Computational Methods in Drug Discovery and Repurposing for Cancer Therapy

Dr. Nagaraju obtained his MSc and his PhD, both in Biotechnology, from Sri Venkateswara University in Tirupati, Andhra Pradesh, India. He received his DSc from Berhampur University in Berhampur, Odisha, India. Dr. Nagaraju’s research focuses on translational projects related to gastrointestinal malignancies. He has published over 100 research papers in highly reputed international journals and has presented more than 50 abstracts at various national and international conferences. Dr. Nagaraju is author and editor of several published books in Elsevier and Springer Nature. He serves as editorial board member of several internationally recognized academic journals. Dr. Nagaraju has received several international awards including FAACC. He also holds memberships with the Association of Scientists of Indian Origin in America (ASIOA), the Society for Integrative and Comparative Biology (SICB), The Science Advisory Board, The RNA Society, The American Association for Clinical Chemistry (AACC) and the American Association of Cancer Research (AACR). Dr. V. Amouda is an Assistant Professor with a demonstrated history of working in higher education at the Department of Bioinformatics, Pondicherry University, Puducherry, India. She possesses a doctoral degree in Computer Science & Engineering from Pondicherry University. She has more than a decade of research and development experience in new computational data technologies focusing on Data analysis, Machine learning, Data mining, and Big Data analytics across biomedical areas. She has published more than 50 research articles in peer-reviewed international journals and presented many abstracts at national and international conferences. Dr. V. Amouda is a co-author of 4 book chapters published by International publishers and a reviewer of many international journals. She promotes science and progress by organizing 35 national and international conferences at various platforms in the Bioinformatics and Computer Science fields. She has a strong administrative professional experience with a track of contributing science to society by associating with various scientific bodies of Government & NGOs. She has experience in extension activities as she is a member of learned societies of national professional agencies. Dr. Ampasala earned his PhD degree in biochemistry from Sri Venkateswara University. He spent 12 years in research and teaching at Pondicherry University, Puducherry, India, and 10 years in postdoctoral studies in cancer research in United States and Canada. Dr. Ampasala’s current research focuses are on functional genomics, molecular mechanisms of cancer, signal transduction, and computational neurobiology and has published nearly 70 peer-reviewed scholarly research articles and book chapters, which are extensively cited globally. He is a reviewer and editorial member for several biomedical/bioinformatics journals and has received several competitive research grants for his research accomplishments/contributions. He has been honored with fellow award from the Society of Plant Research (2019), and he has bagged other prestigious awards such as DST-Young Scientist (2008), DBT-Young Investigator (2015), Science Communicator-ISCA (2013, 2014), and the Best Teacher Award (2011, Pondicherry University). He received VA Merit Award from Wayne State University, Detroit, MI, United States (2005–2007) for his postdoctoral studies; research associate fellowship funded by Genomic Canada, Canada (2001-2005); and postdoctoral fellowship funded by DBT, Indian Institute of Science (1999–2001).

• Contributors About the editors Preface 1. Computational approaches for anticancer drug design Tha Luong, Grace Persis Burri, Yuvasri Golivi, Ganji Purnachandra Nagaraju, and Bassel F. El-Rayes1. Introduction 2. Current computational approaches for cancer drug designs 3. Applications of computational approaches in cancer drug designing 4. Challenges and future directions 5. Conclusion References 2. Molecular modeling approach in cancer drug therapy Bhavini Singh, Rishabh Rege, and Ganji Purnachandra Nagaraju1. Introduction 2. Drug designing 3. Molecular modeling 4. Methods of molecular modeling 5. Applications of molecular modeling 6. Applications in multidrug-resistant proteins 7. Conclusion References 3. Discovery of anticancer therapeutics: Computational chemistry and Artificial Intelligence-assisted approach Subrata Das, Anupam Das Talukdar, Deepa Nath, and Manabendra Dutta Choudhury1. Introduction 2. Drug repurposing 3. Computational chemistry in drug designing 4. Structure-based drug designing 5. ADME/Tox screening and drug-likeness prediction 6. Molecular docking 7. Quantitative structure-activity relationship modeling 8. Molecular dynamics simulation 9. Artificial Intelligence in drug discovery 10. Conclusion References 4. Artificial intelligence in oncological therapies Shloka Adluru1. Introduction 2. Importance of early diagnosis 3. How AI can improve accuracy and speed of cancer diagnoses 4. How AI can assess patient background information to determine risk of cancer 5. Diagnosis of cancer subtype and stage 6. AI in cancer drug discovery and development 7. De novo drug design 8. AI in recommending drug combinations and repurposing drugs 9. AI in identifying drug-target interactions 10. Deep learning, black boxes, and hidden layers 11. Future of AI in oncology 12. Conclusion References 5. Approach of artificial intelligence in colorectal cancer and in precision medicine Grace Persis Burri, Yuvasri Golivi, Tha Luong, Neha Merchant, and Ganji Purnachandra Nagaraju1. Introduction2. Applications of AI in CRC3. Robotic-assisted surgery 4. Precision medicine in CRC 5. Benefits 6. Limitations 7. Current challenges and prospects 8. Conclusion Conflict of interest Funding References 6. Artificial intelligence in breast cancer: An opportunity for early diagnosis Rama Rao Malla and Vedavathi Katneni1. Machine learning 2. Breast cancer 3. Conclusion References 7. Quantitative structure-activity relationship and its application to cancer therapy Bhavini Singh and Rishabh Rege1. Introduction 2. Function 3. Origin of QSAR 4. Advanced techniques of QSAR 5. Application in drug design 6. Application in cancer therapy 7. Concerns 8. Conclusion References 8. Structure-based virtual screening strategy for the identification of novel Greatwall kinase inhibitors Anbumani VelmuruganIlavarasi, Tulsi, Saswati Sarita Mohanty, Katike Umamahesh, Amouda Venkatesan, and Dinakara Rao Ampasala1. Introduction 2. Computational methods 3. Results and discussion 4. Conclusion Acknowledgments Conflict of interest References 9. Strategies for drug repurposing Aparna Vema and Arunasree M. Kalle1. Introduction 2. Computational drug repurposing 3. Experimental drug repurposing 4. Conclusions and perspectives Author contributions Financial disclosures Conflict of interest References 10. Principles of computational drug designing and drug repurposing—An algorithmic approach Angshuman Bagchi1. Introduction 2. Overview of basic thermodynamic principles involved in computational algorithms 3. Fundamentals of computational algorithms 4. Searching the conformational space 5. Analysis of protein flexibility 6. Drug repurposing 7. Conclusion Acknowledgment References 11. Drug discovery and repositioning for glioblastoma multiforme and low-grade astrocytic tumors Asmita Dasgupta, Sanjukta Ghosh, Kastro Kalidass, and Shabnam Farisha1. Introduction 2. Approved therapeutics for astrocytic tumors 3. Drug discovery approaches against astrocytic tumors 4. Drug discovery for astrocytic tumors by virtual screening 5. Drug repositioning in astrocytic tumor therapy 6. Conclusion Acknowledgments Conflict of interest References 12. Repurposing of phytocompounds-derived novel bioactive compounds possessing promising anticancer and cancer therapeutic efficacy through molecular docking, MD simulation, and drug-likeness/ADMET studiesRajalakshmi Manikkam, Vijayalakshmi Periyasamy, and Indu Sabapathy1. Drug repurposing 2. Strategies in drug repurposing 3. Pros and cons of drug repurposing 4. Computational advancements in oncology research 5. Structure-based and target-based virtual screening 6. Systems biology integrated approach in drug repositioning 7. In silico databases and web-based tools for drug repurposing 8. Phytochemicals repurposed in cancer therapy 9. Antidiabetic phytocompounds repurposed for cancer therapy 10. Conclusion References 13. Old drugs and new opportunities—Drug repurposing in colon cancer prevention Vemula Sarojamma, Manoj Kumar Gupta, Jeelan Basha Shaik, and Ramakrishna Vadde1. Introduction 2. Principles and tools used in drug repurposing 3. Categories of repurposed drugs against human cancers 4. Drugs used in the treatment of colon cancer 5. Drug repurposing in the prevention of colon cancer 6. Drug repurposing pitfalls 7. Computational approaches in drug repurposing for colorectal cancer 8. Conclusions and perspectives Conflicts of interest References 14. Repurposing cardiac glycosides as potent immune system modulators in cancer therapy Honey Pavithran, Angelina K. Job, and Ranjith Kumavath1. Introduction 2. Repurposing cardiac glycosides in cancer treatment 3. CGs hamper Na+/K+-ATPase signaling complex in cancer 4. Role of the immune system in cancer 5. Conclusions Acknowledgments Consent for publication Ethics approval and consent to participate Conflict of interest References 15. Systems biology tools for the identification of potential drug targets and biological markers effective for cancer therapeutics Gayathri Ashok, P. Priyamvada, Sravan Kumar Miryala, Anand Anbarasu, and Sudha Ramaiah1. Introduction 2. Current problems in cancer therapies 3. Need for alternative approaches in cancer 4. GIN: A systems biology approach 5. Types of biological networks 6. Cancer databases 7. Databases for interaction data curation 8. Network construction and visualization 9. Network analysis 10. How can the identified targets be used for cancer therapy? 11. Conclusion Acknowledgments Conflict of interest Authors’ contribution References 16. Role of human body fluid biomarkers in liver cancer: A systematic review Dahrii Paul, Vigneshwar Suriya Prakash Sinnarasan, Rajesh Das, Dinakara Rao Ampasala, and Amouda Venkatesan1. Introduction 2. Methods 3. Results 4. Discussion 5. Conclusions References 17. Study on biomarkers in endometrial cancer using transcriptome data: A machine learning approach Dahrii Paul, Vigneshwar Suriya Prakash Sinnarasan, Rajesh Das, Dinakara Rao Ampasala, and Amouda Venkatesan1. Introduction 2. Materials and methods 3. Results 4. Discussion 5. Conclusion References 18. Multifaceted functions of PIWI/piRNA complex in cancer and as a therapeutic target for retinoblastoma Rupa Roy, Muthuramalingam Karpagavalli, Athira Ramesh, Jayamuruga Pandian Arunachalam, Sudha Rani Sadras, and Subbulakshmi Chidambaram1. Introduction 2. Biological functions of PIWI/piRNA in physiological conditions 3. Emerging significance of PIWI/piRNA in various cancers 4. Retina and its structure 5. Potential role of PIWI and piRNA in RB 6. PIWI/piRNA as future biomarkers in cancer 7. Conclusion Acknowledgments Declaration of competing interest References 19. Emerging role of biosimilars: Focus on Bevacizumab and hepatocellular carcinoma Anum Jalil, James Wert, Akriti Gupta Jain, and Sarfraz Ahmad1. Introduction 2. Biologics and biosimilars 3. FDA approved biosimilars to date 4. Role of Bevacizumab and its biosimilar in hepatocellular carcinoma 5. Clinical trials with Bevacizumab and its biosimilar in HCC 6. Conclusions and future perspectives Funding Authors’ contribution References 20. Integrated computational approaches to aid precision medicine for cancer therapy: Present scenario and future prospects Hithesh Kumar, Sravan Kumar Miryala, Anand Anbarasu, and Sudha Ramaiah1. Introduction 2. Precision cancer medicine: Prospects and hurdles 3. Next generation sequencing and computational genomics in PCM 4. Drug repositioning using translational bioinformatics 5. Future perspectives 6. Conclusion Acknowledgment Author contributions Declaration of interests References Index