Senior Scientist II
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.
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.
May 19, 2025
This work tackles a central challenge in oncology drug development: predicting benefit-risk for dosing regimens that have not yet been studied. Semi-mechanistic modeling can be used for this purpose, but model validation is complicated by the presence of safety-driven dose modifications—such as delays and reductions—commonly observed in real-world settings.
April 16, 2025
This piece explores the evolving role of AI across the drug development continuum—from explainable ML models to immune digital twins. The article reinforces that scientific rigor and innovation go hand in hand. AI tools are accelerating time to insight, helping teams make smarter, faster, and more ethical decisions in complex therapeutic areas.