terms of genomic stability. The morphological heterogeneity of liver cancer is demonstrated in tumor differentiation status, tumor growth patterns, and pathological features of paratumoral tissue [1].
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Therapies against HCC include on one hand local that brings together surgical resection, transplantation, ablative techniques, chemoembolization or radioembolization, and external radiotherapy and on the other hand systemic treatments. The current first line of systemic treatments combines programmed cell death inhibitors (PD-1 or PDL-1, atezolizumab, pembrolizumab) and antiVEGF monoclonal antibodies (bevacizumab). Monoclonal antibodies against PD-1 or anti-CTL-4 (nivolumab) are also used as immunotherapy. Second line of treatment or alternatives due to contraindications are tyrosine kinase inhibitors [3, 4]. These former treatments are used in the case of unresectable HCC or metastatic disease or macroscopic vascular invasion. The most commonly used tyrosine kinase inhibitors (TKI) are sorafenib, lenvatinib, regorafenib, or carbozantinib, but they are contraindicated in the case of altered liver function. Considering the current challenge to treat HCC and the fact that a chronic hepatocellular insufficiency is associated in most of the cases, it is evident that new and more efficient targeted therapies are needed. The use of therapeutic peptides combining a tumorpenetrating peptide (specifically targeting tumoral hepatocytes) and an interfering peptide targeting a protein/protein interaction (PP2A/SET) deregulated in HCC can be a new and smart therapeutic approach against HCC. 1.2 TumorPenetrating and Interfering Peptides
Even if great progresses have been made on cancer research, few targeted anticancer therapies have been developed [5–7]. The main problem with treatments against cancer therapies is the lack of targeting tumoral cells, so poor selectivity with low proportion of administrated drug that reach its target, in addition to adverse effects. So, the design of new therapies that bypass these problems resulting in more effective and well-tolerated treatments is a priority goal. To overcome these limitations, several approaches have been developed. Among them is the use of tumor-penetrating peptides (TPP), which are able to deliver a cargo into tumoral cells [8, 9]. TPP have affinity for specific receptors that are presented in tumor cells and tumor vasculature. They are defined by the presence of the C-end Rule (CendR) motif, R/KXXR/K [10– 12]. This motif must be C-terminal exposed to allow binding to neuropilin-1 receptor (NRP-1) [13]. NRP-1 is overexpressed in the tumor vasculature and in malignant cells [14]. RPARPAR is a prototype of TPP that binds and internalizes into cells expressing NRP-1 [13, 15]. Another TPP is iRGD, identified by phage display [14, 16]. This TPP interacts with integrin receptor and, after that, is cleaved by tumor proteases to expose the CendR motif, to allow interaction with NRP-1. This TPP has been used to increase penetration of anticancer drugs in several tumor models [17].
Tumor Penetrating and Interfering Peptides
29
Cyclic TT1 is other TPP identified by phage display. Its binds to p32 protein, a mitochondrial protein aberrantly expressed in malignant cells (NRP-1). The linear version of TT1, Lin TT1, follows the same mechanism of tumoral internalization. Protein/protein interactions are considered as promising therapeutic targets and molecules able to modulate these interactions are considered as potential drugs [18]. Peptides able to disrupt these interactions (interfering peptides) are becoming very important. Conversely, most of anticancer treatments have been focused on the development of small molecules and monoclonal antibodies. However, peptides are better adapted than small molecules to target the large interaction surface of protein/protein interactions. The most targeted approach to identify interfering peptides is the PEP scan [19–23]. The principle of PEP scan is to cut the sequence of one of the partners as a series of overlapping peptides that are associated to a solid support, which is hybridized with the other partner protein. The overlap between consecutive segments allows the identification of the binding site between the two proteins of interest [19–23]. Taking advantage of this approach, we have identified and in vitro an in vivo validated an interfering peptide targeting the interaction between the serine/threonine phosphatase PP2A and the oncoprotein SET [20]. 1.3 Serine/Threonine Phosphatase PP2A
PP2A is a holoenzyme composed of three subunits (A, B, and catalytic) that belongs to serine/threonine phosphatase family. PP2A is involved in several signaling pathways controlling cell cycle, apoptosis, metabolism, stress, and memory, among others [24]. PP2A has been described as a tumor suppressor and its pharmacological inhibitor, okadaic acid, is a tumor promoter [25]. Inactivating mutations, as well as decreased expression or enzymatic activity of PP2A, have been found in a great variety of human cancers [26]. The disruption of PP2A/SET interaction using interfering peptides could lead to the recovery of antitumoral activity of PP2A [25]. According to these results, the tumor suppressor function of PP2A makes it a potential target for novel anticancer therapies [25, 27, 28]. PP2A is genetically modified or functionally inactivated in many solid cancers, as well as in hematological cancers. Several articles show that inhibition of expression and/or function of PP2A contribute to carcinogenesis, characterized by an aberrant activity of oncogenic kinases [25, 29, 30]. Inhibition of enzymatic activity of PP2A is essential to promote cell transformation, tumor progression, and angiogenesis, indicating its tumor suppressor role [31]. It has been shown that pharmacological restoration of PP2A tumor suppressor activity blocks tumor progression [32, 33]. In addition to serine/threonine phosphatase PP2A, other phosphatases have been involved in HCC development. Among them is PTEN, a well-characterized tumor suppressor that prevents
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proliferation of hepatocytes through AKT/mTOR inactivation. In accordance with this information, reduced or absent expression of PTEM is observed in around 50% of HCC [34, 35]. In addition, hepatic PTEN is associated with non-alcoholic steatohepatitis (NASH) HCC. Finally, PTEN reduction has been correlated with HCC prognosis and recurrence. Other phosphatase involved in HCC is SHP-1, a tyrosine phosphatase that plays an important role in HCC by regulating the function of some non-parenchymal cells. SHP-1 increases in patients with chronic hepatitis B and advanced fibrosis [36]. SHP-2, other tyrosine phosphatase that is involved in cell survival and proliferation, has been shown to be deregulated in HCC [37]. This phosphatase can have a dual role: promotes the development of HCC by their involvement in proliferation and survival of tumor cells or suppresses antitumoral immunity in immune cells by its downregulation. Finally, the dual serine/threonine or tyrosine phosphatase downregulation expression can induce liver injury and chronic liver disease [38]. 1.4
Oncoprotein SET
SET is an oncoprotein involved in apoptosis, transcription, nucleosome assembly, and histone binding. SET is localized in the nucleus and in the cytoplasm where it plays a role in the regulation or normal and tumoral signal transduction. High level of SET expression has been linked to cell growth and transformation in several types of cancer such as hematological cancer, among others. SET inhibits PP2A by forming a complex with PP2Ac [39–41]. SET is involved in the initiation and progression of cancer [42]. Its overexpression has been detected in many hematological cancers as well as in solid tumors [43]. The activity of SET is mediated by the inhibition of one of its partners, the protein PP2A. Upregulation of SET expression was observed in gastric cancer, correlating also with aggressiveness grade of the tumor. It has been published that SET overexpression correlates with large tumor size in HCC and decreased overall survival of the patients, suggesting that SET can be used as a prognosis indicator and novel target therapy in HCC patients [44]. Finally, overexpression of SET is also associated with tumor progression and poor prognosis in human non-small cell lung cancer [45]. We have developed a bifunctional peptide composed of a tumor-penetrating peptide (TPP) that selectively internalizes into tumoral hepatocytes [20, 46, 47], associated to an interfering peptide (IP) that blocks the interaction between PP2A and SET [47] and induces apoptosis of tumoral hepatocytes and B cells. We have analyzed the expression of SET and PP2A in a group of liver cancer patients with different aggressiveness score (Table 1) and observed that expression of PP2A and SET correlates with aggressiveness of the liver tumor (Fig. 1). This peptide can be a strong candidate for development as therapeutic peptide for liver
F
M
F
M
F
F
F
3
4
5
6
7
8
9
HCC
HCC
12.5
10.4
20.1
1298
35.3 HCC
46.7 noHCC 5.3
45.9 CCK
41.2 HCC
90.1 HCC
65.3 HCC
74.4 HCC
61.6 HCC
86.7 HCC
67.8 HCC
11 F
12 F
13 M
14 M
16 F
17 M
18 M
19 M
20 M
21 M
0
0
0
0
0
1
0
1
0
0
0
1
1
1
0
0
0
0
1
0
0
0
1
0
0
0
0
CCK cholangiocarcinoma, HCC hepatocellular carcinoma Number 15: Necrotic tissue not retained
2.59
6.1
2.1
42.4
8.7
18.6
340,839 5
4.1
385.9
2
1
29
68.4 HCC
10 M
1
0
30.6
0
1
1
1
3
0
45.5 noHCC 4.8
59.1 HCC
39.7 noHCC 1.1
68
72.1 HCC
51.8 HCC
83
68.5 HCC
3.7
M
1
2
0
8.6
M
1
73.9 HCC
AFP Partial (ng/mL) AFPlog10 capsule
N° Sex Age Tumor
Table 1 Aggressiveness score of the patients used in this study
0
0
1
1
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
Satellite nodule
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
1
0
0
0
0
Microvascular invasion
2
1
2
3
3
2
3
2
0
2
2
0
1
0
2
2
1
2
2
2
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
1
2
1
4
6
4
4
9
2
0
6
4
0
2
0
5
4
2
7
2
4
Medium
Medium
Medium
High
Medium
Medium
High
Medium
Null
High
Medium
Null
Medium
Null
Medium
Medium
Medium
High
Medium
Medium
Differentiation Macrotrabecular Aggressiveness Class
Tumor Penetrating and Interfering Peptides 31
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Eric Savier et al.
Fig. 1 Patient distribution plot. Synthetic view of PP2A and SET protein expression and aggressiveness score plotted for each patient. White circles, null aggressiveness; gray circles, medium aggressiveness; black circles, high aggressiveness. p 0.05 for the aggressiveness score of quadrant B versus A, C, and D
Fig. 2 The iRGD-IP peptide has antitumoral activity in the xenograft mouse model of liver cancer. (a) Mice were IP injected with 5 mg/kg of the peptide 5 days per week for 4 weeks. Tumor size was monitored over time. Solid black line: control group. Dashed gray line: treatment group. Statistical comparisons were performed using ANOVA. (b) Variation of the initial body weight in the xenograft mouse model of liver cancer treated with iRGD-IP peptide or with saline solution (control)
cancer treatment (Fig. 2). In addition, PP2A and SET expression can be used as biomarkers of aggressiveness score in HCC. The characteristics of an ideal biomarker should be as follows: it must be sensitive, inexpensive, specific, little operator experience required,
Tumor Penetrating and Interfering Peptides
33
Fig. 3 Characters of an ideal biomarker
high reproducibility, produces rapid results, and correlates with tumor stage and available samples need no pretreatments (Fig. 3). Figure 4 shows the schematic representation of the generation of xenograft models for the proof of concept of new therapeutic molecules. Our xenograft models have been generated using the cell line HepG2. 1.5 Molecular Aspects of iRGD-IP Internalization
Events triggering iRGD internalization are long known and some aspects well documented [48]. First the full iRGD interacts with an integrin, which results in its cleavage to get a C-terminus sequence of RGDK. This matches the motif of consensus sequence R/KXXR/K (X, any amino acid) often named the C-end Rule (CendR). This motif is reported as required to trigger the interaction of iRGD with NRP1 and its internalization. This positiondependent motif must be C-terminally exposed, which implies that the IP must be linked at the Nter of the iRGD peptide. However, few molecular details of the internalization after the interaction with NRP1 are known. The receptor-bound peptides are endocytosed through the receptor interaction with a cytoplasmic machinery [49]. It has been demonstrated that the CendR peptides and their cargo appear in cytoplasmic vesicules [11], but the details about how this process is triggered by the binding of the CendR motif to NRP1 are so far unknown.
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Fig. 4 Schematic representation of the generation of xenograft models. A cell line or a patient-derived sample is grafted into immunodeficient mice. If the tumor is visible, mice are treated with the peptide for several weeks and the tumor size is measured
We have recently investigated in silico the binding of different peptides encompassing the cleaved and uncleaved iRGD-IP with NRP1 [50]. We have started from models of the complex NRP1/ iRGD-IP built using the colabfold 1.3.0 [51] implementation of alphafold 2.2.4 [52], using as a control the structure of the neuropilin-1 b1 domain in complex with the SARS-CoV-2 S1 C-end rule peptide of sequence NSPRRAR (PDB: 7JJC) [53]. Note that the 6-aminohexanoic acid (Ahx) linker could not be included in the sequence input and was replaced with the GG sequence, which we identified as a better mimic of the Ahx linker, at least when considering the geometric insertion. Models involving both the cleaved and uncleaved iRGD-IP showed a good agreement with the reference structure 7JJC. It was slightly better for the cleaved form that could be expected since the 7JJC structure encompasses the cleaved form of the peptide. The stability of the models built was then challenged using molecular dynamics for 1 μs using for replica. In total it is thus 4 μs simulations that were performed for each complex (details about the simulations in [50]). Surprisingly, molecular dynamics simulations did not reveal that the cleaved form of iRGD is mandatory to get a stable binding of iRGD to NRP1. Figure 5 depicts the interaction of the cleaved and uncleaved forms of iRGD-IP with NRP1 for a typical frame of the MD simulations. Clearly, the CendR motif of the iRGD-IP is able to interact with the residues
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Fig. 5 NRP1 structural organization. (a) Alphafold model (afdb:AF-O14786-F1). (b) Experimental structure of three domains (PDB:2QQM). Domains (uniprot: O14786) interaction of the CendR peptide with NRP1 F5/8 type C1 domain (PDB: 7JJC). Domain definition and names
Fig. 6 Interaction of iRGD-IP with MRP1 in its cleaved (a) and uncleaved (b). The color codes are similar to that of Fig. 1. Hot red: the IOP. Light green: the C terminus residues of the uncleaved iRGD
of the experimental binding site in both cases and in a stable manner (see detail in [50]). One observes however that the cendR motif of the uncleaved peptide adopts an orientation different from that of the cleaved peptide. Finally, one also notes that the IP part of the peptide behaves as expected for a cargo, i.e., does not interact with NRP1 (Fig. 6). These results remain to be confirmed since for simulation times of 4 μs, the effectiveness of the conformational sampling could be questioned. However, if the stability of the interaction of the uncleaved peptide with NRP1 is confirmed, this would suggest
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that the conformation adopted by the uncleaved RGD-IP is not compatible with the transmission of the signal triggering the cell internalization, opening new directions for investigation. 1.6 Other Targetable Protein/Protein Interactions in Liver Cancer
Signaling pathways are complex processes of signal transduction involving activation of protein cascades transmitting signals from the cell surface to the cytoplasm and nucleus [54, 55]. In recent years, emerging studies have improved the understanding of liver tumorigenesis through investigation of some signaling pathways such as PI3K/AKT. Protein/protein interactions act as connectors that mediate signal transduction [56]. The PI3K/AKT pathway is involved in cell cycle, growth, survival, and proliferation, among others. Enhanced PI3K/AKT activity has been reported in several cancers, including liver cancer. This signaling pathway is activated by cytokines, G-proteins, and integrins [57–59]. AKT can interact with proteins of other signaling pathways. Thus, activation of PI3K/AKT signaling pathway in liver cancer can directly affect the activity of other signaling pathways such as Hippo/Yap NF-κB, Wntβ-catenin, Notch, p53, JAK/STAT, and MAPK/ERK. The activation of AKT can directly induce activation of the NF-kB pathway and suppress apoptosis in liver cancer cells [60, 61]. AKT is also able to interact and phosphorylate MST kinases, key components of the Hippo/Yap signaling pathways (Fig. 7), as well as with the natural inhibitor of
Fig. 7 Implication of Hippo-Yap/TAZ signaling pathway in HCC
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β-catenin. There is also a close relation between the activation of PI3K/AKT and the upregulation of Wnt/β-catenin activity in liver cancer [62]. The MAPK/ERK is another signaling pathway involved in a great variety of cell processes, usually activated in liver cancer due to activating mutations or amplification of several components such as Ras, Raf, and MEK [63]. Protein/protein interaction between PI3K/AKT and Notch signaling pathway has been identified. Upregulation of AKT is shown to induce activation of Notch and these two signaling pathways are always activated in liver cancer [64]. In the same direction, protein/protein interaction between AKT and mTOR has been described as deregulated in liver cancer [65, 66]. Deregulation of mTOR expression is present in around 50% of liver cancers and correlates with poor prognosis and recurrence of hepatocellular carcinoma [67, 68]. It has also been shown that apoptosis induced by Bad pro-apoptotic molecule is also deregulated in liver cancer [69]. In addition to the protein/protein interactions between AKT and the proteins described above, AKT can also interact with other proteins involved in liver cancer. These proteins include T-cell leukemia/lymphoma protein I (TCL1) [70, 71], BCRCA1 [72], vimentin [73], ILK [74], and heat shock protein 27 (Hsp 27). In summary, targeting PI3K/AKT protein/protein interactions may become new targets in the clinical treatment of liver cancer.
2
Materials
2.1 Isolation of Proteins from Liver Tissues
1. Lysis buffer: 50 mM Tris HCl pH 8, 1% NP40 137 mM ClNa, 1 mM Cl2Mg, 1 mM Cl2Ca, 10% glycerol, protease inhibitors cocktail. 2. Precellys lysis kit. 3. Precellys evolution sonicator.
2.2 Western Blot Analysis
1. Washing buffer: 20 mM Tris HCl pH 7.5, 150 mM ClNa, 0.05% Tween 20. 2. Blocking buffer: 5% non-fat dry milk in PBS. 3. PBS: 137 mM ClNa, 10 mM Na2HPO, 1.8 mM KH2 PO4. 4. Primary antibodies. 5. Conjugated PO-secondary antibodies. 6. ECL system. 7. Running buffer: 30 g of Tris, 144 g of glycine 10 g SDS, for 1 L. 8. Transfer buffer: 30 g of Tris, 10 g SDS, for 1 L.
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9. Nitrocellulose membrane. 10. Polyacrylamide-SDS gels. 2.3 Immunohistochemistry
1. Fluorescence microscopy.
2.4 Quantification of Proteins
1. Bradford reagent.
2. Primary antibodies against: CK19, HepPar GP C3, β-catenin, glutamine synthase.
2. Colorimetric spectrophotometer. 3. Bovine serum albumin (BSA).
2.5 NRP1/iRGD-IP Sequences
1. NRP1 sequence was taken from Uniprot entry O14786. 2. The uncleaved TPP-IP sequence used was ETVTLLVALKV RYRERITGGCRGDKGPDC. Note that since colabfold accepts only standard amino acids, the linker between the TPP and the IP has been replaced by a di-glycine. 3. The cleaved TPP-IP sequence used was ETVTLLVALKVRYR ERITGGCRGDK.
3 3.1
Methods Peptides
3.2 Isolation of Proteins
1. The purity and composition of the peptides were confirmed by reverse phase high-performance liquid chromatography (HPLC) and mass spectrometry (MS) (see Notes 1 and 2). 1. Liver tissue samples were obtained from resected human liver of patients with hepatocellular carcinoma or benign liver disease. 2. Total proteins were isolated using Precellys lysis kit. 3. A total of 20 mg of tissue were resuspended in a tube of Precellys lysis kit supplemented with 500 μL of lysis buffer. 4. The liver tissue Is shake for 30 s with a pause of 30 s. The process is repeated five times in Precellys evolution machine. 5. The extracts were centrifuged and the supernatant transferred to a fresh tube and centrifuged at 12,000 rpm for 20 min at 4°C. 6. The supernatant was recovered and protein concentration estimated. 7. The supernatant was stored at -80°C.
3.3 Visualization of Protein Expression by Western Blot
1. A total of 60 μg of liver proteins were separated by SDS-PAGE. 2. The gel is transferred to nitrocellulose and the membrane blocked with non-fat dry milk.
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3. The membrane was blotted with an anti-PP2A or anti-SET antibody. 4. The membrane was washed and incubated with PO-conjugated secondary antibody. 5. The proteins were detected using the ECL system and an Amersham Imager 600. 6. The expression of actin was used as internal control for normalization of PP2A and SET expression. 3.4 Histological Characterization of Patient Samples
1. The immunostaining was performed on formalin-fixed deparaffinized 3 μm sections. 2. Samples were incubated with primary antibody: CD19, HepPar, Glypican 3, β-catenin, and glutamine synthase. 3. After washing steps, samples were incubated with secondary antibodies. 4. Samples were visualized by microscopy.
3.5 Generation of Xenograft Models
1. We used SCID mice 6–8 old weeks. 2. A total of 1 × 106 HepG2 cells were injected into the right flank of each mouse (see Note 3). 3. Tumoral progression was monitored three times per week. 4. Once the tumor reaches volume of 50 mm3, mice were treated with the peptide at 5 mg/kg, 5 days per week, for 4 weeks. 5. Control group was treated with saline solution. 6. Tumoral regression was monitored three times per week.
3.6 Complex Structure Modeling
1. The structure of the NRP1/TPP-IP complex was performed using colabfold 1.3.0. 2. The colabfold dropout option was activated. 3. Ten models were generated starting from different random seeds, for a total of 50 models. 4. Models were compared to the structure of the crystal structure of NRP1 in complex with the SARS-CoV2 S1 C-end rule peptide [PDB code: 7JJC] using the dockQ score and the best model was retained for further analyses.
3.7 Molecular Dynamics Simulation
1. We used the OpenMM package. 2. Structure preparation and protonation were performed using the OpenMM’ pdbfixer module, and the TIP3P water model was used for solvation. 3. Sodium and chloride ions were added to neutralize the overall electric charge of the system, to reach an ionic concentration of 150 mM.
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4. Simulations were performed using the Amber14SB force field. 5. The protocol was of 10,000 minimization steps. 6. Equilibration during 10 ns in the NPT ensemble using position restraints on all Cα atoms of 10.0 KJ·mol-1 nm-2—temperature and pressure were equilibrated using a Monte Carlo barostat at a temperature of 300 K and a pressure of 1 atmosphere. 7. Production during 1 μs without any position restraints. 8. Four replicas were simulated for each system.
4
Notes 1. The peptides are solubilized in water. 2. The solubilized peptides are stored at 4 °C for 3 weeks. For longer periods of time, peptides must be frozen at -20°C in aliquots. 3. The HepG2 cell line must be cultured in collagen-coated flasks. In addition, the in vivo test on xenograft models of hepatocellular carcinoma made using the cell line HepG2 showed an antitumoral effect of the tumor-penetrating and interfering peptide iRGD-IP, a bifunctional peptide blocking PP2A/SET interaction in tumor cells (Fig. 2a). This peptide does not show toxicity as shown by not significant variations of weight (Fig. 2b).
Acknowledgments This work was supported by Inserm. References 1. Lu XY et al (2011) Hepatocellular carcinoma expressing cholangiocyte phenotype is a novel subtype with highly aggressive behavior. Ann Surg Oncol 18(8):2210–2217. https://doi. org/10.1245/s10434-011-1585-7 2. Llovet JM, Montal R, Sia D, Finn RS (2018) Molecular therapies and precision medicine for hepatocellular carcinoma. Nat Rev Clin Oncol 15(10):599–616. https://doi.org/10.1038/ s41571-018-0073-4 3. Llovet JM et al (2016) Hepatocellular carcinoma. Nat Rev Dis Primers 2:16018. https:// doi.org/10.1038/nrdp.2016.18 4. Llovet JM et al (2022) Molecular pathogenesis and systemic therapies for hepatocellular carcinoma. Nat Cancer 3(4):386–401. https://doi. org/10.1038/s43018-022-00357-2
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Chapter 3 Computer-Aided Design for Cancer-Targeted Peptide Drugs Yan Degenhardt, Michael Poss, and Xin Gao Abstract Peptide drugs have recently attracted global attention as a promising cancer therapy due to their abilities to disrupt protein-protein interaction and target intracellular “undruggable” targets. However, developing peptide drugs purely through wet-lab experiments is challenging. Computational methods, especially artificial intelligence-based approach, turns out to be a viable solution for peptide drug design because they can benefit from the know-how and feature engineering in both the small molecule field and the antibody field. In this chapter, we set out to provide a comprehensive overview for computer-aided design for cancer-targeted peptide drugs. In particular, we will cover structural modeling, virtual screening, membrane permeation, multi-objective optization, and dry-wet closed-loop development for peptide drug design. Key words Peptide drugs, Bioinformatics, Artificial intelligence, Structural biology, Cancer therapy
1
Introduction With the advancement of human genomics and genetics, many new potential drug targets have been identified that are associated with various diseases. So far over 5000 proteins have been found to have associations with human diseases; however only about 1300 targets have drugs approved with additional 700 targets that are in development. The rest of around 3000 targets, which is about 60% of total targets, are considered “non-druggable” by small molecules or large molecules. A lot of these targets are involved in proteinprotein interactions (PPIs) in signal transduction pathways that play important roles in disease-causing mechanism. Breaking/ interfering with these PPIs is a viable strategy for disease treatment. Antibodies and other protein drugs are suitable for binding to the large, flat interface of PPI targets, and that is the reason for their high specificity, high efficiency, low toxicity, and few side effects. However, because antibodies cannot cross the cell membrane, they have only been used against extracellular targets. Small molecules can enter the cell; however the PPI interface area usually reaches
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1000–2000 Å2, which is too large for small molecules to be effective, since the interface between small molecules and protein target is only about 400–600 Å2 [1, 2]. One promising solution is to use peptides to disrupt PPI and target intracellular “undruggable” targets, especially cyclic peptides. Peptides are between large molecules and small molecules. Comparing to linear peptides, cyclic peptides have large binding surface that can span from 800 to 2000 Å2, which is equivalent to the PPI interface, and they have very little requirement about binding pocket as for small molecues. As a drug modality, peptide has very appealing features. Like antibodies and other protein-based molecules, cyclic peptides can bind flat protein-protein interaction surfaces with high specificity and affinity, but unlike biologics, they are synthetically accessible. They also generally have good safety profile with low toxicity and immunogenicity and minimal risk of drug-drug interaction potential. Instead of liver metabolism, its excretion is mostly through protein degradation and kidney filtration. In recent years, macrocyclic peptides have been proven to bind to diverse targets with high affinity and selectivity, and identification of macrocyclic peptide binders has been reported to target PPIs (e.g., K-Ras, HIV capsid, and TNFα), proteases (e.g., uPA, trypsin, and HCV), and many other targets. However, until recently, peptide drugs only occupied a small market comparing to small molecules and antibody therapies. Historically peptide drug development has been hindered by its less desirable PK profile and absorption, distribution, metabolism, and excretion (ADME) properties. Unmodified peptides usually have very short half-lives (e.g., minutes) resulting from extensive proteolysis in blood, kidneys, or liver and/or rapid renal clearance. Even more difficult, unlike small molecules, peptides usually have low cell membrane mermeability, and very poor oral bioavailability, limiting their administration to injection and rendering targeting intracellular targets extremely difficult. Most peptides have low cell membrane permeability owing to high hydrogen bonding capacity and low lipophilicity. Low oral bioavailability of peptides is caused by a plethora of factors including low absorption and enzymatic- and pH-mediated hydrolysis in the gastrointestinal (GI) tract and liver. With a few exceptions (e.g., cyclosporine A), most peptides have less than 1% oral bioavailability. In recent years, significant understanding and progress have been made to improve the “drug-like” properties of the peptides. To improve pharmacological properties of peptides, various chemical modifications have been developed, including L to D amino acid substitution, N-methylation, incorporation of turn mimetics, helix mimetics or other non-proteinogenic amino acids, using N-cap for a-helix nucleation, and cyclization. Combining the chemical modifications with the development of oral formulations that contain
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permeability enhancers has resulted in recent approvals of orally available semaglutide and octreotide, which are big commercial success, and also demonstrated that peptides can be developed as orally active drug products like traditional small molecules. However significant challenges remain to fully realize the potential of peptide therapy. Most of the oral peptides in development have very low bioavailability even in the presence of permeability enhancers with minimal intrinsic oral bioavailability on their own. For cell penetration, the exact mechanism of how these much larger molecules truly enter the cells through passive diffusion is still under debate. Traditional tools used for small molecule drugs, such as Lipinski’s Rule-of-5, are less reliable for peptides [3–5]. In recent years, breakthrough made by AI, in particular AlphaFold 2 [6], allows accurate prediction of the 3D structure of single chain proteins, and AlphaFold-Multimer [7] allows prediction of the complex structures of protein-protein interactions. Peptides can be considered as mini-proteins in terms of size and thus can directly benefit from AlphaFold2 and AlphaFold-Multimer, which make them the most promising modality for AI-based drug development. AI/ML has great potential for detecting how slight nuances in peptide structure can impact functionalities like stability in the GI tract. In fact, AI/ML has potential to contribute to many aspects of peptide therapy discovery process as listed below: • Hit identification. Even with powerful screening libraries of phage display library and mRNA display library, for a peptide with over 10 amino acid length, if you consider both canonical and unnatural amino acids, the possible permutations are astronomical and would be impossible to be represented in the actual wet lab screen. However the initial hits can be screened with a virtual AI-built library that covers a much bigger chemical space. • AI design of cyclic peptides is also becoming increasingly powerful. De novo prediction of hyperstable and ordered cyclic peptides has been demonstrated. Recent results have also demonstrated the possibility of designing cyclic peptides with membrane permeability. • Hit optimization. In a peptide drug discovery program, particularly for oral delivery, medicinal-chemistry-based lead optimization efforts are typically required to improve various properties beyond binding affinity, such as metabolic stability, solubility, permeability, and various pharmacokinetic parameters. AI algorithms can be developed to optimize each of the above features in addition to enhancing binding affinity. And more importantly AI algorithms can be developed for multi-objective optimization to improve multiple features simultaneously. This would significantly accelerate the drug discovery process and shorten the timeline.
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This chapter would provide detailed examples of how AI algorithms can be developed to facilitate each aspect of peptide drug discovery, to significantly accelerate the process and shorten the timeline.
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Structural Modeling Structural modeling plays a crucial role in peptide drug development. By predicting the three-dimensional structure of peptides and their interactions with target proteins, it provides essential tools for deeply understanding the mechanisms of peptide drugs. Accurate structural modeling aids researchers in identifying key binding sites and interaction patterns, thereby guiding the rational design and optimization of peptides to enhance drug affinity, specificity, and stability.
2.1 Peptide Structure Prediction 2.1.1 Energy-Based Peptide Structure Prediction
Peptide structure prediction is the first step in peptide drug design. Traditional methods, such as molecular dynamics (MD) simulations and tools like Rosetta, are widely used in this field. MD simulations track the movement of peptides over time, providing valuable insights into their dynamics and stability [7]. Rosetta, a molecular modeling software based on energy functions, predicts peptide three-dimensional structures through local energy minimization and global sampling [8]. The strength of these traditional methods lies in their ability to capture complex molecular interactions, resulting in detailed structural predictions.
2.1.2 Deep?LearningBased Peptide Structure Prediction
In recent years, the rapid development of deep learning technologies has revolutionized peptide structure prediction. The introduction of AlphaFold 2 [6] marked a significant breakthrough in this field, as it successfully predicted the three-dimensional structures of numerous proteins and peptides by mapping sequences to structures using deep neural networks. In many cases, its accuracy has approached that of experimental techniques. Subsequently, advancements such as RoseTTAFold [9] have further propelled this field. Based on these algorithms, the researchers also further explored the structure prediction of peptide-protein complexes [10] and cyclic peptides [11], making deep learning more widely applicable in peptide structure prediction tasks. The advantages of these methods include their ability to predict large-scale data with high accuracy in a relatively short time, making them suitable for various peptide structure prediction tasks.
2.1.3 Structure Prediction for Synthetic Peptides: Combining Energy-Based and Deep Learning-Based Methods
Structure prediction for synthetic peptides is a significant challenge in peptide drug development. Due to their structural complexity and diversity, single-method predictions often fail to deliver accurate results. Therefore, combining deep learning-based methods with traditional MD simulations has become a research focus.
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A common approach involves using deep learning models to predict the general three-dimensional structure of the peptide, followed by MD simulations to refine the structure, capturing potential dynamic changes in solution. This hybrid approach leverages the strengths of both methods—rapid prediction from deep learning and high-precision optimization from traditional techniques—yielding more reliable structural predictions. 2.2 Docking of Peptide and Target Protein 2.2.1 Docking Based on Traditional Methods
2.2.2 Docking Based on Deep Learning Methods
Peptide-protein docking is a crucial step in understanding the mechanism of action of peptide drugs. Traditional docking methods can be divided into two categories: rigid docking and flexible docking. Rigid docking (e.g., AutoDock [12]) assumes that both the peptide and protein remain static during the docking process, using energy scoring functions to evaluate and predict potential binding modes. The advantage of this method lies in its high computational efficiency, making it suitable for peptides with rigid structures. However, the limitation of rigid docking is that it neglects the conformational changes of the peptide and protein, which may result in docking outcomes that do not accurately reflect the actual situation. Flexible docking (e.g., FlexPepDock [13]) addresses this issue to some extent by allowing peptides to undergo conformational changes during the docking process, providing more adaptable docking model. This method can handle more complex peptide conformations, particularly in cases where the peptide exhibits significant flexibility or multiple potential binding conformations. However, flexible docking is computationally intensive, has lower throughput, and demands higher computational resources. Additionally, these traditional methods typically require some prior knowledge, such as potential docking sites, to achieve better docking results. This means that in practical applications, researchers often need to combine experimental data and other information to improve the accuracy and reliability of the docking outcomes. With the development of deep learning technology, AI-based peptide-protein docking methods have become a research focus. RoseTTAFold All-Atom [14] and AlphaFold 3 [15] are two representative methods in this field. These approaches not only predict the three-dimensional structures of individual proteins or peptides but also predict the complex structures of peptide-protein complexes. Specifically, these deep learning models input the sequences of proteins and peptides, utilizing deep neural networks to learn from extensive training data and predict possible conformations of the complexes. For complex synthetic peptides, RoseTTAFold All-Atom or AlphaFold 3 can also use the peptide’s SMILES as input to predict the peptide-protein complex conformation. This
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approach is particularly useful in cases lacking experimental data or where traditional docking methods are challenging to apply. 2.3 Affinity Prediction of Peptides and Target Proteins
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In peptide drug development, predicting the affinity between peptides and target proteins is a crucial step. Traditional methods, such as molecular docking, rely on high-resolution three-dimensional structural data and extensive computational resources, making these approaches computationally complex and resource-intensive. In recent years, deep learning technologies have provided more efficient and accurate solutions for predicting peptide-protein interactions. For example, CAMP [16] is a deep learning framework specifically designed for multilayered peptide-protein interaction prediction. CAMP utilizes convolutional neural networks (CNNs) and self-attention mechanisms to fully extract both local and global information from peptide and protein sequences. It predicts binary interactions between input peptide-protein pairs and identifies binding residues within the peptide sequence. Additionally, existing structural prediction algorithms, such as AlphaFold-Multimer [7], can predict the structures of proteinpeptide complexes. By analyzing whether interactions occur between the peptide and protein within the predicted structure, these methods can also be used to predict whether a protein and peptide can bind [17]. These methods offer stronger generalization capabilities and mitigate the risk of overfitting that can arise when training on small datasets.
Virtual Screening of Peptides Peptides have gained significant attention as therapeutic agents due to their high specificity, potency, and versatility [18]. They bridge the gap between small molecules and biologics, offering unique therapeutic benefits such as lower toxicity and better target engagement [19]. However, the traditional methods of peptide discovery and optimization can be time-consuming and resource-intensive. Virtual screening (VS) has emerged as a powerful computational approach to accelerate the identification of peptide candidates, including those with non-natural amino acids, which offer enhanced stability and bioavailability [20]. In this section, we discuss the design of peptide libraries, the workflow of virtual screening, and the scoring functions employed to evaluate peptide candidates.
3.1 Peptide Library Design
The core step for virtual screening is the design of a diverse and representative peptide library. This library should encompass a wide range of sequences, including both natural and non-natural amino acids, to explore the full potential of peptide therapeutics.
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3.1.1 Natural and Nonnatural Amino Acids
Peptides composed of natural amino acids may face limitations such as rapid degradation by proteases, poor membrane permeability, and limited bioavailability. To overcome these challenges, peptides can be designed to include non-natural amino acids. These modifications can enhance the stability, binding affinity, and selectivity of peptides while maintaining or improving their biological activity [21].
3.1.2 Combinatorial Approaches
Combinatorial chemistry techniques allow for the generation of large peptide libraries with a diverse array of sequences. By systematically varying the amino acid sequence, it is possible to generate peptides with a wide range of physicochemical properties [22]. These libraries can then be screened virtually to identify candidates with optimal binding characteristics.
3.1.3 Structure-Based Design
In cases where the structure of the target protein is known, structure-based design methods can be employed to generate focused peptide libraries [23]. These libraries are designed to interact with specific binding sites, increasing the likelihood of identifying high-affinity binders. The incorporation of non-natural amino acids in these peptides can further enhance their binding properties and stability.
3.2 Virtual Screening Workflow
The virtual screening of peptides involves several key steps, including the preparation of the target and peptide libraries, molecular docking, and post-docking analysis. These steps are outlined below.
3.2.1
To prepare the target protein for virtual screening, it is essential to ensure that the protein is in the correct conformation. This process includes removing any bound ligands, adding missing hydrogen atoms, and assigning appropriate charge states. The binding site of the protein is then identified, either through experimental data or computational predictions. In contrast, sequence-based virtual screening does not require the protein’s structure. Instead, it relies on analyzing the protein sequence to predict potential binding sites or interaction regions.
Target Preparation
3.2.2 Peptide Library Preparation
The peptide library is created by generating either sequences or three-dimensional structures for each peptide, utilizing tools like RDKit [24] or Rosetta [32]. When dealing with peptides that include non-natural amino acids, specialized force fields or parameter sets are often necessary to accurately model their structural and behavioral properties.
3.2.3
Scoring is the core of the virtual screening process [25]. Each peptide in the library is scored with target protein, and sometimes the binding pose is also evaluated based on a scoring function. This process can be highly computationally intensive, particularly for
Scoring
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large libraries, but advances in parallel computing and cloud-based resources have made it more feasible [26]. 3.2.4 Post-docking Analysis
After scoring, the top-ranked peptides are subjected to further analysis to refine the selection. This may include molecular dynamics simulations [27] to assess the stability of the peptide-protein complex or the use of more sophisticated scoring functions to reevaluate binding affinities.
3.3 Scoring Functions for Peptides
The scoring function is a critical component of virtual screening, as it determines the predicted binding affinity of each peptide. Several types of scoring functions can be employed, depending on the nature of the peptides and the target protein.
3.3.1 Computer-Aided Drug Design (CADD)
CADD tools incorporate a variety of computational techniques [28], including quantitative structure-activity relationship (QSAR) models and pharmacophore-based methods, to predict the binding affinity of peptides [29]. These methods can be particularly useful when screening peptides with non-natural amino acids, as they can accommodate the unique physicochemical properties of these residues.
3.3.2 Machine LearningBased Scoring
Recent advances in machine learning have led to the development of novel scoring functions that can improve the accuracy of virtual screening [30, 31]. These models are trained on large datasets of peptide-protein interactions and can capture complex patterns that traditional scoring functions may miss. Machine learning-based scoring is particularly advantageous when screening diverse libraries, as it can generalize across different types of peptides and target proteins.
3.4
Virtual screening has become an indispensable tool in the discovery and optimization of peptide therapeutics. By leveraging computational methods, researchers can efficiently explore large peptide libraries, including those containing non-natural amino acids, to identify candidates with optimal properties. The combination of energy-based, CADD, and machine learning-based scoring functions provides a robust framework for evaluating peptide-protein interactions and advancing peptide drug development. As computational techniques continue to evolve, virtual screening will play an increasingly critical role in the design of next-generation peptide therapeutics.
Conclusion
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Membrane Permeation
4.1 Challenges and Advances in Passive Membrane Permeation
Intracellular cancer targets pose significant challenges for small molecule drugs due to the difficulty in achieving effective binding, often necessitating larger biomolecules, such as peptides and antibodies, which can better engage these targets [33, 34]. Peptides, however, usually face significant challenges in crossing cell membranes and typically violate Lipinski’s Rule of Five [35], a guideline in drug development. Despite these limitations, certain peptides, such as those in the CsA series [36] and Emodepside [37], have demonstrated an unexpected ability to cross membranes with relative ease. This phenomenon has spurred considerable interest in discovering new principles that could extend beyond traditional drug design paradigms [38]. Traditionally, the ability of molecules to cross cell membranes was assessed using experimental methods such as PAMPA (parallel artificial membrane permeability assay) [39], Caco2 (human colorectal adenocarcinoma cell line), RRCK (renal proximal tubular cell line) [40], and MDCK (Madin-Darby canine kidney cell line) [41]. While these techniques are reliable, they are often timeconsuming, costly, and labor-intensive. Recently, computational approaches have emerged as efficient and cost-effective alternatives for evaluating the membrane-penetrating capabilities of macrocyclic peptide molecules. These computational methods can be broadly classified into molecular dynamics simulations and artificial intelligence-based models. Researchers have been utilizing atomistic molecular dynamics simulations to investigate the membrane permeability of cyclic peptides for years [42–44]. Carpenter et al. introduce a computational model based on umbrella sampling molecular dynamics for predicting drug membrane permeability, calibrated and validated against in vitro assay data [45]. They demonstrate a strong correlation with PAMPA data and outperform traditional LogP calculators in predicting the permeability of structurally related compounds. Linker et al. also delineated a four-step process for passive membrane permeation with molecular dynamics, involving (1) initial anchoring with lipid headgroup residues, (2) membrane insertion and orientation, (3) interconversion to a closed conformation, and (4) final traversal across the membrane [43]. Additionally, David Baker and his team developed a Rosetta-based algorithm to identify peptides with cell permeability, emphasizing the critical role of N-methylation [46]. These findings offer valuable insights into the design of permeable cyclic peptides and underscore the complex role of amino acids in the membrane permeation process. Artificial intelligence-based models also play a crucial role in predicting the cell permeability of peptides, particularly following the release of the CycPep-tMPDB [47], which includes over 7000
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macro-peptides with detailed permeability data. For example, the MultiCycGT [48] model leverages transformer and graph convolutional networks (GCN) to process molecular features and integrates physicochemical properties through a fully connected layer. Additionally, the CycPeptMP [49] model further improves prediction accuracy and generalization by designing features at the atom, monomer, and peptide levels and applying deep learning techniques with data augmentation. These AI-based models surpass both traditional and molecule dynamic-based methods, providing a powerful tool for cyclic peptide drug discovery. 4.2 Mechanisms and Computational Modeling of Active Membrane Penetration
Since the early 2000s, advances in peptide science and technology have led to the discovery of potent cyclic peptide ligands and highly active cell-penetrating peptides (CPPs) [50, 51]. These peptides can enter cells through multiple mechanisms [52], particularly ATP-dependent active penetration, and show significant potential for targeting intracellular proteins, enhancing oral bioavailability, and facilitating the delivery of intracellular biologics [53, 54]. The field of computer-aided CPP design has seen significant advancements with the development of various computational methods, including statistical learning and deep learning algorithms such as CPPpred [55], CellPPD [56], and C2Pred [57]. However, many of these approaches are limited by their reliance on balanced datasets, which do not accurately represent real-world conditions where positive CPP samples are scarce and many peptides remain uncharacterized. PractiCPP [58] addresses these challenges by incorporating hard negative sampling to mitigate class imbalance, alongside a feature extraction and prediction module (PractiCPPbase). PractiCPP also demonstrates superior performance on both balanced and imbalanced datasets, underscoring its effectiveness in real-world scenarios with limited positive samples. These advancements in computer-aided methods mark a significant step forward in the predictive accuracy and practical application of CPP design, paving the way for more effective drug delivery systems and therapeutic interventions.
4.3 Oral Bioavailability and the Challenges of Peptide Therapeutics
Human oral bioavailability (HOB) is a critical factor in drug development, especially for peptide-based therapeutics [59–61]. Poor HOB frequently results in late-stage failures, underscoring the need for accurate prediction models. Efforts have been made to develop machine learning strategies for predicting and improving peptide stability in simulated gastric and small intestinal fluids, which is highly related to oral bioavailability [62]. Currently, there are no dedicated methods specifically designed for predicting the oral bioavailability of peptides. Several studies have explored the endto-end prediction of human oral bioavailability (HOB) using various methodologies, primarily focusing on small molecules. For instance, Falco´nCano et al. developed a consensus model based
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on 1448 small molecules [63], while Yang et al.’s admetSAR method utilizes random forest algorithms and a dataset of 995 small molecules [64]. Similarly, Kim et al. employed logistic classifiers on the same dataset and achieved improved accuracy [65]. While these small-molecule-based methods offer valuable insights into predicting oral bioavailability, there remains an unmet need for computational approaches specifically designed to optimize the oral bioavailability of peptides. Existing computational methods primarily focus on enhancing membrane permeability [46, 66, 67] and are often supplemented by metabolism and stability predictions [68]. This highlights the significant challenges and opportunities in developing robust and accurate models tailored specifically to peptide therapeutics. In conclusion, integrating computational methods into peptide drug design represents a powerful approach to overcoming the traditional limitations of peptide therapeutics. By leveraging advanced modeling techniques, researchers can more effectively predict and enhance the permeability, stability, and overall efficacy of peptide drugs, paving the way for new treatments targeting previously inaccessible intracellular cancer targets.
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Peptide Optimization with AI-Based Methods With the ongoing accumulation of experimental data, advancements in algorithms, and increasing computational power, AI-based methods are poised to play an increasingly significant role in the optimization of peptides, both from known starting points and from scratch.
5.1 Peptide Optimization from Known Start Points
The presence of specific motifs in peptide sequences is essential for mediating particular biological activities. In peptide design research, existing peptide sequences often serve as templates for modification, with the goal of optimizing the sequences to enhance biological activity or reduce toxicity toward normal cells. Typically, peptide sequences are treated as text, where the sequence composition is altered by mutating amino acids at positions critical for specific biological functions. Optimization-based approaches have played a significant role in this domain. Evolutionary algorithms, inspired by natural evolution, have been widely used to design peptides with targeted properties [69]. Yoshida et al. [70] employed evolutionary algorithms combined with machine learning (ML) to explore the sequence space, starting from template peptides to design novel anticancer peptides. In this approach, genetic algorithms processed natural peptide sequences, with in vitro assays serving as the fitness function. ML predictive models further enhanced the efficiency of generating the next generation of sequences. Their experiments
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demonstrated a 160-fold increase in anticancer activity after three rounds of optimization. However, this method relied heavily on statistical techniques to generate mutations and lacked deeper insights into the effective design of peptides using amino acid substitution frequencies. To address the need for more transparent knowledge in peptide design, Boone et al. [71] applied a codon-based genetic algorithm (CB-GA) integrated with rough set theory, a transparent ML approach, to explore and optimize peptide sequences. Starting with known natural anticancer peptides (ACPs), they converted these sequences into codon representations to utilize the reading frame for generating new ACPs. By mutating individual codons, they generated new peptide sequences, which were then screened using a highly specific rough set classification method. Among the three ACPs selected from this approach, one exhibited anticancer activity, demonstrating the potential of incorporating transparency into the design process. Despite these successes, optimization-based methods have limitations. They primarily focus on amino acid composition without considering the interactions between amino acids that influence the peptide structure. The impact of specific residue substitutions is context-dependent, and these methods often result in peptides that remain similar to the original sequences, thereby restricting the exploration of the sequence space. As the field progresses, integrating peptide structural information and developing optimization algorithms that consider sequence context will be crucial for advancing peptide design. 5.2 De Novo Peptide Design with Generative Models
The success of early peptide design largely depends on prior knowledge and predefined rules derived from existing peptides, which are often difficult to identify. In contrast, generative models provide a powerful alternative by modeling the distribution of training data and generating new data with properties similar to the training dataset. These models allow for the efficient exploration of vast sequence spaces. Among deep generative models, neural language models, variational auto-encoders (VAE), and generative adversarial networks (GANs) have been employed to generate novel peptides [72]. Neural language models, particularly those utilizing recurrent neural networks (RNNs) and generative pretrained transformers (GPTs), predict the next amino acid in a peptide sequence based on the preceding amino acids. For example, Muller et al. [73] trained RNNs with long short-term memory units to design antimicrobial peptides (AMPs) in a de novo manner. The network was trained to predict the next amino acid at each position, focusing on linear cationic peptides that form amphiphilic helices, which are crucial for antimicrobial activity. Of the 2000 sequences generated,
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85% were predicted to be active AMPs, showcasing the potential of this approach for generating functional peptides [74]. VAEs offer a different approach by mapping peptide sequences to a low-dimensional embedding that captures the statistical distribution of the samples. New peptide sequences are generated by sampling and decoding these embeddings. Dean et al. [75] used a VAE for de novo AMP design, training the model on thousands of known AMP sequences. The resulting peptides were experimentally verified to be active, demonstrating the model’s effectiveness. Das et al. [76] extended this approach by employing a classifier-guided VAE, which used a deep learning classifier to refine the generated sequences. Their method led to the identification of two peptides with high performance against multiple pathogens, highlighting the promise of VAEs in peptide discovery. GANs, which consist of a generator and a discriminator, offer another powerful tool for peptide design. Allison et al. [77] developed the GANDALF system, which utilizes GANs to generate peptides targeting specific protein targets. Unlike traditional GANs that generate small molecules, GANDALF employs two networks to generate both peptide sequences and structures, including active atom data. This allows the system to generate complete peptide structures and predict their binding affinities to specific targets. The study demonstrated that peptides generated by GANDALF exhibited binding affinities and three-dimensional conformations comparable to FDA-approved drugs while also presenting unique sequences. Deep generative models represent a significant advancement in peptide discovery, but they still face challenges such as the evaluation and screening of generated peptides and the effective integration of structural information. Combining optimization algorithms with generative models offers a promising direction for overcoming these challenges. For instance, integrating feedback mechanisms between generated peptides and experimental data, coupled with active learning, could continuously refine generative models, enhancing their accuracy and applicability. Future research could also explore the design of multifunctional peptides, peptide drugs, and peptide vaccines, expanding beyond single-function peptides. Moreover, the rapid emergence of large language models (LLMs) presents new opportunities for peptide generation. As these models continue to advance, they are likely to become valuable tools in peptide design, enabling the generation of more diverse and complex peptide structures. 5.3 Multi-objective Optimization
One of the primary challenges in designing therapeutic peptides lies in the simultaneous optimization of multiple, often competing, properties [78]. These properties include affinity, cell penetration, bioavailability, stability, toxicity, and others. In many cases, these objectives conflict with one another, creating a trade-off where
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enhancing one property may lead to the deterioration of another. For example, increasing a peptide’s cell penetration ability might elevate its potential to disrupt cell membranes, thereby increasing cytotoxicity. To address this challenge, one approach is to adjust the weights assigned to each objective based on specific requirements and expert knowledge [70]. Another effective method is to search for the Pareto frontier, which represents the set of solutions where no single objective can be improved without compromising another. This approach is supported by advanced optimization algorithms. For instance, state-of-the-art multi-objective optimization evolutionary algorithms, such as SMS-EMOA, have been successfully applied to the design of peptide ligands with reasonable affinity and selectivity for a specific isoform of 14-3-3 proteins [79]. Pareto optimality can serve as a valuable guide in experimental design, accelerating the development of peptides with well-balanced properties.
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Closed-Loop Systems Data from publications, patents, in-house databases, and assay results are used to initially train new models as they are developed and implemented. However, when initially deployed, machine learning models are rarely perfect. However, using a closed-loop system provides a mechanism for continuous improvement and refinement that leads to better performance over time. Technically, closed-loop testing refers to a process in which the outputs of the system or assay under test are fed back as inputs to maintain and improve the performance of the system. Ideally, automated informatics processes are implemented to provide continuous enhancement of the closed-loop systems. Continually updating, testing, and revalidating the models is generally both required and preferred on an ongoing basis as new data is generated and becomes available from program assays to further refine and enhance a closed-loop system. To accelerate drug discovery program efforts, several different models and closed-loop systems can be implemented depending upon the stage of the program. In particular, at the initiation of a program targeting peptide therapeutics, screening can be conducted to identify hits using phage display libraries or mRNA display libraries. Both technologies enable the panning of libraries of related peptides typically consisting of ~109 unique members for phage display and ~1012 unique members for mRNA display. With both screening methods, next-generation sequencing (NGS) is commonly employed to analyze the samples and identify the hits. The NGS data provides the amino acid sequences detected in the samples together with a numerical count, which is the number of
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times that a specific sequence was detected in the samples. In many cases, analysis of the NGS data simply involves sorting the sequences based on the value of the count and then synthesizing and testing some subset of the sequences having the largest number of counts. Alternatively, utilizing a model that has been trained on the NGS data to identify hits, a deeper more informative analysis can be provided. Specifically, within the NGS dataset, clusters of structurally related sequences can be identified using one or several different similarity metrics. The enrichment of specific hit sequences can be tracked through successive rounds of the selection to confirm binding to the target. Recurring nonspecific binders, which are also known as target-unrelated peptides (TUPs), can be identified and excluded from hit lists. Finally, early structureactivity relationships (SAR) can be extracted from sequences that are closely related to the top hit sequences having the largest number of counts. Overall, the model can efficiently pick out the best hits, exclude false positives such as TUPs, identify different clusters or families of hits that may bind to alternative epitopes, and provide preliminary SAR information to accelerate initial optimization of the confirmed hits. After hit identification, models can also be implemented to direct and advance the optimization of several different criteria in a medicinal chemistry program such as binding affinity, selectivity, functional activity, cell penetration, and/or oral bioavailability. At one level, ML models can propose new molecules having specific properties. Alternatively, by analyzing sequence patterns and data relationships, ML models can also provide insights that reveal complex correlations within the data to guide and accelerate the drug development process leading to the more efficient identification of advanced therapeutics. References 1. Ponte´n F, Jirstro¨m K, Uhlen M (2008) The human protein atlas—a tool for pathology. J Pathol J Pathol Soc GB Irel 216(4):387–393 2. Knox C, Wilson M, Klinger CM, Franklin M, Oler E, Wilson A et al (2024) DrugBank 6.0: the DrugBank knowledgebase for 2024. Nucleic Acids Res 52(D1):D1265–D1275 3. Brayden DJ, Hill TA, Fairlie DP, Maher S, Mrsny RJ (2020) Systemic delivery of peptides by the oral route: formulation and medicinal chemistry approaches. Adv Drug Deliv Rev 157:2–36 4. Lohman DC, Aydin D, Von Bank HC, Smith RW, Linke V, Weisenhorn E et al (2019) An isoprene lipid-binding protein promotes eukaryotic coenzyme Q biosynthesis. Mol Cell 73(4):763–774
5. Nielsen DS, Shepherd NE, Xu W, Lucke AJ, Stoermer MJ, Fairlie DP (2017) Orally absorbed cyclic peptides. Chem Rev 117(12): 8094–8128 6. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O et al (2021) Highly accurate protein structure prediction with AlphaFold. Nature 596(7873):583–589 7. Evans R, O’Neill M, Pritzel A, Antropova N, Senior A, Green T et al (2021) Protein complex prediction with AlphaFold-Multimer. bioRxiv:2021-10 8. Karplus M, McCammon JA (2002) Molecular dynamics simulations of biomolecules. Nat Struct Biol 9(9):646–652 9. Baek M, DiMaio F, Anishchenko I, Dauparas J, Ovchinnikov S, Lee GR et al (2021) Accurate
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Chapter 4 Advances in the Manufacturing of CAR-NK Cells for Cancer Immunotherapy Julia Uhlig, Dominik Schmiedel, U. Sandy Tretbar, and Anna Du¨nkel Abstract Chimeric antigen receptor (CAR)-engineered natural killer (NK) cells have emerged as a promising new frontier in cancer immunotherapy. Building on the successes of CAR-T cell therapies, CAR-NK cells offer several potential advantages that make them an attractive option for treating various malignancies. NK cells can recognize and eliminate malignant or infected cells using an array of invariant activating and inhibitory receptors, without prior sensitization or antigen presentation. This inherent ability combined with genetic engineering to express CARs allows CAR-NK cells to target specific tumor antigens while retaining their natural cytotoxic mechanisms. Additionally, CAR-NK cells have an excellent safety profile, even when being transferred from healthy donors to patients without HLA matching. Recent advances in CAR design, NK cell sourcing, and genetic modification techniques have accelerated the development of CAR-NK cell therapies. However, difficulties in the cultivation, genetic modification, functional testing, and upscaling of NK cells present a major challenge and limit the development of innovative NK cell-based gene therapy approaches. This chapter will guide through optimized processes for research and large-scale CAR-NK cell manufacturing including NK cell isolation, genetic engineering, CAR-NK cell expansion, and quality controls. Thereby, we aim to accelerate the development of novel CAR-NK cells to expand the reach and effectiveness of cellular immunotherapies against cancer. Key words NK cells, Genetic engineering, CAR-NK cells, Allogeneic cell therapy, Process development, Cancer immunotherapy, Large-scale manufacturing
1
Introduction Natural killer (NK) cells are innate immune cells found in tissues and blood, which are capable of effectively recognizing and lysing stressed, malignant, or virus-infected cells. Additionally, NK cells were shown to directly interact with pathogens like fungi and bacteria. Therefore, NK cells are an integral part of the immune response against all kinds of threats [1]. The activation of NK cells is determined by an array of invariant receptors, which provide either activating signals by inducing
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downstream kinase activity and ultimately transcription factor activity. Cellular ligands for activating receptors, like NKG2D or the NCR family (NKp46, Nkp44, Nkp30), are frequently expressed on the tumor cell surface in response to a variety of stresses occurring during malignant transformation. Inhibitory receptors, in contrast, recruit upon activation phosphatases, like SHP-1, SHP-2, or SHIP, that erase phosphorylation patterns induced by kinases, thereby effectively suppressing signal transduction and cellular activation. NK cells are predominantly inhibited by classical and nonclassical HLA class I, like HLA-E, which is recognized by inhibitory receptors from the KIR family or NKG2A, respectively [2]. NK cell activation is determined by integrating the overall signals provided by both activating and inhibitory receptors and lyse target cells either due to the lack of inhibitory signals (“missing-self hypothesis”) or due to the dominance of activating ligands leading to cellular activation (“induced-self hypothesis”). NK cells are capable of serially killing target cells by repeatedly secreting perforin and granzymes but can also induce apoptosis in target cells using death receptors like FAS and TRAIL. NK cells are well-recognized to play a major role in suppressing tumor growth, orchestrating immune cell interplay by releasing cytokines and chemokines, and responsiveness to immunotherapies [3]. To therapeutically exploit these antitumoral features, ex vivo NK cell expansion and activation followed by donor-lymphocyte infusions (DLI) to patients was established over 20 years ago [4]. While NK cell transfer is considered extremely safe, when using NK cells both from the patient (autologous) and from HLA-matched or HLA-mismatched healthy donors (allogeneic), the clinical efficacy has been, overall, limited for most patients [4]. With the rise of T cells engineered with chimeric antigen receptors (CARs) as frontrunner of cellular immunotherapies, with therapies targeting CD19 as well as BCMA being approved and highly effective in treating certain B cell malignancies and multiple myeloma, the interest in NK cell-based therapies enhanced by genetic engineering reemerged. By equipping NK cells with CARs, it seems possible to combine efficacy in targeting CAR antigen-positive cells with the advantages of innate properties of NK cells over T cells [2, 3, 5], reviewed in Table 1. By now, numerous preclinical studies as well as clinical studies for CAR-NK cells have been conducted and published, showing efficacy, safety in allogeneic settings, and persistence [4, 6, 7]. Most CARs used in NK cells are assembled from the same domains used in CAR-T cells, like the CD3ζ chain, CD28, and/or 4-1BB signaling domains. However, few studies aim to exploit NK-cell-specific transmembrane regions and signaling adapters, e.g., derived from DAP12, 2B4, and DNAM-1. Though there are only a few studies comparing the effects of different signaling adapters in CAR-NK cells, the data so far suggests that
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Table 1 Comparison of key parameters of CAR-T cells and CAR-NK cells sourced from blood Parameter
CAR-T cells
CAR-NK cells
Tumor cell recognition
CAR-mediated
CAR-mediated Natural cytotoxicity ADCC
Safety/side effects
Occurrence of CRS or ICANS, potentially of No/little CRS or ICANS higher grades
Sourcing
Autologous peripheral blood Allogeneic (using TCR-KO)
Allogeneic without need for genetic engineering
Scalability
Autologous: low Allogeneic: very high
Very high
Proliferation in vitro
Very high
Cytokine-driven: low/intermediate Feeder cell-driven: very high
Persistence in vivo
Long-term memory formation for many years Probably months
ADCC antibody-dependent cell-mediated cytotoxicity, CRS cytokine release syndrome, ICANS immune effector cellassociated neurotoxicity syndrome, TCR-KO T cell receptor knockout
modifications in signaling may further enhance CAR-NK cell functionality. Apart from the CAR itself, additional modifications can further enhance the functionality of CAR-NK cells, like the overexpression of cytokines like IL-15 or IL-21 as well as CRISPRmediated genetic knockouts for inhibitory receptors (e.g., NKG2A), cytokine receptors (e.g., TFG-β receptor), regulators of cytokine signaling (e.g., CISH), or transcription factors (e.g., HIF1a) [4]. Consequently, the complete therapeutic potential of CAR-NK cells has yet to be fully explored, and additional research is anticipated to result in the development of more powerful cell therapies. Not only the manipulation and genetic engineering of NK cells pose significant challenges, restricting new advancements in NK cell research and slowing the rapid adaptation of novel technologies from T cell research to NK cell studies in the past years. In addition, the advantage of CAR-NK cells for allogenic use and the ability to manufacture them as “off-the-shelf” cell therapy products present several challenges. These include maintaining cell viability and functionality during large-scale expansion, ensuring the genetic stability of CAR-NK cells throughout the process, designing of robust manufacturing processes despite high donor variability, and achieving consistent product quality. Scalability issues also arise from the need for specialized equipment and facilities that can support the large-scale, sterile manufacturing required for clinical and commercial use. Although the production of CAR-T cells has become highly professionalized in recent years, the production
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of CAR-NK cells requires a different approach. Given the biological differences between NK cells and T cells, it is necessary to develop optimized and adapted culture protocols, equipment, and procedures. This is why process development for NK cell manufacturing represents an ongoing aspect of our research. Here, we share optimized protocols for both research scale and large-scale CAR-NK cell manufacturing, including the isolation of NK cells from peripheral blood of healthy donors, their cytokinedriven expansion, and genetic modification using lentiviruses in G-Rex® bioreactors and a combination of both, the CliniMACS Prodigy® platform and G-Rex® bioreactor-based expansion.
2 2.1
2.1.1
Materials Equipment
Research Scale
The specific instrumentation used in this experiment is described, but can be easily substituted for any other similarly equipped model. 1. Easy 50 EasySep™ Magnet | STEMCELL™ Technologies. 2. NucleoCounter® NC-202™ | ChemoMetec. 3. Herasafe KSP Class II Biological Safety Cabinets Sterile bench | ThermoScientific. 4. PIPETBOY (Pipette controller) | Integra Biosciences™. 5. Vortex Mixer | Gilson®. 6. Centrifuge | not specified. 7. Microscope | Zeiss. 8. Single channel pipette 10 μL/100 μL/1000 μL | Eppendorf. 9. TSCD® II Sterile Tubing Welder-II | Terumo BCT. 10. Navios Ex | Beckman Coulter. 11. G-Rex® 24 Well Plates | Wilson Wolf.
2.1.2 CAR-NK Cell Production: CliniMACS Prodigy® Combined with G-Rex® Bioreactor Expansion
1. CliniMACS Prodigy® | Miltenyi Biotec. 2. CliniMACS Prodigy® TS 320 | Miltenyi Biotec. 3. CliniMACS Prodigy® TS 520 | Miltenyi Biotec. 4. Transfer Set Coupler/Coupler | Miltenyi Biotec. 5. Sampling Site Coupler | Miltenyi Biotec. 6. Extension Set (10 cm) | B. Braun. 7. Triple-Sampling-Adapter | Miltenyi Biotec. 8. NucleoCounter® NC-202™ | ChemoMetec. 9. Herasafe KSP Class II Biological Safety Cabinets Sterile bench | ThermoScientific.
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10. PIPETBOY (Pipette controller) | Integra Biosciences™. 11. Vortex Mixer | Gilson. 12. Centrifuge | not specified. 13. Microscope | Zeiss. 14. Single channel pipette 10 μL/100 μL/1000 μL | Eppendorf. 15. TSCD® II Sterile Tubing Welder-II | Terumo BCT. 16. Navios Ex | Beckman Coulter. 17. G-Rex® 24 Well Plates | Wilson Wolf. 18. G-Rex® 100M and G-Rex® 500M RUO | Wilson Wolf. 2.2 Reagents and Solutions
1. NK MACS® Basal Medium, human + NK MACS® Supplement (100×), human | Miltenyi Biotec.
2.2.1
2. IL 2 (Proleukin® S) | Healthcare B.V.
Research Scale
3. Human IL-15, premium grade | Miltenyi Biotec. 4. Human AB plasma, male | Sigma-Aldrich®. 5. Vectofusin®-1 | Miltenyi Biotec. 6. HISTOPAQUE®-1077 | Sigma-Aldrich®. 7. EasySep™ Buffer | STEMCELL™ Technologies. 8. EasySep™ Human NK Cell Isolation Kit | STEMCELL™ Technologies. 9. Red Blood Cell Lysis Solution (10×) | Miltenyi Biotec. 10. Distilled Water, sterile | Thermo Fisher Scientific. 11. Viral vector encoding for the CAR gene pseudotyped with BaEV or RD114-TR or else homemade according to [8]. 12. FcR Blocking Reagent, human | Miltenyi Biotec. 13. autoMACS Running Buffer | Miltenyi Biotec. 2.2.2 CAR-NK Cell Production: CliniMACS Prodigy® Combined with G-Rex® Bioreactors
1. CliniMACS® PBS/EDTA Buffer (3L) | Miltenyi Biotec. 2. NK MACS® GMP Medium and NK MACS® GMP Supplement | Miltenyi Biotec. 3. autoMACS® Running Buffer | Miltenyi Biotec. 4. Gamunex® (10% immunoglobulin solution) | Griffols. 5. CliniMACS® CD3 Reagent | Miltenyi Biotec. 6. CliniMACS® CD56 Reagent | Miltenyi Biotec. 7. MACS® Cytokine hIL-1ß | Miltenyi Biotec. 8. MACS® Cytokine hIL-15 | Miltenyi Biotec. 9. MACS® Cytokine hIL-2 | Miltenyi Biotec. 10. MACS® GMP Vectofusin®-1| Miltenyi Biotec.
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11. Viral vector encoding for the CAR gene pseudotyped with BaEV or RD114-TR or else homemade according to [8]. 12. FcR Blocking Reagent, human I Miltenyi Biotec. 2.3 Antibodies Flow Cytometry
Panel 1 1. BD Pharmingen™ FITC Mouse Anti-Human CD3, BD Biosciences, 555332. 2. PE anti-human CD8a Antibody, Biolegend®, 301008. 3. BD™ CD14 PerCP, BD Biosciences, 345786. 4. BD™ CD56 PE-Cy™7, BD Biosciences, 335826. 5. BD™ CD19 APC, BD Biosciences, 345791. 6. BD Pharmingen™ APC-H7 Mouse Anti-Human CD4, BD Biosciences, 560158. 7. BD Horizon™ BV421 Mouse Anti-Human CD16, BD Biosciences, 562874. 8. BD Horizon™ V500 Mouse Anti-Human CD45, BD Biosciences, 560777. Panel 2 1. PE anti-human CD56 (NCAM) Antibody, Biolegend®, 362508. 2. PerCP/Cyanine5.5 anti-human CD337 (NKp30) Antibody, Biolegend®, 325216. 3. PE/Cyanine7 anti-human CD336 (NKp44) Antibody, Biolegend®, 325116. 4. APC anti-human CD335 (NKp46) Antibody, Biolegend®, 331918. 5. Brilliant Violet 421™ anti-human CD314 (NKG2D) Antibody, Biolegend®, 320822. 6. Brilliant Violet 510™ anti-human CD226 (DNAM-1) Antibody, Biolegend®, 338330. 7. Alexa Fluor® 700 anti-human CD16 Antibody, Biolegend®, 302026. 8. BD Horizon™ Fixable Viability Stain 780, BD Biosciences, 565388. Panel 3 1. Alexa Fluor® 700 anti-human CD56 (NCAM) Recombinant Antibody, BioLegend®, 392417. 2. Brilliant Violet 421™ anti-human CD223 (LAG-3) Antibody, BioLegend®, 369314.
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3. APC anti-human CD279 (PD-1) Antibody, BioLegend®, 621609. 4. PerCP/Cyanine5.5 anti-human CD366 (Tim-3) Antibody, BioLegend®, 345015. 5. PE/Cyanine7 anti-human CD16 Antibody, BioLegend®, 302016. 6. BD Horizon™ Fixable Viability Stain 780, BD Biosciences, 565388.
3
Methods
3.1 Research Scale CAR-NK Cell Production
1. Day 0—NK cell isolation. To isolate human primary NK cells, peripheral blood mononuclear cells (PBMCs) are separated from buffy coats via density gradient centrifugation based on Histopaque®1077 (Sigma-Aldrich®) using SepMate™ Tubes (STEMCELL™ Technologies). Please follow the manufacturer’s instructions (see Note 1). 2. Following the initial wash, use EasySep™ buffer for a second PBMC wash. Subsequently, the cells are treated with red blood cell lysis solution (Miltenyi Biotech) to remove residual erythrocytes. Please follow the manufacturer’s instructions. 3. Centrifuge for 10 min at 300× g at room temperature with brake and remove the supernatant by aspiration. If the pellet appears to be red, repeat the erythrocyte lysis procedure one more time. The final cell pellet is resuspended in 10 mL of EasySep buffer. 4. NK cell isolation is performed using the EasySep™ Human NK Cell Isolation Kit (STEMCELL™ Technologies) by following the manufacturer’s instruction (see Note 2). 5. Gently mix the NK cells by pipetting and take 200 μL for QC testing (cell count and viability) using the NucleoCounter® NC-202™ instrument (see Note 3). 6. Determine the total volume of the cell suspension and calculate total isolated NK cell numbers. 7. Centrifuge the NK cell sample for 5 min at 300× g at room temperature (RT). 8. Prepare appropriate volume of NK cell medium (8 mL per G-Rex® 24 Well) including IL-2 (500 IU/mL) and IL-15 (140 U/mL).
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9. Remove the supernatant from the centrifuged NK cell sample (see step 7) using a serological pipette without disturbing the cells. 10. Resuspend the cell pellet in the prepared volume of NK cell medium and seed the NK cells in the G-Rex® 24 Well Plate. Incubate at 37 °C and 5% CO2 until transduction (see Note 4). 11. QC: Determination of purity (and receptor expression) by flow cytometry: (a) Take 2 × 105 cells (per well and panel) and transfer them in a V-bottom 96-well-plate. (b) Wash the cells once with 150 μL autoMACS® Running Buffer and centrifuge for 2 min at 300× g. (c) Block the cells for 10 min at 4 °C using 1× FcR-Blocking solution (100 μL/well). (d) Wash the cells once with 150 μL autoMACS® Running Buffer and centrifuge for 2 min at 300× g. (e) Stain the cells with antibody Panels 1 and 3 (see Subheading 2.3). (f) Incubate for 30 min at RT in the dark. (g) Centrifuge for 2 min at 300× g. (h) Wash the cells with 150 μL autoMACS® Running Buffer and centrifuge for 2 min at 300× g. (i) Remove the supernatant by aspiration. (j) Transfer the cells in an FC tube with a total volume of 300 μL and start the acquisition (see Notes 5 and 6). 12. Day 2—Transduction. (a) Thaw vector on ice. (b) Remove 6 mL of cell culture supernatant from the respective NK cell culture (G-Rex® 24 Well from step 10). (c) Gently mix the NK cells by pipetting and take 200 μL for QC testing (cell count and viability) using the NucleoCounter® NC-202™ instrument (see Note 3). (d) Prepare transduction medium: NK MACS® Basal Medium + 1% NK MACS® Supplement + 5% human AB serum + IL-2 (500 IU/mL) + IL-15 (140 U/mL). (e) Transfer 2.5 × 105 cells/500 μL to a 24-well suspension plate (to reach optimal cultivation conditions, 1 × 106 cells should be available for post-transduction G-Rex® 24 Well Plate seeding). (f) Add Vectofusin®-1 2.5 μg/mL + viral vector (amount dependent on vector and protocol, example [8]).
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(g) A process control (NK cells without virus) or a transduction with a control vector should be included in the transduction process. (h) Perform spinoculation for 60 min at 900× g and subsequently incubate the 24-well plate overnight at 37 °C, 5% CO2. 13. Day 3—Start of G-Rex®-based CAR-NK cell cultivation. (a) Visual inspection of the cells using a microscope (see Note 7). (b) Pool the cells separately in 50 mL centrifuge tubes, depending on whether they have undergone viral transduction or are carried separately as process control/vector control, and pellet the cells by centrifugation at 300× g for 10 min, RT. (c) Remove the supernatant by aspiration and gently resuspend the cells in ~5 mL fresh NK MACS® Basal Medium + 1% NK MACS® Supplement + 5% human AB serum. (d) Take 200 μL for QC testing (cell count and viability using the NucleoCounter® NC-202™ instrument). (e) Transfer at least 1 × 106 cells to a fresh G-Rex® 24 Well [0.5 × 106 cells/cm2]. (f) Fill G-Rex® 24 Well up to a final volume of 8 mL and add IL-2 (500 IU/mL) and IL-15 (140 U/mL). (g) Incubate at 37 °C, 5% CO2. 14. Day 6—Media Change. (a) Remove 6 mL cell culture supernatant from all seeded G-Rex® 24 Wells and add 6 mL fresh NK MACS® Basal Medium + 1% NK MACS® Supplement + 5% human AB serum and add IL-2 (500 IU/mL) and IL-15 (140 U/ mL) (see Note 8). 15. Day 8. (a) Remove 6 mL cell culture supernatant from all seeded G-Rex® 24 Wells. (b) Resuspend cells gently and determine culture volume. (c) Take 200 μL for QC testing (cell count and viability using the NucleoCounter® NC-202™ instrument) (see Notes 3 and 9). (d) The expression level of the CARs is determined by flow cytometry (see Note 10). (e) Stain the transduced and untransduced/process control cells with Panel 1 (see Subheading 2.3).
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16. Continue cultivation of undisturbed cells in G-Rex® 24 Wells and change media every 2–3 days. 17. Approximately every 7 days (one time per week), take 200 μL for QC testing (cell count and viability using the NucleoCounter® NC-202™ instrument) to monitor NK cell culture/ expansion. 18. Maximal cell counts of 35 × 106 cells/cm2 should not be exceeded (70 × 106 cells/G-Rex® 24 Well). 19. Harvest the cells at day 21 for final analysis: Quality control: Dependent on the design of the CAR, different flow cytometry panels are necessary. (a) Take 200 μL for cell count and viability testing using the NucleoCounter® NC-202™ instrument. (b) Calcein release assays are performed for functionality testing of the NK cells and CAR-NK cells using K562 and CAR-specific target cells, respectively (see Note 11). (c) Perform flow cytometric analyses for purity (Panel 1) and surface receptor expression of the expanded CAR-NK cells (Panels 2 and 3) (see Subheading 2.3). 3.2 Feeder-Free CAR-NK Cell Production: CliniMACS Prodigy® Combined with G-Rex® Bioreactors
The manufacturing process of CAR-NK cells involves many manual steps that are labor-intensive, expensive, and error-prone and can affect the quality of the final CAR-NK cell product. To minimize these difficulties and to establish a standardized process, Miltenyi Biotec’s NK Cell Transduction process on the CliniMACS Prodigy® is used for the isolation and transduction of the primary NK cells. Since this platform has a limited capacity for NK cell expansion, post-transduction cultivation is performed in G-Rex® bioreactors from Wilson Wolf. 1. Day 0—Isolation of NK cells from fresh leukapheresis using the CliniMACS Prodigy®. (a) Determination of CD3/CD56 status using Panel 1 by flow cytometry (see Note 12). (b) Depletion of CD3+ cells using the CliniMACS Prodigy® Tubing Set 320 and the CliniMACS CD3 Reagent following the process software LP-3-56 Separation (see Note 13). (c) At the end of the process, the Non-Target Cell Bag (NTCB, CD3+) and Target Cell Bag (TCB) 1 (CD3fraction) are properly welded off. (d) QC: Aseptically draw a sample (approx. 1.0 mL) from each of the homogenized cell suspensions of NTCB and TCB using a syringe and transfer to an appropriate sample vessel. Take 200 μL for cell count and viability testing
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using the NucleoCounter® NC-202™ instrument. Flow cytometric analyses are performed using Panel 1 (see Notes 14 and 15). (e) Deinstall Tubing Set 320 and install Tubing Set 520 (see Note 13). (f) Seal TCB 1 from (c) to Application Bag of Tubing Set 520 and transfer the cell suspension from TCB 1 into the Application Bag. Weld-off the empty TCB 1. (g) Enrichment of CD56+ cells using the Tubing Set 520, CliniMACS® CD56 Reagent following the process software PD-56 Engineering (see Notes 13 and 16). (h) After CD56 enrichment, the target cells are located in the Reapplication Bag (RAB) where a QC sample is drawn using the QC Pouch. (i) QC: Cell count and viability using the NucleoCounter® NC-202™ instrument and flow cytometric analyses using Panel 1 (see Note 14). (j) Start culture of NK cells in the same Tubing Set. (k) Activation and cultivation in NK MACS® GMP Basal Medium and NK MACS® GMP Supplement with IL-2 (500 IU/mL), IL-15 (140 U/mL), and IL-1β (1500 U/ mL) (see Notes 17 and 22). 2. Day 2—Transduction with viral vector encoding for a CAR. (a) The transduction step is performed automatically by spinoculation on the CliniMACS Prodigy® instrument using the process software PD-56 Engineering. (b) Use a Transfer Bag with NK medium and connect a Sampling Site Coupler. Use Sample Site Coupler for the application of the viral vector (thawed on ice) and MACS® GMP Vectofusin®-1 (2.5 μg/mL) to the Transfer Bag (see Note 18). (c) Seal the prepared Viral Vector Bag to the Tubing Set 520. 3. Day 3—Harvest and transfer to G-Rex® 100M. (a) After an automated Culture Wash by the instrument 24 h after transduction, the (CAR) NK cells are harvested in the Target Cell Bag 1 (without filter) of the CliniMACS Prodigy® Tubing Set 520 with a total volume of 100 mL (see Note 19). (b) Weld the Target Cell Bag 1 off the Tubing Set 520. (c) QC: Take 200 μL for QC testing (cell count and viability) using the NucleoCounter® NC-202™ instrument (see Notes 3 and 20).
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(d) Seed a minimum of 0.5 × 106 cells/cm2 in the G-Rex® 100M with 1 l NK MACS® GMP Basal Medium and 1% NK MACS® GMP Supplement with IL-2 (500 IU/mL) and IL-15 (140 U/mL) (see Note 21). (e) Deinstall Tubing Set 520 (see Note 13). 4. Day 7. (a) Add IL-2 (500 IU/mL) + IL-15 (140 U/mL) to the NK cell culture. 5. Day 10. (a) Gently aspirate 750 mL of cell culture medium from the G-Rex® 100M bioreactor. (b) Swirl and resuspend cells in G-Rex® 100M bioreactor. (c) Take 200 μL of cell suspension for QC testing (cell count and viability) using the NucleoCounter® NC-202™ instrument (see Note 22). (d) Seed 2,5 × 108 cells in a new G-Rex® 100M bioreactor and add 1 l NK MACS® GMP Basal medium + 1% NK MACS® GMP Supplement + 5% human AB serum + IL-2 (500 IU/mL) + IL-15 (140 U/mL) or seed 1,25 × 109 cells in a G-Rex® 500M bioreactor with 5 l NK MACS® GMP Basal medium + 1% NK MACS® GMP Supplement + 5% human AB serum + IL-2 (500 IU/mL) + IL-15 (140 U/mL). (e) Additional QC: (i) Calcein release assays are used for functionality testing of the NK cells and CAR-NK cells using K562 and CAR-specific target cells, respectively. (ii) Perform flow cytometric analyses for purity (Panel 1) and surface receptor expression of the expanded CAR-NK cells (Panels 2 and 3) (see Subheading 2.3). See how staining is performed in Subheading 3.1, step 11. 6. Day 14. (a) Add fresh interleukins to the NK cell cultures, IL-2 (500 IU/mL) + IL-15 (140 U/mL). 7. Day 17. (a) Remove 0.75 l or 3.75 l cell culture medium from the G-Rex® 100M bioreactor or G-Rex® 500M bioreactor, respectively. (b) Swirl and resuspend cells in G-Rex® 100M/500M. (c) Take 200 μL of cell suspension for QC testing (cell count and viability) using the NucleoCounter® NC-202™ instrument.
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(d) Use appropriate bioreactor and seed NK cells with adjusted cell numbers/cm2 and recommended volumes of NK cell medium as described in step 4 of Day 10 (see Note 23). 8. Day 21. (a) Remove 0.75 l or 3.75 l cell culture medium from the G-Rex® 100M bioreactor or G-Rex® 500M bioreactor, respectively. (b) Swirl and resuspend cells in G-Rex® 100M/500M. (c) Take 200 μL of cell suspension for QC testing (cell count and viability) using the NucleoCounter® NC-202™ instrument. (d) Additional QC: (i) Calcein release assays are used for functionality testing of the NK cells and CAR-NK cells using K562 and CAR-specific target cells, respectively. (ii) Perform flow cytometric analyses for purity (Panel 1) and surface receptor expression of the expanded CAR-NK cells (Panels 2 and 3) (see Subheading 2.3).
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Notes 1. When working with our established blood bank, we expect 6 × 108–1 × 109 PBMCs from one Buffy Coat with a proportion of 2–20% of NK cells (dependent on the donor). After isolation we expect 15–80 × 106 NK cells from one Buffy Coat. 2. NK cells can also be isolated using the human NK Cell Isolation Kit (Miltenyi Biotec) following the manufacturer’s instruction. 3. Use appropriate cell suspension dilution for optimal detection range of the NucleoCounter® NC-202™ instrument (e.g., 20 μL NK cell suspension + 180 μL EasySep buffer/NK cell medium). 4. When using isolated, non-activated NK cells we recommend seeding at least 2.5 × 106 cells/cm2 (5 × 106 cells/G-Rex® 24 Well). 5. Acquire at least 20,000 CD45+ events in Panel 1 and 10,000 CD56+ events Panels 2 and 3. 6. Isolated NK cells should present as CD56+, CD14-, CD19-, and CD3-. Successful isolation of NK cells from PBMCs is indicated by absence of CD3+ events in the flow cytometry analysis. 7. It is expected that round luminous clusters are visible. A slight reduction in viability and a minor loss of cells may also be
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observed. These outcomes can vary considerably between donors and may also differ between vectors. 8. Always use the total volume of culture in the well (8 mL) to calculate the units of interleukins to be used. 9. The cells should have expanded, and viability should have recovered. 10. We recommend using 2 × 105 cells per sample for both an unstained control and a CAR check sample. 11. Historically, chromium (or calcein) release assays were the gold standard when testing the activity or functionality of immune cells (e.g., NK or T cells, macrophages, monocytes, etc.). If clinical development is planned in addition to R&D activity testing, we recommend an early switch and the use of appropriate flow cytometric analyses. 12. Flow cytometry panels and devices can differ. Established flow cytometry protocols and trained staff are required to obtain comparable and reliable flow datasets. Determining the CD3 status of the blood product is mandatory to start the process on the CliniMACS Prodigy® instrument. 13. Handling information of the relevant tubing sets, devices, and processes are described in brief here [9]. 14. Use appropriate cell suspension dilution for optimal detection range of the NucleoCounter® NC-202™ instrument (NTCB: 20 μL NK cell suspension + 180 μL buffer, TCB: 10 μL NK cell suspension + 90 μL buffer). 15. At the end of the depletion process, little or no CD3+ events should be detected by flow cytometry. CD14+ events can be expected immediately after CD3 depletion, but should not be detected in the final product. 16. To continue with the process on the CliniMACS Prodigy® instrument and starting the PD-56 Engineering, transfer of CD56 measurement data is required. These datasets are obtained in Subheading 3.2, step 1(a) simultaneously with datasets of CD3 expression. 17. IL-1β is only added on Day 0 of the manufacturing workflow. Check NK cell culture daily between Day 1 and Day 3 using the 560 camera and microscope of the CliniMACS Prodigy 561® instrument. 18. Viral vector activity has to be determined previously, i.e., by functional titration on NK cells. 19. Use NK MACS® GMP Basal Medium and NK MACS® GMP Supplement with IL-2 (500 IU/mL) and IL-15 (140 U/mL) for harvesting to reduce handling stress of the cells during transfer.
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20. A significant cell loss between 30% and 60% can be expected. 21. The process is flexible and can be performed in both R&D and GMP environments. If the CAR-NK cell process is developed for GMP compliance, a closed setup is required. This can be achieved by using a GatheRex™ liquid handling/cell harvest pump from Wilson Wolf, the CliniMACS Prodigy®, or other liquid pumping instruments that have to be included into the process flow. 22. Cell counts and viability should have recovered. Maximal cell counts of 35 × 106 cells/cm2 (G-Rex® membrane) should not be exceeded. 23. Note: To study expansion dynamics, it is plausible to use extrapolated cell numbers to reduce overall material costs. More relevant information—good to know: 4.1 Expected NK Cell Numbers After Isolation from Peripheral Blood and Alternative Sources of NK Cells
NK cells are predominantly sourced from blood. Both peripheral blood (PB) of healthy donors and umbilical cord blood (UCB) are suitable sources due to their availability and ease of handling. The outlined protocols in this chapter are optimized for PB but are also applicable to UCB-derived NK cells. PB contains around 5–15% of NK cells, and several studies showed similar numbers of NK cells in UCB. The quality of the initial blood donation largely determines the quality of the resulting NK cell product, with hemolysis being used as a predictive parameter to assess the quality of the starting material. However, there are major phenotypic and functional differences, primarily in terms of responsiveness or maturation status [10, 11]. While fresh NK cells from UCB show lower cytotoxic potential than their PB counterparts from healthy adults, proper activation of these UCB-derived NK cells, e.g., using K562 feeder cells engineered with membrane-anchored IL-21 and 4-1BBL, greatly enhances their capabilities to recognize and kill malignant cells. Still, receptor expression, homing behavior, and their capacity to produce cytokines and chemokines diverge, and it has yet to be discovered how these differences between UCB- and PB-derived CAR-NK cells translate to clinical outcome. Eventually, the suitability of the NK cell source may depend on the specific biology of the patients’ tumor as well. Next to PB and UCB, CAR-NK cells can also be generated by differentiating NK cells from hematopoietic stem cells (HSCs) and induced pluripotent stem cells (iPSC). Both the differentiation of NK cells from induced iPSCs [12, 13] or HSCs [14, 15] involve specialized protocols that span several weeks. These methods offer an almost limitless supply of NK cells with a controllable phenotype and quality. Additionally, genetic engineering can be conducted before differentiation, allowing for the selection of precursors with precise genetic modifications for further development.
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Lastly, NK cell lines, like NK-92, can be CAR-modified and easily expanded. Due to their highly cytotoxic nature, this cell line is being used also in clinical studies. For clinical-scale expansion of NK-92, batch cultures can be used to grow CAR-NK-92 in large quantities in GMP-compliant X-Vivo 10 medium [16]. However, to restrict the lifetime and proliferation rates of these malignant cells within the patient, irradiation must be performed prior to application to patients [17]. As a result, the in vivo persistence of NK-92 cells is limited and may not be adequate to achieve a curative effect in many cancer scenarios. 4.2 Alternative Engineering Approaches for NK Cells
This chapter outlined the generation of CAR-NK cells based on lentiviral or γ-retroviral vectors, a technology that all currently available CAR-T cell products are based on. We show how to generate CAR-NK cells based on viral particles pseudotyped with BaEV, which was, according to recent studies [18], highly effective for the generation of CAR-NK cells, or, alternatively, using RD114-TR [8]. Many preclinical as well as the clinical study performed previously used RD114-TR-derived viral particles [19, 20], which are also used for some T cell studies. Therefore, RD114-TR is more established and widely available from commercial producers of lentiviral products; however, in our hands and according to literature [18], BaEV shows superior transduction efficacies in NK cell generation. However, newly developed technologies are also emerging in the field of NK cell technologies, such as nonviral vectors for CAR transfer. Nonviral vectors include plasmid DNA, mRNA, transposon-based systems (like Sleeping Beauty and PiggyBac), and precision genome-editing tools, e.g., zinc finger nucleases (ZFN), transcription activator-like effector nucleases (TALEN), CRISPR-Cas, and base editing systems [21]. These methods avoid the use of viruses, reducing safety concerns, manufacturing complexity, and cost [22, 23]. Plasmids and mRNA offer transient expression, while genome-editing tools can integrate the CAR gene into the genome for stable expression. Nonviral vectors can be delivered to primary NK cells using several methods [24]. Most commonly used is electroporation, a technique that applies electrical pulses to temporarily open cell membranes, allowing plasmid DNA or mRNA to enter [25]. It is effective but can cause cell stress or reduce viability. More recently developed are lipid nanoparticles encapsulating genetic material (like mRNA or DNA) to fuse with the cell membrane, enabling the material to enter the cell with less toxicity compared to electroporation [26]. However, the transfer efficacy of LNPs, especially for genome-editing tools, requires optimization and is subject of current research.
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4.3 Freezing and Storage of CAR-NK Cells
For an “off-the-shelf” product, a long shelf-life and recovery from a freeze/thaw cycle is critical. NK cells are sensitive to freezing, and many efforts to improve cell survival and functionality in GMP-compliant protocols have been undertaken [27]. To preserve CAR-NK cells, we utilize a controlled rate freezing protocol based on CryoStor® CS10 Freezing medium, which results in decent post-thaw CAR-NK cell recovery and function. In small-scale lab productions, CAR-NK cells are frozen using a density of 10 Mio/mL per vial. Importantly, recent studies are investigating the mechanisms behind sensitivity to freezing conditions to identify new strategies for improvement, such as stimulating NK cells with IL-18 before freezing [28]. This cytokine reduces intracellular granzyme B levels, preventing its leakage into the cytosol and subsequent apoptosis after freeze-thaw cycles. Such cell-type-specific adaptations in freezing protocols could enhance the post-thaw quality of the product and ensure cellular functionality in vivo, even after prolonged storage.
4.4 Quality Control and Characterization
In terms of NK cell assessment for preclinical purposes, flow cytometry panels give important information on purity, transduction rates, and NK cell activation status. Such parameters were outlined above. The release parameters for clinical-grade cell therapy products like CAR-T cells are crucial to ensuring their safety, purity, potency, identity, and stability and can be translated to CAR-NK cells. These parameters are evaluated through a series of tests and assessments before the product can be released for clinical use. Here are the key release parameters and considerations adjusted from CAR-T cells [29] according to standardized expectations from the FDA. These include: Safety Tests for mycoplasma, sterility, endotoxin levels, residual viral agents, and cell viability are conducted to ensure the safety of the CAR-NK cell product. Purity Purity assessments involve measuring NK cell purity, viability, transduction efficiency, and the presence of any contaminating tumor cells or residual reagents. Potency Potency is evaluated through transduction efficacy, CAR expression, cytotoxicity, and cytokine production (e.g., IFN-γ) upon antigen stimulation. Identity Identity tests confirm CAR expression, visual appearance, clarity, dose, and viability of the cell product. Stability Stability assessments focus on the formulation, shipping, and storage conditions to ensure the product remains effective until it is administered.
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References 1. Vivier E, Tomasello E, Baratin M et al (2008) Functions of natural killer cells. Nat Immunol 9:503–510. https://doi.org/10.1038/ni1582 2. Vivier E, Rebuffet L, Narni-Mancinelli E et al (2024) Natural killer cell therapies. Nature 626:727–736. https://doi.org/10.1038/ s41586-023-06945-1 3. Wolf NK, Kissiov DU, Raulet DH (2023) Roles of natural killer cells in immunity to cancer, and applications to immunotherapy. Nat Rev Immunol 23:90–105. https://doi.org/ 10.1038/s41577-022-00732-1 4. Laskowski TJ, Biederst€adt A, Rezvani K (2022) Natural killer cells in antitumour adoptive cell immunotherapy. Nat Rev Cancer 22 (10):557–575. https://doi.org/10.1038/ s41568-022-00491-0. Epub 2022 Jul 25. PMID: 35879429; PMCID: PMC9309992 5. Ruppel KE, Fricke S, Ko¨hl U, Schmiedel D (2022) Taking lessons from CAR-T cells and going beyond: tailoring design and signaling for CAR-NK cells in cancer therapy. Front Immunol 13:822298 6. Marin D, Li Y, Basar R et al (2024) Safety, efficacy and determinants of response of allogeneic CD19-specific CAR-NK cells in CD19+ B cell tumors: a phase 1/2 trial. Nat Med 30:772–784. https://doi.org/10.1038/ s41591-023-02785-8 7. Moscarelli J, Zahavi D, Maynard R, Weiner LM (2022) The next generation of cellular immunotherapy: chimeric antigen receptor-natural killer cells. Transplant Cell Ther 28:650–656 8. Renner A, Stahringer A, Ruppel KE, Fricke S, Koehl U, Schmiedel D (2024) Development of KoRV-pseudotyped lentiviral vectors for efficient gene transfer into freshly isolated immune cells. Gene Ther 31:378–390 9. Miltenyi biotec manufacturing geneengineered NK cells via viral transduction. https://static.miltenyibiotec.com/asset/150 655405641/document_rtt0 9dgqu55tfemj5khnscqh4b?content-disposi tion=inline 10. Karadimitris A (2020) Cord blood CAR-NK cells: favorable initial efficacy and toxicity but durability of clinical responses not yet clear. Cancer Cell 37:426–427 11. Zhao X, Cai L, Hu Y, Wang H (2020) Cordblood natural killer cell-based immunotherapy for cancer. Front Immunol 11:584099 12. Lupo KB, Moon J-I, Chambers AM, Matosevic S (2021) Differentiation of natural killer cells from induced pluripotent stem cells under
defined, serum- and feeder-free conditions. Cytotherapy 23:939–952 13. Karagiannis P, Kim S-I (2021) iPSC-derived natural killer cells for cancer immunotherapy. Mol Cells 44:541–548 14. Oberoi P, Kamenjarin K, Ossa JFV, Uherek B, Bo¨nig H, Wels WS (2020) Directed differentiation of mobilized hematopoietic stem and progenitor cells into functional NK cells with enhanced antitumor activity. Cells 9(4):811 15. Dezell SA, Ahn Y-O, Spanholtz J, Wang H, Weeres M, Jackson S, Cooley S, Dolstra H, Miller JS, Verneris MR (2012) Natural killer cell differentiation from hematopoietic stem cells: a comparative analysis of heparin- and stromal cell-supported methods. Biol Blood Marrow Transplant 18:536–545 16. Nowakowska P, Romanski A, Miller N, Odendahl M, Bonig H, Zhang C, Seifried E, Wels WS, Tonn T (2018) Clinical grade manufacturing of genetically modified, CAR-expressing NK-92 cells for the treatment of ErbB2-positive malignancies. Cancer Immunol Immunother 67:25–38 17. Walcher L, Kistenmacher A-K, Sommer C, Bo¨hlen S, Ziemann C, Dehmel S, Braun A, Tretbar US, Klo¨ß S, Schambach A, Morgan M, Lo¨ffler D, K€ampf C, Blumert C, Reiche K, Beckmann J, Ko¨nig U, Standfest B, Thoma M, Makert GR, Ulbert S, KossatzBo¨hlert U, Ko¨hl U, Du¨nkel A, Fricke S (2021) Low energy electron irradiation is a potent alternative to gamma irradiation for the inactivation of (CAR-)NK-92 cells in ATMP manufacturing. Front Immunol 12: 684052 18. Colamartino ABL, Lemieux W, Bifsha P, Nicoletti S, Chakravarti N, Sanz J, Rome´ro H, Selleri S, Be´land K, Guiot M, Tremblay-Laganie`re C, Dicaire R, Barreiro L, Lee DA, Verhoeyen E, Haddad E (2019) Efficient and robust NK-cell transduction with baboon envelope pseudotyped lentivector. Front Immunol 10:2873 19. Mu¨ller S, Bexte T, Gebel V, Kalensee F, Stolzenberg E, Hartmann J, Koehl U, Schambach A, Wels WS, Modlich U, Ullrich E (2019) High cytotoxic efficiency of lentivirally and alpharetrovirally engineered CD19-specific chimeric antigen receptor natural killer cells against acute lymphoblastic leukemia. Front Immunol 10:3123 20. Suerth JD, Morgan MA, Kloess S, Heckl D, Neudo¨rfl C, Falk CS, Koehl U, Schambach A (2016) Efficient generation of gene-modified
CAR-NK Cell Development and Manufacturing human natural killer cells via alpharetroviral vectors. J Mol Med (Berl) 94:83–93 21. Tretbar US, Rurik JG, Rustad EH et al (2024) Non-viral vectors for chimeric antigen receptor immunotherapy. Nat Rev Methods Primers 4:74. https://doi.org/10.1038/s43586-02400348-w 22. Wagner DL, Koehl U, Chmielewski M, Scheid C, Stripecke R (2022) Review: sustainable clinical development of CAR-T cells – switching from viral transduction towards CRISPR-Cas gene editing. Front Immunol 13:1–13 23. Sheridan C (2023) Why gene therapies must go virus-free. Nat Biotechnol 41:737–739 24. Wang C, Pan C, Yong H, Wang F, Bo T, Zhao Y, Ma B, He W, Li M (2023) Emerging non-viral vectors for gene delivery. J Nanobiotechnol 21:272 25. Muralidharan A, Boukany PE (2023) Electrotransfer for nucleic acid and protein delivery. Trends Biotechnol 42:780–798
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Chapter 5 Addressing the Blood-Brain Barrier: Overcoming Glioblastoma Drug Delivery € Langel Ly Porosk and Ulo Abstract The blood-brain barrier (BBB) is a natural protective semipermeable membrane that shields the central nervous system (CNS) from harmful substances in the bloodstream. While this barrier is essential for protecting the brain, it also presents significant challenges for therapy in treating brain tumor such as glioblastoma multiforme (GBM). This limitation is particularly relevant for most chemical drugs and biopharmaceuticals. They struggle to penetrate the BBB, making efficient delivery to the brain highly sought after. Inadequate drug delivery to the brain can reduce therapeutic effectiveness and lead to increased side effects due to accumulation in other organs and tissues, especially after systemic administration. Overcoming the challenges related to drug delivery would open new treatment options for GBM and other CNS diseases. Nanoparticle-based approaches are highly promising, potentially offering a safer and less invasive alternative to current treatment options. This review provides a summary of the structure of the BBB and the main cell types that contribute to its function, with a specific focus on the barrier’s relevance in GBM. It also examines strategies for crossing the BBB, current treatment options, and prospects for GBM therapies. It emphasizes the significance of efficient delivery across the BBB and the potential of nanoparticles in advancing improved GBM treatments. Additionally, it introduces versatile nanoparticle-based drug delivery systems, including organic, inorganic, and biologics-derived approaches. Key words Nanoparticles, Glioblastoma, Therapeutics, Delivery
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Introduction: Healthy Brain and Glioblastoma The blood-brain barrier (BBB) is a natural semipermeable protective membrane, shielding the central nervous system (CNS) from harmful substances in the bloodstream while permitting essential nutrients to cross. The history behind its discovery and first studies is reviewed more thoroughly in [1, 2]. The BBB is an anatomical and physiological barrier that separates the circulating blood from the extracellular fluid of the CNS. Despite significant advancements in genomics and neurobiology, diseases affecting the CNS, such as brain tumors, continue to
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present a significant global healthcare challenge [3]. This is primarily attributed to the complexity inherent within the CNS [4], rendering it difficult to replicate through simplified experimental models to find better therapeutic approaches. Additionally, understanding the fundamental etiologies of various CNS diseases and treating conditions related to these diseases without adversely impacting other physiological aspects remains a significant challenge. The delivery of therapeutics to the brain is hindered by different barriers, such as intracranial tissue pressure, difficulty penetrating the skull, and inadequate accumulation in the target site, among many others. Delivering therapeutic substances to the brain encounters distinct obstacles not found in other bodily tissues. For example, the skull provides a rigid protective barrier externally, limiting the selection of possible noninvasive approaches. Additionally, the brain is enveloped by three layers of meningeal membranes. These membranes, collectively known as the meninges, regulate intracranial pressure by confining tissue volume and the cerebrospinal fluid (CSF) circulating within them. The brain is further shielded by dynamic interfaces—the already mentioned BBB, the blood-CSF barrier (BCB), and the arachnoid barrier [5–7] that selectively control the passage of substances. The BCB is located in the choroid plexus, a structure in the brain’s ventricles responsible for producing CSF. It regulates the transfer of substances between the blood and CSF, maintaining strict control over the composition of the CSF. The arachnoid barrier consists of the arachnoid membrane and the underlying subarachnoid space filled with CSF, providing physical protection and controlling the exchange of substances between the blood and CSF. The BBB primarily consists of endothelial cells that line the blood vessels and are joined together via tight junctions (TJ). It separates the circulating blood from the extracellular fluid in the CNS. The BBB plays a key role in regulating the passage of substances from the blood into the brain, guarding it from toxins, pathogens, and fluctuations in the blood that could disrupt normal brain function. While ensuring the exchange of essential nutrients and metabolic waste, the BBB is a demanding obstacle for therapeutics [8], rejecting about 98% of small molecule substances and nearly 100% of large molecules [2]. This review explores the nanoparticle- and nanotechnologybased approaches aimed at overcoming the limitations due to the BBB to enhance the potential of various treatments for glioblastoma (GBM). In the following chapter, we will provide a brief overview of the constituents of the blood-brain barrier, the transport mechanisms involved in crossing it, as well as GBM and the cells associated with its progression.
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Fig. 1 The BBB structural components, transport routes, and brain in glioblastoma. (a) The BBB’s main structural components include pericytes, endothelial cells, tight junctions between the endothelial cells, basement membrane, and astrocyte end feet. (b) The main routes used to get substances and molecules across the BBB. (c) The glioblastoma alters several functions in the brain BBB. The tumor invades the soft brain tissues collectively, using diffuse infiltration, perineuronal satellites, or metastasizes upon co-option and invasion of BBB blood vessels. During vessel co-option and invasion, the intactness of the BBB is reduced. (The figure is compiled using Biorender.com and modified versions of figures in [9–11]) 1.1 Components of the BBB
The BBB consists of different cellular and structural components (Fig. 1a), which include endothelial cells, tight junctions (TJ), basement membrane, pericytes, and astrocytic end-feet [12]. The endothelial cells, one of the main components of BBB, tightly line the interior surface of the brain’s blood vessels. Unlike endothelial cells in other parts of the body, those within BBB have TJ that restricts the passage of substances and allows only selective permeability. The BBB endothelial cells have specific transporters for regulating the inflow and outflow of substances (Fig. 1b), and they exhibit minimal pinocytic vesicular transport [6]. Endothelial cells of the BBB have a higher mitochondrial volume [13], a net negative membrane charge, and a low amount of leukocyte adhesion molecules, which limits the entry of immune cells. A thin, dense layer of extracellular matrix, base membrane (basal lamina), surrounds the endothelial cells, providing structural support and anchoring the cells to nearby tissues. Embedded within the basal membrane, pericytes help maintain the integrity of the
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BBB, regulate blood flow, and are involved in the formation of TJ. They contribute to the physical stability of the blood vessels. The astrocytes are glial cells in the brain with projections called end-feet that almost completely surround the blood vessels of the BBB. They play a role in maintaining homeostasis and BBB integrity and function. They release factors that promote TJ formation, help to mediate the transport of nutrients and waste products over BBB, and facilitate the communication between neurons and blood vessels. The microglia are the resident immune cells of the CNS and play a role in maintaining the health of the BBB. They can influence the barrier’s properties by releasing various cytokines and other molecules. The role of these components is thoroughly reviewed in [6, 14]. Together, they regulate what substances can enter the brain from the bloodstream, thus protecting and maintaining its internal environment. 1.2 Transport Mechanism Across the BBB
The transport mechanisms in the BBB include paracellular and transcellular pathways [6, 11]. Paracellular transport is highly restricted due to the TJs that connect the endothelial cells. They selectively allow certain small, water-soluble substances to pass. The main pathways crossing the endothelial cells of the BBB [6, 11] (Fig. 1b) include lipophilic (passive) diffusion, where small, lipophilic molecules can pass through the BBB by dissolving in the cell membrane of the endothelial cells. This includes gases like oxygen, carbon dioxide, and other small molecules soluble in the cell membrane. Solute carrier protein influx is a form of facilitated transport with the help of specific carrier proteins. This mechanism does not require energy and is used for molecules like glucose and amino acids, which are critical for brain function. With the help of ATP, certain amino acids, peptides, and vitamins present in blood at low concentrations are actively transported to the brain. Transcytosis and endocytosis involve engulfing materials by the cell membrane to form vesicles that transport the materials across the cell. Meanwhile, unusually low levels of vesicle trafficking limit transcellular transport or transcytosis [15]. Transcytosis transports large molecules like insulin and transferrin across the BBB by moving vesicles across endothelial cells. This passing route includes absorptive transcytosis [16, 17], receptor- or cell-mediated transcytosis, and transport protein-meditated transcytosis. This mechanism is used for various molecules, including larger molecules that cannot pass through other routes due to their size or other characteristics. Additionally to getting substances into the brain, efflux pumps are used to transport them out of the brain. For example, P-glycoprotein exports a wide variety of substrates to limit exposure of the vulnerable brain environment to waste buildup and neurotoxic compounds [18]. A more thorough review of this can be found in [14].
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Several receptors are involved in different transport routes [6, 19], including the transferrin receptor, insulin receptor, leptin receptor [6], IGF1 receptor, LDL receptor, LDLR-related proteins 1 and 8, and melanotransferrin. Insulin receptors transport insulin across the BBB, managing glucose levels and influencing cognitive functions. Transferrin receptors are involved in iron transport, a vital element for biochemical processes, including oxygen transport, DNA synthesis, and electron transport in mitochondria. Leptin receptors transport the hormone responsible for regulating energy balance, signaling satiety, and modulating energy expenditure. LDL receptors transport cholesterol and other lipids essential for cell membrane synthesis and repair, synapse formation, maintenance of dendric spine morphology, and neurotransmitter receptor functions. These receptors highlight the sophistication of the BBB and its selective permeability, ensuring that essential molecules can enter the brain while maintaining a protective barrier against harmful substances. By modifying the carriers of therapeutics, the transcytosis can be activated. For example, fucoidan-based nanocarriers induced active crossing of the BBB via caveolin-1-dependent transcytosis [20]. 1.3
Glioblastoma
Glioblastoma, also referred to as glioblastoma multiforme (GBM), is an aggressive and the most common type of primary brain cancer [21]. Brain tumors continue to be the most frequently diagnosed solid tumors in children and adolescents, as well as the primary cause of cancer-related mortality among young adults [22]. Symptoms of glioblastoma can vary but often include headaches, seizures, memory problems, personality changes, and neurological deficits, such as weakness or problems with speech and vision, depending on the tumor’s location in the brain. Due to glioblastoma’s aggressive nature, the prognosis for this condition is often poor, with median survival rates ranging from 12 to 18 months after diagnosis. However, some patients may live longer with treatment. The exact cause of glioblastoma is unknown, but several genetic mutations have been marked to play a crucial role [23] in both occurrence and prognosis. The role of viral infections has also been revised in [24]. The World Health Organization (WHO) classifies glioblastomas as grade IV tumors, indicating their high degree of malignancy due to their rapid growth and ability to spread quickly within the soft brain tissue. Glioblastomas belong to a group of brain tumors known as gliomas, which start in the star-shaped glial cells called astrocytes—cells in the brain and spinal cord that support and protect neurons. However, according to gene profile analysis and genetic modeling of GBM in mice, there is evidence that GBM is derived from neural stem cells, neural stem cell-derived astrocytes, and oligodendrocyte precursor cells [25].
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1.3.1
GBM Variants
Glioblastoma mainly affects the brain and spinal cord. Depending on the tumor’s location, it can cause neurological deficits of different degrees. Additionally, it releases factors into the surrounding environment, promoting angiogenesis and progression while affecting normal tissue functions. GBM can be characterized into several types based on genetic, molecular, and histological factors. More suitable treatment options and accurate patient prognosis can be given based on the characteristics and classification of the patient’s GBM variant. GBM are categorized based on their development to primary and secondary GB [26]. The primary GB develops rapidly without a preceding lower-grade lesion, appearing spontaneously. It is the most common type [27], usually occurring in older patients, and is characterized by genetic alterations such as EGFR amplification, PTEN mutations, and loss of heterozygosity on chromosome 10. Secondary GB develops more slowly from lower-grade astrocytomas, progressing to glioblastoma. It occurs in younger patients and is characterized by mutations in the IDH1 [28] or IDH2 genes, p53 mutations, and ATRX loss [29]. Secondly, GBM has several main variants based on genetic and molecular characteristics. Indications from The Cancer Genome Atlas (TCGA) project have pointed to distinct genetic and molecular signatures within glioblastoma tumors, which can further classify the disease into subtypes. The most common or classical variants include glioblastomas characterized by epidermal growth factor receptor (EGFR) [30] amplification, PTEN mutations, and loss of chromosome 10q [31]. The other variants are associated with mutations in the neurofibromatosis type 1 (NF1) gene [32] features of mesenchymal differentiation. The subtypes such as mutations in isocitrate dehydrogenase (IDH) [33] genes (more common in secondary GB) and platelet-derived growth factor receptor A (PDGFRA) [34, 35] amplification and the ones characterised by the expression of neuronal markers [36], influence the tomour´s behaviour and response to treatment. In the case of IDH enzyme, in wild-type GB, the majority of cases are characterized by this, which is a more aggressive type with a poorer prognosis. In IDH, mutant GB, which is less common and is associated with secondary GB, generally indicates a better response to treatment and a more favorable prognosis. The O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation variants are more likely to respond to alkylating agent chemotherapy, such as temozolomide, due to reduced repair of chemotherapyinduced DNA damage [25, 37–39].
1.3.2
Cells in GBM
The cell types involved in the GBM progression mainly include glial cells and glioblastoma stem-like cells. The glial cells are vital for maintaining homeostasis, forming myelin, and providing support and protection for neurons in the CNS. In GBM, these cells mutate and proliferate uncontrollably, forming the tumor. Glial cells, particularly astrocytes, can dramatically influence glioblastoma’s
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progression, invasiveness, and therapeutic resistance by supporting tumor-favorable microenvironment, angiogenesis, and immune evasion pathways. Glioblastoma growth requires angiogenesis [40] to supply nutrients to rapidly dividing tumor cells. Astrocytes and other glial cells can secrete vascular endothelial growth factor (VEGF) and other pro-angiogenic factors, contributing to the formation of new blood vessels within the tumor and supporting its growth and spread. Targeting the interactions between glioblastoma cells and glial cells is an area of active research and holds the potential for developing new therapeutic strategies against this aggressive brain cancer [40–42]. At the same time, as the tumor grows and infiltrates the brain tissue, it disrupts the normal functioning of the surrounding neuronal and glial cells. For example, due to growth, the GBM causes compression and displacement of normal cells—solid stress [43], impairing the communication between neurons and inhibiting the ability of glial cells to perform their normal functions. In the affected brain regions, loss of function can be detected, as synaptic connections and neurotransmitter release needed for neurons to communicate are interrupted. Depending on the GBM location, the BBB may also be disrupted at the early stages of tumor development by affecting the TJ and the selective permeability of the BBB. The presence of a tumor can change the chemical balance in its vicinity; trigger an inflammatory response in the brain, leading to swelling (edema) [43]; and increase intracranial pressure. The inflammatory response attracts microglia, and their presence can lead to chronic inflammation, further damaging healthy brain tissue. The inflammatory cytokines can exacerbate neuronal damage and contribute to the neurological symptoms seen in glioblastoma patients. GB can manipulate microglial cells, the brain’s resident immune cells, to support tumor growth [44]. Modified microglia can suppress immune responses against the tumor, facilitate tumor proliferation, and promote the tumor-supportive environment through the secretion of immunosuppressive factors and cytokines [45]. Tumor-associated astrocytes, for instance, can secrete factors that promote tumor cell survival and invasion [41]. These cells can also alter the extracellular matrix, providing pathways for tumor invasion [46]. GB tumors require oxygen and nutrients to sustain their rapid growth, often diverting these critical resources away from normal brain cells, including neurons and glial cells [47]. This can starve neurons of the energy they need to function correctly and impede the ability of healthy glial cells to support neuronal functions, maintain the extracellular environment, and repair damage. Cancer cells, including glioblastoma, can release toxic substances as a byproduct of their metabolism. These harmful substances can
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damage or kill neurons and other brain cells, further deteriorating brain function or affecting the signaling between glial cells and neurons. The role of neurons in GBM progression is still controversial, with studies suggesting that neuronal activity can influence the growth and spread of GB cells [48–50]. Neurons do not directly turn into cancer cells in glioblastoma, as the cancer originates, most probably, from the glial cells or their precursors. Neurons that are actively firing can release factors that make the brain environment more favorable for glioblastoma cell growth and invasion. This interaction may explain why glioblastoma tumors often form around brain areas with high neuronal activity. Neurons can impact glioblastoma progression by releasing neurotransmitters, especially glutamate, that can promote tumor growth and aggressiveness by activating signaling pathways that encourage glioblastoma cells’ proliferation, survival, and migration. At the same time, the presence of the tumor can lead to an abnormal increase in glutamate levels, which is neurotoxic at high concentrations and can lead to neuronal death. Evidence suggests that glioblastoma cells can form synapse-like connections with neurons [51]. These pseudo-synaptic connections allow the glioblastoma cells to hijack the neuronal signaling mechanisms, promoting tumor growth and survival. Integrating into the neuronal network could facilitate the spread of glioblastoma cells along existing neural pathways. GBM can impact or originate from the neural stem cell population [25, 52]. This co-option aids in the tumor’s infiltration and spread throughout the brain [47], complicating surgical attempts to eradicate the tumor and contributing to the high recurrence rate. The glioma stem-like cells [53] can hijack astrocytic functions to degrade the extracellular matrix [54], facilitating tumor spread. The impact of GB stem cells emphasizes the complexity of GBM and the challenges it poses for treatment and can also contribute to the resistance to standard therapies, including chemotherapy and radiation [55, 56]. For instance, the protective barriers formed by glial cells can limit the delivery of therapeutic agents to the tumor site.
2
Current Challenges and Treatment Options The challenges in treatment efforts stem from inherent biological glioblastoma characteristics and current medical technology limitations. One of the most significant obstacles to GBM treatment is the difficulty of efficiently delivering therapeutic agents across the BBB. This section summarizes the challenges of GBM treatment and current treatment options.
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Main Challenges
2.2 Current Treatment and Perspectives
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Glioblastomas are highly heterogeneous [45], comprising a diverse mix of cell types. This cellular diversity within the same tumor can lead to variable responses to therapy, where some cells might be killed, but others, potentially more aggressive, may survive and continue to proliferate. This heterogeneity poses a significant challenge in developing universally effective treatments across all tumor cells in GBM. The GBM tumor microenvironment, which includes the blood vessels, immune cells, and other components surrounding the tumor cells, can influence the growth and spread of the tumor. Glioblastomas are known for their aggressive invasion into surrounding brain tissue, making them difficult to remove through surgery. This invasion also contributes to the tumor’s resilience against treatments due to the protective effects of the microenvironment. The cells often develop resistance to the standard treatments—chemotherapeutic agents and radiation therapy. This resistance can emerge through various mechanisms, such as the upregulation of drug efflux pumps, DNA repair enhancement, and alterations in cell survival pathways [57]. Additionally, GBM features a high rate of recurrence. Residual cancer cells often remain even after seemingly successful treatment that removes the bulk of the tumor. Identifying actionable targets for molecular and immunotherapy in GBM has been challenging. While progress has been made in understanding the genetic and molecular landscape, translating this knowledge into effective targeted therapies has been limited. Addressing these challenges requires a multifaceted approach that seeks to improve drug delivery across the BBB and aims to develop novel therapeutic strategies to overcome the tumor’s heterogeneity, invasive nature, and resistance mechanisms. The approaches for treating GBM can be broadly categorized as invasive and noninvasive [58]. The current management (Fig. 2a) of glioblastoma predominantly involves a combination of surgery, radiotherapy, and chemotherapy. Surgical resection is the cornerstone of therapeutic intervention to achieve maximal tumor removal. However, the complete resection of tumor cells presents a challenge, predominantly due to the infiltration of the cancer cells into adjacent cerebral tissues. Surgery is typically followed by radiation therapy, which aims to eradicate the residual cancer cells. The goal is to remove and destroy as many tumor cells as possible while preserving brain function. In radiotherapy, precisely targeted radiation destroys residual tumor cells post-surgery or in inoperable cases. Techniques such as intensity-modulated radiation therapy (IMRT) [59] and volumetric modulated arc therapy (VMAT) [60] enable the concentration of radiation doses at the tumor site while sparing the surrounding healthy tissue. Generally accompanying the surgery and/or radiation therapy, chemotherapy is often used [61]. Although not invasive in the
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Fig. 2 Current treatment options and nanoparticles in GBM treatment. (a) Standard treatment for GBM currently used, often in combination. (b) Invasive and noninvasive approaches supporting GBM treatment or used independently. (c) Nanoparticles, their broad classification, applications, and benefits
traditional sense, such as surgery, the entire body is affected by chemotherapy. Among the array of chemotherapeutic agents, temozolomide is the standard care for glioblastoma. It is a chemotherapeutic that can cross the BBB, but is not sufficient in many cases. It is used concomitantly with radiation therapy after surgical resection and then as adjuvant treatment. Furthermore, intracavity chemotherapy agents can be directly placed in the cavity left by the tumor post-surgery. This is aimed at eliminating remaining cancer cells with minimal systemic side effects. For this, Carmustine Wafers (Gliadel), which slowly releases the chemotherapeutic to targeted residual tumor cells, is implanted in the surgical cavity after tumor resection and has shown benefits [62]. Additional approaches involve the disruption of the BBB mediated by external stimuli to facilitate the delivery of therapeutics
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to the brain [14]. These include tumor-treating fields (TTF) [63], which leverage electromagnetic fields to impede cancer cell mitosis. It involves using an FDA-approved portable medical device, Optune®, that creates low-intensity electric fields to disrupt cancer cell division [64]. This therapy is typically used in conjunction with chemotherapy for recurrent glioblastoma. Although both small molecules and macromolecules are investigated for their therapeutic potential, the majority of therapeutics in use and clinical trials are small molecules. Some clinical trials are summarized in [65]. For GBM treatment, small molecules such as irinotecan, etoposide, carboplatin, topotecan, carmustine, and temozolomide [66] are considered. Temozolomide, lamustine, and carmustine are FDA-approved [66]. Currently, the most used chemotherapeutic agents are small molecule inhibitors that target key dysregulated signaling pathways in glioblastoma, including a receptor tyrosine kinase, PI3K/AKT/mTOR pathway, DNA damage response, TP53, and cell cycle inhibitors. Drugs that target specific pathways or mutations—oncogenes—in tumor cells or immune-targeted therapies are also under investigation [58]. For example, inhibitors targeting epidermal growth factor receptor (EGFR) mutations [67] present in some glioblastoma cases. Additionally, with FDA-approved antibody-based therapy, bevacizumab (avastin) [66] inhibits angiogenesis by targeting VEGF, especially in managing recurrent GBM. In other immunotherapeutic approaches [68], checkpoint inhibitors, cancer vaccines, and CAR T-cell therapy [69], aiming to harness the patient’s immune system to fight the tumor, are being explored. However, their clinical efficacy and application have not yet been conclusively demonstrated. On the other, biological approach, stem cell therapies have been effectively used in preclinics as antiglioma agents, thoroughly reviewed in [70]. Each treatment method has its benefits and risks, and the choice of therapy is highly individualized, considering the tumor’s location, size, genetic markers, and the patient’s overall health and preferences. Emerging research and the development of new technologies continue to evolve the landscape of glioblastoma treatment. Often, a combination of different approaches is used, and under trials, therapeutics aimed to enhance the efficacy of already used treatments. From https://clinicaltrials.gov several examples can be found. TFF is often used as combination therapy, for example when started concurrently with five fractions stereotactic radiosurgery and temozolomide (ID NCT04474353) or using a smallmolecule WEE1 inhibitor, adavosertib, it can enhance the antitumor effect of chemotherapy (ID NCT01849146). Additional approaches include suppression of myeloid-derived suppressor cells in recurrent GBM [71] with a combination of capecitabine and bevacizumab (ID NCT02669173). There are several trials,
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where the use of nanoparticles, such as nanocells (ID NCT02766699), gold nanoparticles (ID NCT03020017), or liposomes (ID NCT02340156), among a few others.
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Nanoparticles Considering the selective nature of healthy, integral BBB, the dysfunction is often related to various neurological diseases and conditions. In the case of brain tumors, such as GBM, the BBB may become more allowing for crossing different substances, including therapeutics. One of the phenomena is enhanced permeability and retention [72] due to the increased permeability of the tumor vasculature. This way NPs may passively accumulate in the GBM to a certain degree. Nanoparticles have shown potential as a noninvasive approach (Fig. 2b), crossing the BBB and delivering therapeutics into the brain. Although considered invasive, intravenous administration of NPs can be considered one of the least invasive approaches when considering delivery across the BBB [73]. Different entry mechanisms are used by the nanoparticles, covering some of the abovementioned: receptor-mediated transcytosis, absorptive transcytosis, cell-mediated transcytosis, opening TJ, and, to a lesser extent, passive diffusion. Each of these strategies aims to deliver therapeutic agents effectively to the brain without compromising the integrity of the BBB. This section discusses different nanoparticles that could be applied to cross the BBB and deliver therapeutics.
3.1 NanoparticleAided Therapeutic Approaches
Nanoparticles represent a versatile category of nanosized carrier systems that facilitate the delivery of various cargoes. Over the years, numerous NPs have been developed (Fig. 2c), each with its advantages and disadvantages. Often, these systems integrate features from others to overcome their limitations. In the case of GBM, overcoming the BBB remains a significant challenge in efficiently delivering therapeutics [73]. However, targeted therapies may hold even more significant value [74] due to their potential effects on BBB permeability and, more importantly, reaching the tumor. In addition to the nanoparticles, the administration route, particularly intranasal delivery, has demonstrated significant promise [75, 76].
3.1.1
Viral vectors harness the evolutionary strategies viruses have developed over millions of years for invasion but for therapeutic purposes. Viral vectors in GBM treatment show promise in gene therapy applications, with immunogene therapy, oncolytic virotherapy, suicide gene therapy, gene suppression, and gene correction and editing listed as possible applications [77, 78]. Despite the
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success of preclinical studies, concerns surrounding the safety of viral immunotherapies have also been highlighted on several occasions [79]. Oncolytic virotherapy [80] involves the use of the inclination of some viruses to replicate in cancer cells, leading to their destruction. In addition, various viral vectors [81], such as adenovirusbased vectors, herpes simplex virus-based vectors, and, to a lesser extent, vectors based on human wild-type reovirus, Newcastle disease virus, oncolytic wild-type parvovirus, poliovirus type 1, recombinant Edmonston strain of measles, recombinant vaccinia viral vectors, and replicating gamma-retrovirus [77], are used in other applications. Several candidates have been tested and are tested in clinical trials, such as adenovirus-based vectors (ID NCT01811992, NCT03330197, NCT02026271) and retroviral vector Toca 511 (ID NCT04327011, NCT02576665, NCT04105374). Additionally, viral vectors are used for engineering PBMCs derived from patients with GBM to encode the CAR and infuse directly into the patient’s tumor to mediate regression of their tumors (ID NCT03283631). An oncolytic virus called rQNestin34.5v.2 is in clinical trials Phase I is currently recruiting (ID NCT03152318). 3.1.2 Peptide-Mediated Transport and PeptideAided Approaches
Specific peptides have demonstrated the ability to cross the BBB through saturable transport systems [82, 83]. Due to the increased interest in peptides, several new tools have been developed to characterize and predict peptides with specific properties and bioactivities. For example, new BBB-crossing peptides such as augur can be designed using prediction models [84]. By conjugating the peptides to therapeutics or other carrier systems, they can facilitate transport across the BBB using the transport pathways available. Additionally, peptides with high affinity to specific targets overexpressed in GBM can be used as targeting modalities. Furthermore, as many modification possibilities are available, the peptides offer a versatile approach; they can be conjugated to the therapeutic, have high biocompatibility, and offer efficient and scalable synthesis. Although peptides feature these advantages, possible immunogenicity, stability in the organism, and often complicated conjugation of various therapeutics may be considered the main challenges. A specific type of peptide called cell-penetrating can mediate the cellular uptake of different cargoes, aiding in reaching the tumor cells. Peptides can also be designed to undergo structural changes triggered by factors such as pH or enzymes found in the tumor microenvironment. This capacity can be used to release chemotherapeutics at the target site, reducing systemic side effects. Angiopep-2 [85] is an example of a compound that targets the LDL receptor-related LRP1 and can facilitate the transportation of
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conjugated drugs across the blood-brain barrier. Similarly, the peptide iRGD [86] targets integrins and neuropilin-1 on tumor cells and tumor vasculature, thereby enhancing the penetration and accumulation of therapeutic agents in GBM. Peptides can also be utilized in immunotherapy, such as in peptide-based cancer vaccines (ID NCT01403285, NCT04280848, NCT02722512, NCT02465268, NCT00626015, NCT00643097, NCT02287428, NCT04116658, NCT04842513, NCT05283109, NCT05557240, NCT04808245) or where dendritic cells are prepared from autologous mononuclear cells pulsed with synthetic peptides derived from tumor-associated antigens of GBM (ID NCT02049489, NCT01280552). Additionally, dendritic cells have been loaded with multiple tumor neoantigen peptides (ID NCT04968366, NCT02546102, NCT05163080, NCT01222221, NCT06253234), long peptides (ID NCT02510950), peptides against cytomegalovirus antigens (ID NCT01854099, NCT06132438), and peptide PEP-CMV (ID NCT02864368). Peptides have also found application in imaging, such as 131I-TM601 (ID NCT00683761, NCT01806675), or used in combination with TTF (ID NCT03223103), and in more personalized approaches (ID NCT02722512, NCT03422094). Cell-penetrating peptides have also been employed to address GBM (ID NCT01975116). Exosomes
Exosomes occur naturally and can be collected and applied by loading the therapeutics into them. Exosomes are extracellular vesicles involved in cell-to-cell communication, capable of transporting proteins, lipids, and nucleic acids across biological barriers [87]. Exosomes have emerged as promising tools for GBM treatment due to their potential in drug delivery and immunomodulation and as biomarkers for diagnosis and monitoring the disease’s progression [88]. Exosomes derived from dendritic cells, T cells, and B cells or modified to express tumor antigens can be equipped with tumor antigens and used to stimulate the immune system against glioblastoma cells and have been shown to inhibit tumor growth [89]. In clinical trials, exosomes and vesicles are mainly used as indicators and immune markers (ID NCT05328089, NCT05864534, NCT05698524, NCT03576612).
3.1.4 Liposomes and Lipid-Based Nanoparticles
Liposomes are one of the most used lipid-based NPs. They are biodegradable, non-toxic, and capable of encapsulating both hydrophilic and hydrophobic drugs, facilitating the delivery of chemotherapeutic agents across the BBB to the glioblastoma site [10]. Liposomes can carry a wide range of therapeutic agents, including traditional chemotherapeutics, nucleic acids for gene therapy, and proteins or peptides. Liposomes enhance the
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therapeutic efficacy of encapsulated drugs by improving drug solubility, stability, and bioavailability in the brain. Doxorubicin is a potent chemotherapeutic agent that has been encapsulated in liposomes to reduce its systemic toxicity and improve its distribution to tumor sites, including glioblastoma. PEGylated liposomes loaded with doxorubicin have been studied [90, 91] for their ability to cross the BBB and target brain tumors. Cationic magnetic liposomes loaded with nucleic acids and CPT-11 (irinotecan) have also been formulated to improve GBM delivery Beyond these, liposomal encapsulated Ara-C [92]. (ID NCT01044966), liposomal verteporfin (ID NCT04590664), liposomal curcumin (ID NCT05768919), and 186RNL nanoliposomes (ID NCT05460507, NCT01906385) have been used. RNA-lipid particles (ID NCT06389591, NCT04573140) are also listed in clinical trials. 3.1.5
Polymers
3.1.6 Metallic and Magnetic NP
Designed to mimic natural processes, polymeric NPs can be modified to respond to specific stimuli within the brain environment, triggering the release of the drug. Polymers such as poly(lactic-co-glycolic acid) (PLGA) are widely used for this. Compared to PLGA, poly(ε-caprolactone) (PCL) offers a slower degradation rate and is used for long-term drug delivery applications [93], and poly(butylcyanoacrylate) (PBCA) [94] has been used in combination with other agents. Chitosan is a natural polymer derived from chitin. It has shown BBB penetration ability [95] and possibly can cross other biobarriers. Chitosan is especially attractive in intranasal delivery, as its mucoadhesive properties help prolong the residence time of therapeutics in the nasal cavity, enhancing therapeutics absorption [96]. Dendrimers are highly branched polymers that have been used to encapsulate therapeutics and imaging agents and to deliver them to target sites in the brain [97]. Polyethylene glycol (PEG) is often used to modify other polymers and to extend the circulation time for NPs [98] and therapeutics. Silk fibroin is a natural protein polymer also investigated for its potential [99]. In clinical trials, polymers have been used mainly after surgery as implantable wafers loaded with drugs (ID NCT00984438, NCT00003876, NCT03234595). By applying an external magnetic field, magnetic nanoparticles loaded with therapeutic agents can be guided across the BBB and directed to specific regions within the brain. This approach allows for targeted therapy with minimal off-target effects. Magnetic NPs such as iron oxide feature unique physicochemical properties and can be used as contrast agents in magnetic resonance imaging (MRI), targeted drug delivery, and thermal therapy. Silver and gold NPs can also be applied for delivery purposes. For example,
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gold NPs can be used for photothermal therapy, where nearinfrared light is used to kill the cancer cells after accumulation into the tumor. These NPs can be functionalized with various targeting ligands, allowing for targeted therapies. The therapeutic agent can be released after accumulation in the target site, reducing systemic effects. Additionally, modifying the NP surface improves delivery efficacy and BBB crossing capability. Small spherical nucleic acids conjugated on gold nanoparticles (ID NCT03020017) and iron oxide nanoparticles in thermal therapy (ID NCT06271421) have been used in clinical trials. 3.1.7
Other Approaches
3.2 Advantages of NPs
Carbon materials [100], especially nanotubes, can be used for PTT imaging alongside metallic NPs. Additionally, fullerenes have been used to improve the efficacy of radiotherapy. Silica nanomaterials have a high surface area, pore volume, and tunable pore size; these NPs can load a high quantity of drugs and be modified on the surface for targeting and stimuli-responsive drug release and used to improve delivery, imaging, and diagnostic applications locally triggered drug release, delivery of immunotherapeutics, and RNAi meditating RNA. Some cells can naturally cross the BBB, and techniques involving coating the NP with such membranes facilitate the delivery to the brain. For example, loaded nanocells have been used for this (ID NCT02766699). NPs have the potential to mediate delivery across the BBB and are being investigated for minimally invasive and noninvasive treatment strategies for GBM. These NPs can be customized to specifically target the BBB [38] in order to facilitate the transcytosis and transportation of therapeutics without compromising the integrity of the BBB. They can also be tailored to target GBM and its microenvironment, such as the pH or overexpressed enzymes. A comprehensive review detailing the recent developments in targeting GBM with NPs was recently published [38]. NPs hold considerable promise for GBM treatment as they have demonstrated the ability to efficiently deliver therapeutics to target cells, encapsulate drugs to minimize potential side effects, protect them from degradation, extend their half-life, facilitate BBB crossing, improve bioavailability, and enable targeted delivery. Furthermore, the therapeutic agent can be attached to the carrier, and nanoparticles can be modified after assembly to achieve specific surface properties. Depending on the carrier system, nanoparticles can also be utilized to present antigens to activate the immune system and target cancer cells.
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3.3 Applications of NPs
Nanoparticles can be tailored for diagnostic and imaging purposes, effectively enhancing techniques like MRI [19] to improve earlystage tumor detection. Additionally, when engineered to bind to cancer cells, nanoparticles can aid in localizing tumors. Furthermore, they can deliver therapeutic genes in the form of nucleic acid molecules, which can trigger apoptosis in cancer cells, sensitize tumor cells to chemotherapy or radiation therapy, and correct genetic mutations that help tumor cells survive. Nanoparticles also have potential applications in immunotherapy by stimulating the body’s immune system to recognize and combat GBM cells. In personalized and precision medicine, nanoparticles can target specific mutations or characteristics of a patient’s GBM, thus tailoring treatment for enhanced effectiveness and reduced side effects. Furthermore, nanoparticles can be loaded with different entities for the co-delivery of therapeutics, targeting various aspects of GBM for maximum effect. Some nanoparticles can also be utilized for thermal therapy and activated to elevate the temperature and destroy cancer cells upon reaching the tumor.
3.4
The current application of nanoparticles mainly revolves around complementing other treatments or using them alongside existing therapies. Finding effective solutions for the complexities of GBM and improving current treatments remains a considerable challenge. Despite years of research, only a handful of nanoparticlebased approaches have demonstrated success in this field, with limited presence in clinical trials. As potential targets advance, gene therapy, immunotherapy, and other more targeted approaches may soon be applied with nanoparticles.
Current State
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Chapter 6 Advances in Cancer Gene Therapy: Strategies, Delivery Methods, and Challenges Anni Lepland and Kadi-Liis Veiman Abstract Cancer gene therapy has rapidly evolved from experimental development into clinically validated treatment modalities, offering new opportunities in the fight against cancer. This chapter highlights advances in geneediting technologies such as CRISP-Cas9, zinc-finger nucleases (ZFNs), and transcription activator-like effector nucleases (TALENs), which target cancer-related mutations and immune evasion pathways, addressing key cancer hallmarks. Chimeric antigen receptor (CAR) T-cell therapies highlight the success of ex vivo gene editing, moving toward more accessible and personalized cancer treatments. Moreover, strategies like suicide gene therapy (SGT) and cytokine gene therapy further demonstrate the versatility in modulating tumor microenvironment (TME) for therapeutic purposes. Challenges remain in safe and efficient gene delivery with widely used viral vectors presenting risks like immunogenicity, while nonviral delivery systems offer safer alternatives. Continued innovation in gene delivery, regulation, and combination therapies is essential for improving the precision and scalability of cancer gene therapy. This chapter concludes by discussing the future directions, emphasizing research aimed at optimizing genome editing and delivery methods to minimize off-target effects and their widespread clinical use. As understanding about those mechanisms and strategies advances, cancer gene therapy holds great promise for personalized and precision oncology, tailored to the unique makeup of individual tumors. Key words Cancer gene therapy, Gene delivery systems, CRISPR-Cas9, CAR T-cell therapy, Precision medicine
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Introduction Gene therapy as defined by the European Medicines Agency (EMA) is “a biological medication that contains an active ingredient that is a recombinant nucleic acid and is used or given to humans to alter, correct, add, replace, or delete genetic sequences, and those therapeutic, prophylactic, or diagnostic effects are directly related to that nucleic acid or to the gene expression product of that sequence” [1]. Over the past decades, gene therapy has evolved from an experimental, high-risk treatment into clinically validated treatment
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modality [2]. Advances in precise gene-editing tools and expanding delivery technologies have enabled gene therapy to incorporate into most advanced treatments, including chimeric antigen receptor (CAR) T-cell therapies. Various gene therapy strategies have been developed for cancer therapy such as the correction of genetic defects, gene silencing, suicide gene therapy, anti-angiogenic gene therapy, immunomodulatory gene therapy, induction of apoptosis, and gene-editing techniques [2]. Each approach focuses on directly targeting cancer cells or modifying tumor microenvironment (TME) to inhibit tumor growth. However, for all these approaches, safe and efficient delivery of therapeutic genetic material to its target sites remains a persistent challenge. While historically many developed gene therapies were targeting rare monogenetic diseases such as adenosine deaminasedeficient severe combined immunodeficiency (ADA-SCID) and β-thalassemia, most indications nowadays addressed by gene therapy in clinical development are targeting cancer [3]. Cancer therapy poses unique challenges and opportunities. Breakthroughs in genome editing, such as the regulatory approval of clustered regularly interspaced short palindromic repeat (CRISPR)-based geneediting therapy [4], have expanded ways to target cancer. These advancements highlight the growing suitability of gene therapy for cancer treatment, given the unique genetic and molecular dynamics of cancer. The complexity of cancer is reflected in hallmarks of cancer as defined by Hanahan and Weinberg [5], describing the core properties of cancer to survive, proliferate, and metastasize, making cancer cells resistant to conventional therapies such as chemotherapy and radiation. Gene therapy, however, is able to tackle these hallmarks by precisely altering underlying genetic and molecular pathways via tailored interventions. The scope of the chapter focuses on exploring the various mechanisms and strategies for cancer gene therapy, by shedding light on the latest advancements and approaches that are paving the way for more effective and personalized cancer treatments.
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Mechanisms and Strategies of Cancer Gene Therapy Cancer progression is driven by the acquisition of specific functional capacities known as the hallmarks of cancer, which enable tumor cells to survive, proliferate, and evade conventional therapies. These hallmarks include the ability to sustain proliferative signaling, evade growth suppressors, resist cell death, enable replicative immortality, induce angiogenesis, activate invasion and metastasis, reprogram cellular metabolism, and avoid immune destruction [6, 7]. Tumor cells, in particular, suppress the function
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of host immune cells by altering antigen presentation, and this creates localized immunosuppressive microenvironment. Such immune evasion makes it difficult for conventional therapies to achieve long-term therapeutic impact. Gene therapy presents a powerful set of tools that can address these challenges by directly modifying cancer cells and modifying TME. Recent advancements in various gene therapy strategies such as gene editing, immune cell reprogramming, and targeted gene therapy have demonstrated the potential to disrupt multiple cancer hallmarks, achieving significant clinical impact. In the following sections, we provide an overview of various approaches used in cancer gene therapy. 2.1 Zinc-Finger Nucleases and TALENs as Pioneers of Targeted Gene Editing
Zinc-finger nucleases (ZFNs) are artificial restriction enzymes consisting of zinc-finger protein and the cleavage domain of the FokI restriction endonucleases [8]. ZFNs function as dimers, recognizing specific DNA sequences and cutting DNA within a short spacer region enabling targeted genome editing [9]. Zinc-finger domains can be used to target a variety of DNA sequences using modular assembly or selection-based methods [10]. The main limitation of the ZFNs for therapeutic applications is their off-target activity. It has been addressed by the development of heterodimeric ZFNs to prevent off-target dimerization and enhance FokI cleavage. This is achieved using structure-based design of ZFNs that efficiently cleave DNA only when paired as heterodimer, and with about 40-fold reduction in homodimer function, leading to much lower levels of off-target cleavage [11]. Additionally, it has been demonstrated that when delivering ZFNs as proteins by relying on their cell permeable properties, it enables to reduce off-target effects and insertional mutagenesis in various mammalian cells [12]. This approach was demonstrated to be particularly effective for modifying difficult-to-transfect cells like leukemia cell lines and human lymphocytes, making it a potential tool for nonviral gene editing. Another recent study showed that using protein modeling tools can be applied to improve the construction and efficiency of ZFNs, where genome-editing efficiency improved by 5%, demonstrating the structural modeling potential of ZFNs [13]. However, further improvements are necessary for widespread clinical use. ZFN-based viral in vivo gene editing has been evaluated as therapeutic approach in small single-dose phase 1/2 study in mucopolysaccharidosis (MPS) I, MPS II, and hemophilia B patients [14]. It was concluded that treatment was well tolerated with no serious treatment-related adverse events, and with some evidence of targeted genome editing in the liver, further development is needed to improve ZFNs and/or to use alternative delivery system. Transcription activator-like effector nucleases (TALENs) are similar to ZFNs—they are modular in form and function and compose TALE DNA-binding domain fused to a FokI cleavage
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domain [10, 15, 16]. Dimerization of TALEN proteins is mediated by the FokI cleavage domain, which cuts within a 12- to 19-bp spacer sequence that separates each TALE binding site [16]. Each TALE repeat binds a single base pair, and to create double-stranded break in the target DNA, FokI must dimerize to ensure precise genome editing in target sequence [11, 17]. TALENs have demonstrated to be applicable in adoptive immunotherapy using autologous CAR T cells, where simultaneous TALEN-mediated gene editing was used to target TRAC and CD52 genes to manufacture off-the-shelf TCR/CD52-deficient CD19 CAR T cells (dKO-CART19) enabling antitumor activity [18]. The most promising aspect about this work is the development of a scalable process for manufacturing dKO-CART19, where TALENmediated genome editing enables large-scale production of such cells, offering significant advantage over the traditional custommade autologous CAR T-cell therapies. This approach enables such therapy to be more accessible for a wider range of patients. Both ZFNs and TALENs are considered powerful tools and important milestones in genome editing, and they are widely used in a range of applications. However, for therapeutic use, they are still considered labor-intensive due to the protein engineering needed for every single target and delivery challenges of the components (e.g., limited size for the viral delivery systems), leading to limited access [19]. 2.2 CRISPR-Cas9 Advancing Precision Gene Editing in Cancer Therapy
The clustered regularly interspaced short palindromic repeat (CRISPR) and CRISPR-associated protein 9 (Cas9), originally identified as part of bacterial adaptive immunity [6, 7], have emerged as powerful gene-editing technology. The CRISPR-Cas9 system uses short RNA molecules known as single guide RNA (sgRNA), to identify and bind to specific DNA sequences via complementary base pairing. This is followed by induction of precise DNA cleavage by the Cas9 enzyme. When multiple sgRNAs are incorporated, CRISPR-Cas9 can target several genomic sites simultaneously, making it a very versatile tool in cancer research [20]. The potential of CRISPR-Cas9 was demonstrated on human cells firstly in 2013 [18], and since then technology has been rapidly developed for many applications, including in vitro and in vivo and has progressed to clinical trials. CRISPR-Cas enables precise modification of genes directly involved in cancer progression, such as oncogenes and tumor suppressors, and also modification of regulatory pathways. This is widely applied in cancer research to study the effects of genetic mutations and to develop targeted therapies [21]. One specific application is the knockout of mutated oncogenes, such as KRAS mutation in pancreatic cancer. Pancreatic ductal adenocarcinoma (PDAC) is one of the most challenging cancers with very limited therapeutic options, having a 5-year survival rate less than 10% [22]. Activating KRAS mutations are found in
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approximately 90% of pancreatic cancer cases, while mutations in other driver oncogenes are rare, highlighting KRAS as a central driver in the progression of pancreatic cancer [23]. CRISPR-Cas9 is used to selectively disrupt KRAS in PDAC cells, resulting in the significant suppression of tumorigenic properties and reduced growth of these cancer cells [24]. By enabling precise gene editing, CRISPR-Cas9 can directly modulate cancer-driving mutations, highlighting its potential to develop targeted therapies that address the genetic basis of tumour progression. In this study [25] authors have applied CRISPR-Cas9-based prime editing (PE) technique to correct KRAS and TP53 oncogenic mutations. PE allows precise genetic correction without the introduction of double-stranded DNA breaks. This approach enables to correct specifically targeted mutations and reduce the unintended mutational rates. The study demonstrated notable correction of both KRAS G12D and TP53 R24Q mutations in cancer cell lines. While this study demonstrated the potential of PE as a therapeutic tool, the limitation was the low editing efficiency in primary human cells which can be overcome by more efficient and optimized delivery methods and conditions. CRISPR-Cas9 has shown potential in modulating tumor suppressor pathways to address key regulatory mechanisms in cancer progression. Haapaniemi and colleagues [26] have shown that CRISPR-Cas9 can be used to modify the TP53 gene pathway, triggering a p53-mediated DNA damage response. While this presented a challenge by causing cell cycle arrest and reducing genome-editing efficiency in cells with intact p53, it also offered a therapeutic opportunity to selectively target TP53-deficient cancers, enhancing the specificity and safety of CRISPR-based cancer therapies. The clinical study by Lu et al. [27] explored the safety and feasibility of CRISPR-Cas9 therapeutic strategy in patients with advanced non-small cell lung cancer (NSCLC). In this study CRISPR-Cas9 was used to knock out PD-1 gene in T cells, thereby enhancing the immune cells’ ability to target tumor cells. While authors concluded that PD-1-edited T-cell therapy was safe with no severe treatment-related adverse events, the clinical efficacy was limited, and future effort should focus on optimizing sgRNA design, using high-fidelity Cas9 variants, and improving delivery methods. As discussed briefly above, CRISPR-based approaches have been demonstrated to target cancer hallmarks, including immune evasion and tumor growth, marking a significant leap toward precision oncology. Ongoing research is warranted to overcome delivery challenges and off-target effects ensuring safe and effective application of CRISPR-Cas9 in cancer treatment. CRISPR-Cas9 is under intensive clinical research. Table 1 lists examples of ongoing and completed CRISPR-Cas9-based gene-editing trials in cancer therapy.
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1 and B-cell malignancies: B-cell lymphoma, CD19-directed T-cell immunotherapy CRISPR Therapeutics AG comprised of allogeneic T cells 2 non-Hodgkin lymphoma, B-cell genetically modified ex vivo using malignancy, chronic lymphocytic CRISPR-Cas9 gene-editing leukemia (CLL)/small lymphocytic components lymphoma (SLL), follicular lymphoma, mantle cell lymphoma, marginal zone lymphoma, large B-cell lymphoma CRISPR Therapeutics AG
NCT05643742 Ongoing
NCT04035434 Ongoing
D19-directed T-cell immunotherapy 1 and B-cell malignancies, non-Hodgkin comprised of allogeneic T cells 2 lymphoma, B-cell lymphoma, adult genetically modified ex vivo using B-cell ALL
107
CD70-directed T-cell immunotherapy CRISPR 1 and Hematologic malignancies: T-cell Therapeutics comprised of allogeneic T cells 2 lymphoma, B-cell lymphoma, acute myeloid leukemia genetically modified ex vivo using CRISPR-Cas9 gene-editing components
CD70-directed T-cell immunotherapy CRISPR Therapeutics AG comprised of allogeneic T cells genetically modified ex vivo using CRISPR-Cas9 gene-editing components
26
Enrollment
NCT06492304 Ongoing
Renal cell carcinoma
CRISPR Therapeutics AG
Sponsor
1
B-cell maturation antigen (BCMA)directed T-cell immunotherapy comprised of allogeneic T cells genetically modified ex vivo using CRISPR-Cas9 gene-editing components
Treatment
NCT04438083 Ongoing
Multiple myeloma
Phase Conditions
1
Study status
NCT04244656 Ongoing
NCT number
Table 1 Examples of CRISPR-Cas9 gene-editing trials in cancer therapy. Data obtained from clinicaltrials.gov
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NCT05812326 Completed 1 and Advanced breast cancer 2
NCT03081715 Completed NA
Esophageal cancer
Advanced hepatocellular carcinoma
1
NCT04417764 Ongoing
1 and Metastatic gastrointestinal epithelial 2 cancer
NCT04426669 Ongoing
Metastatic non-small cell lung cancer
T-cell lymphoma, diffuse large B-cell lymphoma (DLBCL)
1
NCT04502446 Ongoing
NCT02793856 Completed 1
1 and Solid tumors: clear cell renal cell carcinoma, cervical carcinoma, 2 esophageal carcinoma, pancreatic adenocarcinoma, malignant pleural mesothelioma
NCT05795595 Ongoing
CRISPR Therapeutics AG
Sichuan University
Hangzhou Cancer Hospital
16
10
12
20
45
250
PD-1 knockout targeting MUC1 CAR Sun Yat-Sen 15 T cells Memorial Hospital of Sun Yat-Sen University
PD-1 knockout T cells programmed cell death 1 (PD-1) gene will be knocked out by CRISPR-Cas9
The PD-1 knockout engineered T cells Central South University are prepared from autologous origin using CRISPR-Cas9 technology
Programmed cell death protein 1 (PDCD1) gene will be knocked out by CRISPR-Cas9 in the laboratory (PD-1 knockout T cells)
Tumor-infiltrating lymphocytes in Intima Bioscience, which the gene encoding CISH was Inc. inactivated using the CRISPR-Cas9 system
Anti-CD70 allogeneic CRISPR-Cas9engineered T cells
CD70-directed chimeric antigen CRISPR receptor (CAR) T-cell Therapeutics AG immunotherapy comprised of allogeneic T cells that are genetically modified ex vivo using CRISPRCas9
CRISPR-Cas9 gene-editing components Advances in Cancer Gene Therapy: Strategies, Delivery Methods, and Challenges 111
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2.3 Ex Vivo Genome Editing in CAR T-CellBased Cancer Therapies
Ex vivo genome editing plays a crucial role in enhancing the function of cells used in cancer therapies, particularly in T cells. In cellbased cancer treatments like CAR T-cell therapy, genome editing is employed to precisely modify T cells, improving their ability to target and eliminate cancer cells. CAR T-cell therapy has transformed the treatment of hematological malignancies where patient-derived T cells are engineered to express these synthetic receptors that are designed to target tumor-specific antigens [28]. CAR T-cell therapy has been successfully demonstrated to treat patients with B-cell malignancies, such as acute lymphoblastic leukemia (ALL) and diffuse large B-cell lymphoma (DLBCL). Clinical investigations are extensive, and several of them have been successfully approved in recent years [29, 30]. The most advanced example of genome-edited cell therapy is the axicabtagene ciloleucel (axi-cel), tradename YESCARTA, which is used to treat patients with refractory large B-cell lymphoma, who have relapsed within 12 months after first-line chemoimmunotherapy [31, 32]. The ZUMA-7 trial demonstrated that axi-cel therapy significantly outperformed second-line standard of care in relapsed or refractory large B-cell lymphoma patients [33]. Although treatment was associated with a high incidence of severe adverse events, the toxicity profile was in line with previous findings in third-line therapy and showed minimal fatal outcomes. This makes axi-cel a promising alternative to traditional approaches such as chemoimmunotherapy, high-dose chemotherapy, and autologous stem-cell transplantation for second-line treatment, despite the unique challenges associated with CAR T-cell therapy. Similarly, another innovative application is tisagenlecleucel, known with tradename KYMRIAH, which is a CD19-directed CAR T-cell therapy approved for the treatment of relapsed or refractory B-cell malignancies, including pediatric and young adult patients with B-cell acute lymphoblastic leukemia (ALL) and adult patients with relapsed or refractory DLBCL or follicular lymphoma (FL) [34]. The JULIET trial [35] demonstrated that KYMRIAH achieved a 52% overall response rate with 40% complete responses, in relapsed/refractory DLBCL patients. At 12 months, 65% of responders remained relapse-free. While showing promising durable responses in relapsed or refractory DLBCL, long-term safety needs further evaluation, as severe toxicities like cytokine release syndrome, though manageable, could pose significant risks. As CAR T-cell therapies like YESCARTA and KYMRIAH demonstrate promising results in treating B-cell malignancies, genome editing for precise engineering of T cells will continue to be a critical step in advancing these therapies further. Ongoing research to optimize genome-editing technologies to reduce off-target effects will be essential to improve safety. Secondly, much effort needs to be put into the development of feasible and efficient
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delivery methods to improve safety but even more efficacy and scalability of cell-based cancer treatments. 2.4 Targeted Cell Destruction Via Suicide Gene Therapy
The concept of suicide gene therapy (SGT) is that suicide gene is introduced into target cell, which typically is tumor cell. Expression of that gene triggers cell death via specific toxic mechanisms. One example is using cytosine deaminase (CD) to convert nontoxic prodrug 5-fluorocytosine (5-FC) into the active drug 5-fluorouracil (5-FU), which commonly used chemotherapeutic drug in anticancer therapy [36]. In this study optimized yeast CD gene was inserted into the thymidine kinase (TK) region of vaccinia virus VG9 strain, resulting in an engineered virus that could selectively replicate in tumor cells due to the deletion of TK region. The expression of CD enzyme within tumor cells converted 5-FC into 5-FU, leading to rapid and targeted antitumor response in colorectal cancer models. Another prominent example of CD/5-FC system is the use of Toca 511, a retroviral replicating vector (RRV) to deliver CD gene into tumor cells [37]. Unlike the previous approach, Toca 511 is RRV derived from a murine leukemia virus, a member of the retrovirus family. These vectors integrate genetic material into the host’s genome and can replicate without causing cell lysis. This can be an advantage as long-term expression of CD in tumor environment results in strong local chemotherapy effect. Despite mixed results in clinical studies involving RRVs such as Toca 511, expert opinion [37] suggests that positive outcomes could be achieved via optimizing dosing and using combinations of different therapies. As research into SGT progresses, the integration of this approach with advanced gene delivery technologies and innovative therapies could offer new and more effective cancer treatment strategies.
2.5 Introduction of Cytokine Genes to Enhance Immune Response
The use of cytokines for cancer treatment has been of interest as they are small proteins playing a key role in innate and adaptive immune responses [38, 39]. The promise of cytokine therapy for cancer treatment relies on increasing cytokines with antitumoral properties, such as interleukin (IL)-2, IL-4, IL-6, IL-12, interferon alpha (IFN-α) and IFN-γ, and tumor necrosis factor alpha (TNF-α) [40]. Cytokines act on tumors by directly limiting tumor cell growth, inducing apoptosis, or indirectly through stimulating cytotoxic activity of immune cells [41]. The first cytokines received FDA approval in the 1990s, and they were IL-2 for renal cell carcinoma [42] and metastatic melanoma [43] and IFN-α for indications such as hairy cell leukemia [44], follicular non-Hodgkin lymphoma [45], and melanoma [46]. These were a milestone in cancer immunotherapy; however, severe toxicities such as hypotension, respiratory failure, and neurological symptoms caused by pleiotropic effect and modest effects due to small
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therapeutic window and short half-life are hindering their use in clinical settings [38, 41, 47, 48]. Moreover, using cytokines as monotherapy provided disappointing results as its effects are not targeted to the tumor and require the body to activate the immune system [48]. The next generation of cytokine-based drugs relies on three concepts: synergistic combinations, such as with PD-L1, increased concentration, and using gene therapy vectors, such as oncolytic viruses and lipid nanoparticles to direct cytokines [41]. For IL-2 the approach is to avoid the binding of IL-2 to high-affinity receptors through, for example, PEGylation [41]. The effect of modified IL-2 has been analyzed in different clinical trials: in combination with nivolumab (NCT03282344), atezolizumab (NCT03138889), or nivolumab and ipilimumab (NCT02983045) with mixed results and need for additional studies [49–51]. Another highly studied cytokine is IL-12 which has shown potent antitumoral effects in preclinical studies [52–56], but in clinical trials the high doses needed induce severe toxicities, and clinical benefit has been lacking [57, 58]. IL-12 modulates and targets natural killer (NK) cells, APCs, and T cells and therefore is still considered for cancer immunotherapy [58, 59]. Localized delivery systems such as viral vectors, liposomes, immunostimulatory antibodies, and mRNA-based methods may provide solutions for IL-12-based cancer therapies to reduce toxicities for improved clinical response [41, 60]. Promising results might come from ongoing clinical trials (NCT00323206, NCT02483312, NCT06171750, NCT04303117, NCT06343376). IL-10 is a potent immunosuppressive cytokine, and it has been shown that in cancer, IL-10 prolongs the effect of cytotoxic T cells. This is under evaluation in phase I studies (NCT02009449), and so far, the results are promising [61]. Finally, IL-15 is crucial in the development of NK cells and activation of cytotoxic T cells, and it does not stimulate regulatory T cells [62, 63]. First in human clinical trial with aglycosylated IL-15 increased NK and cytotoxic T cells, but the study was terminated due to high toxicity [64]. In another study where IL-15 was administered subcutaneously, no objective clinical response was achieved, but several patients had disease stabilization for 2 years [65]. In recently concluded phase I clinical trials where IL-15 was administered either intravenously (NCT01385423) or subcutaneously (NCT02395822), up to 40% of patients had clinical benefit. The results from varied clinical trials with different cytokines indicate the need for more studies with targeted delivery methods and varied dosing regimens to provide the best clinical benefit with lowest toxicity profiles. Moreover, there is a need to study potential combinations of cytokines and other treatments for more effective tumor control.
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Gene Delivery Strategies in Cancer Therapy Efficient delivery systems for ex vivo and in vivo gene therapies are critical to their success. Gene delivery methods must ensure that therapeutic genetic material reaches target cells in sufficient quantities by maintaining their stability and achieving precise genetic modifications, all while minimizing off-target effects and potential toxicity. In ex vivo gene therapy, cells are extracted from the patients and modified to express the gene product, and modified cells are readministered to the patients. This approach forms the basis for many cell therapies, including CAR T-cell therapy. In contrast, in vivo gene therapy involves delivering the therapeutic genetic material directly into its target site, which can be a specific tissue, cells, or even organelle. In vivo gene therapy is often administrated intravenously and offers advantages in scalability and accessibility as there is no need for cell extraction and modification outside the body. However, the primary challenge in in vivo therapy is ensuring the genetic material reaches the correct target site. Delivery techniques are broadly classified into viral and nonviral methods, each with distinct benefits and challenges for ensuring effective transfer and stability and minimizing adverse effects.
3.1 Viral Vectors for Targeted Gene Therapy
For viral cancer gene therapy, engineered viruses deliver therapeutic genetic material into cancer cells with the aim to selectively kill them or to stimulate the immune system to attack cancer cells. Viral vectors are one of the primary vectors studied for gene therapy, due to their efficiency in transferring genetic material into cells. Adenoviruses are widely used for gene transfer, and about 15% of gene therapy clinical trials used adenoviral delivery systems [66]. Their main advantages are episomal high transgene expression and simple production. Limitations are high immunogenicity and preexisting immunity that can hinder efficacy. Adenoassociated viruses (AAVs) are developed for many applications since they have wide tissue tropism and can infect many types of cells and tissues; they have good safety profile, they usually do not integrate into the host’s genome, and they have long-term transgene expression [67]. A major barrier however is the host’s immune response to adenoviral infection, which, although as different strategy, can be beneficial for immunotherapeutic approach [68]. Retroviruses are also widely used for clinical applications [66]. They are single-stranded RNA viruses that have reverse transcriptase activity converting their RNA genome into double-stranded DNA (dsDNA) enabling integration into host’s genome [69]. While chromosomal integration is useful for long-term transgene expression, the risk of disrupting the host’s genome has raised serious safety concerns in clinical applications. One notable case was about
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early trials for X-linked severe combined immunodeficiency (SCIDX1) patients, where four patients out of nine patients developed T-cell leukemia several months after gene therapy treatment [70]. Vector-induced oncogenesis has been addressed by developing self-inactivating γ-retrovirus vector that contains deletions in viral enhancer sequences that was the cause for the adverse effects described before [71]. The treatment with this vector was mostly successful, restoring immune function without causing long-term side effects after follow up. Lentiviruses, belonging also to the family of retroviruses, share similar features with retroviruses, but they can transduce both dividing and nondividing cells. Lentiviral vectors have been derived from human immunodeficiency virus (HIV) and have been developed and improved for the last few decades. Lentiviral vectors are less immunogenic and therefore have been studied extensively [72, 73]. The limited use of lentiviral vectors in clinical trials is mainly due to not being able to get high-enough titer and safety due to the fact that most vectors are derived from HIV [74]. There are some ongoing early-phase clinical trials using lentiviral vectors as delivery vehicles for IL-12 [75] (NCT02483312) and IL13Ralpha2 CAR T cells (NCT04119024) [76], and results are still to be determined. Moreover, lentiviral vectors have been used to deliver CAR T-cell therapy, and results with patients with B-cell malignancies have been promising [74]. 3.2 Nonviral Approaches with Enhanced Tumor Targeting
The nonviral vectors are considered to be less immunogenic, in contrast to viral vectors. Moreover, they do not have limited payload size, they can protect therapeutic nucleic acids against degradation by serum nucleases, and they are easily scalable on industrial scale for commercial applications. Lipid nanoparticles (LNPs) are widely used tool in biomedical applications, emerging as an effective delivery system for nucleic acid-based therapeutics such as messenger RNAs (mRNA), small interfering RNAs (siRNA), small activating RNAs (saRNA), microRNAs (miRNA), and antisense oligonucleotides (ASO) and delivery of CRISPR components for genome editing [77– 79]. Prominent examples of clinically validated LNP-based gene therapy are Onpattro, an LNP-based siRNA drug for the treatment of polyneuropathies induced by hereditary transthyretin amyloidosis [80] and mRNA-based COVID-19 vaccines developed by Pfizer-BioNTech and Moderna [81]. In Onpattro the drug is delivered directly to the liver without producing disease-causing proteins [82]. LNP-based clinical trials, including cancer gene therapy trials, have recently been thoroughly reviewed [78, 83]. In the field of cancer therapy, translating LNP-mediated gene therapy into clinical strategies has been limited by efficient uptake of LNPs by tumor cells.
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Polymeric nanoparticles (PNPs) consist of polymers, the most common materials studied as nanocarriers due to their abilities: biodegradable, ease of synthesis, functionalization versatility, low cost, and scalable production. Also, PNPs accumulate to the tumor through enhanced permeability and retention effect or through active receptor-mediated targeting [82, 84, 85]. Moreover, conjugating drug and/or gene to the PNP will enable the use of poorly soluble drugs [84]. There are two aspects to consider when designing new PNPs: optimum gene delivery and minimal retention inside the body for lower toxicity [82]. PNPs have been used to co-deliver drug and gene to the cancer cells, but suitable carriers for delivering both remain an obstacle [86]. The first polymeric gene and drug co-delivery system was using poly{(N-methyl dietheneamine sebacate)-co-[(cholesteryl oxocarbonylamido ethyl) methyl bis(ethylene) ammonium bromide] sebacate} (P(MDS-co-CES)). They confirmed that co-delivering IL-12 and paclitaxel will decrease tumor growth in a synergistic manner in vivo [87]. Polyethyleneimine (PEI) is a cationic synthetic polymer that is widely used as a nonviral vector in cancer research to deliver genetic material into cells because of its ability to assist in endosomal escape. Due to its high molecular weight and cationic charge density, it is often linked with higher cytotoxicity. Nevertheless, PEI has been investigated in several promising cancer therapy applications [84]. The CALAA-01 delivery system was the first in human where siRNA inhibited cancer growth due to RNA interference with M2 subunit of ribonucleotide reductase (RRM2) (NCT00689065). Although the in vivo results were promising, the clinical trial was terminated due to high toxicities [88]. As a natural polymer, chitosan is the most used one [82]. Chitosan (poly [β-(1–4)-linked-2-amino-2-deoxy-D-glucose]) is a natural linear polymer derived from chitin. Chitosan nanoparticles are nanometric in size, nontoxic, and biodegradable, have good stability in serum and long half-life, and also have great efficacy of drug loading [85]. Chitosan has also been used to deliver siRNAs to the tumor cells [89]. Physical methods like electroporation are another category of nonviral gene delivery techniques, where electrical pulses temporarily disrupt the cell membrane, allowing the genetic material to enter the cell. Electroporation has been widely used for ex vivo genome editing in many clinical studies [90]. Due to its carrier-free delivery mechanism, cells modified through electrotransfer are generally regarded as less immunogenic in patients compared to those altered using viral vectors [91]. Developed automated electrotransfer systems can produce engineered cells while meeting clinical Good Manufacturing Practice (GMP) requirements and ensuring scalability. Various automated and closed systems for T cell and hematopoietic stem and progenitor cell (HSPC) engineering have
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successfully implemented GMP-compliant electroporation methods [92, 93]. Nonviral delivery approaches, including LNPs and polymeric nanoparticles as well as electroporation, present safe and scalable alternatives to viral delivery systems. LNPs have been effective in delivering nucleic acids such as mRNA and components of CRISPR, while polymeric nanoparticles provide versatile options for drug and gene delivery. Electroporation is widely used for ex vivo genome editing offering a carrier-free approach. However, challenges like tumor targeting and clinical translation remain, requiring further research and development.
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Challenges and Future Directions For long-term therapeutic effects of cancer gene therapy, there is a need to study therapeutic interventions closely. There are different strategies for cancer gene therapy: expression of proapoptotic genes, wild-type suppressor genes, genes capable of inducing antitumor immune response, and targeted silencing of oncogenes. Choosing the best delivery system and achieving optimal expression levels still remain challenging [94]. For effective gene therapy, there is a need to precisely regulate the expression of genes based on clinical need, also to lower possibility of side effects. For that, promoters and enhancers play an essential role. There are two types of promoters: constitutive and inducible. Constitutive are the ones that allow continuous transcription of genes, and they can be tumor specific, such as prostate specific antigen for prostate cancer, or be functional in many different tumors, such as hTERT. Inducible promoters can be induced to express genes through external introduction of small molecules and hormones [95]. Cancer-specific promoters are important as they are only expressed in cancer cells spearing healthy ones [96]. For sufficient expression of effector genes, the promoter needs to be specific, and for that, enhancers can be used which are small DNA sequences near or far from the promoter [97]. Moreover, a silencer can be used to keep the vector inactive in healthy cells and active in tumor cells [95]. There are different strategies for cancer gene therapy: expression of proapoptotic genes, wild-type suppressor genes, genes capable of inducing antitumor immune response, and targeted silencing of oncogenes. Choosing the best delivery system and achieving optimal expression levels still remain challenging [94]. For effective gene therapy, there is a need to precisely regulate the expression of genes based on clinical need, also to lower possibility of side effects. For that, promoters and enhancers play an essential role. There are two types of promoters: constitutive and inducible. Constitutive are the ones that allow continuous transcription of genes, and they can be tumor specific, such as prostate
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specific antigen for prostate cancer, or be functional in many different tumors, such as hTERT. Inducible promoters can be induced to express genes through external introduction of small molecules and hormones [95]. Cancer-specific promoters are important as they are only expressed in cancer cells spearing healthy ones [96]. For sufficient expression of effector genes, the promoter needs to be specific, and have good strength and for that, enhancers can be used which are small DNA sequences near or far from the promoter [97]. Moreover, a silencer can be used to keep the vector inactive in healthy cells and active in tumor ones [95]. Targeted delivery is one of the main goals for successful cancer gene therapy. One of the main methods is to use viral and nonviral vectors to allow successful binding to tumor cells and not to healthy ones. The use of viral vectors is still challenging as there is toxicity to viral proteins, the host can produce neutralizing antibodies and induce tissue inflammation as a reaction to viral vectors, and the issues of random integration and adenovirus-associated hepatic toxicity fuel these challenges [95]. These aspects need to be kept in mind when choosing a vector for delivery. Another option for targeted therapy is to implement regulatory sequences to direct gene expression [96].
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Conclusions Cancer gene therapy has demonstrated significant progress, evolving from experimental approaches to clinically validated treatments. These therapies target key hallmarks of cancer such as genetic mutations, immune evasion, and tumor progression, offering new promising opportunities for personalized and precise cancer treatments. However, challenges in delivery systems remain as a major barrier in the clinical application of cancer gene therapy. While viral vectors have most widely used and researched clinically, their immunogenicity and insertional mutagenesis remain a challenge. Nonviral approaches such as LNPs, PNPs, and electroporation offer alternative approach, but they require further investigation for wider clinical translation to overcome issues like tumor targeting, efficient delivery, and safety. Ex vivo gene therapy, such as CAR T-cell therapy, has shown great success, particularly in hematological malignancies. Integrating genome editing and CAR T-cell therapy has significantly improved patients’ outcomes. Beyond delivery challenges, precise regulation of gene expression through promoters, enhancers, and silencers must be considered as a key to target tumor cells while minimizing harm to healthy cells. Future efforts must focus on overcoming delivery and targeting challenges, enhancing geneediting accuracy, and enabling scalable treatments for clinical use.
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66. Ginn SL, Mandwie M, Alexander IE et al (2024) Gene therapy clinical trials worldwide to 2023—an update. J Gene Med 26:e3721 67. Wang J-H, Gessler DJ, Zhan W et al (2024) Adeno-associated virus as a delivery vector for gene therapy of human diseases. Signal Transduct Target Ther 9:1–33 68. Shaw AR, Suzuki M (2019) Immunology of adenoviral vectors in cancer therapy. Mol Ther Methods Clin Dev 15:418–429 69. Lesbats P, Engelman AN, Cherepanov P (2016) Retroviral DNA integration. Chem Rev 116:12730–12757 70. Hacein-Bey-Abina S, Garrigue A, Wang GP et al (2008) Insertional oncogenesis in 4 patients after retrovirus-mediated gene therapy of SCID-X1. J Clin Invest 118:3132–3142 71. Hacein-Bey-Abina S, Pai S-Y, Gaspar HB et al (2014) A modified γ-retrovirus vector for X-linked severe combined immunodeficiency. N Engl J Med 371:1407–1417 72. Milone MC, O’Doherty U (2018) Clinical use of lentiviral vectors. Leukemia 32:1529–1541 73. Breckpot K, Aerts JL, Thielemans K (2007) Lentiviral vectors for cancer immunotherapy: transforming infectious particles into therapeutics. Gene Ther 14:847–862 74. Ghosh S, Brown AM, Jenkins C et al (2020) Viral vector systems for gene therapy: a comprehensive literature review of progress and biosafety challenges. Appl Biosaf 25:7–18 75. Vacchelli E, Aranda F, Bloy N et al (2016) Trial watch—immunostimulation with cytokines in cancer therapy. Onco Targets Ther 5: e1115942 76. Haanen J, Los C, Phan GQ et al (2024) Adoptive cell therapy for solid tumors: current status in melanoma and next-generation therapies. Am Soc Clin Oncol Educ Book 44:e431608 77. Pozzi D, Caracciolo G (2023) Looking back, moving forward: lipid nanoparticles as a promising frontier in gene delivery. ACS Pharmacol Transl Sci 6:1561–1573 78. Thi TTH, Suys EJA, Lee JS et al (2021) Lipidbased nanoparticles in the clinic and clinical trials: from cancer nanomedicine to COVID19 vaccines. Vaccines 9:359 79. Kazemian P, Yu S-Y, Thomson SB et al (2022) Lipid-nanoparticle-based delivery of CRISPR/ Cas9 genome-editing components. Mol Pharm 19:1669–1686 80. Akinc A, Maier MA, Manoharan M et al (2019) The Onpattro story and the clinical translation
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Chapter 7 Targeted Tumor Delivery Using Extracellular Vesicles Hema Saranya Ilamathi, Samir El Andaloussi, and Oscar P. B. Wiklander Abstract Over the past decade, precision medicine has made significant advances in oncology, particularly through the use of targeted therapies designed to hit cancer cells precisely while minimizing the off-target effects of traditional treatments. A promising new avenue in cancer therapy involves the use of natural nanoparticles, extracellular vesicles (EVs), which play a crucial role in intercellular communication. With their inherent ability to transport molecules and their potential for precise targeting, EVs hold great promise for drug delivery. Here we explore the latest advancements in utilizing EVs for targeted cancer therapies, examining their surface features for targeting, engineering strategies to enhance their effectiveness, and the challenges and opportunities in customizing EVs for cancer treatment. Key words Extracellular vesicles, Cancer, Targeting, Surfaceome, EV engineering
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Introduction Extracellular vesicles (EVs) encompass a heterogeneous group of lipid-bilayered natural nanoparticles produced by nearly all cells. These vesicles range from nano- to micrometer-sized vesicles that mediate intercellular communication by transferring bioactive molecules. EVs are typically subdivided into exosomes, microvesicles, and apoptotic bodies, each distinguished by their biogenesis, size, and cargo composition [1–3]. They play a key role in the regulation of various physiological and pathological processes such as immune response modulation, tissue repair, and cancer metastasis [2, 3]. The cargo within EVs reflects characteristics of their parental cell and constitutes a myriad of actively and passively sorted molecules which differ depending on the cell’s physiological state and molecular signals received from their surrounding microenvironment. This cargo includes various lipids, glycans, and protein molecules involved in cell signaling, adhesion molecules, cytoskeletal proteins, heat shock proteins, cytokines, growth factors, enzymes, and nucleic acids (DNA, RNA) (Fig. 1) [4]. Additionally, EV
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Fig. 1 EV surfaceome. The EV surface is a rich landscape of biomolecules, including proteins, glycans, and lipids. This image highlights the most abundant molecules present on the EV surface, showcasing the diverse and complex nature of these extracellular vesicles
surface signatures influence their targeting to specific recipient cells [5–7]. EVs act as a natural shuttle of cargo throughout the body, surpassing biological barriers such as the blood-brain barrier (BBB), and show inherent organotropism, biocompatibility, and low immunogenicity [3]. These features, along with their engineerability and the potential to equip them with various targeting moieties, have spurred an immense interest in exploiting EVs for therapeutic delivery. In this chapter, we highlight the surface features of EVs that facilitate targeting, explore various engineering strategies to improve EV targeting, and discuss the challenges and opportunities in tailoring EVs for effective cancer treatment. 1.1 Surface Features of EVs
The surface of extracellular vesicles (EVs) serves as a dynamic hub, teeming with diverse biomolecules that orchestrate EV tropism, mediate communication with recipient cells, and shape the vesicles’ functional roles. This intricate “surfaceome” encompasses proteins, lipids, nucleic acids, and glycans, each playing a pivotal part in determining EV behavior (Fig. 1). By delving into the complexities of the EV surfaceome, we can unlock insights into how these biomolecules contribute to EV function and identify potential biomarkers for disease diagnosis. Additionally, profiling the EV
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surfaceome holds significant promise for optimizing EVs as innovative tools for targeted drug delivery. 1.1.1
EV Surface Proteins
Proteins on the EV surface often mirror their parental cells and vary depending on the cell’s composition and physiological state. Among the most commonly expressed EV surface proteins are tetraspanins such as CD9, CD63, and CD81, which are primarily utilized as exosome markers [8]. Other commonly found EV surface proteins include integrins such as ITGA5, ITGAV, and ITGB5 [9]. They regulate the homing properties of EVs and help in their attachment to the matrix and recipient cells. Additional transmembrane proteins identified on the EV surface include CD109, CD44, CD46, CD70, TSPAN1, TSPAN14, and VAMP7 (Fig. 1) [9]. Solute carrier proteins such as SCL12A6, (K-Cl cotransporters), SLC2A3 (glucose transporter), and SLC7A6 (amino acid transporter) are also found in the EV membrane, sometimes with reverse orientation [9, 10]. Some EVs can express transmembrane protein CD47 which acts as a “do not eat me signal” and protects EVs from macrophage clearance [9]. Small EVs are known to carry proteins involved in endosomal sorting including Flotilin-1, syntaxin-4, and secretory carrier membrane protein 3 [8, 10, 11]. Certain proteins are peripherally attached to the EV membrane via noncovalent or electrostatic interactions. Some of the EV peripheral membrane proteins include RNA-binding proteins such as eukaryotic translation initiation factor (EIF2S1, EIF3A, EIF3B, EIF3L, EIF4G2), RNA helicase (DDX17, DDX6, DHX9), 60S ribosomal protein (RPL6, RPL7, RPL9), and SERPINE1 mRNA-binding protein 1 [9, 10]. Other studies have reported the presence of heat-shock proteins, immunoglobulin superfamily proteins, cytokines, growth factors, DNA-binding proteins, lipoprotein receptors, major histocompatibility complexes I and II (MHC I and II), metabolic enzymes, annexin family proteins, and tubulin on the EV surface (Fig. 1) [9, 10, 12–15]. The EV surface is surrounded by layers of biological molecules called corona, which they absorb from the surrounding fluid. Hence, some of the peripheral proteins reported in these studies could be associated with the corona. Further investigation is needed to distinguish between inherent EV proteins and the acquired corona compartment. Surfaceome of EV changes with cancer, and they carry signaling molecules to reprogram the tumor microenvironment for tumor proliferation and survival. For instance, the surfaceome of pancreatic cell-derived EVs showed differential expression of certain proteins (CLDN4, EPCAM, CD151, LGALS3BP, HIST2H2BE, and HIST2H2BF) compared to control cells. LGALS3BP, a galectinbinding protein, was more abundant in tumor-derived EVs (tEVs) than in control cells [16]. Additionally, C19-positive EVs from
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B-cell chronic lymphocytic leukemic patient plasma were enriched with CD62L, a homing receptor, compared to the healthy volunteers [17]. Overall, EV has a distinct protein signature on its surface, which varies by cell type. Furthermore, even from the same cell type, different populations of EVs with varying levels of protein expression exist. This is further influenced by the stress that cells are subjected to. Hence it is important to identify highly expressed EV membrane proteins before further manipulation for targeted therapies. The characterization of EV surfaceome by techniques that rely on antibodies such as flow cytometry, dot blot, and microscopy is limited to known proteins with available commercial antibodies. However, some of the transmembrane EV proteins have reversed topology and hence fail to be detected by antibody-based approaches due to the inaccessibility of the epitope [10]. Therefore, antibody-independent approaches, such as mass spectrometry, can help to further dissect the EV surfaceome. 1.1.2
EV Surface Glycans
Glycosylation is one of the most common posttranslational modifications in the cell. Proteins and lipids are glycosylated by different glycosyltransferases expressed by the cell. There are over 200 genes in the genome that encode different glycosyltransferases. Around 167 of them orchestrate the structure of glycoproteins and glycolipids [18, 19]. However, depending on the expression of these genes, the glycosylation profile of proteins may vary among the tissue. Glycosylation regulates the stability and folding of proteins and prevents them from degradation. Glycans are linked to proteins through different glycosidic linkages with amino acids. Based on the linkage there are multiple types of glycans including N-linked, O-linked, C-linked, and glypiation where glycans are attached to the amine group of asparagine, hydroxyl group of serine, threonine, and tyrosine, the carbon atom of tryptophan, and covalent addition of glycophosphatidylinositol to the C-terminus of a protein, respectively. Glycans form a protective layer around the surface of the membrane called glycocalyx. These glycosylated proteins play an important role in cell-cell interaction, host-pathogen interactions, regulate immune response in immune cells, turn on/off expression of genes regulating cellular metabolism, regulate ligand-receptor interaction and downstream signaling, and facilitate endocytosis [20–29]. As glycosylation is important for protein function in the cell, including the immune response, cancer cells hijack the cellular glycosylation machinery to suppress this immune response [30– 32]. Glycosylation plays a crucial role in cancer development, metastasis, and escape immunosurveillance mechanisms. Extracellular signaling through cellular receptors regulates the expression of cellular proliferation, metabolism, and survival genes. The
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glycosylation profile of receptors modulates its activation and downstream signaling. Receptor tyrosine kinase is an integral part of the cancer signaling pathway that controls the growth of tumor cells. Depending on the glycans and the site of glycosylation on the protein, the receptor function could be differentially regulated. N-linked glycosylation of epidermal growth factor receptor (EGFR) or its core fucosylation causes aberrant activation of the receptor and its downstream signaling [33, 34]. The glycocalyx layer on the cancer cell surface influences its metastasis processes including invasion, intravasation, and extravasation [35, 36]. Many of the serum-based markers used in the clinics for cancer diagnosis are glycoproteins including cancer antigen 124 (CA125), CA15.3, CA19.9, prostate-specific antigen (PSA), and carcinoembryonic antigen (CEA). However, these markers are normally found in the sera of healthy individuals, thereby affecting the specificity of the test. On the other hand, recent studies have reported aberrations in glycosylation resulting in truncated or altered protein glycoforms in cancer [37–39]. These neoantigens could find a potential application in diagnostics. Glycans account for 60% of extracellular space and plasma membrane proteins [40, 41]. EVs appear to contain glycoproteins that are similar to 80% of the parental cell membrane surface from which they are derived [42]. EV surfaces carry different N-glycosylated clusters of differentiation (CD) proteins which in turn regulate chemotaxis and migration of myeloid-derived suppressor cells (MDSC), which aids tumor progression by suppressing the immune system [42]. EVs derived from different sources carry diverse glycan molecules on their surface. α-2,6 sialic acid is predominantly found on the EVs derived from melanoma cells, colon cancer cells, bronchial epithelia, ovarian cancer, glioma, HEK293 cells, and breast milk (Fig. 1) [43–46]. In addition, EVs carry other glycans such as high mannose, polyLacNAc, and complex N-linked glycans (Fig. 1) [43]. However, melanoma, colon cancer, and T-cell-derived EVs lack terminal blood group A and B antigens [43]. Depending on the EV source and the recipient cells, different glycans influence the cellular uptake of EVs. For instance, desialylation of EVs from mouse liver progenitor 29 (MLP29) cells decreases its uptake by lung cancer cells, whereas it is more readily uptake by colorectal cancer (LS174) cells [47]. The EV surface carries different glycosaminoglycans, and they interact with chemokines through electrostatic interactions. For instance, heparinase III treatment reduces cellular uptake of EVs by glioblastoma cells [48]. Alternatively, incubating EVs with heparin also halted their cellular uptake in a leukemic model [49]. Overall, glycosylation plays an important role in EV tropism and its function.
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Lipids are integral components of EVs that compartmentalize them and protect the luminal content from degradation. EVs are usually presented as unilamellar structures with a lipid bilayer. However, EVs could be multilamellar depending on their origin. Exposure to bacterial endotoxin, lipopolysaccharide (LPA), or prion-infected cells enhances the release of multilamellar EVs [50, 51]. These findings suggest that distinct mechanisms regulate the lamellarity of EV, but this is still poorly understood. EVs display a distinct lipid profile depending on their size. Large vesicles (lEVs), microvesicles, or ectosomes from MSCs were abundant in lysophosphatidylcholine, ceramides, cholesterol esters, and acylcarnitines, while MSC exosomes or small vesicles (sEVs) were enriched with cardiolipins [52]. In contrast to MSCs, adipocyte-derived SEVs have more cholesterol than IEVs (Fig. 1) [53]. The biogenesis of these vesicles is regulated by independent mechanisms which could account for the difference in their lipid profile [54, 55]. Altogether, the lipid content of EV membranes differs based on the nature of the source cell and the type of vesicles. Unlike proteins and glycans, the lipid composition on the EV surface is distinct from the cell of its origin. For instance, breast cancer EV membranes were twofold enriched with ceramide, sphingomyelins, hexosylceramides, lysophosphatidic acid, lysophosphatidylethanolamines, phosphatidylcholines, ether PE, and phosphatidylinositol [56] compared to the parental cell membrane, while EVs from prostate cancer cells (PC-3) show enrichment of cholesterol, sphingomyelin, phosphatidylserine, and less phosphatidylcholine content to the cell membrane [57]. The lipid composition of the EV membrane varies depending on the type of cancer. For instance, EVs from glioblastoma cells (U87) lack cardiolipin but are enriched with sphingomyelin. On the other hand, hepatocellular carcinoma cells (Huh7) and human bone marrow-derived mesenchymal stem cells (MSC)-derived EVs are enriched with cardiolipin but depleted of phosphatidylcholine [52]. Phosphatidylserine (PS) is present mainly in the inner leaflet of the membrane to prevent its removal by PS receptor-expressing phagocytic cells. However, certain tEVs display increased PS molecules on their surface [58–60]. The lipid composition of EVs is thus sorted differently than its parental cell, yet it depends on the size and type of EV as well as its cellular origin.
Targeted Delivery of EVs EV’s outer composition equips them with a natural ability to shuttle molecular cargo throughout the body and cross biological barriers, which has spurred a great interest in the drug delivery field. In addition, their low immunogenicity and high biocompatibility make them highly compelling to be explored as natural vehicles.
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EVs can be targeted to the site of interest by utilizing their intrinsic passive targeting mechanisms or by actively bioengineering them with enhanced targeting capabilities. 2.1 Passive Targeting
The passive targeting strategy depends on EVs’ innate tropism, which relies on the functional moieties on their surface. The homing properties thus depend on the parental cell producing the EVs [61]. Exosomes-mimetic nanoparticles have natural homing properties. These are generated by a serial cell extrusion process. Exosomes-mimetic nanoparticles derived from macrophage/ monocytic cells have been shown to target endothelial cells, possibly via an interaction between exosome surface protein and endothelial cell adhesion molecule (CAM) [62]. tEVs have previously been reported to preferentially home toward the cell of origin. For instance, fibrosarcoma cells, HT1080-derived EVs, target their parental cells to a higher degree than Hela cell-derived EVs in vivo [63]. EVs from brain cells have shown the potential to cross the blood-brain barrier, which is a common challenge for many drug delivery systems.. EVs from patient-derived glioblastoma cells increase vascular permeability in vivo, which is regulated by pro-permeability factor semaphorin 3A (Sem3A) on the EV surface [64]. However, experiments in the zebrafish model suggest that EVs from brain endothelial cells could cross BBB and enter brain tissue better than EVs from neuroectodermal, glioblastoma, and glioblastoma-astrocytoma cells in vivo [65]. In addition to this property, EVs could travel to distant organ sites, thereby playing an important role in pre-metastasis niche formation [66, 67]. For instance, pretreating mice with colorectal cancer EVs increases the spread of tumor cells from the spleen to the liver [68]. The natural homing properties of EV are attributed to the surface proteins, and integrins have been reported to play a vital role in dictating this organotropism. For instance, α6β4 and α6β1 integrins on the tEV surface direct them to the lung resulting in pre-metastatic niche site formation. On the other hand, αvβ5 on tEVs increases liver metastasis [69]. In addition to integrins, CD151 and Tspan8 on exosomal surface regulate matrix degradation and reprogram tumor microenvironment for metastasis. Rat pancreatic adenocarcinoma cells lose their migratory potential in the absence of CD151 and Tspan8 [70]. Integrin-binding sialoprotein (IBSP) on the breast cancer EVs directs them to the osteoclast cells, resulting in metastasis niche formation in the bone [71]. Detailed information on the other homing receptors that have been shown to regulate EV tropism can be found elsewhere [72, 73]. In addition to the surface proteins of EVs, the surrounding protein corona can affect EV tropism. A recent study showed that coating EVs with albumin enhances their uptake by hepatocytes, endothelial cells, and stellate cells in vivo. This approach can
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improve EV biodistribution and reduce their clearance by the liver macrophages [74]. Overall, tEVs naturally carry homing molecules on their surface which regulate their organotropism. However, tEVs contain other tumorigenic factors, so their clinical safety must be thoroughly investigated before using tEVs for targeted therapy. On the other hand, by understanding factors regulating EV tropism, we could exploit them to generate EVs through bioengineering. 2.2
Active Targeting
2.2.1 Engineering EV Surface
Active targeting involves tailoring EVs by genetic or non-genetical approaches to target them to the site of action. As discussed previously, EVs have the inherent ability to target different cells in the body. However, studies have shown that this innate ability may not be sufficient to deliver the therapeutic cargo molecules [75]. EV surface engineering thus has the potential to target these delivery vehicles with cargo to the site of interest. Also, this will reduce nonspecific targeting and clearance via the mononuclear phagocyte system (MPS). Furthermore, modulating the EV surface can enhance bioavailability and pharmacokinetic properties. The EV surface can be further engineered to carry different antigens, offering potential applications in inducing antitumor immunity [76, 77]. Also, EV surface engineering may facilitate the tracking of EVs in in vivo models. For instance, the EV’s surface can be labeled with fluorescent molecules to track its movement and biodistribution in an in vivo system. Given the chapter’s focus on EV targeting, we have highlighted studies where the EV surface was modified for targeting purposes. There are two different approaches to modifying the EV surface features: (i) genetic engineering of the donor cells from which EV is isolated and (ii) physical or chemical modification of EV surface. Genetic engineering of EV donor cells offers a way to generate EVs carrying targeting peptides or proteins on their surface. As discussed in the previous section, the EV surface has distinct proteins, lipids, and glycan features. For instance, the EV surface predominantly features various transmembrane proteins such as tetraspanin (D63, CD9, CD81), Lamp2b, and CD47 (Fig. 1). The donor cells can be genetically engineered to display the targeting peptides or proteins fused to these transmembrane proteins. Several previous studies investigated the feasibility of creating a fusion transmembrane protein through a genetic engineering approach. The pioneering work by Alvarez-Erviti et al. showed that EVs expressing rabbit virus glycoprotein (RVG) fused with lamp2b facilitate its delivery to the brain [78]. Alternatively, expressing Lamp2b fused with integrin-specific peptide IRGT promotes selective delivery of EVs to tumor tissue in vivo [79]. Recently our group has shown that fusing CD63 with an Fc-binding domain can be used to decorate the EV surface with antibodies, enabling targeted delivery
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of EVs to tumor tissue. These Fc-EVs were targeted toward different antigens including human epidermal receptor 2 bracket (HER 2) and programmed death ligand 1 (PDL1). EVs decorated with antibodies such as trastuzumab and atezolizumab showed around 340-fold and more than 500-fold increased selective uptake by HER2 and PD-L1-expressing cancer cells, respectively [80]. Alternative to full-length antibody coating, previous studies have tested decorating EV surfaces with recombinant llama nanobody. This study showed that nanobody targeting alpha-CD206 on the EV surface increases the cargo delivery to antigen-presenting cells [81]. EVs can also be engineered to express antibody fragments targeting different antigens simultaneously. SMART-Exos are engineered EVs that express single-chain variable fragments targeting CD3 and epidermal growth factor receptor (EGFR) on T cells and cancer cells, respectively. These EVs specifically target EGFRexpressing cancer cells and increase the recruitment of killer T cells, thereby enhancing antitumor immunity [82]. Alternative to tetraspanins, EVs can be engineered to carry nanobodies by fusing them with proteins that bind to glycosylphosphatidylinositol (GPI) on the surface. EGFR plays an important role in cell proliferation and survival. Many cancer cells have high levels of epidermal growth factor receptor (EGFR) expressed on their surface. EVs engineered to carry anti-EGFR nanobody fused with GPI-anchoring proteins demonstrate increased binding to EGFR-expressing cells [83]. Peptides show a promising alternative to large antibodies. Previous studies have shown peptides with specific binding capacity. Targeting peptides could be fused with abundant EV surface proteins for the targeted delivery of engineered EVs. CD47 can be highly expressed on the EV surface in addition to tetraspanin (Fig. 1). Genetic engineering of the N-terminal of CD47 with CDX (FKESWREARGTRIERG) or CREKA peptide results in enhanced delivery of EVs (ExoT) to the brain in vivo model [76]. The GE11 peptide (YHWYGYTPQNVI) and EGF peptide bind specifically to EGFR though the earlier is less mitogenic [84]. Engineered EVs carrying PDGFR transmembrane domain fused to GE11 peptide on its surface selectively migrate toward EGFR-positive tumors in vivo. These EVs with let-7 miRNA inhibited the growth of tumors [85]. Instead of proteins or peptides, EVs can be engineered to present specific glycans, which can influence their targeting capabilities. For example, modifying EVs to display Sialyl Lewis-X or Lewis-X glycans on their surface has been shown to enhance their uptake by endothelial and dendritic cells, respectively [86]. Enhancing EV half-life is crucial for drug delivery. Albumin, with a plasma half-life of about 3 weeks, is commonly utilized to prolong the half-life of drugs and delivery vehicles [87]. Liang et al.
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demonstrated that EV circulation time can be increased by decorating their surface with albumin. To achieve this, EV surface proteins like tetraspanin or Lamp2B were engineered to express an albuminbinding domain. This modification resulted in longer EV circulation, improving their accumulation in lymph nodes and tumors in vivo [88]. Thus, genetic engineering holds promise for redesigning the EV surface and improving target specificity. Also, it avoids the use of any foreign material (e.g., chemicals) to manipulate the EV surface. However, there are some limitations to this approach. It can be technically challenging to engineer and create a stable cell line that expresses fusion proteins for immediate clinical applications. In addition, it is important to choose a suitable EV surface protein to express the target protein or peptide. Previous studies have used CD63, CD9, CD81, Lamp2b, PDGFR transmembrane domain, phosphatidylserine (PS) binding C1C2 domain of lactadherin, and GPC-anchoring peptide to display targeting moieties (Fig. 2) [89]. However, the expression of some EV transmembrane
Fig. 2 Engineering EVs for targeted therapy. There are several strategies to customize the EV surface for precise targeting to specific sites. This image illustrates both source cell-dependent methods (such as genetic and metabolic engineering) and independent approaches (including physical methods and chemical engineering)
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proteins varies with the donor cell. For instance, tetraspanin protein level varies with cell type [89–91]. In addition to the classical tetraspanin markers, our recent study identified 24 proteins with sorting abilities on the EV surface. Notably, TSPAN2 and TSPAN3 were found to be more abundant than the classical EV marker, CD63 [91]. In some cases, fusion proteins are sensitive to endosomal proteases and hence degrade quickly before being packed into EV. For instance, peptide fused with Lamp2b in the N-terminus is susceptible to lysosomal degradation. The addition of a glycosylation motif (GNSTM) to the N-terminal can prevent this degradation [92]. It is important to note that EVs from a single cell are heterogeneous, with varying levels of specific sorting domains. The targeting strategy of fusions with a specific EV-sorting domain will thus only generate bioengineered EVs of the population expressing that particular EV marker. Given these challenges, alternative approaches to modify the EV surface have been explored as discussed below. 2.2.2 Other Engineering Strategies
Rather than genetically manipulating the cells, they can be metabolically tuned to release EVs with surface features of interest. Glycans play an important role in receptor-ligand interaction. For instance, dendritic cell-specific intercellular adhesion molecule-3grabbing non-integrin (DC-SIGN) binds strongly with highmannose residues. Treating cells with kifunensine, a mannosidase I inhibitor, results in the release of EVs enriched with high mannose residues on their surface. These EVs effectively activate dendritic cell-mediated antitumor immune response when loaded with tumor-associated antigens [93]. Alternative to this strategy, cells can be metabolically manipulated to express azide residues. Azidebound EVs from these cells could then be conjugated to different targeting molecules (peptides, proteins, aptamers) via biorthogonal chemistry (Fig. 2). For instance, culturing cells with L-azidohomoalanine, an azide-conjugated methionine analog, results in the synthesis of proteins with the azide group. Alternatively, cells can be cultured with tetra-acetylated N-azidoacetyl-Dmannosamine (ManNAz) resulting in EV surface decorated with azide-conjugated glycans [94]. While these methods can modify the EV surface, it is difficult to control the amount and location of modifications. Also, these approaches are dependent on the donor cells for producing customized EVs, making them unsuitable for EVs isolated from body fluids.
2.2.3 Exogenous Engineering of EV
Exogenous modification facilitates the labeling of EVs independent of the donor cells. This approach is not limited to peptide and protein-based targeting. Any targeting moieties could be introduced on the EV surface including aptamers and small molecules. Furthermore, these modifications are independent of the proteins
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naturally expressed on the EV surface. EVs could be exogenously modified based on physical adsorption/fusion or by chemical modification (Fig. 2). Engineering EVs by Physical Methods
The EV surface could be modified with the help of lipophilic molecules (Fig. 2). For instance, the insertion of EGFR nanobodies conjugated to PEGylated phospholipid (1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (DMPE)) on the EV surface increases its targeting capacity and in vivo circulation time [95]. A similar approach has been employed to improve the EV uptake by dendritic cells. The insertion of mannose-conjugated PEGylated phospholipid (1,2-distearoyl-sn-glycero-3-phosphoethanolamine (DSPE)) on EV surface enhances its uptake by dendritic cells which preferentially recognize high mannose residues [96]. In another study, dioleoyl phosphatidylethanolamine (DOPE) proved to be more effective than DSPE as a lipid anchor for EVs. When DOPE was conjugated with targeting peptides, such as RVG, and added to the EV surface, it enhanced targeting efficiency in vitro [97]. Alternative to phospholipid, cholesterol conjugates can be used to modify EV surface (Fig. 2). Previous work has tested its ability to successfully incorporate siRNA into EV. However, these cholesterol conjugates could also be used for decorating EVs with targeting moieties [98]. While lipids could be a feasible way for EV modification, there are some limitations to this approach. Certain lipids have limited insertion stability. For instance, cholesterol has poor retention on the EV surface, thus increasing the risk of targeting moieties leaching. The EV membrane surface can also be tailored to carry targeting moieties through membrane fusion with customized liposomes. The fusion between EV and liposome can be achieved through repeated freeze-thawing or using PEG. Although hybrid EVs generated by the freeze-thaw method demonstrated increased cellular uptake, it is important to study the impact of this method on EV integrity as the method may compromise the integrity of EVs [99]. On the other hand, PEG-mediated fusion results in a hybrid EV that could efficiently escape macrophage-mediated clearance [100]. Additionally, the incorporation of antibodies onto these hybrid EVs could direct them to the site of interest. For instance, anti-EGFR conjugated hybrid EVs demonstrate enhanced cellular uptake [101]. This approach can be used to simultaneously modify the EV surface and load cargo molecules [99, 102]. However, it is important to consider proper purification strategies to remove any hemi-fused and non-fused particles. There are also other ways to modify the EV surface based on their physical characteristics. Since EVs are negatively charged particles, positively charged moieties could be introduced on the EV surface through electrostatic interaction (Fig. 2). For instance,
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cationized pullulan, a linear glycan polymer made of maltotriose, preferentially coats the EV surface. Pullulan is recognized by the asialoglycoprotein receptor, and hence these modified EVs could be delivered to the liver, where this receptor is highly expressed by hepatocytes [103]. The targeting moieties could be alternatively introduced on the EV surface using peptides with affinity for EV surface proteins (Fig. 2). CP05 (CRHSQMTVTSRL) has a specific affinity for tetraspanin CD63. CP05 conjugated with targeting peptide could effectively drive EVs to the site of action in vivo [104]. Altogether, EVs can be modified effectively without the use of chemicals or genetic manipulations. Engineering EVs by Chemical Methods
Functional groups on the EV surface could be exploited to introduce targeting moieties by covalent reactions. The amino group in the lysine residue, N-termini of protein, or phosphatidylethanolamine is mostly commonly targeted for EV surface modification by chemical methods. Additionally, new functional groups can be introduced to EVs by metabolic engineering as discussed in the previous section. Targeting moieties can be covalently incorporated on EV surface using different methods such as activated N-hydroxysuccinimide esters (NHS), copper-dependent alkyneazide cycloaddition, copper-independent strain-promoted alkyneazide cycloaddition, or based on the hydrazine-aldehyde interaction (Fig. 2) [105–107]. The chemical approach has been widely tested to introduce labeling molecules such as fluorescent dyes and quantum dots [107] but can also be used to attach targeting moieties to EVs. For instance, a previous study developed brain targeting EV using copper-independent chemistry. It involves two steps: (i) introduction of DBCO to amine group on EV surface using NHS and (ii) azide-conjugated cyclo(Arg-Gly-Asp-D-TyrLys) brain-targeting peptide bind to DBCO by copper-free click chemistry [106]. These EVs have enhanced brain tropism and demonstrate enhanced drug delivery compared to unmodified EVs [106]. Thus, the chemical-based method could be an alternative approach for the modification of EVs isolated from biological fluids. However, depending on the reaction, it is important to investigate the impact of chemicals on EV properties. Also, the success of direct EV engineering depends on the proper removal of residual chemicals or unmodified EVs. Mostly size-exclusion chromatography (SEC), ultrafiltration, or density-gradient ultracentrifugation is employed to remove any chemical residues. However, each of these approaches comes with some limitations. For instance, SEC can effectively remove any residues from EVs, but it dilutes the sample at the same time. On the other hand, ultrafiltration can remove residues and concentrate the sample at the same time. However, it is inevitable to prevent the loss of EVs which firmly attach to the membrane. Ultracentrifugation, though
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effective, is a tedious process with limited clinical application. Overall, exogenous engineering of EVs could find immediate applications in the clinical setting if the engineering strategies and purification techniques are optimized. Furthermore, much of the inspiration for EV modification could stem from the field of synthetic nanoparticles. 2.2.4 Other Target Molecules for EV Engineering
Most of the engineering strategies employed for EV targeting are based on peptides, proteins, antibodies, glycans, and lipids. In addition to these targeting moieties, other molecules with potent targeting properties include aptamers and small molecules. However, the targeting abilities of these moieties have mainly been tested with synthetic nanoparticles.
Aptamers for EV Targeting
Aptamers are DNA or RNA of 20–100 nucleotides with a specific target binding capacity. They are smaller (6–30 kDa) and easier to manufacture in larger quantities than antibodies [108]. Unmodified aptamers have a short half-life in vivo (~10 min) and are quickly cleared by renal filtration. The optimized versions of aptamers have a longer circulation time in vivo. The first commercialized RNA-based aptamer Macugen®/Pegaptanib targeting VEGF has a half-life of 9.3 h when injected intravenously [109]. One limitation of the incorporated chemical modifications is potential toxicity. For instance, REG1, a PEGylated RNA-based aptamer, binds to coagulation factor IXa and prevents coagulation, causing severe allergic reactions due to the presence of antibodies targeting PEG in the blood [110, 111]. Hence it is important to consider the toxicity effect associated with unnatural modifications. Previous studies have demonstrated the potential of aptamers in therapeutics and diagnostics. Aptamer, either alone or in combination with other targeting moieties, is effective in targeting with improved circulation time in vivo. For instance, Pegaptanib conjugated to anti-cotinine, an antibody targeting a pharmacologically inert metabolite, effectively blocked tumor angiogenesis and reduced tumor burden in vivo. Further, this modification increases the circulation half-time of this oligobody [112]. There are other aptamers reported to have specificity against cancer-related antigens including 2′-fluoropyrimidine-modified aptamers targeting prostate-specific membrane antigens and RNA aptamers targeting EGFR. In addition to that, some aptamers are being tested clinically. AS1411 is a DNA-based aptamer that targets nucleolin, a Bcl-2 mRNA-binding protein, which has demonstrated significant anticancer properties in preclinical studies [113]. In phase II clinical trials, however, this DNA aptamer showed minimal effect in patients with acute myeloid leukemia (NCT00740441). On the other hand, an RNA-based aptamer, NOX-A12, which targets stromal cell-derived factor-1 (SDF-1), is being tested for anticancer
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properties in relapsed CLL patients in a phase II clinical trial (NCT01486797) [114]. Altogether, aptamers have potent therapeutic properties. They could be used for targeting due to selectivity and affinity for the targeting moieties. In addition to targeting, aptamers can be conjugated to drugs, antibodies, proteins, siRNA, miRNA, and shRNA. Aptamer-conjugated EVs have previously been tested for their application in cancer-targeted therapy. For example, the aptamer sgc8 targets protein tyrosine kinase 7 (PTK7), a receptor highly expressed in leukemia cells [115]. Diacyllipid-conjugated aptamer binds to the EV surface through hydrophobic interaction with phospholipids, enabling selective targeting of PTK7expressing leukemic cells in vitro [116]. In another study, an aptamer was designed to target Mucin 1 (MUC1), a cell surface glycoprotein regulating cellular adhesion and intracellular signaling. It is abundantly expressed in most epithelial cancers [117]. EVs coated with amine-conjugated MUC1 aptamer targets specifically MUC1overexpressing colon cancer cells in vitro [118]. In addition, the abovementioned AS1411 aptamer (anti-nucleolin) has further been functionalized to doxorubicin-loaded EVs, which reduced tumor size by twofold and significantly improved survival rates compared to non-targeted EVs in animal studies [119, 120]. Thus, aptamer has the potential application in targeted therapy. Further, in vivo studies could shed light on the targeting efficiency, immunogenicity, and pharmacokinetics of aptamer-conjugated EVs. Small Molecule-Based EV Targeting
Small molecules are often used in synthetic nanoparticles to direct them to specific sites of interest. These molecules can be easily conjugated to EVs from any source, offering a more cost-effective and faster alternative to genetic engineering approaches. For instance, cancer cells express a higher number of folate receptors compared to healthy cells [121–123]. Previous studies have demonstrated that synthetic nanoparticles can be effectively targeted using folic acid [124, 125]. This suggests that folic acid could potentially find application in targeting extracellular vesicles (EVs). EVs coated with lipid-conjugated folate were demonstrated to target folate receptor-expressing tumor cells both in vitro and in vivo [126, 127]. Previously, EV uptake via direct membrane fusion has been shown to be rare [3]. Here, the authors were however able to demonstrate that these folate-displaying EVs deliver their cargo directly into the cytosol, bypassing receptormediated endocytosis [126]. Thus, folate-mediated EV delivery not only ensures targeted delivery but also overcomes a significant challenge in drug delivery by preventing the cargo from being trapped in endosomes.
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Challenges and Possibilities with EV-Based Targeting As aforementioned, EVs hold immense promise as therapeutic entities that can convey beneficial properties of their cell source, be engineered to express targeting and therapeutic fragments on their surface, and be loaded with a great range of therapeutic cargo, including small molecule drugs, chemotherapy, and nuclei acidbased therapies. Paradoxically, one of the main hurdles to overcome for EV-based therapies still lies in targeted delivery, as discussed below together with suggested approaches to overcome these limitations.
3.1 Pharmacokinetics
Systemically injected EVs have been shown to be mainly distributed to the organs of the MPS with the liver, spleen, and lungs often reported as the predominant organs of accumulation of injected EVs [61, 128]. In addition, EVs also circulate and accumulate in other tissues throughout the body, including kidneys, brain, and tumor tissue in tumor-bearing mice [61, 128–130]. The distribution pattern of EVs is thus similar to that of other nanoparticles with the predominant fraction designated to clearance by macrophages of the MPS [131–133]. Several strategies to evade immune clearance have been suggested, including EV surface expression of the anti-phagocytic moieties CD47 as discussed above. Another example involves scavenger receptor class A (SR-A), which is present in macrophages and has been shown to play a role in the clearance of EVs [129]. Saturation of SR-A by dextran sulfate resulted in lower liver and higher tumor accumulation of therapeutic EVs in a breast cancer model [129]. Another study identified macrophage recognition of phosphatidylserine (PS), as an important uptake mechanism. They showed that incubation with annexin V, which binds PS, resulted in a decreased macrophage uptake and that pretreatment with PS-expressing liposomes reduced the liver clearance of EVs [134]. Another approach involves camouflaging β-galactosides on the surface of EVs to reduce the liver clearance [135]. Other clearing mechanisms focus on EV phagocytosis governed by macrophage C-type lectin receptor [135], recognition of EV-expressed sialic acid by CD169positive macrophages [136], and clearance due to complement activation by the presence of complement protein C5 on EVs [75]. The MPS clearance explains the short half-life of EVs, which has been reported to be from 2 to 20 min in circulation [128, 137]. In fact, the abovementioned strategies for overcoming the MPS uptake also increase the circulation time of EVs. As discussed above, approaches including PEGylation [95] and, more recently, EVs displaying albumin-binding properties [88] have been successfully explored to increase the half-life of EVs. Interestingly, the albumin-binding EVs displayed greater accumulation in
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tumor tissue and lymph nodes compared to control EVs. Of note, the immune uptake of EVs could also be exploited for EV-based cancer vaccines to stimulate the immune system’s recognition and activation. 3.2
Immunogenicity
3.3 Route of Administration
Two decades ago, EVs were shown to function as a novel cell-free tumor vaccine in murine models, and two clinical trials conducted in 2005 with EVs pulsed with tumor antigens showed that these are safe in humans [138]. The immunotherapeutic effect was however modest. More recent research has now elucidated the reasons for this limited response, including the importance of the cell origin, cargo, and adjuvants as discussed in recent reviews [3, 138]. In fact, multiple preclinical studies have shown that EVs have minimal toxicity and immunogenicity from various cell sources. Human embryonic kidney (HEK293T) cell-derived EVs, which are widely used in EV therapy studies, did not have any immunotoxicity in vitro when tested on human monocytes (THP-1 and U937) [139] or human hepatocytes (HepG2) [140]. Several reports of HEK293T EVs have also shown nontoxicity or minimal toxicity and immunogenicity in mice studies with single [140] as well as repetitive dosing [141]. Similarly, EVs from mesenchymal stromal cells [142, 143], immature dendritic cells [78], bovine milk [144], and erythrocytes [130] have been reported to be minimal or nontoxic when assessed in vivo. Results of toxicity assessments from clinical trials of allogenic EV-based therapies are still awaited, but the preclinical data and the fact that patients are exposed to allogenic EVs in various cell therapies, blood product transfusions, organ transplantation, etc., all indicate that EVs are well tolerated. From a safety perspective, the complexity of EVs necessitates rigorously controlled isolation and characterization within Good Manufacturing Practice (GMP) standards for clinical applications. Beyond this, other considerations, such as the optimal delivery methods for EVs, must also be addressed before EVs can be fully translated into clinical settings. Several studies have explored the local administration of therapeutic EVs by intratumoral injections [75, 130, 145–147]. As expected, this leads to a therapeutic effect on the treated solid tumor, accompanied by an increased accumulation of EVs in the tumor tissue [75, 130, 145, 147]. Though these studies show important proof of concept for the use of EV-based therapies in tumor settings, the primary therapeutic potential for EVs in cancer predominantly lies in the treatment or prevention of disseminated disease. Most EV distribution studies have also focused on systemic treatments with intravenous (IV) [61, 75, 79, 86, 128, 132, 137, 146–150], intraperitoneal (IP) [61, 130, 137, 151–153], and subcutaneous (SC) [61, 137, 150] injections being the most common administration routes. Other routes such as oral [137, 149], intracerebral
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[137], and intramuscular [154] injections have also been explored. Comparison between studies is difficult due to differences in EV source, isolation strategy, dosing, and not least different means of tracking the EV biodistribution, including the use of luminescent and fluorescent fusion proteins, nuclear tracers, and fluorescent dyes. A few studies have however conducted direct comparisons between different injection routes. In a study employing a lipophilic near-infrared fluorescent dye to track EVs, the distribution of SC and IP-injected HEK293T EVs had significantly lower liver and spleen accumulation compared to IV injection [61]. The authors further showed that SC injections resulted in lower systemic tissue distribution, including liver and spleen, and IP injections marginally higher, compared to IV injections. Similarly, another study employing a radioactive label to trace hepatic EVs compared to SC and IV treatment showed that SC injections had lower spleen and liver accumulation [150]. In addition, the SC-injected EVs had a greater distribution to lymph nodes compared to IV injections. These results were further corroborated in a study employing highly sensitive luminescent proteins (NanoLuc and ThermoLuc) fused to EV-anchoring proteins [137, 155]. The authors showed that SC injections resulted in lower levels of EVs in the liver and plasma compared to IV and IP injections, which had similar levels. As previously shown the plasma levels of IV and IP injections were rapidly cleared with only 10% remaining after 5 min. Interestingly the SC injections showed, albeit low, stable plasma levels for 24 h after a slight increase from 1 to 60 min, indicating a more sustained release. In a more recent study, the pharmacokinetics of EVs were also studied in nonhuman primates. Macaques were IV injected with repeated but increasing EV doses at weekly intervals. The EVs could be detected within an hour in CSF, liver, and spleen. At lower doses (7 × 1010–2 × 1012) the half-life following IV injections was 36–42 min, whereas a higher dose (9 × 1012) resulted in a shorter half-life of 11 min. Of note, EV circulation time decreased after repeated IV administration and also when repeated in inverse order, possibly due to immune clearance [155]. The authors further investigated intranasal delivery, which led to negligible systemic uptake. The route and dose of injection thus significantly influence the biodistribution and plasma half-life of EVs and must be carefully considered when designing EV therapy-based trials. 3.4 Uptake and Endosomal Escape
The process of internalization of EVs into cells is still to be unraveled in detail. Though membrane fusion events have been reported [156], most data support endocytosis pathways, especially clathrin/caveolin-mediated endocytosis, to be the most common cellular access route of EVs [157, 158]. The internalized EV will thus be trapped inside an early endosome designated for degradation in the lysosome or recycled back to the plasma membrane. However, a substantial body of evidence supports the functional
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delivery of EV cargo, including RNA, suggesting that some EVs successfully undergo endosomal escape, although at very low level [158]. The mechanism is still debated, but rupture or, more likely, fusion with the endosome during its maturation process has been suggested [158]. In fact, preventing acidification and cholesterol accumulation during endosomal maturation has been shown to inhibit EV cargo exposure [159]. Still, only a fraction of the EVs successfully deliver their cargo into the cytoplasm, and enhancing endosomal escape is a critical strategy for improving the functional delivery of therapeutic EVs. Endosomal escape-enhancing compounds, such as chloroquine, rupture the endosome through swelling caused by the influx of Cl-, H+, and H2O as the compound is protonated during endosome maturation. This approach has been successfully explored for various biological agents as well as EVs [160, 161]. In addition, methods overcoming endosomal entrapment can be drawn from bacteria and viruses. These strategies include fusion, rupture, and pore formation of the endosomal membrane as well as the proton sponge effect as previously described [162]. Of note, several viral endosomolytic peptides, including arginine-rich peptides, such as the cell-penetrating viral peptide TAT (transactivator of transcription) [45, 161], and non-cationic peptides, such as the pH-responsive amphipathic viral protein mimic GALA [163], have been successfully used to increase endosomal escape with improved EV cargo delivery. Furthermore, pseudotyping EVs by expression of viral envelope proteins, such as the fusogenic vesicular stomatitis virus (VSV-G) protein, have been shown to greatly enhance the delivery of functional EV cargo [164, 165]. Endosomal escape is thus another bottleneck of EV therapeutics for which promising strategies are being explored. Further combinational approaches that integrate endosomal escape mechanisms with tissue targeting methods and ways to overcome or stimulate immunogenic properties, depending on the indication, are still awaited. Such advancements are crucial to fully address the delivery challenges associated with EVs.
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Conclusion There is a great body of evidence of successful targeting with EVs against numerous cancer types and by different approaches. The extent of the targeting and effect of the general EV biodistribution pattern are however often poorly or not reported at all, making it difficult to assess how effective the EV targeting strategy truly is. The cancer targeting of EVs is thus highly encouraging, but there is an unmet need for detailed pharmacokinetic studies, preferably in nonhuman primates, of promising candidates, which is needed for the next step toward the clinical transition of tumortargeting EVs.
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Chapter 8 Peptide-Assisted CRISPR/Cas9 Delivery to Tumors Oskar Gustafsson, Samir EL Andaloussi, and Joel Z. Nordin Abstract The field of peptide-mediated cargo delivery has seen an evolution in the last four decades, starting with the naturally derived transactivator of transcription (TAT) and penetratin cell-penetrating peptides (CPPs) and gradually evolving to rationally designed peptides for specific cargos. Early work in the field focused on oligonucleotide CPP delivery but has lately pivoted to gene editors, such as clustered regularly interspaced short palindromic repeats (CRISPR)/Cas9. CPP-mediated Cas9 delivery has great potential as a cancer treatment to treat the underlying cause of the disease and/or disrupt cancer-specific cellular pathways. The combination of CRISPR/Cas9 with conventional chemotherapy is especially exciting. Furthermore, novel peptide designs can create activatable peptides that are capable of responding to the tumor microenvironment and thereby reducing off-target effects of the peptide and the CRISPR/Cas9 system simultaneously. The work in this field is still in its early stages, but results are promising, and progress is being made. Currently, the main application of peptides in cancer is the use of targeting peptides to increase cargo delivery to the tumor, but research into other peptide applications is progressing. Lastly, developments in peptide-mediated gene editor delivery into immune cells are promising to enhance cellmediated cancer treatments, such as augmented chimeric antigen receptor (CAR) T cells. Thus, the future of peptide-mediated CRISPR/Cas9 delivery to tumors is exciting and promising. However, much work is still required in the field, such as showing the clinical benefit of CPP-mediated CRISPR/Cas9 delivery to cells. Key words CRISPR/Cas9, RNP, Cell-penetrating peptides, Activatable cell-penetrating peptides, Tumor, Cancer
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CRISPR/Cas9 Applications in Cancer Treatment and Research The primary cause of cancer is due to DNA damage (e.g., singlenucleotide changes, deletions, and fusions) but also epigenetic dysregulation [1, 2]. Mutations that lead to cancer typically cluster around the mechanisms controlling cellular division, DNA repair, tumor suppressor genes, and oncogenes—genes that, once mutated, drive cancer formation. These mutations collectively result in abnormal cell growth, deviating from the normally stringent control of cellular division.
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The evolution of mutations commonly progresses slowly, often taking years to develop. However, the process commonly accelerates once the DNA repair mechanisms are sufficiently compromised and immune evasion mechanisms are established. These disruptions lead to alterations in numerous cellular pathways. The interactions among these mutations foster a form of cellular evolution within the cancer cell population, resulting in cells with altered structures, motility, and metabolism. The changes enable cancer cells to grow and survive in challenging environments [3]. Significant efforts have been made to characterize the mutations and pathways driving cancer growth. Unfortunately, cancer is an extremely heterogeneous disease, with many patients possessing a unique set of mutations in combination with mutations that are common in that specific cancer type. Moreover, different subpopulations of cells within the same tumor often harbor distinct mutations [4]. Despite this variability, certain mutations are frequently shared within a tumor, likely those that emerged early in the evolution of the tumor and are key drivers of cancer progression. The prerequisite of mutations in cancer makes them an attractive target, essentially addressing the root of the disease. Consequently, the CRISPR/ Cas9 system has garnered significant attention in cancer research. While the CRISPR system comprises a large family of enzymes and gene systems, the CRISPR/Cas9 system is currently the most frequently used. The CRISPR/Cas9 system is an adaptive immune mechanism found in bacteria and archaea, used to defend against phages by cleaving phage DNA and thus halting infection. Researchers have adopted this system to target specific DNA sequences in almost all types of cells, including mammalian and human cells, due to its ease of use facilitated by RNA-guided targeting. This allows for simple target redirection by changing the guide RNA sequence, making it a powerful tool in cancer to address the large variation in genome targets observed between patients. Cas9 forms a ribonucleoprotein (RNP) complex with its guide RNA (gRNA), which in bacteria consists of a trans-activating CRISPR RNA (trRNA) and a CRISPR RNA (crRNA) but has been engineered into a single guide RNA (sgRNA) for simplicity of use and at the same time increasing the efficiency. This RNP complex scans the genome for protospacer adjacent motif (PAM) sites. Upon finding a PAM site, the Cas9 protein samples the adjacent DNA, and if the DNA sequence matches the guide RNA target sequence, the DNA cleavage machinery is induced, resulting in a double-stranded break (DSB). The cell repairs the DSB, commonly using the nonhomologous end-joining (NHEJ) system, frequently leading to the introduction of insertions and deletions (indels). If the Cas9 target is chosen correctly, then the formation of indels generally results in frameshifts within the targeted gene’s coding region, creating a premature stop codon and consequently causing a gene knockout (KO). This is the most straightforward
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method involving Cas9 and cancer, where the Cas9 is directed toward genes associated with apoptosis resistance, chemoresistance, proliferation, migration, invasion, etc. to inhibit tumor growth and metastasis [5]. A limitation of this approach is that editing non-mutated DNA sequences can lead to unintended modifications in bystander cells affected by the treatment. Therefore, it is crucial to either choose genes that are non-essential to healthy cells, target mutated DNA sequences, or utilize highly cell-specific delivery vectors for the Cas9 machinery. Another application of Cas9-mediated NHEJ is knockout screens, where Cas9 is transduced with gRNAs targeting all genes or selected pathways/genes of interest in the human genome. This can be done in both healthy and cancer cells to identify which genes are essential for the transition from healthy to cancerous states, as well as those critical for cancer growth and metastasis [5]. This application represents one of the most significant uses of Cas9 in cancer research, serving as a powerful tool to understand the biology of cancer. Similarly, genomic screens can be used in immune cells to identify genes involved in cancer cell killing [6, 7]. In contrast to NHEJ, homology-directed repair (HDR) can be used to restore a gene to its functional state or revert an oncogene to its normal state. HDR works by providing the edited cells with a DNA template that matches the sequences flanking the doublestrand break (DSB) site, with the desired sequence positioned between the matching arms of the template. The cell subsequently uses this DNA template to repair the damaged DNA, incorporating the edit of interest. This method can be employed in both the coding and promoter regions, thus enabling a broader array of repair mechanisms compared to NHEJ. However, HDR is often inefficient, with most repairs completed by error-prone mechanisms like NHEJ. Additionally, cells must be in the correct replicative state for HDR to occur, limiting its usefulness in vivo [8]. However, small molecules inhibiting the NHEJ pathway can considerably increase the HDR efficiency [9]. Despite these limitations, HDR can be highly valuable as a research tool to create specific mutations in a controlled manner and can be used to modify immune cells for immunotherapy purposes. An example of this would be the generation of chimeric antigen receptor (CAR) T cells, where the CAR receptor is knocked in at the TRAC locus, which is beneficial for their function [10, 11]. A solution to the shortcomings of NHEJ and HDR lies in the recently developed Cas9 fusion proteins, such as base editors and prime editors. These engineered versions of Cas9 have additional domains that enable precise editing of single base pairs (base editors) or multiple base pairs (prime editors). Base editors combine Cas9 with a deaminase enzyme capable of converting cytosine to thymine or adenine to guanine. New base editors with various
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conversion capabilities are being developed, potentially allowing for the conversion of any single base to another. Prime editors are more complex, consisting of a reverse transcriptase fused with Cas9, along with a long guide RNA that acts as a template for the reverse transcriptase. This allows for the specific integration of defined DNA sequences. These new editors are more efficient and versatile than HDR, as they do not require a DNA template or a specific cellular cycle state, however, they cannot knock in as long sequences as HDR (the upper limit for prime editing is typically around 44 bp, with newer derivatives of prime editing efficiently inserting up to 250 bp [12]). Cas9 and related proteins have also been engineered for transient modulation of gene expression. This can be done by sterically blocking transcriptional and/or epigenetic modulation, leading to the increase or decrease of the specific gene expression [13, 14]. These techniques are mainly used in the study of cancer [15, 16]. One of the main uses of various Cas9-based DNA modification tools has been in cancer screening, targeting cancer-driving mutations and genes in both cancer and immune cells [5]. The simplicity of the CRISPR/Cas9 system allows for large-scale screens, facilitating the identification of genes associated with immune evasion, T cell-mediated tolerance, antigen presentation, immune cell activation, and more [6, 7, 11]. Identifying these driver genes enables the development of optimal therapeutic approaches and immunomodulatory drugs [17]. These screens can be conducted in vitro or in vivo, allowing for the selection of cancer-inhibitory or enhancing mutations [5]. While these screens do not directly impact a patient’s cancer, they significantly impact the field of cancer research and represent one of the most widely used applications of the CRISPR/Cas9 system concerning cancer.
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Delivery of CRISPR/Cas9 Can Be Performed with DNA, RNA, and RNP Modalities The potential to repair, restore, or inhibit mutated genes of cancer cells allows for a fascinating approach to treating cancers. However, one limitation of this concept is efficient delivery to a large-enough fraction of the cells in a tumor, as unmodified cells can potentially outcompete the edited cells. Cas9 and its derivatives can be delivered in three different formats: as DNA, RNA, or protein (Fig. 1), each with its advantages and disadvantages. DNA is generally considered the easiest but therapeutically least useful format due to the risk of plasmid genomic integration, which can lead to long-term Cas9 expression, increased off-target effects, and a higher risk of immunogenic reactions [18–21]. A commonly used example of this is the use of adeno-associated virus (AAVs) to deliver non-integrating DNA cargo
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Fig. 1 Gene editing: cargo, vectors, and editing types. Top: The three different formats of Cas9, DNA, mRNA, and RNP. Middle: Common delivery vectors and their capability to deliver one or several formats of Cas9. Bottom: Minimum steps required for successful intracellular delivery and the most common and useful editing techniques employed to date
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[22, 23]. These vectors have proven to be generally safe and effective. However, recent data show they have a high risk of genomic integration, resulting in long-term expression and genotoxicity [24]. Furthermore, large parts of the human population have antibodies against many of the most commonly used AAV serotypes, rendering them ineffective from a gene editing point of view [24]. The second option is delivery as mRNA, which eliminates the risk of genome integration and has a shorter half-life, reducing the duration of Cas9 expression and associated risks [25–29]. However, mRNA is not as stable as DNA and requires careful manufacturing and vector packaging. The delivery of mRNA has been significantly advanced by the development of lipid nanoparticles (LNPs) and research related to SARS-CoV-2 vaccines. The development in the LNP field has led to a plethora of publications showing mainly mRNA delivery, but also protein delivery, of CRISPR/Cas9 [25, 30]. These results have been astonishing, with clinical trials ongoing for LNP-mediated CRISPR/Cas9 delivery [31]. However, successes are mainly limited to the hepatic system, with little extrahepatic successful delivery. It is important to note that progress has been made in targeted LNP delivery, either to organs or to specific cell types [32, 33]. Lastly, some LNP formulations have been shown to cause a strong innate response, which is advantageous during vaccination but potentially hampers their clinical utility in certain circumstances [34]. The third format is the delivery of Cas9 as a preformed RNP, which has garnered considerable interest. The RNP remains in the cell for the shortest duration of time of the three delivery modalities and is expected to produce the fewest off-target edits [35]. A drawback of this format is that Cas9 antibodies are present in a large portion of the human population since it is derived from the human pathogenic bacteria Streptococcus pyogenes making it a prerequisite to package the Cas9 RNP in a way that antibody recognition is prevented [36]. It is not only the format of Cas9 that is important; moreover, the delivery vector is of utmost importance, and in general the delivery of Cas9 delivery can be divided into physical- and particle-based methods. Physical methods, such as electroporation, are generally unsuitable for in vivo use except under very specific circumstances in small areas, making them generally impractical for cancer treatment. Particle-based methods include viruses and synthetic nanoparticles, such as the aforementioned LNPs. Viruses are naturally very efficient as a delivery system but suffer from drawbacks such as preexisting antiviral antibodies (e.g., AAV), immune reactions, and toxicity [24, 37, 38]. Lastly, synthetic nanoparticles can be utilized for Cas9 delivery. These can be made from a variety of materials, including metals, DNA, polymers, lipids, and peptides. Peptides are interesting as delivery vectors due to their flexibility. They can serve either as targeting agents or as cellular uptake
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and cytoplasmic release enhancers (e.g., CPPs), depending on the peptide sequence used (Fig. 1). Cancer cells frequently overexpress receptors that targeting moieties can be developed against, such as antibodies or peptides, making peptide-based delivery vectors a promising approach for targeted cancer therapy. While cancer-targeting antibodies represent a fast-growing field of research, with more than 100 different antibody-drug conjugates in development, they suffer from several drawbacks [39]. These drawbacks include the large size of antibodies (approximately 150 kDa), which limits their ability to penetrate malignant tissue, and the limited cargo available for conjugation, thus far limited to small molecules and oligonucleotides [40–44]. Additionally, the process of conjugating cargo to antibodies is expensive, time-consuming, and nonselective [45]. In contrast, peptides do not suffer from these drawbacks. Their small size, ease of synthesis and modification, and the ability to incorporate nonnatural amino acids allow for simpler and cheaper production. Furthermore, peptide chemistry is well-defined, resulting in low product heterogeneity [46, 47]. Peptides are also generally considered safe due to their rapid proteolytic cleavage and clearance by the kidneys and liver, producing nontoxic metabolites and exhibiting low immunogenicity [48, 49]. However, due to the small size of peptides, careful design is essential when selecting a cargo to conjugate with the peptide, as receptor binding and selectivity could be affected by the cargo.
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Peptides as Targeting Moieties for Tumor Delivery To date, a multitude of different targeting peptides have been published, and they have been excellently reviewed by Beck-Sickinger et al. [50]. One example of peptide-assisted targeting is the R8-dGR modified cationic liposome, which has been used to deliver Cas9 and sgRNA plasmids to disrupt the hypoxia-inducible factor-1 alpha in the BxPC-3 pancreatic cancer cell line [51]. This system was also tested in vivo, where the liposomes were injected intravenously (IV) to treat subcutaneously (SC) or IV-established BxPC-3 tumors with promising results. The treatment reduced the growth of the SC-established tumor and inhibited the metastasis of the IV-injected pancreatic cells [51]. The use of the iRGD peptide is common for tumor delivery due to its tumor-homing properties and its ability to increase endocytosis, enhancing tumor uptake [52]. These properties were employed by fusing the iRGD peptide to target liposometemplated hydrogel nanoparticles for the delivery of Cas9 protein and sgRNA minicircles to U87 glioblastoma cells [53]. This successfully delivered Cas9 into U87 cells in vitro and increased the accumulation and delivery of the nanoparticles to tumors, both
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subcutaneous and intracranial tumors, in vivo after IV injection. Treatment with the nanoparticle formulation resulted in reduced tumor volumes and increased survival in mice. Another example of the use of iRGD is the nanoparticle formed from the copolymers NTA-disulfanediyldipropionate-polyethyleneglycol-b-polycaprolactone (NTA-SS-PEG-PCL), iRGD-PEG-bpolyasparte-g-1,4-butanediamine, the antitumor photosensitizer chlorin e6 (Ce6), and Nrf2 targeting Cas9 RNP [54]. The iRGD peptide increased tumor targeting in vivo, while the Ce6 enabled endosomal escape through the production of light-induced reactive oxygen species (ROS). Although ROS generation has been used to kill cells, tumor cells often develop antioxidant mechanisms to cope with excessive ROS. This makes the choice of Nrf2 as the Cas9 target particularly interesting [55]. Nrf2 is an antioxidant gene, and its disruption sensitizes the tumor to Ce6-induced ROS generation. This treatment showed promising results, demonstrating a synergistic effect between Ce6 and Cas9, with decreased CEN-2 xenograft tumor growth and 80% survival after 60 days when the experiment was terminated compared to the control group. An obvious drawback of this approach is the need for direct light stimulation.
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Peptides as Delivery Vectors of CRISPR/Cas9 While peptides can assist in targeting treatment to specific cells, they do not solve the bottleneck of functional delivery, namely, endosomal cargo escape. Proteins and large nucleic acid species generally do not escape the endosomes, and it is believed that only a small portion (typically less than 1%) of endocytosed cargo escapes the endosomes even when using endosomal escape moieties, meaning that the vast majority of cargo is degraded in the lysosomes [56]. Several systems for cargo escape have been developed to ameliorate this limitation. One of them is CPPs, which are generally short (10–30 amino acids) peptides that can enter the cytoplasm of living cells to deliver membrane-impermeant cargo [57]. These peptides can be combined with cargo in a covalent or non-covalent fashion and have been shown to enhance cellular uptake and endosomal escape both in vitro and in vivo. The first CPP reported was the TAT peptide, derived from a human immunodeficiency virus protein and can deliver both nucleic acid and proteins [58, 59]. CPPs are derived both from natural and rationally designed peptides with many examples in both categories. These peptides’ uptake and release mechanism not fully understood; however, two main theories have been proposed. The first is direct penetration of the plasma membrane to gain access to the cytoplasm, while the second is endocytosis-based and subsequent induced endosomal escape. Initially, the direct penetration
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hypothesis gained significant support. However, it was quickly challenged since several studies showed that the observed direct penetration effects are due to artifacts from cell fixation [60, 61]. The endocytosis hypothesis posits that CPPs and their cargo are internalized via endocytosis, followed by a rupture event in the endosomal pathway. Determining the exact mechanism is challenging due to the complicated relationship between uptake and release with factors such as CPP design, concentration, cargo, and cell type. Most peptides in use are cationic and are believed to bind to ubiquitous anionic membrane proteins, carbohydrates, and the membrane itself of recipient cells, with some also binding scavenger receptors [62–70]. As mentioned, the CRISPR/Cas9 system can be delivered as DNA, mRNA, or RNP, with RNP likely being the most advantageous delivery format for the above-mentioned reasons. Consequently, it is unsurprising that many examples of peptide-mediated CRISPR/Cas9 delivery have utilized the Cas9 RNP format. An early example of peptide-mediated CRISPR/Cas9 delivery is the work by Ramakrishna et al. where a maleimide-linked 9R CPP was covalently tethered to the Cas9 protein and complexed with the guide RNA [71]. This complex was able to transfect cells successfully, although with low efficiency. The low efficiency could partially be attributed to the interference of the conjugated cationic CPPs with Cas9’s interactions with the anionic guide RNA and DNA, potentially explaining the suboptimal results observed after several treatment rounds [71]. A more recent example involves the work by the Doudna Lab in 2017 and a follow-up in 2023, where 1–7× nuclear localization signals (NLS) were added to Cas9, functioning similarly as a CPP. This naked RNP was successfully delivered into neural progenitor cells and brain parenchyma following injection into the cerebrospinal fluid [72, 73]. A drawback of using covalently conjugated peptides is that the Cas9 RNP is exposed to the environment without protection, which may lead to Cas9’s and/or the gRNA’s rapid degradation. This exposure can be particularly detrimental in environments rich in proteases, nucleases, and neutralizing antibodies, such as the bloodstream or the tumor microenvironment. The directly conjugated approach is more likely to work in environments such as the CNS, where the cerebrospinal fluid is not as rich in degrading enzymes or neutralizing antibodies, as showcased by the Doudna labs work in this field. Conjugated CPP and Cas9 are contrasted by the use of CPPs that interact with the Cas9 cargo using interactions such as electrostatic, hydrophobic, and more, to form nanoparticles with the Cas9 RNP. The main interaction type is generally the electrostatic interactions between the anionic RNP and cationic peptides, forming a protective shell around the Cas9 cargo. An early example of this approach is the work by Lostale´-Seijo et al., who used an
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amphipathic peptide while varying the side groups to optimize the hydrophobicity, to deliver Cas9 RNPs in vitro to HeLa cells [74]. To the best of our knowledge, this represents the first published CPP-mediated supramolecular strategy for Cas9 RNP delivery. Nonconjugated CPP approaches offer greater flexibility in peptide composition, as too much cationic charge linked with Cas9 can disrupt the Cas9-gRNA interaction during RNP formation, and the ratio of peptide to Cas9, as multiple CPPs may be required per Cas9 to achieve efficient delivery. Non-covalent CPPs do not always need to form nanoparticles with the RNP to be effective. For instance, Krishnamurthy et al. developed shuttle peptides, including the S10 peptide, for delivering Cas9 RNP into cultured ciliated and non-ciliated epithelial cells, as well as mouse airway epithelia [75]. More recently, the same lab demonstrated the delivery of base editor RNPs to rhesus monkey airway epithelial cells in vivo using a derivative of their original S10 peptide, called S315 [76]. Ongoing research continues to explore both covalent and non-covalent RNP delivery methods [77–81].
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CPP-Mediated CRISPR/Cas9 Delivery to Tumors The majority of published studies using CPPs for delivery of gene editors focus on in vitro experiments, often using cancer cell lines, primarily due to the challenges associated with in vivo systems. Nevertheless, a few promising studies have demonstrated Cas9 delivery to tumors in vivo. Khairkhah et al. recently reported the successful delivery of Cas9 plasmids complexed with the LL37 peptide to subcutaneous C3 tumors via tail vein injection [82]. This approach reduced in tumor volume and 100% survival at 60 days, following NHEJ-mediated knockout of the human papillomavirus 16 (HPV16) oncogene. The C3 cells used in these studies are primary epithelial mouse cells transformed with HPV16, rendering them cancerous. The LL-37 peptide is a human antimicrobial peptide with cell-penetrating properties previously demonstrated to facilitate DNA delivery in vitro [69]. Moreover, the antimicrobial properties of LL-37 could offer synergistic antibacterial and antiviral effects in addition to the antitumoral effects demonstrated by Khairkhah et al. [83] and could enhance cancer therapy by upregulating the immune response and modulating inflammation, as reported in ovarian cancer studies [84]. The general drawback of CPPs—non-specificity—is partially mitigated by the LL-37 peptide and the chosen target. The cationic LL-37 peptide exhibits selectivity for anionic membrane compartments, such as cholesterol-rich lipid rafts, which are often upregulated in cancer cells. LL-37 binds to glycosaminoglycans, found on most cell membranes, similarly to most amphipathic CPPs
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[62, 85]. Additionally, targeting HPV-introduced oncogenes ensures that any nonspecific Cas9 delivery would result in an inactive Cas9 due to the lack of genomic targets; however, the off-target edits need to be carefully considered [86]. Another example of CPP-mediated intertumoral Cas9 delivery is the work done by Wang et al., where CRISPR/Cas9 plasmids were complexed with a cationic alpha-helical polypeptide, poly(γ-4((2-(piperidin-1-yl)ethyl)aminomethyl)benzyl-l-glutamate) (PPABLG), and then PEGylated to form PEGylated nanoparticles (P-HNPs) [87]. The P-HNPs efficiently delivered Cas9 plasmid to HeLa cells in vitro and in vivo following intertumoral (IT) injections. In vivo editing rates reached 35% for NHEJmediated knockout of the polo-like kinase 1 gene. This resulted in a >71% reduction in tumor growth and increased animal survival to 60% at the end of the 60-day experiment compared to the control group where 0% survived. However, for the intertumoral experiments, the P-HNPs containing Cas9 plasmid and sgRNA had to be injected separately on different days, necessitating daily intertumoral injections on 12 consecutive days. Although promising, these results highlight the need for further development before reaching clinical relevance. Additionally, the authors demonstrated effective editing in fibroblasts, dendritic cells, and human endothelial progenitor cells, suggesting that P-HNPs have broad applications. Both Khairkhah et al. and Wang et al. utilized plasmid formats for delivering the CRISPR/Cas9 system, likely due to their consistent anionic charge and stability. This approach has been corroborated by others that use CPP-mediated delivery of plasmid-based CRISPR/Cas9 for tumor treatment [51, 88]. However, plasmid-based Cas9 delivery has notable limitations, such as the long-term Cas9 expression and risk of genomic integration as mentioned above. Few examples of CPP-mediated Cas9 RNP delivery for cancer treatment exist to date. One notable study by Kim et al. utilized low molecular weight protamine (LMWP) fused to Cas9, forming ternary RNP complexes with gRNA [89]. The LMWP is a membrane translocator peptide sourced from a naturally derived CPP. This system demonstrated effective delivery both in vitro and in vivo. In A549 cells, in vitro NHEJ indel rates reached 43.9%, with a significant reduction in KRAS protein expression by approximately 75%. In vivo, intertumoral injections on two consecutive days resulted in a 50% reduction in KRAS fluorescence in A549 xenografts. Additionally, combining KRAS-targeting Cas9 RNP nanoparticles with anticancer drugs revealed that AZD6244, a MEK inhibitor, synergized with KRAS knockout, leading to a 73% inhibition of tumor growth. The authors suggest that this synergy is due to AZD6244 targeting the MEK pathway, which is closely associated with KRAS.
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Table 1 Summary of selected studies of CPP-mediated CRISPR/Cas9 delivery to tumors Summary of selected peptide-mediated CRISPR/Cas9 tumor delivery Cas9 format Animal model Administration Vector IV injections every 3rd day for 15 days
Result
R8-dGR-modified cationic liposomea
Disrupt the Decreased hypoxiatumor inducible growth and factor-1 alpha metastasis [51]
IV injections Cas9 protein SC and 3 times a and intracranial week for gRNA U87 tumors 3 weeks minicircle
iRGD-targeted liposometemplated hydrogel nanoparticlesa
PLK1 disruption Decreased tumor growth, increased survival [53]
Cas9 RNP
SC CEN-2 xenograft tumors
NTA-SS-PEG-PCL, Ce6 ROS iRGD-PEG-bgeneration polyasparte-gand Cas91,4mediated butanediaminea Nrf2 disruption
Decreased tumor growth, 80% survival at day 60 [54]
Cas9 and gRNA plasmid
SC C3 tumors IV injections every 7th day for 3 weeks
LL-37 CPPb
HPV16 oncogene disruption
Tumor reduction, 100% survival at day 60 [82]
Cas9 plasmid and gRNA
HeLa tumors
IT injections daily for 10 days
Cationic alphahelical polypeptide (PPABLG)b
PLK1 disruption Decreased tumor growth, 60% survival at day 60 [87]
Cas9 RNP
SC A549 xenografts
IT injections daily for 3 days
LMWP peptide fused to Cas9b
KRAS Cas9 Reduced disruption + tumor [89] MEK pathway inhibitor
Cas9 and gRNA plasmid
SC or IV BxPC-3 pancreatic tumors
MoA
IV injections every 2nd day for 10 days
gRNA guide RNA, SC subcutaneous, IV intravenous, PLK1 polo-like kinase 1, ROS reactive oxygen species, CPP cellpenetrating peptides, HPV human papillomavirus, IT intratumoral, KRAS Kirsten rat sarcoma virus, MEK mitogenactivated protein kinase kinase Peptide function: atargeting. buptake and/or endosomal escape
Table 1 summarizes the selected studies discussed for CRISPR/Cas9 tumor delivery. To the author’s knowledge, no CPP-mediated delivery of the CRISPR/Cas9 system in mRNA format has been published at this point.
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6 Activatable Cell-Penetrating Peptides (ACPPs) Applied in Tumor Treatment for a Safer and More Effective Delivery A notable drawback of CPPs is their non-specificity, which can lead to unintended effects on non-target cells due to the widespread presence of anionic binding motifs on cell surfaces and shared endosomal escape mechanisms. This non-targeted nature is particularly concerning in CRISPR/Cas9 applications, where edits are permanent. One strategy to mitigate this issue is to target Cas9 to specific mutated DNA sequences, thereby reducing the off-target edits in noncancerous cells; however, as always, the off-target editing frequency needs to be investigated for each gRNA. This approach is limited by the fact that specific sequences are not always known or targetable, and tumors are often genetically heterogeneous. An alternative strategy involves modifying CPPs to either include targeting functions, as previously discussed, or to be activated only within specific microenvironments, resulting in the development of activatable CPPs [90, 91]. The ACPPs are designed with a covalently bound inhibitory domain to prevent premature uptake and/or release. The inhibitory domain is connected via a tumor microenvironment (TME)sensitive linker, allowing for selective activation in the tumor vicinity. The TME is characterized by distinct conditions such as acidic pH, low transmembrane potential, hypoxia, elevated reactive oxygen species (ROS) levels, and upregulation of specific proteases [92–94]. Strategies employing pH, ROS, and light-triggered removal of the inhibitory domain have been explored for both normal and tumor tissues. Comprehensive reviews on these methods are available in the works of Jong et al. and are not covered in detail here [95–98]. Several research groups have exploited the unique protease environment of tumors by utilizing protease-cleavable linkers tailored to the tumor-secreted proteases, facilitating the removal of inhibitory domains. One of the first examples of an ACPP was described by Jiang et al. in 2004 who linked an anionic domain to a polyarginine peptide via a metalloproteinase-sensitive linker, resulting in a tenfold increase in cellular association upon cleavage of the peptide [99]. A similar strategy was employed by Aguilera et al., who used a polyanionic inhibitory domain to mask a cationic polyarginine peptide [90]. This approach reduced the peptide’s accumulation in the liver from over 90% to a more uniform distribution across normal tissues, owing to the reduction in peptide adhesion to local tissues due to its near-neutral overall charge. Additionally, this method significantly decreased toxicity, with the polyarginine peptide causing toxicity at a fourfold lower dose than its ACPP counterpart. Liu et al. demonstrated that the inhibitory
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domain could function by sterically blocking a crucial part of the CPP and charge independently [100]. The TAT peptide has been shown to enhance the cellular uptake of doxorubicin-loaded liposomes, which can lead to detrimental off-target effects when chemotherapy is delivered. To address this, researchers modified the TAT peptide by inserting alanine-alanine-asparagine (AAN) at the fourth lysine position. This modification reduced liposome uptake by 72.65% compared to unmodified TAT. The AAN sequence is a substrate for legumain, an endoprotease overexpressed in many solid tumors. In the tumor microenvironment, the AAN is cleaved, restoring the TAT peptide to its native form and thereby increasing liposome uptake, specifically in cancer cells and surrounding tissues.
7 Near Future Clinical Applications of Peptide-Mediated Cas9 RNPs for Cancer Indications The combination of CRISPR/Cas9 with CPPs, whether targeted or stimuli responsive, presents a significant potential for advancing cancer therapy. Despite this promise, no such therapies have yet received clinical approval, primarily due to challenges including accumulation in the liver or kidneys, poor stability in the bloodstream, and limited biodistribution. Activatable strategies, as outlined, offer a promising approach to address these issues, potentially facilitating the transition of these technologies from research settings to clinical applications. A notable application of CRISPR/Cas9 in clinical cancer trials is its use in the generation of CAR T cells, an FDA-approved treatment modality, where the combination with CRISPR/Cas9 knockout of T-cell regulatory genes or knock in of the CAR site specifically is expected to receive FDA approval soon. A search on ClinicalTrials.gov conducted in July 2024 revealed 12 active trials in the USA that utilize CRISPR/Cas9 for CAR T-cell modification. These trials focus on various applications, including knocking in CAR T constructs at the TCR locus (while simultaneously knocking out the TCR), knocking out HLA proteins to create allogeneic T cells, and disrupting inhibitory receptors like PD-1 on T cells [101]. CPPs are also making strides in this field, as demonstrated by Foss et al., who utilized CPPs for the effective delivery of Cas9 and Cas12a RNPs to T, B, and NK cells using a system termed peptide-enabled RNP delivery for CRISPR engineering (PERC) [102]. Foss et al. simply mixed Cas9 RNP and 10–20 molar equivalents of A5K amphipathic peptide derived from an HA2-TAT fusion to achieve impressive editing in these challenging and clinically relevant cell types. The A5K peptide used in this work partially consists of the TAT CPP, shortened by 3 amino acids in the
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C-terminal. The PERC system achieved editing efficiencies comparable to electroporation but with significantly reduced cellular toxicity. This lower toxicity facilitates sequential treatments without disrupting cell phenotype, allowing for multiple target disruptions without the risk of simultaneous genotoxicity. The PERC system can also be combined with AAVs to deliver templates for HDR-mediated CAR T receptor insertion into the TCR locus. The CAR T cells generated using PERC demonstrated in vivo functionality comparable to those produced by electroporation. This system offers several advantages, including simplicity, requiring only standard laboratory equipment, and its use of Cas9 in RNP form, which minimizes off-target effects. This is particularly crucial for CAR T-cell production, where only a portion of the product is sequenced, potentially allowing oncogenic off targets to evade detection. A very similar Cas9 RNP delivery method was developed by Zhang et al. named Peptide-Assisted Genome Editing (PAGE), where using the unmodified HA2-TAT fusion co-incubated with TAT-Cas9 was able to efficiently edit primary T cells [103]. The Cas9 used in this study contained 4× MYC NLS repeats and 2× SV40 NLS repeats, both of which are highly cationic and have many similarities with CPPs. Similarly to PERC, this editing method displayed modest toxicity and allowed for both simultaneous and sequential multiplex editing, often targeting CAR T relevant genes, such as B2M. The authors didn’t, however, show co-delivery of a DNA template for HDR as was done in PERC and thus relied on lentiviral methods for CAR T generation. One advantage of PAGE was the successful and efficient editing of CD34+ hematopoietic stem and progenitor cells (HSPCs). These methods show the way forward for ex vivo gene therapy of highly therapeutic primary cell sources, such as T, NK, and CD34+ HSPCs, indicating potentially faster and more affordable generation of future cell therapies, taking a burden of already overstretched health systems.
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Future Perspective The outlook for CPP-mediated CRISPR/Cas9 delivery is promising, with advancements addressing initial challenges such as serum instability and low cell-type specificity. Solutions like chemical modifications and the development of targeting peptides or ACPPs are paving the way for more effective delivery. CPPs are emerging as a key player in nonviral CRISPR RNP delivery due to their potential for simple and nontoxic administration. As CRISPR technology evolves to include larger protein constructs like base editors and prime editors, CPP nanoparticles are well suited to accommodate these expanding needs.
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Chapter 9 Identification of Organ- and Disease-Specific Homing Peptides Using In Vivo Peptide-Phage Display Kristina Po˜sˇnograjeva, Karlis Pleiko, and Tambet Teesalu Abstract In vivo peptide-phage display is a powerful, agnostic technique for identification of homing peptides and mapping of the vascular diversity. This chapter provides a practical guide for conducting in vivo T7 phage biopanning experiments and analyzing the data. It details experimental designs and protocols, emphasizing the use of high-throughput sequencing (HTS) technologies to enhance the efficiency of in vivo biopanning and the validation of tumor-targeting peptides. Key words In vivo phage display, Tumor homing peptide, T7 bacteriophage, High-throughput sequencing, Vascular ZIP codes, Affinity targeting
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Introduction Synaphic targeting, also known as active or ligand-directed drug targeting, employs to cancer therapy the “magic bullet” concept— the delivery of the drugs precisely to the tumor site at therapeutically efficacious concentrations, low systemic exposure, and acceptable side effects [17]. To achieve precision and selectivity, therapeutic compounds are coupled to ligands that engage with disease-associated systemically accessible target molecules. Various classes of targeting ligands are used, including small molecules (e.g., folic acid, hyaluronic acid, transferrin), homing peptides, and antibodies. Several factors have contributed to the growing popularity of vascular homing peptides as targeting ligands. They provide moderate affinity, helping to bypass the binding site barrier. Additionally, peptides are typically not species specific, have a smaller size, are easier to synthesize, exhibit low immunogenicity, are biocompatible, and are straightforward to scale up. These
Kristina Po˜sˇnograjeva and Karlis Pleiko contributed equally.
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characteristics have contributed to recent advances in peptideguided precision interventions [2, 4, 11]. Target-engaging peptides can be identified through various approaches, including the use of natural protein interaction modules, de novo design, and selection from combinatorial peptide display libraries. Bacterial viruses, bacteriophages, can be engineered to act as multicopy display scaffolds to enable high-avidity binding and reduced off rates [5, 22]. Peptide-phage display has been successfully used against targets of different complexity, from purified proteins to highly complex systems, including cultured cells, excised (ex vivo) cells and tissues, and live animals [24]. In vivo phage display, an agnostic tool to map vascular diversity in live animals and to identify systemic homing peptides, was pioneered by the laboratory of Erkki Ruoslahti [14]. This seminal study identified homing peptides that are selective for the microvessels of normal brain and kidney. In the years that followed, in vivo peptide-phage biopanning became an established technology, extensively utilized to identify peptides that target differentially expressed vascular markers in both healthy tissues and disease sites, such as malignant, inflammatory, atherosclerotic, and neurodegenerative lesions [18]. The homing peptides identified by in vivo phage display are applied for systemic precision delivery of therapeutic or imaging compounds or nanoparticles [9]. Most homing peptides appear to use simple docking interactions with endothelial receptors overexpressed at the target site. Some homing peptides, due to their interaction with functionally important binding pockets on target proteins, can modulate the activity of these proteins. As a result, these peptides have applications beyond mere affinity targeting, allowing for functional modulation and therapeutic interventions. Examples of such peptides are LyP-1, a P32-targeting peptide with intrinsic antitumor activity [10], and CendR peptides, which bind to neuropilins to modulate vascular function and enhance the ability of nanoparticles to traverse tissue barriers [20]. The power of in vivo phage display is particularly well illustrated by application of the technology for discovery of tumorpenetrating peptides (TPP), a class of tumor homing peptides that use a multistep mechanism [17, 19] unlikely to have been developed by rational design-based approaches. Many laboratories have opted to use for in vivo biopanning the lytic T7 bacteriophage platform [1, 3, 23]. The size (~55 nm) and aspect ratio of T7 phage nucleocapsid are more similar to clinically used nanoparticles than in the case of filamentous phages. Furthermore, lytic T7 phages are less restrictive in the sequence landscape of displayed peptides compared to lysogenic filamentous phages [8] In the T7-Select system of Novagen, the peptides are displayed at the C-terminus of the major coat protein 10A at ≤415 peptides/ phage particle [13]. The T7 peptide-phage libraries used for in vivo display consist of collections of clones of T7 phages that display a
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Fig. 1 In vivo phage display. Schematic representation of the steps of a single round of in vivo biopanning and T7 phage displaying CX7C peptide library (X stands for a random amino acid). (Image source: https://github. com/KarlisPleiko/KristinaP_phage/tree/main)
genetically encoded randomized peptide fused to the C-terminus of the major capsid protein 10A [7]. Typically, such libraries consist of phages expressing ~109 different peptides, approaching the theoretical diversity of a seven-amino-acid random library (1.28 × 109) and cover a broad chemical space. For in vivo display, peptide-phage library is amplified, purified, and administered in live mice followed by perfusion to remove free phages in blood, removal of target and control organs, amplification of the recovered phage, and next round of selection (Fig. 1). Unlike in the case of ex vivo, in vitro, and cell-free biopanning, in vivo peptide-phage selections have an inherent mechanism that reduces the selection of promiscuous, nonspecific peptides. This is because such peptides are depleted at nontarget vascular sites [21]. After circulation, the target tissue is collected, and the phage pool from the target tissue is amplified and used for the next round of selection to further increase the representation phages displaying target-selective peptides. The genomic DNA of the peptide-encoding region of the rescued phages is then sequenced to reveal the peptides displayed on the surface of the phages [14]. Although in vivo phage display has been effective in discovering numerous homing peptides, there are some unresolved issues that can hinder the robust identification of target-specific peptides. For example, amplification bias, where certain peptide-phage clones
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replicate more rapidly than others within the peptide-phage library, presents a significant challenge [12]. Introduction of highthroughput DNA sequencing technologies for in-depth monitoring of landscape of phage displayed peptides throughout selections has provided additional possibilities for streamlining in vivo phage selections and improving reproducibility of the screens. To allow for improved tissue selectivity profiling, we describe a novel bioinformatics approach, differential homing analysis, that allows direct comparison of the representation of each peptide at the target site against the relative abundance of the same peptide in control organs in the same round of selection (Subheading 3.2.2). The effect of amplification bias can be reduced by propagating phage pools in semisolid media instead of liquid culture, as it allows for more equal amplification of every phage clone (Subheading 3.1.3). For the mapping biodistribution of peptide phages we propose two approaches. First, the differential homing analysis method, besides being useful for selectivity profiling, also provides information about the biodistribution of a peptide phages (Subheading 3.2.2). Second, in vivo playoff technique can be used for head-to-head comparative evaluation of biodistribution of multiple peptide phages in the same animal (Subheading 3.3).
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Materials
2.1 In Vivo Phage Display 2.1.1 General Reagents and Materials
1. LB broth with carbenicillin (50 μg/mL) (LB/carbenicillin). 2. LB agar plates with ampicillin (100 μg/mL) (LB/ampicillin plates). 3. Top agar (LB broth with 0.7% agar). 4. IPTG (1 M stock). 5. PBS. 6. mQ water. 7. E. coli BLT5403 glycerol stock (Novagen). 8. E. coli BLT5615 glycerol stock (Novagen). 9. 15 mL and 50 mL Falcon tubes. 10. 1.5 mL Eppendorf tubes.
2.1.2 Basic Manipulation of T7 Phages: Cloning, Titering, and Sequencing
1. Materials listed in Subheading 2.1.1.
Cloning T7 Peptide-Phage Libraries or Single Clones
4. T4 DNA ligase.
2. 5′-phosphorylated DNA oligonucleotides. 3. T7Select 415-1 cloning kit (Novagen). 5. T4 DNA ligase buffer.
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1. Materials listed in Subheading 2.1.1.
Sequencing PeptideCoding Region of Single T7 Phage Clones
1. Materials listed in Subheading 2.1.1.
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2. PCR tubes. 3. PCR master mix stock, such as 5× HOT FIREpol Blend Master Mix (Solis BioDyne). 4. Primers for sequencing of peptide-encoding DNA inserts: T7 up (5′-AGCGGACCAGATTATCGCTA-3′) and T7 down (5′AACCCCTCAAGACCCGTTTA-3′).
2.1.3 Phage Amplification and Purification
1. Materials listed in Subheading 2.1.1. 2. 5 M NaCl in mQ. 3. 50% PEG8000 in PBS. 4. 62.5% (w/w) CsCl in PBS. 5. 11 × 60 mm thin-walled ultracentrifuge tubes (Beckman Coulter). 6. SW 60 Ti swing-out rotor (Beckman Coulter). 7. 21G needles. 8. Syringes (3 mL). 9. Slide-A-Lyzer 3500 MW cutoff dialysis cassettes (Thermo Scientific).
2.1.4 Ex Vivo Phage Display
1. Materials listed in Subheading 2.1.1. 2. Tumor-bearing mice. 3. Anesthetic, such as a mixture of ketamine (75 mg/kg) and dexmedetomidine (0.1 mg/kg) (according to approved animal use protocol). 4. Styrofoam platforms and needles for mouse immobilization during dissection. 5. Lab soakers (Versi-Dry, Nalgene). 6. Surgical scissors and tweezers. 7. Gravity perfusion systems comprising 100-mL catheter syringe (Soft-Ject); Intrafix Primeline (Braun); Safety-Lok Blood Collection Set (Vacutainer). 8. DMEM with 1% BSA (DMEM/BSA). 9. 14 mL snap-cap Falcon tubes. 10. Handheld homogenizer with disposable plastic hard tissue tips (Omni International Inc.). 11. LB broth containing 1% NP40 (Igepal CA-630) (LB/NP40).
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2.1.5 In Vivo Phage Display
1. Materials listed in Subheading 2.1.1. 2. Tumor-bearing mice. 3. Insulin syringes (0.5 mL). 4. Restrainer for mice. 5. 1-L beaker with warm water (~39 °C). 6. Anesthetic, such as a mixture of ketamine (75 mg/kg) and dexmedetomidine (0.1 mg/kg) (according to approved animal use protocol). 7. Styrofoam platforms and needles for mouse immobilization during dissection. 8. Lab soakers (Versi-Dry, Nalgene). 9. Surgical scissors and tweezers. 10. Gravity perfusion systems comprising 100-mL catheter syringe (Soft-Ject); Intrafix Primeline (Braun); Safety-Lok Blood Collection Set (Vacutainer). 11. LB broth containing 1% NP40 (Igepal CA-630) (LB/NP40). 12. 14 mL snap-cap Falcon tubes. 13. Handheld homogenizer with disposable plastic hard tissue tips (Omni International Inc.). 14. Freshly prepared overnight cultures of BLT5615 and BLT5403. 15. Phage elution buffer (20 mM Tris–HCl pH = 8; 100 mM NaCl, 6 mM MgSO4). 16. Materials listed in Subheading 2.1.3.
2.2 Application of High-Throughput Sequencing and Bioinformatic Tools for Mapping of Vascular ZIP Codes
1. Phage DNA Isolation kit (Norgen Biotek #46850). 2. HOT FIREPol® Blend Master Mix Ready to Load (Solis BioDyne, 04-25-00120). 3. Primers for PCR amplification. 4. Agarose. 5. TBE 1× buffer. 6. EtBr or alternative DNA intercalating dye for staining a gel. 7. Isopropanol. 8. Thermocycler. 9. DNase- and RNase-free water for PCR. 10. Zymo Select-a-Size DNA Clean & Concentrator Kit (D4080) or SPRIselect Bead-Based Reagent (B23317) beads. 11. NEBNext® Library Quant Kit for Illumina® (E7630). 12. High-sensitivity DNA Kit (Agilent, 5067-4626).
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13. Installed RStudio (https://rstudio.com/) and R programming language (https://www.r-project.org/). 14. Installed R packages edgeR, readr, tidyverse, ggplot2, and dplyr. 2.3 In Vivo Auditioning of Homing Peptides Using Phage Playoff
1. Materials listed in Subheading 2.1.1. 2. Materials for phage purification (Subheading 2.1.3). 3. Materials for HTS (Subheading 2.2). 4. Tumor-bearing mice. 5. Individual peptide coding phages. 6. Insulin syringes (0.5 mL). 7. Restrainer for mouse. 8. 1-L beaker with warm water (~39 °C). 9. Anesthetic, such as a mixture of ketamine (75 mg/kg) and dexmedetomidine (0.1 mg/kg) (according to approved animal use protocol). 10. Gravity perfusion system comprising 100-mL catheter syringe (Soft-Ject); Intrafix Primeline (Braun); Safety-Lok Blood Collection Set (Vacutainer). 11. PBS with 1% BSA (PBS/BSA). 12. Styrofoam platform and needles for mouse immobilization during dissection. 13. Lab soakers (Versi-Dry, Nalgene). 14. Surgical scissors and tweezers. 15. LB broth containing 1% NP40 (Igepal CA-630) (LB/NP40). 16. 14 mL snap-cap Falcon tubes. 17. Handheld homogenizer with disposable plastic hard tissue tips (Omni International Inc.). 18. Phage elution buffer (20 mM Tris-HCl pH = 8; 100 mM NaCl, 6 mM MgSO4).
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Methods
3.1 In Vivo Phage Display 3.1.1 Basic Manipulation of T7 Phages: Cloning, Titering, and Sequencing
T7 peptide-phage libraries can be purchased ready-made or cloned in-house. Constructing single peptide-phage clones is required for testing candidate peptides found using high-throughput DNA sequencing-based phage display and for verifying that the homing of peptide phages found using Sanger sequencing is dependent on the displayed peptide and not changes elsewhere in the genome. Titer of T7 phage is determined after cloning (Subheading “Cloning T7 Peptide-Phage Libraries or Single Clones”), after amplification and purification of the phage (Subheading 3.1.2), and after
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Fig. 2 Schematic representation of basic manipulation of T7 phages. Main steps of cloning, titering, and sequencing the T7 phages are shown
each round of ex vivo (Subheading 3.1.3) or in vivo phage display (Subheading 3.1.4). The identity of T7 peptide-phage libraries and single clones should be routinely verified by Sanger sequencing following cloning and amplification. To avoid contamination, gloves should be frequently changed, working surfaces and equipment should be periodically wiped with ethanol, and only aerosolproof filter pipette tips should be used. Figure 2 shows schematic representation of basic manipulation of T7 phages described in this section. Cloning T7 Peptide-Phage Libraries or Single Clones
1. For the cloning of peptide-phage library or a single peptidephage clone, you will need 5′-phosphorylated DNA oligonucleotides that encode a peptide displayed on the phage surface after being annealed and ligated into the phage genome. To construct a CX7C peptide-phage library, order following oligonucleotides: (a) Sense oligonucleotide: 5′-Phos-AATTCTTGC NNKNNKNNKNNKNNKNNKNNKTGCTA-3′ (b) Antisense oligonucleotide: 5′-Phos-AGCTTAGCA MNNMNNMNNMNNMNNMNNMNNGCAAG-3′ The nucleotides in italics create overhangs that enable the annealed oligonucleotides to be ligated into phage genome shoulders, nucleotides in bold code for cysteines, and underlined nucleotides code for random amino acids. N = any nucleotide; K = T or G; M = A or C.
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2. If your aim is to construct a phage clone expressing a single peptide on its surface, the amino acids in bold and underlined should be replaced. For sense oligonucleotide, backtranslate the sequence of your peptide of interest against E.coli codon sequences using a Web-based tool such as EMBOSS Backtranseq (http://www.ebi.ac.uk/Tools/st/emboss_backtranseq/). Insert the nucleic acid sequence between the flanking region in italics (step 1(a) in this section). 3. For the antisense oligonucleotide, create a reverse complement of the peptide coding region acquired in step 2 and insert the nucleic acid sequence between the flanking region in italic (step 1(b) in this section). 4. To anneal the oligos, add 1 μL of sense and 1 μL of antisense oligonucleotide stock (100 μM) to 998 μL of mQ. Heat the mixture at 95 °C in a heat block for 5 min. Turn off the heat block and allow it to cool slowly to room temperature. 5. Ligate 1 μL of the annealed oligonucleotides into the T7 phage genome using T7Select 415-1 cloning kit, T4 ligase, and T4 ligase buffer and following the procedure described in T7Select System Manual provided by Novagen. Package the DNA using the T7Select 415-1 cloning kit and the T7Select System Manual. Titering T7 Phage
1. For the titering of T7 phage, it is recommended to use BLT5615 E. coli strain culture as it yields uniform and clear plaques. Inoculate 20 mL of LB/carbenicillin medium with a single colony of BLT5615 from a freshly streaked LB/ampicillin agar plate. Incubate in 100-mL Erlenmeyer flask in bacterial shaker at 37 °C and 200 rpm overnight. 2. Next morning, dilute the overnight culture 1:100 in prewarmed LB/carbenicillin and incubate in a shaker at 37 °C and 200 rpm until the OD600 of the culture reaches 0.5 (see Note 1). 3. Prepare serial dilutions of phage in LB/carbenicillin (see Note 2). 4. Combine 100 μL of diluted phage in 15-mL Falcon tube with 500 μL of BLT5615 culture (OD600 = 0.5). For every phage stock, determine the phage titer from at least two dilutions with different dilution factors. This allows you to count the phage plaques from more than one plate and thus ensure that the dilutions were prepared correctly. After combining phage and bacteria, move onto the next step within 15 min, as the phage may start amplifying, and consequently the determined titer will be incorrect. 5. To each 15-mL tube, add 5 mL of top agar (50–60 °C) containing 2 mM IPTG. Mix thoroughly by vortexing and
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immediately plate on LB/ampicillin agar plate before the top agar starts to solidify (see Note 3). 6. Incubate the plates overnight at room temperature (RT). Plaques will appear as transparent “holes” in bacterial lawn. Alternatively, the plates can be incubated at 37 °C for 3–4 h, but this will result in less clear plaques, and we do not suggest using these plates for sequencing. 7. Count the phage plaques and calculate phage concentration of the undiluted sample. When calculating the phage titers, take into account the number of plaques counted from plates containing 20–200 plaques, as this gives the most accurate result. The plates used for determining the concentration of phage can also be used for sequencing. Sequencing PeptideCoding Region of Single T7 Phage Clones
1. As a first step of sequencing T7 phage, add 30 μL of PBS to a PCR tube. We recommend sequencing at least four plaques per phage stock; thus, prepare at least four PCR tubes per phage stock. 2. Collect phage from a single plaque on a titering plate by lightly touching the plaque surface with a 10-μL pipet tip. Place the pipet tip into an open PCR tube with PBS. 3. Prepare PCR mix using PCR master mix stock, upstream (T7 up; 5′- AGCGGACCAGATTATCGCTA -3′) and downstream (T7 down; 5′- AACCCCTCAAGACCCGTTTA -3′) primers, each with the final concentration of 0.5 μM. 4. In a new PCR tube, combine 14 μL of PCR master mix with 1 μL of the phage resuspended in 30 μL of PBS. Use the same tip you used to pick up the phage from a plaque. Store the phage resuspended in PBS at 4 °C, as it can be used for amplification of a particular peptide-phage clone (see Subheading 3.1.2). 5. Perform PCR with the following cycling conditions: denaturation at 95 °C for 5 min, followed by 35 amplification cycles (50 s at 94 °C, 1 min at 50 °C, 1 min at 72 °C), and final elongation (72 °C for 10 min). 6. Sequence PCR products using the T7 up primer and translate the obtained sequences to amino acid sequence using https:// web.expasy.org/translate/. The sequence of a peptide expressed on the surface of the phage will follow the amino acid sequence “MLGDPNS.”
3.1.2 Phage Amplification and Purification
1. Inoculate 20 mL of LB/carbenicillin with a single colony of E. coli BLT5403 strain from a freshly streaked LB/ampicillin agar plate. Incubate the culture in a 100-mL Erlenmeyer flask in a bacterial shaker at 37 °C and 200 rpm overnight.
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2. Next morning, dilute the overnight culture 1:100 in prewarmed LB/carbenicillin (see Note 4) and incubate in shaker at 37 °C and 200 rpm until the OD600 of the culture reaches 0.5 (equates to ~4 × 108 cells/mL). 3. If you are amplifying phage from a single plaque following cloning, a preamplification step is needed. If you are amplifying a single phage or phage library stock, skip this step and continue from step 4. For preamplification, inoculate 1 mL of BLT5403 (OD600 = 0.1; for this combine 800 μL of LB/carbenicillin and 200 μL of bacterial culture OD600 = 0.5) with 10 μL of phage in PBS that was used for sequencing. Incubate in a 50-mL Falcon tube in a shaker at 37 °C and 200 rpm for 1.5 h. Next, add 26 mL of BLT5403 (OD600 = 0.5) and proceed from step 5. 4. Inoculate the bacteria with phage stock at multiplicity of infection 0.001–0.01 (i.e., 100–1000 bacterial cells per each pfu (plaque forming unit)). 5. Continue incubating in the shaker until the cloudiness of the culture decreases and you start to see floating debris formed by the lysed bacteria. This will take at least 1 h. 6. Chill the lysate on ice and divide between 50-mL Falcon tubes adding 26 mL of lysate per tube. Add 3 mL of 5 M NaCl in each tube and mix by vortexing. 7. Centrifuge at 12,000× g (gravitational force or relative centrifugal force, RCF) and 4 °C for 10 min to clear the lysate of bacterial cell debris. 8. Transfer the supernatant carefully into a new 50-mL tube; avoid disturbing the pellet. 9. To precipitate the phage, 8.4 mL of 50% PEG8000 in PBS should be added to the supernatant. As the 50% PEG8000 is very viscous and thus hard to pipette accurately, we add 9 mL of it using a serological pipette. Mix thoroughly by vortexing and incubate on ice for at least 30 min (up to overnight). 10. Centrifuge at 8000× g and 4 °C for 10 min to pellet the precipitated phage. 11. Discard the supernatant and place the tubes upside down on a paper towel for 15 min to drain the excess liquid. 12. Resuspend the phage pellet in 1.5 mL of PBS (see Note 5). 13. For density gradient purification, use 62.5% CsCl (percent weight/weight) solution in PBS to prepare dilutions with following volume ratios of CsCl (62.5%):PBS; 2:1; 1:1; 1:2. 14. Create a density gradient in ultracentrifugation tubes by pipetting the CsCl solutions in the order and volume specified below (see Fig. 3). Prepare the gradient slowly, using the
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Fig. 3 CsCl density gradient in an ultracentrifugation tube and the position of phage particles after the ultracentrifugation
pipet tips with cutoff ends to avoid mixing the layers. After adding the CsCl:PBS 2:1 layer, mark the edge of the solution with a marker on the ultracentrifugation tube, as you expect to see a layer of phage right above this layer after the centrifugation. Prepare the same gradients for the balance tubes. (a) CsCl (62.5%)
0.25 mL
(b) CsCl:PBS 2:1
1.2 mL
(c) CsCl:PBS 1:1
1.2 mL
(d) CsCl:PBS 1:2
0.25 mL
15. Add 1.5 mL of PEG-purified phage on top of the CsCl gradient. In the balance tubes, add 1.5 mL PBS as the top layer. 16. Ultracentrifuge the gradients using SW 60 Ti swing-out rotor (Beckman Coulter) at 40 000 rpm (113 000–216 000 G, depending on the fraction of the tube) at 22 °C for 45 min. 17. The phage forms a light blue band, which is visible on a dark background when illuminated from above the tube. Use a disposable syringe with a 21G needle to carefully penetrate the tube ~0.5 cm below the phage band with the hole in the needle facing upward. Collect the phage with a syringe in 0.5–1 mL volume.
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18. Dialyze the phage sample in Slide-A-Lyzer 3500 MW cutoff dialysis cassettes against PBS at 500× volume excess at RT for 1 h. Replace the PBS and dialyze again at RT for 1 h or at 4 °C overnight. 19. Gradient-purified peptide-phage stocks can be stored at 4 °C for at least a week without a loss of titer, but for biopanning, using freshly amplified phage pools is recommended to avoid bias against unstable peptides. If a time gap longer than a few days is anticipated between the amplification of the phage pool and the subsequent round of biopanning, it is recommended to add glycerol with final concentration of 10%, snap-freeze and store the amplified phage pool at -80 °C. 3.1.3 Ex Vivo Phage Display
Ex vivo phage display allows the selection of short peptides that bind to tumor tissue. Incorporating 1–2 rounds of ex vivo phage display before in vivo biopanning helps to reduce the loss of promising tumor-homing peptides. The high variability in naı¨ve peptide-phage libraries (in the case of CX7C library, the hypothetical maximum diversity is 1.28 × 109, in our hands, the actual diversity is 2 × 108) means that, on average, only 30 copies of each peptide-phage clone can be injected during the first round of in vivo biopanning. Due to rapid clearance from circulation by the liver and spleen, even the most effective tumor-homing peptide phages may not reach the tumor. An ex vivo biopanning step enriches the library with peptide-phage clones that bind to tumor tissue, removing many nonbinding clones. This reduces library diversity while retaining potential tumor-homing peptides, increasing the likelihood of recovering efficient tumor homing peptides during in vivo phage biopanning. However, many peptide phages identified via ex vivo phage display may bind to targets that are not specific for tumor tissue or that are not accessible from the circulation, making them ineffective in vivo. Therefore, combining ex vivo selection with in vivo phage display is crucial for identifying peptides that successfully target tumor tissue in vivo. The ex vivo phage display process begins with excising the tumor from a tumor-bearing mouse, homogenizing the tissue, and incubating it with a peptide-phage library. After washing to remove unbound phages, the bound peptide phages are amplified for either another round of ex vivo biopanning or the first round of in vivo phage display. The Procedure of Ex Vivo Peptide-Phage Display 1. Anesthetize the mouse and using needles, fix the mouse on a Styrofoam platform covered with a piece of lab soaker.
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2. Perfuse the mouse intracardially by gravity flow tubing with approximately 10 mL of DMEM/BSA until the tumor and control organs (e.g., liver, kidney, and lung) are discolored. 3. Collect the tumor(s) and rinse them thoroughly with DMEM/ BSA. Weigh the tumor(s) and place in 1 mL of DMEM/BSA in a 14-mL snap-cap Falcon tube on ice. You will need at least 50 mg of tissue for one round of biopanning. 4. Homogenize the tissue using handheld homogenizer with disposable plastic hard tissue tips; keep the tissue homogenate on ice. 5. To 1 mL of tissue homogenate in DMEM/BSA, add 5 × 108 pfu of peptide-phage library (50 μL of 1010 pfu/mL stock) and 8 mL of cold DMEM/BSA. 6. Mix end over end at 4 °C for 1 h. Prepare 5 × 15 mL tubes containing 14 mL of cold DMEM/BSA per sample; keep the tubes on ice. 7. To remove phages that did not bind to the tumor tissue homogenate, centrifuge the sample in a swing-out rotor at 4 °C and 450× g for 5 min. Remove the supernatant, add 1 mL of cold DMEM/BSA to the tissue pellet, and resuspend it by pipetting up and down. Transfer 1 mL of suspension into a 15-mL tube containing 14 mL of DMEM/BSA. 8. Repeat the washing step (step 7) four more times. 9. After the last centrifugation, resuspend the tissue pellet in 1 mL of LB/NP40. Determine the amount of bound phage (per mg of tissue) by titering (see Subheading “Titering T7 Phage”). 10. The following steps can be postponed to the day after ex vivo biopanning is performed. The phage pool is amplified in LB semisolid or liquid culture and used for the next round of biopanning and for the high-throughput sequencing. We suggest amplifying the phage pools recovered from tissues in semisolid media as described below, as this method will result in decreased amplification bias and prevent the slowly amplifying peptide phages from disappearing from the phage pool. Nevertheless, the phage pools can also be amplified in liquid bacterial culture, as described in Subheading 3.1.2, steps 1–5. 11. Grow BLT5403 E. coli culture to the density of OD600 = 0.5 as described in Subheading 3.1.2, step 2 using a freshly prepared BLT5403 overnight culture. 12. For amplification of phage pools, it is recommended to use spatially restricted peptide-phage amplification in semisolid media, which reduces the differences in phage amplification speeds between different peptide-phage clones and decreases the chances of losing slowly amplifying peptide phages from the phage pool. The first step for plate amplification is to
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calculate how many plates you will need. We have observed that it is optimal to amplify no more than 10,000 pfu on one LB/ampicillin plate. To calculate the amount of needed plates: n=
t 10, 000
where: n is the number of needed plates, t is the phage titer in the eluate (pfu/mL). It is unreasonable to use more than 20 plates for amplification of phage from tumor sample and 10 plates from control samples. In that case, we suggest taking a fraction of the sample for amplification. To calculate how much of the tumor sample to use for amplification: v=
20 × 10,000 pfu t
where: v = how much of the sample to use for the amplification (mL), t = phage titer in the eluate (pfu/mL) 13. Combine the tissue homogenate with n mL of BLT5403 E. coli (OD600 = 0.5). Add 4n mL of top agar to the mix of phages and bacteria, mix it by pipetting or vortexing, and immediately divide the mixture between LB/ampicillin plates using a serological pipette (5 mL of mixture per plate). 14. Incubate the plates at 37 °C until most or all the bacteria are lysed and the plaques are fused together. This will take 3–8 h. Alternatively, the plates could be incubated at RT overnight. 15. To elute the amplified phage once the bacteria is lysed, add 3 mL of phage elution buffer to each plate used for phage amplification. Incubate the plates at 4 °C for at least 2 h or overnight. 16. Collect the eluted phage by pipetting, washing the surface of the plate with a pipette. Collect the eluted phage into 50-mL tubes. 17. Perform a PEG-purification for all the samples as described in Subheading 3.1.2, steps 6–12, and adjust the volumes of added 5 M NaCl (0.1 mL/1 mL of eluted phage) and 50% PEG8000 (1/5 volume of the final mixture). 18. If the samples are used for sequencing, the phage does not need to be purified further. If the phage stock will be used for another round of biopanning, purify the sample further by density gradient purification (see Subheading 3.1.2, steps 13–18). 19. Determine the amount of phage in the purified sample by titering (see Subheading “Titering T7 Phage”). Use the amplified phage pool as an input for the next round of ex vivo phage
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display (repeat all the steps starting from step 1) or for the first round of in vivo biopanning (see Subheading 3.1.4). The amplified phage pool should be used for the next round of biopanning as soon as possible and should not be reamplified as it introduces a bias for more stable or faster amplifying peptidephage clones. If a longer time gap is expected between the two rounds of biopanning, glycerol with final concentration of 10% should be added to the phage mix, the sample snap-frozen and stored at -80 °C. 20. To prepare the samples for high-throughput sequencing, all the phage pools are diluted in PBS to be in the range of 109–1010 pfu/mL. Store the samples at 4 °C. 3.1.4 In Vivo Phage Display
In vivo peptide-phage display enables an unbiased identification of organ- or disease-specific homing peptides. Below we describe a protocol for in vivo phage display using the T7 peptide-phage library, where each phage displays hundreds of copies of a short (7 aa) random cyclic peptide on its surface (Fig. 1). Following intravenous (i.v.) injection of the library in live mice, the phages expressing tumor-specific peptides accumulate in the target organ/ tissue. The mice are then anesthetized and perfused with PBS to remove blood with unbound phages from the circulation. The tumor(s) and control tissues are collected, and the phage pools from these tissues are rescued using spatially restricted amplification in semisolid media, which reduces the amplification bias introduced due to vastly different amplification rates of different peptide-phage clones. After purification, the phage pool acquired from the tissue of interest is used for the next round of biopanning to further enrich the homing peptide expressing phages (Fig. 1). The peptide-encoding region of the genome of phages rescued from tissues of interest and from control tissues is then sequenced to reveal the displayed peptides. The Procedure of In Vivo Peptide-Phage Display 1. We have observed that for in vivo phage display, it is optimal to inject 5 × 109 pfu of peptide-phage library in 50 μL; however, the amount of phage per i.v. injection can vary between 109 and 1010 pfu and the volume of injection between 50 and 200 μL. To inject 3 mice with 5 × 109 pfu of peptide-phage library in 50 μL, prepare at least 200 μL of peptide-phage library stock with the titer of 1 × 1011 pfu/mL in PBS. 2. Place a tumor-bearing mouse into a restrainer and warm up the mouse tail in a beaker with warm water (~39 °C) to dilate the tail veins. Inject 50 μL of the peptide-phage library stock into the mouse tail vein using an insulin syringe (see Note 6).
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3. Allow the phages to circulate. The circulation time of a peptidephage library can vary from 10 min up to 1 h, but we routinely use 30 min circulation time for in vivo phage display. When deciding on the circulation time, take into consideration that the half-life of T7 phage in blood is approximately 12 min [3]. Anesthetize the mouse 5 min prior to perfusion. 4. Using needles, fix the mouse on a Styrofoam platform covered with a piece of lab soaker. 5. To remove the unbound phage from the pulmonary circulation, perfuse the mouse intracardially with PBS by gravity flow tubing with approximately 10 mL of PBS until the tumor and control organs (e.g., liver, kidney, and lung) are discolored. 6. Collect the organs/tissues of interest and rinse them thoroughly with PBS from a spray bottle. Weigh the tissues and place each in 1 mL of LB/NP40 in a 14-mL snap-cap Falcon tube on ice. 7. Repeat the procedure (starting from step 2) on two more mice. 8. To extract the phage pools from organs/tissues, homogenize the tissues using handheld homogenizer with disposable plastic hard tissue tips (see Note 7). 9. Determine the amount of phages (per mg of tissue) in the tissues of interest and control tissues by titering (see Subheading “Titering T7 Phage”). This allows to judge preliminarily how successful the biopanning was—the amount of phage in the target organ/tissue should increase with each round of biopanning. Figure 4 shows the expected titers in different tissues following an injection of a naı¨ve peptide-phage library following the procedure described above.
Fig. 4 Expected titer range following an injection of 5 × 109 pfu of naı¨ve peptide-phage library into adult mice and 30 min circulation time is indicated in gray
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10. The following steps can be postponed to the day after in vivo biopanning is performed. We suggest amplifying the phage pools recovered from tissues in semisolid media as described above (see Subheading 3.1.3, steps 11–18). Determine the amount of phage in the amplified and purified phage pools by titering (see Subheading “Titering T7 Phage”). In equimolar ratios, combine the three phage samples recovered from tumors from three mice. Use this as an input for the next round of biopanning. The amplified phage pool should be used for the next round of biopanning as soon as possible and should not be reamplified as it introduces a bias for more stable or faster amplifying peptide-phage clones. If a longer time gap is expected between the two rounds of biopanning, glycerol with a final concentration of 10% should be added to the phage mix and the sample snap-frozen and stored at -80 °C. 11. For the next round of biopanning, repeat all the steps starting from step 1. Throughout the rounds of biopanning, the phage titer in target tissue is expected to increase. The phage display is carried on until the titer in target tissue has reached a plateau or at least for three rounds. 12. To prepare the samples for high-throughput sequencing, all the phage stocks are diluted in PBS to be in the range of 109–1010 pfu/mL. Store the samples at 4 °C. 3.2 Application of High-Throughput Sequencing and Bioinformatic Tools for Identification of Homing Peptides
High-throughput sequencing (HTS) can usually yield between 105 and 107 reads per sample and allows thorough investigation of the diversity of peptide phages recovered from tumor site and control organs. HTS data are frequently used for enrichment analysis— evaluation of the representation of candidate peptide phages in the target organ over several selection rounds. In addition, we have recently developed a differential homing approach for parallel comparison of the representation of peptides in the tumor and control organs during the same selection round [15]. Differential homing is expressed as logarithmic fold change (logFC) between target vs. control with false discovery rate (FDR) describing the reliability of the differences. Logarithmic value of counts per million (logCPM) indicates abundance of each peptide phage in target. Data analysis has been adopted from RNA sequencing studies and relies on the edgeR package [16]. Sample dataset has been published as Supplementary Data along with our initial differential homing manuscript [15] and can be used to replicate the analysis. The full code for biopanning data analysis we have developed can be found online: https://github.com/KarlisPleiko/phage_homing.
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1. Extract phage DNA using column-based Phage DNA Isolation Kit (Norgen Biotek #46850) or alternative DNA extraction kit. It is not necessary to extract DNA from CsCl-purified phage, and it can be directly used for PCR (proceed to step 2). (a) If using Phage DNA Isolation Kit, transfer 106–1010 pfu of NaCl cleared (see Subheading 3.1.2, steps 6–8) or PEG purified (see Subheading 3.1.2, steps 9–18) to 2-mL centrifuge tube. (b) Add PBS to reach the total volume of 1 mL. (c) Optional step to avoid host genomic DNA contamination: add 20 units of DNase I (Norgen Biotek #25710 or alternative) and incubate at room temperature for 15 min. Inactivate DNase I afterward by heating at 75 °C for 5 min. (d) Add 500 μL of Lysis Buffer B, and vortex for 10 s. (e) Incubate at 65 °C for 15 min. Mix the lysate every 3–5 min by inverting the tube. (f) Add 320 μL of isopropanol. Vortex to mix. (g) Transfer 650 μL of lysate to the spin column included in the kit. Centrifuge at 6000× g for 1 min. (h) Discard the flowthrough. Reload the column and repeat the centrifugation until all sample volume has been passed through the column. (i) Wash the column three times by adding 400 μL of wash solution A, centrifuging at 6000× g for 1 min, and discarding the flowthrough. (j) Spin the column for 2 min at 14,000× g to dry the column resin. (k) Discard the collection tube and transfer the column to a new DNase- and RNase-free 1.5 mL centrifuge tube. (l) Add 75 μL of elution buffer B directly to the column. Centrifuge at 6000× g for 1 min. (m) Collect the flowthrough containing purified phage DNA, measure the DNA concentration, and either use the samples immediately to continue HTS sample preparation or store at -80 °C until further use. 2. Amplify the peptide-encoding region of the bacteriophage genome by PCR. Perform first overlap PCR (Illumina specific sequences adapted from 16S Metagenomic Sequencing Library Preparation protocol # 15044223 Rev. B) using HOT FIREPol® Blend Master Mix Ready to Load (Solis BioDyne, 04-2500120). Alternatively, it is possible to use Phusion Green Hot Start II High-Fidelity DNA Polymerase (Thermo Scientific, F537L) or other high-fidelity polymerase for increased sequence accuracy.
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(a) T7selectUp-Illumina primer sequence (this primer adds Illumina specific sequences to T7Select peptide coding region in phage): (i) TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGGGAGCTGTCGTATTCCAGTC (b) T7selectDown-Illumina primer sequence (to add Illumina specific sequences to T7Select peptide coding region in phage): (i) GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGAACCCCTCAAGACCCGTTTA (c) Set up a PCR reaction: (i) 34 μL DNase- and RNase-free water. (ii) 2.5 μL 10 μM T7selectUp-Illumina primer. (iii) 2.5 μL 10 μM T7selectDown-Illumina primer. (iv) 10 μL 5× FIREpol MM (Solis BioDyne, #04-2500120). (v) 1 μL phage DNA sample (starting concentration usually 2–40 ng/μL for phage). (d) Run PCR using following conditions—adjust the PCR conditions according to your amplicon specific primer: (i) 12 min 95 °C. (ii) 20 cycles of 95 °C for 30 s, 56.3 °C for 30 s, and 72 ° C for 60 s. (iii) Final elongation at 72 °C for 5 min. 3. Quality control (QC) step: Load 5 μL of reaction directly on 3% agarose TBE gel and run for 40 min at 100 V. Confirm that the amplicon size is of the expected length. 4. Purify the rest of the sample using Zymo Select-a-Size DNA Clean & Concentrator or SPRIselect beads or similar kit. 5. Perform second overlap PCR—Illumina-specific and barcode sequences adapted from Illumina Adapter Sequences #1000000002694 section “Nextera Index Kit—PCR primers” and PCR program adapted from Nextera® DNA Library Prep Reference Guides: (a) This is the first primer (FWD-OH2) for the second PCR: (i) CAAGCAGAAGACGGCATACGAGAT code]-GTCTCGTGGGCTCGG (ii) We have discovered (TCGCCTTA), N702 (TTCTGCCT), N704 (AGGAGTCC), N706
that barcodes (CTAGTACG), (GCTCAGGA), (CATGCCTA),
-[i7-barN701 N703 N705 N707
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(GTAGAGAG), and N710 (CAGCCTCG) work well with this protocol. (b) This is the second primer (REV-OH2) for the second PCR: (i) AATGATACGGCGACCACCGAGATCTACAC[i5-barcode]-TCGTCGGCAGCGTC (ii) Barcodes N501 (TAGATCGC), (CTCTCTAT), N503 (TATCCTCT), (AGAGTAGA), N505 (GTAAGGAG), (ACTGCATA), N507 (AAGGAGTA), (CTAAGCCT), N510 (CGTCTAAT), (TCTCTCCG), N513 (TCGACTAG), and (TTCTAGCT) work well with this protocol.
N502 N504 N506 N508 N511 N515
6. Set up a PCR reaction as follows: (a) 34 μL water. (b) 2.5 μL 10 μM barcoded FWD-OH2 primer. (c) 2.5 μL 10 μM barcoded REV-OH2 primer. (d) 10 μL 5× FIREpol MM (Solis BioDyne, #04-25-00120). (e) 1 μL of purified phage DNA sample from the first overlap PCR. 7. Run the PCR using following conditions (adapted from here https://support.illumina.com/content/dam/illumina-sup port/documents/documentation/chemistry_documenta tion/samplepreps_nextera/nexteradna/nextera-dna-libraryprep-protocol-guide-1000000006836-00.pdf): (a) 98 °C 12 min. (b) 5 cycles of 98 °C for 10 s, 63 °C for 30 s, and 72 °C for 3 min. (c) Hold at 10 °C. 8. QC step: Load 5 μL of reaction directly on 3% agarose TBE gel and run for 40 min at 100 V. Confirm that amplification product is of the expected size. 9. Purify the rest of sample using Zymo Select-a-Size DNA Clean & Concentrator or similar kit. 10. Quantify using qPCR NEB kit for Illumina or Agilent Bioanalyzer 2100 Instrument and the High-sensitivity DNA Kit (Agilent, 5067-4626). 11. Normalize libraries and proceed to sequencing. Single-end reads with a length of 150 cycles are enough to sequence the library with 9 amino acids. If the encoded peptides are significantly longer, it might be necessary to increase the length of sequencing reads.
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3.2.2 Mining of HTS Data for Differential Binding Analysis
1. Use acquired .fastq sequence files for differential analysis. Use at least three mice per each group to draw conclusions based on the statistical significance. Start with loading all necessary libraries for data analysis. # Load necessary libraries required_packages