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Chapter 6 Drug development and clinical trial via artificial intelligence

  • Rishabha Malviya , Shristy Verma , Sonali Sundram and Harshil Shah
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Abstract

Clinical trials and medication development have been profoundly impacted by artificial intelligence (AI), with deep learning techniques outperforming traditional machine learning tactics. Though its potential and accessibility are limited, highthroughput virtual screening (HTVS) has been proposed as an alternative to expensive and time-consuming methods. The chapter overviews computational drug development techniques that identify drug targets, validate drugs, and conduct clinical trials using artificial intelligence (AI). Additionally, by simply acquiring the governing equations from data, a novel deep learning technique has been described that is designed to build PK/PD models that can forecast patient response times and simulate the impact of unknown dosage regimens. The technique keeps the dose-concentration effect pharmacologically sound while enabling the model to predict treatment outcomes and replicate the patient’s dosage schedule. Drug target interactions (DTIs) are essential for locating possible drugs and offering information on side effects and drug mechanisms. Additionally, ethical issues about social gaps, security of data, informed consent, medical consultation, empathy, as well as sympathy, are raised in the last section and affect AI-enabled clinical trials in the healthcare industry.

Abstract

Clinical trials and medication development have been profoundly impacted by artificial intelligence (AI), with deep learning techniques outperforming traditional machine learning tactics. Though its potential and accessibility are limited, highthroughput virtual screening (HTVS) has been proposed as an alternative to expensive and time-consuming methods. The chapter overviews computational drug development techniques that identify drug targets, validate drugs, and conduct clinical trials using artificial intelligence (AI). Additionally, by simply acquiring the governing equations from data, a novel deep learning technique has been described that is designed to build PK/PD models that can forecast patient response times and simulate the impact of unknown dosage regimens. The technique keeps the dose-concentration effect pharmacologically sound while enabling the model to predict treatment outcomes and replicate the patient’s dosage schedule. Drug target interactions (DTIs) are essential for locating possible drugs and offering information on side effects and drug mechanisms. Additionally, ethical issues about social gaps, security of data, informed consent, medical consultation, empathy, as well as sympathy, are raised in the last section and affect AI-enabled clinical trials in the healthcare industry.

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Foreword VII
  4. Contents IX
  5. Chapter 1 Introduction to smart pharma: foundation of AI and big data 1
  6. Chapter 2 Emerging technology in pharma and the role of AI and blockchain 19
  7. Chapter 3 Partnership and collaboration in the pharmaceutical industry 37
  8. Chapter 4 Education and training in smart pharma 55
  9. Chapter 5 Drug manufacturing and quality control with artificial intelligence 77
  10. Chapter 6 Drug development and clinical trial via artificial intelligence 97
  11. Chapter 7 AI-driven clinical decision support systems for pharma executives 121
  12. Chapter 8 AI-based pharmacovigilance and drug safety 143
  13. Chapter 9 Intellectual property and data privacy in the pharmaceutical sector 163
  14. Chapter 10 Regulatory affairs and compliance in the pharmaceutical sector 183
  15. Chapter 11 AI and big data in post-marketing surveillance 201
  16. Chapter 12 Challenges and ethical considerations 219
  17. Chapter 13 Future trends and innovations of AI in healthcare 237
  18. Index 253
https://doi.org/10.1515/9783111574288-006

Chapters in this book

  1. Frontmatter I
  2. Preface V
  3. Foreword VII
  4. Contents IX
  5. Chapter 1 Introduction to smart pharma: foundation of AI and big data 1
  6. Chapter 2 Emerging technology in pharma and the role of AI and blockchain 19
  7. Chapter 3 Partnership and collaboration in the pharmaceutical industry 37
  8. Chapter 4 Education and training in smart pharma 55
  9. Chapter 5 Drug manufacturing and quality control with artificial intelligence 77
  10. Chapter 6 Drug development and clinical trial via artificial intelligence 97
  11. Chapter 7 AI-driven clinical decision support systems for pharma executives 121
  12. Chapter 8 AI-based pharmacovigilance and drug safety 143
  13. Chapter 9 Intellectual property and data privacy in the pharmaceutical sector 163
  14. Chapter 10 Regulatory affairs and compliance in the pharmaceutical sector 183
  15. Chapter 11 AI and big data in post-marketing surveillance 201
  16. Chapter 12 Challenges and ethical considerations 219
  17. Chapter 13 Future trends and innovations of AI in healthcare 237
  18. Index 253
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