How Model-Based Meta-Analysis Leverages Public Data to Support Strategic Drug Development Decision Making


Using public preclinical and clinical data can shorten drug development and decrease costs.

By Mark Lovern, Ph.D., Certara Strategic Consulting

Why Conduct a Model-Based Meta-Analysis (MBMA)?

MBMA represents a smart, quantitative approach to enable a sponsor to supplement its existing proprietary drug data with public preclinical and clinical data, allowing it to make more informed strategic drug development decisions.

MBMA involves the systematic search and tabulation of summary results from external data sources and their combination with in-house clinical trial data to create a richer resource. The resulting highly curated data can be used with parametric pharmacology models to increase drug development productivity, inform portfolio management, and improve clinical trial success.

MBMA can be applied in most established therapeutic areas, ranging from oncology through metabolic diseases such as diabetes to cardiovascular and kidney disease. The primary limiting factor is the richness of the available literature, which is generally only an issue with newly emerging fields.

About 70 percent of the work involved with MBMA is in extracting all the relevant information from the literature or clinical trial registries and then formatting and preparing it for analysis. The extracted information must first be turned into a tabular, analyzable form. Then, it needs to be harmonized because two endpoints might have similar naming conventions but very different interpretations or definitions across therapeutic areas. Those steps have to be completed before the data can be analyzed with a parametric model.

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May 2019

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