Speaker
Description
The development of T cell engagers (TCEs) in immuno-oncology can leverage fit-for-purpose modeling strategy that aligns quantitative tools with the key decisions guiding the development pipeline. In preclinical and first-in-human stages, mechanistic PKPD models translate in vitro and in vivo data into predictions of safe, biologically active starting doses by characterizing trimolecular synapse formation \cite{chen2024}. As programs progress toward a recommended Phase 2 dose, population PK modeling coupled with exposure–response (ER) analyses continue to be widely used to support monotherapy. However, TCE efficacy is primarily driven by trimer formation, so Quantitative Systems Pharmacology (QSP) models that explicitly predict trimer dynamics provide additional mechanistic insight. QSP integrates drug properties, tumor microenvironment, and the immune system to predict interactions, optimize dosing regimens and schedules, and identify determinants of safety and efficacy. Modern QSP platforms—often including 'digital twin' virtual patients—simulate these multiscale dynamics and apply global sensitivity analysis to reveal parameters governing cytokine peaks, attenuation dynamics, bell shaped ER relationships, and efficacy plateaus \cite{Singh2024}. These insights enable CRS risk mitigation, rational step-up dosing, and dose escalation, as illustrated with mosunetuzumab or elranatamab \cite{poels2025}. For late stage (Phase 2b/3), QSP can support dose and regimen optimization, predicts combination and safety outcomes, guides enrichment and trial design, and supports regulatory interactions by translating mechanistic data into decision ready simulations. QSP frameworks also guide combination therapy dose selection by quantifying synergy and therapeutic index boundaries \cite{jafar2023}. Other techniques, such as Physiologically based pharmacokinetic (PBPK) models, further support development by evaluating drug–drug interaction risk. Overall, regulatory agencies increasingly recognize these model-informed approaches as supportive in dose justification and benefit–risk evaluation \cite{musante2024}. This talk will highlight these methods, the decisions they enable, and key agency feedback through case studies of novel T cell engagers.
Bibliography
@article{musante2024,
title={Landscape of regulatory quantitative systems pharmacology submissions to the U.S. Food and Drug Administration: An update report},
author={Musante, Cynthia J and Earp, Justin C and Betts, Alison and Bult-Ito, Gabrielle J and Clark, Timothy and Gieschke, Ronald and Gurbaxani, Brian and Hallow, K Michael and Ivanovic, Maja and Jafar, Muhammad and Ke, Alice and Lled{\'o}-Garc{\'\i}a, Roc{\'\i}o and Penney, Mark and Phipps, Andrew and Popel, Aleksander S and Riggs, Matthew M and Schuck, Virna and Stites, Edward C and van der Graaf, Piet H and Vicini, Paolo and Wu, Frank and Yu, Hao and Zhao, Ping and Zhou, Hao},
journal={Clinical Pharmacology \& Therapeutics},
year={2024},
publisher={Wiley Online Library}
}
@article{jafar2023,
title={Using quantitative systems pharmacology modeling to optimize combination therapy of anti-PD-L1 checkpoint inhibitor and T cell engager},
author={Jafar, Muhammad and Chen, Xinyi and Penney, Mark and Singh, Ishan},
journal={Frontiers in Pharmacology},
volume={14},
pages={1163432},
year={2023},
publisher={Frontiers Media SA},
doi={10.3389/fphar.2023.1163432},
note={PMCID: PMC10229810}
}
@article{chen2024,
title={Development of Bispecific T Cell Engagers: Harnessing Quantitative Systems Pharmacology},
author={Chen, X and Jafar, M and Singh, I and Penney, M},
journal={Clinical Pharmacology \& Therapeutics},
volume={115},
number={2},
pages={349--359},
year={2024},
publisher={Wiley Online Library},
doi={10.1002/cpt.3090},
note={PMID: 37941259; PMCID: PMC10843027}
}
@article{Singh2024, author = {Singh, Fulya Akpinar and Afzal, Nasrin and Smithline, Shepard J. and Thalhauser, Craig J.}, title = {Assessing the performance of QSP models: biology as the driver for validation}, journal = {Journal of Pharmacokinetics and Pharmacodynamics}, year = {2024}, month = {oct}, volume = {51}, number = {5}, pages = {533--542}, doi = {10.1007/s10928-023-09871-x}, url = {https://doi.org/10.1007/s10928-023-09871-x}, issn = {1573-8744}, note = {Published 2024-10-01}, keywords = {QSP, model validation} }
@article{poels2025,
author = {Poels, Kamrine E. and Singh, Ishan and Jafar, Muhammad and Chen, Xinyi and Nalda, Marc and Parra-Guillen, Zinnia P. and van der Graaf, Piet H. and Penney, Mark},
title = {Leveraging Quantitative Systems Pharmacology modeling for elranatamab regimen optimization in relapsed or refractory multiple myeloma},
journal = {npj Systems Biology and Applications},
volume = {11},
number = {1},
pages = {102},
year = {2025},
month = {sep},
doi = {10.1038/s41540-025-00585-z},
url = {https://www.nature.com/articles/s41540-025-00585-z}
}
