Abstract: This talk will provide a brief overview of the evolving regulatory landscape for artificial intelligence and machine learning (AI/ML) in the development of drugs and biologics, with a focus on initiatives led by the U.S. Food and Drug Administration (FDA). It will discuss the use of AI/ML for estimating and inferring causal effects, including key regulatory challenges and considerations as well as emerging opportunities. This talk aims to contribute to the ongoing dialogue on AI/ML for medical product development by providing insights into regulatory thinking and methodological strategies for robust causal inference, while emphasizing the need for continued collaboration between stakeholders to ensure responsible and transparent AI/ML use in this field.

Abstract: Hybrid clinical trials, which integrate real-world data (RWD) from sources such as patient registries, claims databases, and electronic health records (EHRs) to enhance randomized clinical trials, are gaining significant attention. In their forthcoming study, Xu et al. (BRS, 2025) propose an advancement to the two-step design introduced by Yuan et al. (2019), focusing on effective type-I error control. This talk will provide an overview of the newly developed two-step hybrid design, highlighting its enhancements for tighter control over type-I error rates. Additionally, I will discuss methods and algorithms for the optimal selection of design parameters aimed at minimizing sample size, maximizing statistical power, or achieving a favorable treatment-to-control ratio. This research is a collaborative effort by Jiapeng Xu (SU), Arlina Shen (SU), Ruben van Eijk (UMUC), and Lu Tian (SU).

One innovative design in clinical development is the use of externally controlled trial design. Evidence from such trials has recently gained acceptance from regulators as pivotal in their decision-making processes, and this trend is accelerating. However, this approach is not without risks. Recent FDA draft guidance, “Considerations for the Design and Conduct of Externally Controlled Trials for Drug and Biological Products,” and the EMA reflection paper, “Establishing Efficacy Based on Single-Arm Trials Submitted as Pivotal Evidence in a Marketing Authorisation,” discuss several methodological limitations including confounding and bias. These documents also emphasize the critical importance of assessing and controlling for confounding and bias in externally controlled clinical trials. In this presentation, we will discuss important considerations in designing externally controlled clinical trials and use a case study to demonstrate how to incorporate those considerations within and beyond statistics territory and combine RWE and statistical simulation approaches to inform externally controlled design for a phase 3 clinical development program.

Abstract: The rapid advancement of computational technologies has ushered in a transformative era for drug development, where in silico methods are increasingly pivotal. This presentation delves into the relevance and importance of computer-simulated approaches in pharmaceutical research, particularly focusing on their application in clinical trials. In silico methods offer a powerful alternative to traditional experimental approaches, enabling researchers to simulate complex biological processes and predict drug behavior in virtual environments. Such methods are invaluable in streamlining the drug development pipeline, reducing time and costs, and enhancing safety protocols. A prime example of in silico applications in clinical trials is the simulation of pharmacokinetic and pharmacodynamic profiles, which helps in optimizing dosing regimens and predicting adverse reactions. Moreover, these techniques facilitate the identification of potential biomarkers and the assessment of drug efficacy and toxicity across diverse patient cohorts. This presentation will introduce a compelling case study that employs in silico methodologies to address organ impairment issues. By generating virtual cohorts of healthy participants based on existing data from prior studies, we can circumvent the ethical concerns associated with exposing real participants to experimental treatments. This approach not only minimizes potential harm to healthy individuals but also presents significant cost-saving opportunities for sponsors, thereby enhancing the overall efficiency of the trial process. The case study will elucidate the practical implementation and benefits of in silico methods in drug development, underscoring their role in fostering more ethical, economical, and scientifically robust clinical trials. As the pharmaceutical industry continues to evolve, embracing these innovative techniques will be crucial for addressing complex challenges and driving the future of medicine.