Brian Corrigan, Ph.D.

Principal Consultant, Strategic Advisory Solutions

Dr. Brian Corrigan brings nearly 30 years of expertise in clinical pharmacology, quantitative modeling, and global drug development to Metrum Research Group. As former Senior Vice President of Translational Clinical Sciences at Pfizer, he led initiatives in precision medicine, business development, and innovative regulatory strategies. A champion for inclusivity, he founded Pfizer’s Men as Allies initiative and served as President of the International Society of Pharmacometrics. At MetrumRG, Dr. Corrigan provides strategic guidance to biopharma leaders, empowering data-driven decisions that maximize pipeline value.

Recent publications by this scientist

Optimizing Clinical Trial Designs with Quantitative Decision Frameworks: Integrating Pharmacometric, Financial, and Commercial Models to Optimize Value

October 19, 2026

Tyler Dunlap, Pharm.D., DABCP, Brian Corrigan, Ph.D., Akintunde Bello, Ph.D., Marc Gastonguay, Ph.D.

Presented at the 2026 American Conference on Pharmacometrics (ACoP 2026), an International Conference on Pharmacometrics in the US (ICoP – US), on October 11, 2026.

Biopharmaceutical research and development (R&D) productivity, defined as the aggregate return on investment (ROI) for all drug development activities, is declining despite increased global participation and rapid technological advancement. The use of all available data sources, quantitative modeling to inform all decisions, removal of organizational silos, and adoption of quantitative decision frameworks (QDFs) have been proposed as key strategies to improve R&D productivity. This work aimed to propose a framework for linking pharmacometric, commercial, and financial valuation models to support optimization of a hypothetical Phase 3 trial design with respect to the expected ROI (eROI), defined as the ratio of the risk-adjusted net present value (rNPV) to the committed capital (CC).

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Shortening the Regulatory Submission Critical Path via MIDD Acceleration: A Multi-Trial Phase 3 Case Study

October 9, 2026

Marc R. Gastonguay, William Knebel, Timothy Waterhouse, Jeannine Fisher, Brian Corrigan

Presented at the 2026 American Conference on Pharmacometrics (ACoP 2026), an International Conference on Pharmacometrics in the US (ICoP – US), on October 12, 2026.

Regulatory submissions for new drugs (NDAs/BLAs) increasingly rely on MIDD (model-informed drug development) analyses to support dosing, labeling, and design of post-approval trial commitments. Frequently, MIDD related activities (data cleaning/prep, analysis, summary document preparation) are on the filing critical path (CP) following completion of the final registration studies, with delays directly impacting time to file. These delays impact overall medicine value and access for patients. This case study highlights a MIDD acceleration (MIDD-A) strategy that resulted in a meaningful reduction in the NDA submission critical path (CP) for an ongoing Phase 3 drug development program.

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Operationalizing MIDD within R&D Quantitative Decision Frameworks: Identifying Clinical and Biological Drivers of Trial Risk with Global Sensitivity Analysis

October 19, 2026

Tyler Dunlap, Pharm.D., DABCP, Brian Corrigan, Ph.D., Marc Gastonguay, Ph.D

Presented at the 2026 Annual Meeting of the American College of Clinical Pharmacology (ACCP 2026), on September 27, 2026. Winner of the 2026 ACCP Early-Stage Professional Abstract award. 

Biopharmaceutical research and development (R&D) productivity, defined as the aggregate return on investment for all drug development activities, is declining despite rapid technological advancement. While a multifactorial challenge, this trend is exacerbated by increasingly complex therapeutic modalities and rudimentary decision-making frameworks. While Model-Informed Drug Development (MIDD) has become an established scientific framework among clinical pharmacologists, its contribution to strategic risk management remains underleveraged. This work builds upon previous efforts to integrate pharmacometric, commercial, and financial valuation models into a quantitative decision framework to improve R&D productivity. Here, we propose a framework for identifying the mechanistic drivers of risk for clinical trial failure for a hypothetical Phase 3 study.

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