Unleashing the Power of Large Language Models in Drug Development: Transformative Insights and Innovative Applications

Session Description

In recent years, the integration of large language models (LLMs), powered by artificial intelligence (AI), has emerged as a transformative force across various domains. This session will delve into the profound impact of these advanced models on clinical pharmacology and drug development, showcasing their versatility and efficacy.

Amidst the growing emphasis on precision medicine and the need for expedited drug development, LLMs offer unparalleled capabilities. By harnessing vast datasets encompassing biomedical literature, clinical trial data, real-world evidence, and integrating applications tailored to pharmacometrians and clinical pharmacologists, these models provide invaluable insights. They can enable analysis of drug interactions, parameter extraction, facilitate efficacy predictions, and enhance adverse event detection, thereby optimizing drug development pipelines and ultimately improving patient outcomes.

Through an array of insightful presentations by esteemed experts in the fields of AI and pharmacometrics, attendees will witness firsthand the groundbreaking achievements made possible by LLMs. Topics will include extracting parameter values for quantitative systems pharmacology models, developing LLMs chatbots tailored for pharmacometrians and clinical pharmacologists, and extracting insights from real-world data for precision medicine. Attendees will gain firsthand exposure to these innovative uses, highlighting the transformative potential of large language models in the field.

This session serves as a testament to the remarkable advancements in AI and its profound impact on advancing the frontiers of clinical pharmacology and drug development, offering a glimpse into the exciting possibilities that lie ahead.

Learning Objectives

1. Gain an understanding of the transformative potential of large language models (LLMs) in revolutionizing drug development, including opportunities such as personalized medicine and challenges such as data privacy and bias mitigation.
2. Explore how LLMs can enhance efficiency in building QSP models by tailoring LLMs for fit-for-purpose data mining, parameter extraction, and model replication from literature.
3. Learn about the systematic assessment of the LLMs in pharmacokinetic data analysis and community pharmacy, including its strengths in knowledge encapsulation, alongside critical areas for improvement such as numerical accuracy.

Session Schedule

Introduction
Jagdeep Podichetty, PhD and Jamie Connarn, PhD

Transforming Healthcare with Large Language Models: Opportunities and Challenges
Joga Gobburu, MBA, PhD

Tailoring Large Language Models for Use as a Literature Copilot for QSP
Emily Nieves

Evaluation of Large Language Models with Diverse Case Studies from Pharmacokinetic Data Analysis and Community Pharmacy
Murali Ramanathan, PhD

Panel Discussion