A0926
Title: Modeling novel therapeutic modalities in oncology for dose optimization
Authors: Yanguang Cao - University of North Carolina at Chapel Hill (United States) [presenting]
Abstract: The development of novel therapeutic modalities in oncology, such as bispecific antibodies and immune checkpoint inhibitors, presents unique challenges in dose selection and optimization. Unlike traditional small molecules, these therapies often exhibit complex pharmacokinetics (PK), pharmacodynamics (PD), and mechanisms of action, necessitating a more sophisticated approach to guide early clinical decision-making. The aim is to advance a model-informed strategy for first-in-human (FIH) dose selection and optimal dose determination during early-stage oncology drug development. The proposal is to integrate quantitative systems pharmacology (QSP) modeling, exposure-response (E-R) analyses, and advanced statistical methodologies to build a translational framework that supports evidence-based dosing decisions for these cutting-edge therapies. The approach leverages mechanistic QSP models to capture the biological complexity of tumor-immune interactions, drug-target engagement, and downstream signaling pathways, providing a predictive basis for understanding therapeutic responses and potential toxicities. These models will be calibrated using preclinical and early clinical data and extended to simulate a range of clinical scenarios, accounting for patient heterogeneity and disease-specific factors.
