Principal Scientist I
Matthew joined Metrum in 2019 as a Research Scientist. He holds an M.A. in Statistics from Boston University. Prior to Metrum, he worked for a variety of startup technology companies where he applied Bayesian methodologies in predictive models based on remote sensing data. His ongoing interests include communicating and leveraging uncertainty from a Bayesian perspective in scalable modeling and simulation projects.
October 16, 2026
Yuchen Wang¹, Matthew Wiens², Ke Szeto¹, Timothy Waterhouse², Vivek Purohit¹, Leandro L. Santos³, Jessica Wojciechowski¹
¹Clinical and Quantitative Pharmacology, Incyte Corporation, Wilmington, DE, USA; ²Metrum Research Group, Boston, MA, USA; ³Clinical Development, Incyte Corporation, Wilmington, DE, USA
Presented at the 2026 American Conference on Pharmacometrics (ACoP 2026), an International Conference on Pharmacometrics in the US (ICoP – US), on October 14, 2026.
Hidradenitis suppurativa (HS) is a chronic inflammatory skin disease characterized by recurrent nodules, abscesses, and draining tunnels (dTs) that contribute substantially to disease burden. The presence of dTs is indicative of irreversible tissue damage, and a surrogate marker of a more challenging to treat HS, making dT count (dTC) a clinically meaningful endpoint. Povorcitinib is an oral, highly selective Janus kinase 1 inhibitor with demonstrated efficacy in reducing dTC in the phase 3 STOP-HS1 and –HS2 studies. Because dTC changes dynamically over time, a continuous-time Markov model (birth-death process) was developed to characterize the longitudinal relationship between povorcitinib exposure and dTC in patients with moderate to severe HS.
October 6, 2026
Andrew Tredennick¹, Matthew Weins¹, Timothy Knab¹, Cole Zmurchok¹, Ahmed Elmokadem²
¹ Metrum Research Group, Boston, MA, USA; ² GlaxoSmithKline (GSK), Collegeville, PA, USA
Presented at the 2026 American Conference on Pharmacometrics (ACoP 2026), an International Conference on Pharmacometrics in the US (ICoP – US), on October 14th, 2026.
Virtual populations (VPops) are widely used in quantitative systems pharmacology (QSP) to represent variability in clinical populations and support model calibration and simulation of treatment outcomes. Practical guidance on selecting among algorithms to generate VPops, and understanding their relative performance, remains limited. The objective of this work was to compare commonly used and emerging VPop generation approaches based on data-simulation matching and computation time.
June 13, 2025
Presented at ASCPT 2025 Annual Meeting. Neural networks can be integrated with traditional pharmacometric models using several free open-source programming languages. Both Julia and R environments are suitable platforms, but there are tradeoffs regarding development speed, built-in capabilities, and documentation. DCM simplifies the covariate modeling process and uncovers complex, non-linear relationships in computationally efficient workflows.