Using Model-based Meta-analysis to Inform Drug Development for Autoimmune Diseases

The difference between getting a new medication to patients, and it ending up in the scrap heap of failed programs lies in making the right choices. There is a surfeit of publicly available information on approved drugs as well as those currently in development. How can sponsors turn clinical trial data into understanding that helps chart the course for investigational drugs?

Model-based meta-analysis (MBMA) is a quantitative framework for leveraging external data to inform drug development decisions. This strategy involves a systematic search and tabulation of summary results from public sources which may be combined with proprietary clinical trial data. These data are then analyzed to characterize the impacts of drug class, drug, dose, and time on the response(s) of interest. The influence of study population characteristics or the trial conduct may also be explored.

The MBMA approach offers two key advantages over classical meta-analyses. First, it supports bridging across studies to allow comparing treatments that may never have been tested in the same clinical trial. Second, MBMA models use pharmacologic principles which incorporate wider spectrum data (dose, observation time, and clinical trial design). In contrast, traditional meta-analysis generally focuses on treatments that were compared within the same trial and on particular doses for each drug.

The insights gained via MBMA enable sponsors to design less costly and more precise trials with an eye toward achieving commercial success for both the drug and portfolio. Watch this webinar with Dr. Mark Lovern, Vice President at Certara Strategic Consulting, to learn how leveraging public data can provide value by abbreviating the “cash spiral” inherent to proprietary data. He presented case studies of autoimmune drug development programs that illustrate how MBMA has been successfully used to:

  • Compare drugs and therapeutic classes
  • Optimize clinical trial designs
  • Predict long-term responses based on short-term responses and/or biomarkers
  • Guide dosing recommendations for unstudied indications or populations

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