Optimizing Clinical Translation of Bispecific T Cell Engagers through Context Unification with a Quantitative Systems Pharmacology Model
Authors: Liao Xiaozhi , Cao Yanguang , Liu Can , Zhou Jiawei , Qi Timothy 
Published: February 2025

Objectives: Bispecific T cell engagers (bsTCEs) are a promising class of cancer immunotherapy. One pivotal challenge in early clinical trials of bsTCEs is the selection of a First-In-Human (FIH) dose, due to their narrow therapeutic windows. Given the limited translatability of most preclinical animal models, in vitro cell culture systems are more commonly used to assess bsTCE potency for the selection of FIH dose. However, protocol variabilities between assays make it difficult to comprehensively evaluate bsTCE potency. Variations in experimental conditions may lead to different FIH doses. In addition, in vitro systems may not reflect clinical disease conditions, further compromising the translatability of in vitro results. This study is aimed to unify variations in these physiological contexts through a Quantitative Systems Pharmacology (QSP) model and develop a more robust measure of bsTCE potency to guide FIH dose selection.Methods: We developed a Quantitative Systems Pharmacology (QSP) model to simulate the formation of immunological synapses (IS) induced by bispecific T cell engagers (bsTCEs). The model was calibrated using experimental synapse formation data from previous studies. In vitro cytotoxicity data of bsTCEs from literature were digitized, and experimental conditions and bsTCE characteristics were compiled to set up the model. We coupled the model-predicted synapse percentage with observed efficacy to derive synapse-efficacy curves using the Hill equation. We compared the robustness of the synapse percentage at half maximal efficacy (ES50) with conventional EC50. To evaluate the translatability of model-derived synapse-efficacy relationships in clinical predictions, we conducted virtual trial simulations using the QSP model. We predicted the clinical exposure-response relationship for three approved bsTCEs: blinatumomab, epcoritamab, and teclistamab. Results: The QSP model successfully captured the synapse formation dynamics across experimental variations in effector-to-target (E:T) ratio, cell density, target cell lines, and incubation duration. The model-derived synapse-efficacy relationships has been proved to be more robust to conventional concentration-efficacy relationships as the estimated ES50 remained consistent through protocol variations in bsTCE binding affinities, target cell lines and E;T ratios. The model-derived ES50 from preclinical cytotoxicity assays successfully bridged preclinical data and clinical predictions. The model predicted exposure-response relationships fits the clinical observations well. Conclusions: The QSP model shows promise as a tool for translating preclinical findings into clinical predictions. For bsTCEs, the formation of immune synapses serves as a robust measure of potency, capable of accommodating variations in physiological contexts. Our framework provides an example of using in vitro cytotoxicity data to predict clinical responses in virtual patients using a QSP model.Citations: [1] Liu C, Zhou J, Kudlacek S, Qi T, Dunlap T, Cao Y. Population dynamics of immunological synapse formation induced by bispecific T cell engagers predict clinical pharmacodynamics and treatment resistance. Elife. 2023;12:e83659.[2] Liao X, Qi T, Zhou J, Liu C, Cao Y. Optimizing Clinical Translation of Bispecific T Cell Engagers through Context Unification with a Quantitative Systems Pharmacology Model. Clinical Pharmacology & Therapeutics. In Press[3] Saber H, Del Valle P, Ricks TK, Leighton JK. An FDA oncology analysis of CD3 bispecific constructs and first-in-human dose selection. Regulatory Toxicology and Pharmacology. 2017;90:144-152. [4] Jiang X, Chen X, Carpenter TJ, et al. Development of a Target cell-Biologics-Effector cell (TBE) complex-based cell killing model to characterize target cell depletion by T cell redirecting bispecific agents. mAbs. 2018;10(6):876-889.