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

Quantitative systems pharmacology modeling of loncastuximab tesirine combined with mosunetuzumab and glofitamab helps guide dosing for patients with DLBCL

April 10, 2024

Presented at AACR Annual Meeting 2024. QSP model simulations were used to predict anti-tumor efficacy and guide dosing of the antibody-drug conjugate Loncastuximab tesirine combined with T cell-dependent bispecific antibodies, Mosunetuzumab or Glofitamab for the treatment of B-cell malignancies.

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In relapsed or refractory diffuse large B-cell lymphoma, CD19 expression by immunohistochemistry alone is not a predictor of response to loncastuximab tesirine

December 12, 2023

This study explores Lonca’s efficacy across varying CD19 expression levels, revealing its effectiveness in patients, regardless of low or undetectable CD19 levels by conventional methods. The study integrates quantitative systems pharmacology (QSP) modeling to predict treatment responses, indicating that CD19 expression alone may not predict Lonca’s effectiveness, however, response predictions are improved by considering CD19 surface density.

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Deep QSP Modeling: Leveraging Machine Learning for QSP Model Development and Evaluation

November 14, 2023

Presented at ACoP14. DQSP framework successfully implemented a UDE that characterized the PK of remoxipride and its effect on PRL release from lactotrophs to plasma. The model also characterized the positive feedback effect of plasma PRL on lactotroph PRL stimulation using an ANN.

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