Improve Decision-Making in AD Clinical Trials
A recent publication in Drug Discovery Today reviews how to utilize cognitive data for better Go/No-Go decision-making in Alzheimer’s disease (AD) clinical trials, and explores how to apply robust decision-making criteria depending on the specific stage of development, mechanism of action, and design of the trial.
At nearly $6 billion, the total cost to develop a new Alzheimer’s disease (AD) drug is more than double that of the average development program. Adding to this financial burden, it can take up to 13 years to develop an AD therapeutic candidate from bench to bedside. During this long and costly development process, experimental therapies face the potential for termination due to factors such as limited efficacy, adverse safety profiles or commercialization concerns.
To manage these risks, clear Go/No-Go decisions must be made at key points during the drug-development process. The earlier a compound is terminated, the sooner those resources can be reallocated to something more promising. Unfortunately, most AD drugs that have progressed to late-stage clinical trials have failed, suggesting that effective Go/No-Go decision making in early-phase clinical trials remains a costly and common challenge for AD drug developers.
Study Design and Results
Led by Eli Lilly’s Alette Wessels, the paper’s authors—including two of Cogstate’s scientific leaders—discuss how to implement cognitive data for more targeted Go/No-Go decision-making in AD clinical trials. The authors note that these cognitive results are available at various points during drug development and can be utilized for the application of Go/No-Go decisions. The paper describes how cognitive data can be used as a safety marker and applied to decision making criteria depending on the stage of the trial under evaluation. These cognitive measures must be valid, reliable, sufficiently sensitive, and should span multiple relevant domains. At a minimum, measures of attention, episodic memory, working memory and executive function are recommended. Additionally, any cognitive impairments must be deliberately interpreted.
With these parameters in mind, the authors provide a sample algorithm for Go/No-Go decision-making using Phase II data to consider the viability of a Phase III pivotal trial. The algorithm presented is just one example as every drug has unique mechanisms. From this model however, the authors point out that when establishing Go/No-Go decision-making criteria in AD trials, a single type of outcome is not sufficient to maximize confidence; converging lines of evidence across multiple outcomes are required. Tools to consider in the development of a specific algorithm include target engagement, downstream anatomical effects, digital biomarkers including computerized cognitive tests, composite outcomes, and traditional clinical assessments.
Leveraging robust Go/No-Go decision-making criteria has the potential increase the efficiency of AD drug development. While the process of determining an appropriate strategy may be initially burdensome, it is well worth the efforts.
Optimize Clinical Outcome Assessment in AD Trials
Cogstate’s digital cognitive assessments and data quality programs are designed to help your research team enhance signal detection via improved measurement and trial design, and expert study conduct, enabling you to make important AD drug development decisions. Our team of scientific and operational experts deliver innovative technology, proven operational delivery models and leading scientific support for both digital and conventional clinical outcome assessments in AD trials. Learn more here.
