Tyler Dunlap, Pharm.D., DABCP

Research Scientist II

Tyler joined MetrumRG in 2023 after earning his Doctor of Pharmacy from the University of Missouri – Kansas City and completing fellowship training in pharmacometrics/clinical pharmacology at the University of North Carolina at Chapel Hill. His primary research interests are in modeling the relationships between tumor growth, new lesion appearance, and overall survival in patients with solid tumors. He is passionate about pharmacometrics education and dose-optimization in oncology.

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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A Quantitative Medicine Framework for Optimizing Buprenorphine Dosing in Patients with Opioid Use Disorder: Defining Receptor Occupancy Target Trajectories to Promote Successful Transition from Fentanyl

October 19, 2026

Tyler Dunlap, Pharm.D., DABCP¹, Hillary Husband, Ph.D.², Sara Miller, Pharm.D.¹, Alyssa Tilhou, MD, Ph.D.³
¹Metrum Research Group, Boston, MA, USA, ²Louisiana Tech University, Ruston, LA, USA, ³Boston University, Boston, MA, USA

Presented at the 2026 Annual Meeting of the American College of Clinical Pharmacology (ACCP 2026), on September 28, 2026.

Opioid use disorder (OUD) is a chronic relapsing condition characterized by compulsive use of opioids, such as fentanyl, despite significant harms. While buprenorphine, a Mu-opioid receptor (MOR) partial agonist, can effectively stabilize individuals with OUD, withdrawal during induction [due to underdosing or displacement of full agonist opioids (“precipitated withdrawal”)] often leads to treatment discontinuation. Alternative buprenorphine induction strategies have been proposed, but quantitative evidence informing their design is limited. This study introduces a quantitative medicine framework (QMF) to optimize two strategies: macrodosing, characterized by high-dose initiation of buprenorphine with abrupt cessation of nonprescribed opioids to achieve rapid therapeutic receptor occupancy (RO) and avoid underdosing; and microdosing, characterized by low-dose initiation and gradual titration of buprenorphine with continuation of nonprescribed opioids to mitigate risk for precipitated withdrawal.

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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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