Model-based Meta-analysis

An Innovative Strategy to Make Better Use of Available Data

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) quantifies clinical trial efficacy, tolerability, and safety information to enable strategic drug development decisions.

What is MBMA?

MBMA makes better use of available data. The result is increased knowledge and better decision making in clinical development. 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. Compare treatments that may never have been tested together 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.

Web-MBMABenefits of MBMA

MBMA can help answer many important questions in areas including:

Compare your drug vs the competition
What are the dose-response relationships for existing drugs that are in the same class as a new compound? What are typical ranges? How does onset of effect differ between drug classes? How do baseline characteristics or background treatments impact drug response?

Optimize trial design
How does trial design impact treatment effects? How are specific subsets of the population represented? What is the impact of region? How do biomarker and clinical endpoint results compare? Can we predict trial results? How can we optimize dosing to maximize safety and efficacy?

Inform go/no go, portfolio, marketing decisions
What are the safety and efficacy profiles for competitor drugs for a given therapeutic indication? Can we differentiate the drug as best-in-class? What is the therapeutic window of the new drug compared to competitors/SOC (standard of care)? How can we best position a drug between existing and developing competitors?

The insights gained via MBMA enable less costly and more precise trials with an eye toward achieving commercial success for both the drug and portfolio.

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