Learnings Guiding Clinical Trial Design for Heterogeneous Populations

Quick Overview

In this talk from the 2020 FAST Science Summit, Emil Kakkis discusses the challenges of designing clinical trials for rare heterogeneous populations, using Angelman Syndrome as an example. He highlights the variability in symptoms and drug response in neurodevelopmental disorders and emphasizes the need for individualized dosing and measurement of efficacy across multiple domains. Kakkis presents two case studies that demonstrate the importance of dose titration and the use of a multi-domain responder index for evaluating drug effectiveness. He concludes by suggesting that these approaches can improve clinical trial design and evaluation for complex diseases like Angelman Syndrome.

Introduction

In this talk, I will discuss the learnings we have gained in guiding the clinical trial design for rare heterogeneous populations. Patients with rare diseases often exhibit high variability, making it challenging to prove the safety and effectiveness of treatments. I will provide examples related to Angelman Syndrome to illustrate the importance of trial design and measurement in these cases.

The Challenge of Heterogeneity in Neurodevelopment Disorders

Neurodevelopment disorders present a significant challenge due to the complexity and variability of the brain. With over 10,000 genes expressed in the brain, interactions between these genes result in extreme heterogeneity. Even patients with the same genetic defect can exhibit substantial variations in symptoms and response to drugs. This complexity makes it difficult to determine the right dose of medication and measure efficacy.

Individualizing Dosing in Neurologic Disorders

Determining the appropriate dose for neurologic drugs is a complex task. Starting doses for 27% of all neurologic drugs have had to be changed after approval due to safety issues. Traditional measures of drug development have not improved this process. Therefore, it is crucial to individualize dosing in neurologic disorders to optimize effectiveness and minimize safety risks.

Measuring Efficacy Across Multiple Variable Domains

Traditional clinical studies often focus on a single primary endpoint, which may not capture the full range of symptoms and manifestations in complex diseases. In Angelman syndrome, for example, patients exhibit variability across multiple domains such as communication, behavior, sleep, and motor skills. Each patient has a unique combination of problems within these domains, making it challenging to measure efficacy using a single endpoint.

Finding the Right Dose Range

Instead of searching for a single “best” dose, it is more effective to identify a range of doses that are acceptable and effective for different patients. This approach, known as dynamic and individualized dose titration, allows for personalized dosing based on individual patient responses. By using this strategy, we can optimize the safety and efficacy of treatments for rare diseases.

Evaluating Efficacy with Multi-Domain Responder Index

The traditional approach of using single primary endpoints to evaluate complex multi-system diseases is flawed. It fails to capture the full range of symptoms and manifestations experienced by patients. To address this issue, we have developed a novel strategy called the Multi-Domain Responder Index (MDRI). This approach combines multiple clinical endpoints to provide a comprehensive assessment of a patient’s overall health. By analyzing the impact of multiple endpoints, we can better understand how patients feel and measure the effectiveness of treatments.

Conclusion

In conclusion, the learnings we have gained in guiding the clinical trial design for heterogeneous populations are crucial for developing effective treatments for rare diseases. Individualizing dosing and incorporating multiple endpoints in the evaluation process are essential steps in optimizing treatment outcomes. By considering the unique challenges posed by rare diseases, such as Angelman syndrome, we can improve the design and measurement of clinical trials, leading to better outcomes for patients.