Data-Driven Computational Modeling of Cellular Response in CART Therapy

Project Description Narrative:

Cancer is one of the major health burdens in Wisconsin and beyond, and it puts significant strains on society and healthcare systems, with approximately 34,000 new cases in the state each year. Chimeric antigen receptor T cell (CART) therapy is a promising adoptive cell therapy in which a patient’s T cells are genetically modified to express chimeric antigen receptor (CAR) constructs. Response to CART therapy is governed by complex dynamics of CART proliferation, cytotoxicity, apoptosis, and biodistribution. It is additionally complicated by the choice of construct designs and differentiation of CART products into phenotypically distinct heterogenous populations both prior and after therapy administration. Several models studying CART therapy dynamics have been developed using clinical datasets. However, these do not account for the complexity of variables underlying CART function.

This project aims to develop an ordinary differential equation-based method that can be utilized to predict therapy responses for various CART constructs and product compositions dependent on product-specific parameters derived from in vitro assays. The researchers hypothesize that they can define predictive product- and patient-specific exposure-response relationships using a physiologically based pharmacokinetic and pharmacodynamic modeling approach. They will use this model to investigate variables controlling therapy response. Over time, their work can be extended to predict product- and patient-specific therapy responses prior to therapy administration.