New Paradigms in Clinical Trial Design*

Date/Time: Sunday, September 10, 2023 - 11:45 AM – 12:45 PM
Track: Interactive Lunch Workshop
Room: Franklin Hall 3 (4th Floor)
Log in to Add to My Schedule


Session Evaluation Form:

Chair: Cassie Mitchell, PhD

Co-Chair: Lauren Reoma, MD, FAAN

This session will provide an overview of new paradigms in clinical trial design as it relates to neurological disease and disorders. This ILW will include integrated perspectives from the National Institute of Neurological Disorders and Stroke (NIH-NINDS), the Food and Drug Administration (FDA), and clinical trial initiatives in academic neurology. Session topics will include interactive presentation and discussion of new paradigms in clinical trial design, new innovations, and new technology end points for clinical trials. 

Learning Objectives: 

  • Identify and implement novel technologies (recruitment, monitoring/assessment, outcomes) into new clinical trial paradigms.
  • Understand and appropriately implement government perspectives and policies for implementation of technology into new clinical trial paradigms.
  • Identify the strengths and weaknesses of new clinical trial paradigms for neurological disease or injury.

NIH-NINDS Perspectives on New Clinical Trial Designs for Neurological Disorders

Speaker: Lauren Reoma, MD, FAAN

Recent years have seen an explosion in the number of new and innovative therapies developed for neurological diseases. Coincident with this, clinical trial design has advanced to facilitate clinical testing. This includes innovative n=1 clinical trials for rare diseases, leveraging technology to explore new outcome measures in neurologic trials, platform trials to rapidly screen candidate drugs, and multi-center intervention trials. Intramural NINDS trials have focused on early-phase and first-in-human therapeutic programs, supporting long-term goals of bringing novel neurologic therapeutics to the market. This discussion will briefly review current early-phase clinical trial design for neurological conditions, delineate how early phase clinical trial design can be leveraged to advance the neurological field, and explore new clinical trials methodology that advance clinical trial design, including platform trials and n=1 studies.

New Screening Models in Neurological Clinical Design

Speaker: Bryan Traynor, MD, PhD, MMSc, FRCPI, FRCP, FANA

The talk, "New screening models in neurological clinical design," presents our work to accelerate the development of disease-modifying treatments for rare conditions. We aim to reduce the cost and time involved in therapeutic development by utilizing cutting-edge techniques.

The presentation delves into the use of high-throughput electrophysiology and 3-D tissue models to evaluate the central nervous system safety and cellular responses of antisense oligonucleotides (ASOs). These innovative techniques enhance our understanding of ASOs' impact on neuronal networks and potential toxicities, improving their safety profile. We also investigate organoid behaviors and viability following ASO exposure, allowing us to predict adverse effects that might occur during therapy.

The talk further explores our strategic goal of revolutionizing the development of various drug modalities for rare conditions. Addressing the challenges of small patient populations and high developmental costs, we discuss novel approaches based on platform development. By predicting potential toxicities and determining the impact of new treatments on cells, we aim to enhance patient safety, cut costs, and accelerate the path to clinical applications.

Overall, the presentation will underscore how our research program is poised to transform the rare disease therapeutics landscape, bringing hope to patients worldwide and potentially heralding effective treatments for many rare diseases in the coming decade.

AI and Machine Learning for Clinical Trials

Speaker: David Page, PhD

AI and Machine Learning have many potential uses in clinical trials. This talk will review a number of these, and it will focus on the potential of AI/ML for identifying potential trial participants enriched for risk or for conditions not explicit in electronic health records.