Timothy Knab, Ph.D.

Science Advisor, Senior Scientist II

Tim joined Metrum in March 2017 after completing his Ph.D. in Chemical Engineering from the University of Pittsburgh where his dissertation was focused on modeling and controlling stress-induced hyperglycemia in critically ill patients. This work included dynamic optimization of models describing glucose-insulin dynamics and the development of model-predictive controllers and state estimators for clinical applications. Tim’s interests include the application of systems biology models to guide decision making in the drug development process and to advance treatment paradigms.

Recent publications by this scientist

QSP Modeling of Loncastuximab Tesirine-lpyl Combined with T Cell-Dependent Bispecific Antibodies Bridges Knowledge and Dose Regimen Strategy

June 13, 2025

Presented at ASCPT Annual Meeting 2025. The virtual population-based lymphoma QSP model enabled systematic exploration of alternate drug combinations, dosing schemes, clinical covariates and resultant effect on anti-tumor activity in silico. The results of the LOTIS-7 study (NCT04970901), a platform study evaluating loncastuximab tesirine in combination with glofitamab, will soon be available to assess the accuracy of the model predictions.

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Pharmacometric Machine Learning: Integrating Neural Networks for Flexible, Advanced Covariate Analysis

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.

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Symbolic PBPK-PDE Modeling using Open-Source Julia Tools.

December 6, 2024

Presented at ACoP 2024. The poster introduces a framework for developing physiologically based pharmacokinetic (PBPK) models that incorporate partial differential equations (PDEs) to account for spatial drug distribution, using open-source Julia tools. This approach simplifies the integration of spatial components into PBPK models, demonstrated through a case study on naphthalene diffusion, and is applicable to various pharmacometric models requiring spatial considerations, such as topical, inhaled, and antitumor therapies.

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