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. Attend 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 will present 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
About Our Speaker
Dr. Mark Lovern joined Certara (then Quantitative Solutions) in 2012 with 14 years of experience in applying modeling and simulation tools and techniques toward optimally informing drug development decision-making. Since joining Certara, Dr Lovern has led and contributed to numerous consultancy projects involving population PK, PK/PD, and model-based meta-analysis. In June 2014, Dr Lovern assumed leadership of the Eastern US division of Quantitative Solutions, and continues in this capacity within Certara Strategic Consulting (CSC). His previous work history has been split between biopharmaceutical companies (GSK and UCB) and companies that support the biopharmaceutical industry (Quintiles and Pharsight). In addition to modeling pharmacokinetic and pharmacodynamic data across a wide variety of compounds and therapeutic areas, Mark has also taught over 50 technical training workshops on modeling tools and methodology. His most recent therapeutic area experience has been with therapies for infectious disease, metabolic, and autoimmune disorders.