DIA 2025 Global Annual Meeting

Component Type: Session
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-634-L04-P; CME 1.00; RN 1.00

Aftificial Intelligence (AI) is being adopted across the medicine lifecycle to increase insights into data and improve efficiency of processes, for the benefit of public and animal health. This session will address key elements to enable the safe and responsible use of AI.

Learning Objectives

Identify AI opportunities across the medicine lifecycle; Examine the legal ecosystem of AI across jurisdictions and identify opportunities for convergence; Discuss how regulators worldwide are developing guidance to support AI innovation in the medicine lifecycle; State other strategic AI initiatives, including approaches to leveraging AI with healthcare data.

Chair

Luis Pinheiro, PharmD, MSc

Speaker

Panelist
Timothe Menard, PharmD, MSc

Panelist
Tala Fakhouri, PhD, MPH

Panelist
Gabriel Westman, MD, PhD, MSc

Panelist
Julian Beach

Component Type: Session

Join the Clinical Research Community for a follow up round table discussion tied to session: Statistical Challenges and Innovations in Small-Sized Clinical Trials Using Composite Endpoint (Tuesday, June 17 | 1:45pm - 2:45pm EDT). Space is limited.

Learning Objectives

Identify ways to apply concepts and techniques from the session.

Chair

Noel Ellison, MS

Component Type: Session
Level: Basic
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-650-L04-P; CME 1.00; RN 1.00

In the rapidly evolving landscape of medical product development, the utilization of synthetic data and digital twins has gained increasing interest as a transformative approach to accelerate the development and approval of medical products. This session aims to explore the potential and innovative applications of these technologies in the realm of drug development, featuring speakers from health authorities, academia and industry. Dr. Khaled El Emam, Professor at the University of Ottawa, will present his latest research on generating synthetic data to augment clinical trials. Additionally, he will explore practical applications of synthetic data generation using real-world data. Dr. Arman Sabbaghi, Associate Professor at Purdue University, with prior experience at Unlearn, will present AI-Generated Digital Twins to deliver more efficient clinical trials. He will discuss the innovative statistical methodologies and novel trial designs that combine historical data, artificial intelligence (AI), and randomization to deliver smaller, faster, and more powerful RCTs that are built with regulatory guidance in mind. Dr. Ye Li, Mathematical Statistician, CDER, FDA will present statistical challenges leveraging machine learning in clinical trials. Several key factors that may influence the application of ML and AI algorithms in analyzing efficacy data will be discussed, including estimand and type I error rate control, as well as the importance of interpretability and reproducibility of ML/AI models to ensure robust and reliable results. Furthermore, she will address corresponding considerations related to these factors when using Digital Twins.

Learning Objectives

Describe what synthetic data and digital twins are; Discuss ways to apply AI to generate synthetic data and digital twin; Illustrate the potential of synthetic data and digital twins in medical product development.

Chair

Di Zhang, PhD

Speaker

Applications of Synthetic Data in Clinical Trials and Real-World Studies
Khaled El Emam, PhD

Statistical Methods for Unleashing AI-Generated Digital Twins to Deliver More Efficient Randomized Controlled Trials
Arman Sabbaghi, PhD, MA

Leveraging Machine Learning in Clinical Trials: Statistical Challenges
Ye Li, PhD

Component Type: Forum
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-666-L04-P; CME 1.00; RN 1.00

Regulatory, industry, and academic organizations discuss modern approaches to clinical drug development by describing novel methods and use cases integrating real-world data and AI to generate real-world evidence, with focus on oncology and rare diseases.

Learning Objectives

Discuss global regulatory guidance and frameworks supporting innovative designs using real-world data as well as approaches to inclusion of artificial intelligence (AI); Describe innovative approaches and methods through examples of applied AI use in drug development; Discuss areas of research interest and promote community collaboration.

Chair

Donna Rivera, PharmD, MSc, FISPE

Speaker

Applying Artificial Intelligence in Drug Development
Sid Jain, MBA

Real World Data and Artificial Intelligence in Oncology: An FDA Perspective
Catherine Lerro, PhD, MPH

Use of Artificial Intelligence Approaches for Rare Diseases
Christina Mack, PhD, MPH

Panelist
Kelly Robinson, MSc

Component Type: Session
Level: Advanced
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-665-L04-P; CME 1.00; RN 1.00

In this session, we will provide a brief overview of the evolving ICH E20 effort with the goal to provide a transparent and harmonized set of principles for the design, conduct, analysis, and interpretation of adaptive clinical trials.

Learning Objectives

Discuss considerations specific to adaptive design using Bayesian methods; Recognize opportunities and challenges of using Bayesian methods in the regulatory setting.

Chair

Amy Xia, PhD

Speaker

E20 Updates
Amy Xia, PhD

FDA Bayesian Updates
Mark Rothmann, PhD

Bayesian Trial Design Case Studies
John Zhong, PhD

Component Type: Forum
Level: Intermediate
CE: ACPE 1.00 Knowledge UAN: 0286-0000-25-660-L04-P; CME 1.00; RN 1.00

This session will provide an opportunity for key stakeholders, including regulators, product developers and patients to discuss best practices around patient experience data, including planning, collecting, and submitting such data to regulators.

Learning Objectives

Discuss current expectations for regulator/sponsor interactions related to patient experience data that will be used in regulatory submissions; Recognize common pitfalls for submission of patient experience data in regulatory applications, and how to avoid them.

Chair

Robyn Bent, BSN, MS, RN

Speaker

Panelist
Laura Lee Johnson, PhD

Panelist
Laura Jawidzik, MD

Panelist
Pujita Vaidya, MPH

Panelist
Brett Hauber, PhD, MA