List of Contributors xix Preface xxvii List of Abbreviations xxix Part I AI for Drug Design and Formulation 1 1 Integrating AI in Drug Formulation: Influential Factors and Practical Applications 3 Vivek Panwar, Nikita Thakur, Piyush Sewal, Nitin Verma, and Deepak Kumar 1.1 Introduction 3 1.2 AI and Drug Formulation 5 1.3 AI-based Drug Optimization and Pharmaceutical Formulation Parameters 6 1.4 AI-driven Models and Tools for Predicting the Physicochemical Properties of Drugs 7 1.5 Implementing AI in Regulatory and Ethical Aspects 8 1.6 Conclusion 9 2 Artificial Intelligence-driven Innovations in Drug Design, Delivery, and Personalization 13 Amol D. Gholap, Pankaj R.
Khuspe, Sadikali F. Sayyad, Navnath Hatvate, and Machindra Chavan 2.1 Introduction 14 2.2 AI in Predictive Modeling for Drug Formulation 14 2.3 Personalized Medicine and AI 18 2.4 Automation and Optimization in Drug Design 20 2.5 AI in Drug Delivery Systems 24 2.6 Challenges and Limitations 26 2.
7 Future Directions 28 2.8 Conclusion 30 3 Data-driven Approaches in Pharmaceutical Formulation and Drug Development: Case Studies and Insights 41 Eman M. Sarhan and Mandana Akhavan 3.1 Introduction 42 3.2 Data-driven Algorithms in Pharma 44 3.3 Data-driven Application in Healthcare and Pharmaceutical Industry 46 3.4 Tools and Case Studies 58 3.5 Challenges, Limitations, and Ethical Considerations 65 3.
6 Conclusion 68 4 Case Studies on Data-driven Drug Formulation/Development 79 Yujing Zhao, Wenai Chen, Lixiang Guo, Lei Zhang, Jian Du, Jiaqi Lin, Qilei Liu, and Qingwei Meng 4.1 Introduction 80 4.2 Deep Learning Models for Predictions of Binding Affinity and Dissociation Kinetics 81 4.3 Integration of the MINLP-based Drug Design Model with Deep Learning Models 94 4.4 Case Studies 101 4.5 Conclusion 111 5 Data-driven Pharmaceutical Processing and Drug Development 117 Fawad Abidi 5.1 Introduction 117 5.2 Methodology 119 5.
3 Results 128 5.4 Conclusion 132 Part II AI for Pharmaceutical Manufacturing 135 6 Artificial Intelligence and Pharmaceutical Manufacturing 137 R. Yuvaraj, R. Rithika, L. Charlie Jelura, and M. Manigandan 6.1 Introduction 138 6.2 Design Space in Pharmaceutical Industry 139 6.
3 Fundamental Tools for Increased Process 140 6.4 AI Technologies in Pharmaceutical Manufacturing 141 6.5 AI Tools in Pharmaceuticals 142 6.6 Predictive Maintenance in Pharmaceutical Manufacturing 143 6.7 3D Printing Techniques for Pharmaceutical 145 6.8 Pharmaceutical Process Optimization 149 6.9 Challenges and Future Perspectives 151 6.10 Conclusion 152 7 Data-centric Engineering for Real-time Monitoring and Control in Pharmaceutical Manufacturing 159 Amol D.
Gholap, Pankaj R. Khuspe, Sadikali F. Sayyad, Machindra Chavan, and Md. Faiyazuddin 7.1 Introduction 160 7.2 Principles of DTs in Pharmaceutical Manufacturing 162 7.3 Applications of DCE 164 7.4 Real-time Monitoring and Control: Enabling Technologies 169 7.
5 Case Studies 174 7.6 Challenges and Future Directions 176 7.7 Conclusion 179 8 Digital Twin in Pharmaceutical Manufacturing and Quality Control 191 F. Fareedhul Fahmitha, M.S. Swetha, S. Swetha, and P. Saranraj 8.
1 Introduction 191 8.2 DTs in Pharmaceutical Manufacturing 192 8.3 Data-centric Engineering in Pharmaceutical Manufacturing 193 8.4 Real-time Monitoring and Control in Pharmaceutical Manufacturing 195 8.5 Integration of DTs, Data-centric Engineering, and Real-time Monitoring and Control 196 8.6 Regulatory and Quality Considerations 197 8.7 Challenges 197 8.8 Conclusion 198 9 Data-driven Acoustic Approaches to Pharmaceutical Efficacy 201 Mubbashar Mehmood and Javed Akhter 9.
1 Introduction 201 9.2 Applications of SAWs 204 9.3 Conclusion 212 10 Data-driven Practices for Pharmaceutical Coatings 217 Anam Ijaz and Abdul Wasy Zia 10.1 Introduction 217 10.2 Data-driven Approaches in Pharmaceutical Industries 219 10.3 Material and Formulation Design Using Data Analytics 223 10.4 Coating Techniques Enhanced by Data-driven Insights 223 10.5 Quality Control and Performance Evaluation Using Data 228 10.
6 Trends and Future Directions in Data-driven Coating Technologies 229 10.7 Conclusion 229 11 Nanotechnology and AI-driven Drug Repurposing and Combination Therapy 233 Naveed Ahmad, Muzna Ali Khattak, Talaya Hidayatullah, Muhammad Asim Sajid Al-Hanafi, Bruno Arnaud Dzokwe Kene, Zahid Hussain and Shahzeb Khan 11.1 Introduction 236 11.2 Role of Nanotechnology in Addressing Challenges Associated with Repurposed Drugs and Combination Therapies 239 11.3 Key Concepts in Nanotechnology-driven Drug Delivery 240 11.4 Functionalization and Surface Modifications of NPs for Targeted Delivery 245 11.5 Drug Repurposing: Concept and Role of Nanotechnology 245 11.6 Combination Therapy: Challenges and Nanotechnology Solutions 250 11.
7 Drug Repurposing and Combination Therapy 253 11.8 Artificial Intelligence in Nanoparticle Design for Drug Repurposing 262 11.9 Preclinical Studies Demonstrating the Effectiveness of Nanotechnology-based Drug Repurposing and Combination Therapy 263 11.10 Regulatory Considerations for Nanoformulations in Repurposed Drugs and Combination Therapies 267 11.11 Challenges and Limitations 268 11.12 Scale Up and Manufacturing Challenges 273 11.13 Stability and Storage of Nanocarriers 281 11.14 Future Directions and Opportunities 283 11.
15 Integration of Nanotechnology to the Medical Field, Specifically to PM 283 11.16 Role of AI and Machine Learning in Optimizing Nanoformulation Design 293 11.17 Potential for Nanotechnology in Tackling Rare Diseases with Repurposed Drugs 294 11.18 Nanotheranostics 296xiv Contents 11.19 Ethical Dimensions of Nanomedicine 298 11.20 Conclusion 300 12 Nanotechnology-based Repurposing and Combination Therapy with Case Studies and Mechanisms 315 Amol D. Gholap, Pankaj R. Khuspe, Sadikali F.
Sayyad, Navnath Hatvate, and Machindra Chavan 12.1 Introduction 316 12.2 Nanotechnology in Drug Repurposing 318 12.3 Nanotechnology in Combination Therapy 324 12.4 Challenges and Limitations 329 12.5 Future Perspectives 333 12.6 Conclusion 334 13 Nano-enabled Drug Repurposing and Chemotherapy 347 Sareer Ahmad, Jawairia Ali, Salah Uddin, Safia Ali, and Kefayat Ullah 13.1 Introduction 347 13.
2 Nanotechnology in Combination Therapy 353 13.3 Applications 359 13.4 Conclusion 364 13.5 Future Recommendations 364 14 Data-driven Performance Evaluation of Drug Development and Pharmaceutical Manufacturing 373 Amol D. Gholap, Pankaj R. Khuspe, Navnath Hatvate, and Md. Faiyazuddin 14.1 Introduction 374 14.
2 Data-driven Approaches in Drug Development 374 14.3 Data-driven Pharmaceutical Manufacturing 379 14.4 Challenges and Solutions 381 14.5 Future Directions 385 14.6 Conclusion 387 Part III AI for Performance Evaluation and Regulatory Compliance 399 15 A Comprehensive Survey of Advanced Control Chart Applications in Data-driven Pharmaceutical Manufacturing 401 Raiha Mumtaz, Tahir Mahmood, and Naeem Ramzan 15.1 Introduction 401 15.2 Study Selection and Eligibility Criteria 403 15.3 Statistical Process Control 405xvi Contents 15.
4 Application Domains of Control Charts in Healthcare and Pharmaceutical Sciences 409 15.5 Clinical and Healthcare Process Monitoring and Quality Improvement 417 15.6 Laboratory and Diagnostic Process Monitoring 424 15.7 Animal and Veterinary Applications 433 15.8 Summary and Future Recommendations 433 16 Artificial Intelligence in the Management of Pharmaceutical Manufacturing 451 Rida Sagheer, Samra Siddiqui, and Aisha Badruddin 16.1 Introduction 451 16.2 Industrial Revolution in Pharmaceutical Manufacturing 453 16.3 Pharmaceutical Manufacturing 454 16.
4 Artificial Intelligence 456 16.5 History of AI in Pharmaceutical Industry 458 16.6 AI in Pharmaceutical Manufacturing 459 16.7 Case Studies of AI Application in Pharmaceutical Management 467 16.8 Limitations of AI in Pharmaceutical Manufacturing 470 16.9 Challenges in AI Adoption 470 16.10 Conclusion 471 17 Regulatory Compliance on Data-driven Pharmaceutical Research and Development 477 Md Akbar, Hasan Ali, Md. Faiyazuddin, Shrestha Sharma, Rakesh Kumar, and Ankur Sharma 17.
1 Introduction 478 17.2 Data-driven Approaches in Drug Regulatory Compliance 480 17.3 Characteristics of Drug Regulatory Compliance in Data-driven Research and Development 482 17.4 Data Governance 486 17.5 Data Quality Assurance 488 17.6 Data Privacy 489 17.7 Standardization 491 17.8 Regulatory Compliance 493 17.
9 Compliance Frameworks 495 17.10 Key Considerations for AI/ML in Drug Development 495 17.11 Challenges of Data-driven Drug Regulatory Compliance 497 17.12 Conclusion 498 18 Responsible AI and Data Governance in Pharmaceutical Settings 503 Piyush Sewal, Sriti Thakur, and Deepak Kumar.