When AI Meets Drug Development

Session Description

AI has been utilized across various stages of drug development, presenting a continuously evolving landscape. Its applications span from drug discovery and clinical research to postmarket safety surveillance and advanced pharmaceutical manufacturing. Recent endeavors have explored novel AI algorithms for PK/PD modeling, demonstrating the growing exploration of AI's potential in this field. Notably, the number of drug and biological product applications incorporating AI/machine learning has witnessed a significant surge, exceeding 100 submissions in 2021. Given AI's increasingly pivotal role in delivering new treatments to patients, it is imperative for scientists to stay abreast of the latest developments. Equally important is the assessment of potential risks and harms associated with AI implementation.

This session aims to address these vital aspects by focusing on the advancement and application of AI in supporting drug development. Led by experts from academia, industry, and regulatory agency, this session will offer valuable insights and experiences, providing different perspectives of the current state of AI across different phases of drug development. Attendees will delve into case studies showcasing AI applications in drug discovery and PK/PD modeling. Furthermore, the session will shed light on key challenges and gaps encountered in AI-based drug development while offering perspectives on overcoming these obstacles. Attendees will gain a better understanding of how AI has the potential to accelerate drug development, enhance the efficiency and safety of clinical trials, all while remaining mindful of the specific risks and challenges it presents. Through case studies, discussions, and identification of challenges, the session aims to inspire new research endeavors, advance translational science, and ultimately contribute to improved patient outcomes in the context of AI-based drug development. (Reference 1: FDA. Using Artificial Intelligence and Machine Learning in the Development of Drug and Biological Products ; 2: Q.Liu et al. 2022,Clin Pharmacol Ther.)

Learning Objectives

1. Gain a comprehensive understanding of the current opportunities that AI used in drug development.
2. Identify the methodologies currently utilized in the field, including their strengths and limitations.
3. Identify gaps, ethical concerns, and obstacles that arise in AI-based drug development, and discover effective strategies for overcoming these challenges and promoting the advancement of translational science approaches

Session Schedule

3:00 PM - 3:05 PM MT


Introduction: From Data to Drugs
Zhu Zhou, PhD

3:05 PM - 3:15 PM MT
When AI meets drug discovery
Anshul Kanakia, PhD

3:15 PM - 3:25 PM MT
AI and Pharmaceutical Intelligence
Mark Sale, MD

3:25 PM - 3:35 PM MT
When AI Meets Pharmacometrics
Liang Zhao, PhD, MBA

3:35 PM - 4:00 PM MT
Q&A and Closing Remarks