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 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

18.1 Introduction 503
18.2 Governance and Compliance in the Age of Digital Pharma 505
18.3 AI in Healthcare: Best Practices and Policy Frameworks 514
18.4 Transforming Pharmaceuticals with Digital Intelligence and Responsible AI 516
18.5 Transforming Innovation and Development Through AI in Pharma and Drug Discovery 522
18.6 Optimizing Pharmaceutical Excipients with AI Technology and AI-driven Discoveries in Natural Product Science 523
18.7 Robust Framework of IPR in AI-driven Pharma 524
18.8 Conclusion and Future Scope 525

References 526
Appendixes 531
Index 557