Data-Driven Pharmaceutical Processing and Drug Development

Md. Faiyazuddin, PhD, is a senior professor at Al-Karim University, India, with seventeen years’ experience in pharmacy education, product development, and research consultation. He has authored more than 145 published papers in Q1 journals, holds 12 patents, and has written 12 books and 48 book chapters. Abdul Wasy Zia, PhD, is an Assistant Professor at the Institute of Mechanical Process and Energy Engineering at Heriot-Watt University, Edinburgh. A Fellow of the Higher Education Academy and endorsed as a Global Talent in Materials Performance by the Royal Academy of Engineering, he leads the Advanced Materials and Manufacturing Laboratory.

• List of Contributors xixPreface xxviiList of Abbreviations xxixPart I AI for Drug Design and Formulation 11 Integrating AI in Drug Formulation: Influential Factors and Practical Applications 3Vivek Panwar, Nikita Thakur, Piyush Sewal, Nitin Verma, and Deepak Kumar1.1 Introduction 31.2 AI and Drug Formulation 51.3 AI-based Drug Optimization and Pharmaceutical Formulation Parameters 61.4 AI-driven Models and Tools for Predicting the Physicochemical Properties of Drugs 71.5 Implementing AI in Regulatory and Ethical Aspects 81.6 Conclusion 92 Artificial Intelligence-driven Innovations in Drug Design, Delivery, and Personalization 13Amol D. Gholap, Pankaj R. Khuspe, Sadikali F. Sayyad, Navnath Hatvate, and Machindra Chavan2.1 Introduction 142.2 AI in Predictive Modeling for Drug Formulation 142.3 Personalized Medicine and AI 182.4 Automation and Optimization in Drug Design 202.5 AI in Drug Delivery Systems 242.6 Challenges and Limitations 262.7 Future Directions 282.8 Conclusion 303 Data-driven Approaches in Pharmaceutical Formulation and Drug Development: Case Studies and Insights 41Eman M. Sarhan and Mandana Akhavan3.1 Introduction 423.2 Data-driven Algorithms in Pharma 443.3 Data-driven Application in Healthcare and Pharmaceutical Industry 463.4 Tools and Case Studies 583.5 Challenges, Limitations, and Ethical Considerations 653.6 Conclusion 684 Case Studies on Data-driven Drug Formulation/Development 79Yujing Zhao, Wenai Chen, Lixiang Guo, Lei Zhang, Jian Du, Jiaqi Lin, Qilei Liu, and Qingwei Meng4.1 Introduction 804.2 Deep Learning Models for Predictions of Binding Affinity and Dissociation Kinetics 814.3 Integration of the MINLP-based Drug Design Model with Deep Learning Models 944.4 Case Studies 1014.5 Conclusion 1115 Data-driven Pharmaceutical Processing and Drug Development 117Fawad Abidi5.1 Introduction 1175.2 Methodology 1195.3 Results 1285.4 Conclusion 132Part II AI for Pharmaceutical Manufacturing 1356 Artificial Intelligence and Pharmaceutical Manufacturing 137R. Yuvaraj, R. Rithika, L. Charlie Jelura, and M. Manigandan6.1 Introduction 1386.2 Design Space in Pharmaceutical Industry 1396.3 Fundamental Tools for Increased Process 1406.4 AI Technologies in Pharmaceutical Manufacturing 1416.5 AI Tools in Pharmaceuticals 1426.6 Predictive Maintenance in Pharmaceutical Manufacturing 1436.7 3D Printing Techniques for Pharmaceutical 1456.8 Pharmaceutical Process Optimization 1496.9 Challenges and Future Perspectives 1516.10 Conclusion 1527 Data-centric Engineering for Real-time Monitoring and Control in Pharmaceutical Manufacturing 159Amol D. Gholap, Pankaj R. Khuspe, Sadikali F. Sayyad, Machindra Chavan, and Md. Faiyazuddin7.1 Introduction 1607.2 Principles of DTs in Pharmaceutical Manufacturing 1627.3 Applications of DCE 1647.4 Real-time Monitoring and Control: Enabling Technologies 1697.5 Case Studies 1747.6 Challenges and Future Directions 1767.7 Conclusion 1798 Digital Twin in Pharmaceutical Manufacturing and Quality Control 191F. Fareedhul Fahmitha, M.S. Swetha, S. Swetha, and P. Saranraj8.1 Introduction 1918.2 DTs in Pharmaceutical Manufacturing 1928.3 Data-centric Engineering in Pharmaceutical Manufacturing 1938.4 Real-time Monitoring and Control in Pharmaceutical Manufacturing 1958.5 Integration of DTs, Data-centric Engineering, and Real-time Monitoring and Control 1968.6 Regulatory and Quality Considerations 1978.7 Challenges 1978.8 Conclusion 1989 Data-driven Acoustic Approaches to Pharmaceutical Efficacy 201Mubbashar Mehmood and Javed Akhter9.1 Introduction 2019.2 Applications of SAWs 2049.3 Conclusion 21210 Data-driven Practices for Pharmaceutical Coatings 217Anam Ijaz and Abdul Wasy Zia10.1 Introduction 21710.2 Data-driven Approaches in Pharmaceutical Industries 21910.3 Material and Formulation Design Using Data Analytics 22310.4 Coating Techniques Enhanced by Data-driven Insights 22310.5 Quality Control and Performance Evaluation Using Data 22810.6 Trends and Future Directions in Data-driven Coating Technologies 22910.7 Conclusion 22911 Nanotechnology and AI-driven Drug Repurposing and Combination Therapy 233Naveed Ahmad, Muzna Ali Khattak, Talaya Hidayatullah, Muhammad Asim Sajid Al-Hanafi, Bruno Arnaud Dzokwe Kene, Zahid Hussain and Shahzeb Khan11.1 Introduction 23611.2 Role of Nanotechnology in Addressing Challenges Associated with Repurposed Drugs and Combination Therapies 23911.3 Key Concepts in Nanotechnology-driven Drug Delivery 24011.4 Functionalization and Surface Modifications of NPs for Targeted Delivery 24511.5 Drug Repurposing: Concept and Role of Nanotechnology 24511.6 Combination Therapy: Challenges and Nanotechnology Solutions 25011.7 Drug Repurposing and Combination Therapy 25311.8 Artificial Intelligence in Nanoparticle Design for Drug Repurposing 26211.9 Preclinical Studies Demonstrating the Effectiveness of Nanotechnology-based Drug Repurposing and Combination Therapy 26311.10 Regulatory Considerations for Nanoformulations in Repurposed Drugs and Combination Therapies 26711.11 Challenges and Limitations 26811.12 Scale Up and Manufacturing Challenges 27311.13 Stability and Storage of Nanocarriers 28111.14 Future Directions and Opportunities 28311.15 Integration of Nanotechnology to the Medical Field, Specifically to PM 28311.16 Role of AI and Machine Learning in Optimizing Nanoformulation Design 29311.17 Potential for Nanotechnology in Tackling Rare Diseases with Repurposed Drugs 29411.18 Nanotheranostics 296xiv Contents11.19 Ethical Dimensions of Nanomedicine 29811.20 Conclusion 30012 Nanotechnology-based Repurposing and Combination Therapy with Case Studies and Mechanisms 315Amol D. Gholap, Pankaj R. Khuspe, Sadikali F. Sayyad, Navnath Hatvate, and Machindra Chavan12.1 Introduction 31612.2 Nanotechnology in Drug Repurposing 31812.3 Nanotechnology in Combination Therapy 32412.4 Challenges and Limitations 32912.5 Future Perspectives 33312.6 Conclusion 33413 Nano-enabled Drug Repurposing and Chemotherapy 347Sareer Ahmad, Jawairia Ali, Salah Uddin, Safia Ali, and Kefayat Ullah13.1 Introduction 34713.2 Nanotechnology in Combination Therapy 35313.3 Applications 35913.4 Conclusion 36413.5 Future Recommendations 36414 Data-driven Performance Evaluation of Drug Development and Pharmaceutical Manufacturing 373Amol D. Gholap, Pankaj R. Khuspe, Navnath Hatvate, and Md. Faiyazuddin14.1 Introduction 37414.2 Data-driven Approaches in Drug Development 37414.3 Data-driven Pharmaceutical Manufacturing 37914.4 Challenges and Solutions 38114.5 Future Directions 38514.6 Conclusion 387Part III AI for Performance Evaluation and Regulatory Compliance 39915 A Comprehensive Survey of Advanced Control Chart Applications in Data-driven Pharmaceutical Manufacturing 401Raiha Mumtaz, Tahir Mahmood, and Naeem Ramzan15.1 Introduction 40115.2 Study Selection and Eligibility Criteria 40315.3 Statistical Process Control 405xvi Contents15.4 Application Domains of Control Charts in Healthcare and Pharmaceutical Sciences 40915.5 Clinical and Healthcare Process Monitoring and Quality Improvement 41715.6 Laboratory and Diagnostic Process Monitoring 42415.7 Animal and Veterinary Applications 43315.8 Summary and Future Recommendations 43316 Artificial Intelligence in the Management of Pharmaceutical Manufacturing 451Rida Sagheer, Samra Siddiqui, and Aisha Badruddin16.1 Introduction 45116.2 Industrial Revolution in Pharmaceutical Manufacturing 45316.3 Pharmaceutical Manufacturing 45416.4 Artificial Intelligence 45616.5 History of AI in Pharmaceutical Industry 45816.6 AI in Pharmaceutical Manufacturing 45916.7 Case Studies of AI Application in Pharmaceutical Management 46716.8 Limitations of AI in Pharmaceutical Manufacturing 47016.9 Challenges in AI Adoption 47016.10 Conclusion 47117 Regulatory Compliance on Data-driven Pharmaceutical Research and Development 477Md Akbar, Hasan Ali, Md. Faiyazuddin, Shrestha Sharma, Rakesh Kumar, and Ankur Sharma17.1 Introduction 47817.2 Data-driven Approaches in Drug Regulatory Compliance 48017.3 Characteristics of Drug Regulatory Compliance in Data-driven Research and Development 48217.4 Data Governance 48617.5 Data Quality Assurance 48817.6 Data Privacy 48917.7 Standardization 49117.8 Regulatory Compliance 49317.9 Compliance Frameworks 49517.10 Key Considerations for AI/ML in Drug Development 49517.11 Challenges of Data-driven Drug Regulatory Compliance 49717.12 Conclusion 49818 Responsible AI and Data Governance in Pharmaceutical Settings 503Piyush Sewal, Sriti Thakur, and Deepak Kumar18.1 Introduction 50318.2 Governance and Compliance in the Age of Digital Pharma 50518.3 AI in Healthcare: Best Practices and Policy Frameworks 51418.4 Transforming Pharmaceuticals with Digital Intelligence and Responsible AI 51618.5 Transforming Innovation and Development Through AI in Pharma and Drug Discovery 52218.6 Optimizing Pharmaceutical Excipients with AI Technology and AI-driven Discoveries in Natural Product Science 52318.7 Robust Framework of IPR in AI-driven Pharma 52418.8 Conclusion and Future Scope 525References 526Appendixes 531Index 557