Math Modeling Seminar: Quantitative Systems Pharmacology: Mechanistic Models for Drug Development Decisions Under Uncertainty

Math Modeling Seminar
Quantitative Systems Pharmacology: Mechanistic Models for Drug Development Decisions Under Uncertainty

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Dr. Timothy Knab
Principal Scientist
Metrum Research Group

Abstract: Quantitative Systems Pharmacology (QSP) is a scientific field that combines pharmacology, engineering, molecular biology, and computer science to create computational representations of biological processes and how these processes are affected, systematically, by therapeutic interventions. Using virtual patients and therapeutics for in silico clinical trials, the effects of medical interventions (existing or hypothetical), dosing regimens or co-medications may be evaluated across a variety of “what if” scenarios in different patient groups. Pressure testing thousands of hypotheses and counterfactuals through computer simulations can yield insight into the biology and pharmacology, and inform which experiments to run in the lab or the clinic. After providing background on the aspirations, approaches and limitations of QSP, case studies will be presented demonstrating its utility across different diseases and stages of drug development, including first-in-human dose selection, combination regimen design, and prediction of gene therapy durability. A deeper focus on T cell engagers, a class of bispecific antibodies that induce T-cell-mediated cytotoxicity, will examine how parameter non-identifiabilities and biological uncertainties propagate through model-based clinical dose projections, and how QSP models can benchmark novel immunotherapies in indications where head-to-head clinical trials would be infeasible to execute.

Speaker Bio: Timothy Knab is a Principal Scientist in the Quantitative Systems Pharmacology (QSP) group at Metrum Research Group. He earned his B.S. in Chemical Engineering from the University of Rochester and his Ph.D. in Chemical Engineering from the University of Pittsburgh, where his dissertation focused on mathematical modeling and model-predictive control of stress-induced hyperglycemia in critically ill patients. His current research interests include mechanistic and physiologically based pharmacokinetic modeling for drug development, with a focus on immuno-oncology, T cell engagers, bispecific antibodies, and antibody-drug conjugate combination therapies. He is also interested in the integration of Bayesian inference and machine learning with systems models.

Intended Audience: Beginners, undergraduates, graduates. Those with interest in the topic.

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