Cole Zmurchok, Ph.D.

Group Leader QSP Principal Scientist I

Cole Zmurchok joined Metrum Research Group in 2025 as a Group Leader in Quantitative Systems Pharmacology. He advances model-informed drug development through the creation of mechanistic systems pharmacology models. Cole brings industry experience in applying modeling and simulation approaches to the development of antibody and cellular therapies, gained during his time at AbCellera and Notch Therapeutics. His academic background includes postdoctoral research at Vanderbilt University, where he investigated pattern formation in biological systems. He earned a PhD in Mathematics from the University of British Columbia in 2018, with a focus on multi-scale modeling of cell biology. Cole is an active member of the applied mathematics community, currently serving as Secretary of the Society for Industrial and Applied Mathematics’ Activity Group on the Life Sciences.

Recent publications by this scientist

Comparison of Virtual Population Generation Methods for Quantitative Systems Pharmacology Models

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.

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A Quantitative Systems Pharmacology Platform for Atopic Dermatitis and Asthma: One Immune Cascade Calibrated to Four Biologics

October 19, 2026

Timothy Knab¹, Cole Zmurchok¹, Jimena Davis¹, Dan Kirouac¹, Richard Allen², Cody Herron²
¹Metrum Research Group, Boston, MA, USA; ²Pfizer Inc., Cambridge, MA, USA

Submitted to the 2026 American Conference on Pharmacometrics (ACoP 2026), an International Conference on Pharmacometrics in the US (ICoP – US).

Atopic dermatitis (AD) and asthma share a type-2 immune axis and the same biologics: dupilumab (anti-IL-4Rα), lebrikizumab (anti-IL-13) and tezepelumab (anti-TSLP), with mepolizumab (anti-IL-5) in asthma. Published QSP models describe each disease on its own. The objectives were to build one reaction network in which the same immune cascade, placed in skin or
airway, gives AD or asthma, and calibrate it to biologic responses in both, as well as use it to compare a combination across indications and asthma phenotypes, and to rank untested targets.

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A Modular PBPK-QSP Platform in Julia for Optimizing the Therapeutic Window of Antibody-Drug Conjugates: ADC In Silico Design Engine (AIDE)

October 19, 2026

Yuezhe Li, Jimena Davis, Timothy Knab, Cole Zmurchok, and Daniel C. Kirouac

Submitted to the 2026 American Conference on Pharmacometrics (ACoP 2026), an International Conference on Pharmacometrics in the US (ICoP – US).

Antibody-drug conjugates (ADCs) are a rapidly evolving class of oncology therapeutics designed to expand the therapeutic index of potent cytotoxic payloads by leveraging the specificity of monoclonal antibodies. Despite clinical successes, many ADC candidates fail in late-stage development due to dose-limiting toxicities or insufficient efficacy. To support preclinical evaluation and de-risk ADC development, a modular, customizable PBPK-QSP platform using the open-source Julia package ModelingToolkit.jl was developed. This platform integrates a whole-body PBPK submodel to characterize ADC and payload biodistribution with a QSP submodel designed to predict both potential off-target toxicities and anti-tumor efficacy.

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