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

ACCELERATION OF CRITICAL PATH MODEL INFORMED DRUG DEVELOPMENT (MIDD) SUBMISSION ACTIVITIES VIA DATA AND TOOL STANDARDIZATION

March 18, 2026

ACCELERATION OF CRITICAL PATH MODEL INFORMED DRUG DEVELOPMENT (MIDD) SUBMISSION ACTIVITIES VIA DATA AND TOOL STANDARDIZATION Brian W. Corrigan¹, Matthew Riggs¹, Mike Ferguson¹, Joydeep Bhattacharya¹, Marc R. Gastonguay¹ 1 Metrum Research Group, Boston, MA, USA

Regulatory submissions for new drugs (NDAs/BLAs) increasingly rely on
pharmacometric (PK/PD) analyses to support dosing, labeling, and design of
post-approval trial commitments.
More 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.
Accelerating MIDD related CP activities via methods standardization,
process automation, and proactive planning offers a potential mechanism for
increasing speed without compromising quality

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A MODEL INFORMED DRUG DEVELOPMENT (MIDD)-BASED QUANTITATIVE DECISION FRAMEWORK (QDF) FOR IMPROVING R&D PRODUCTIVITY: PROOF OF CONCEPT FOR ATOPIC DERMATITIS (AD)

March 18, 2026

A MODEL INFORMED DRUG DEVELOPMENT (MIDD)-BASED QUANTITATIVE DECISION FRAMEWORK (QDF) FOR IMPROVING R&D PRODUCTIVITY: PROOF OF CONCEPT FOR ATOPIC DERMATITIS (AD)

E. Anderson¹, BW. Corrigan¹, M. Cala Pane¹, A. Tredennick¹, T. Dunlap¹, L. Lomeli¹, B. Davis¹, MR.Gastonguay¹1Metrum Research Group, Boston, MA

Project Rationale

QDF Components QDF Components
Competitive Landscape
MIDD Enhanced Valuations

Rising costs, uncertain reimbursement, competition, and declining success rates have
reduced drug R&D productivity and investment over the last decade.
Proposed strategies to improve R&D productivity include four key factors: 1) leveraging all
data sources; 2) utilizing quantitative models; 3) elimination of information silos across R&D
and commercial organizations; and 4) application of decision frameworks to reduce
cognitive bias and improve decision making.1

A QDF for a drug development program in atopic dermatitis (AD) was developed to: 1) link
MIDD models aligned with a target product profile (TPP) to risk-adjusted net present value
(rNPV); and 2) integrate context-sensitive large language models (LLMs) to incorporate
non-structured data from novel sources into the decision-making framework in a responsible
manner.

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Impact of Model-Informed Drug Development on Drug Development Cycle Times and Clinical Trial Cost

April 21, 2025

While the application of MIDD has grown, there have been no clear examples across programs to demonstrate its value at the portfolio level. This manuscript offers a methodology and examples to demonstrate MIDD value in terms of time and cost savings.

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