(S-087) A Quantitative Systems Pharmacology (QSP) Model Platform to Study the Impact of Intra-tumoral Heterogeneity on Antibody-Drug Conjugate (ADC) Treatment Efficacy

Chang Gong – AstraZeneca; Linda Irons – AstraZeneca; Florencia Tettamanti – AstraZeneca; Meghna Verma – AstraZeneca; Md Shahinuzzaman – AstraZeneca; Hanwen Wang – AstraZeneca; Holly Kimko – AstraZeneca; Cesar Pichardo-Almarza – AstraZeneca

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Objective:
Intra-tumoral heterogeneity is one of the hallmarks of cancer and is known to significantly affect the efficacy of Antibody-Drug Conjugates (ADCs). Variability in vascularization affects blood perfusion and ADC delivery to different tumor regions. Target expression levels vary across cancer cells, resulting in differential binding and uptake of the drug and release of the payload in different regions of the tumor. Such differential ADC uptake further prompts the need to better understand the role of by-stander activities and nonspecific/non-target mediated uptake mechanisms. Furthermore, heterogeneities in inherent genetic- and epigenetic-alterations as well as therapy induced adaptation and selection can lead to a complex pattern of intrinsic and acquired drug resistance. Our goal is to develop a Quantitative Systems Pharmacology (QSP) model that captures this heterogeneity to enhance our predictive capabilities for ADC treatment efficacy.

Methods:
We designed a flexible QSP model framework using MATLAB's SimBiology toolbox, enabling programmatic instantiation and simulation of multiple sub-models within each virtual patient. Each sub-model can represent individual lesions (primary/metastatic; target/non-target), or different regions within a single tumor lesion. Each sub-model is governed by its own copy of parameter values and initial conditions, capturing its distinct characteristics (e.g., target expression level, blood perfusion, drug resistance, cancer cell growth rate, etc.) to accommodate the intricate variations in cancer cell properties and the tumor microenvironment. It allows for the adjustment of interconnections between sub-models to represent diverse heterogeneity scenarios, including intra-tumoral and inter-lesion heterogeneity.

Results:
Using this model platform, we were able to show the influence of different mechanisms involving tumor heterogeneity on ADC efficacy. By adding variability in tumor vascular density, target expression, membrane permeability, drug resistance, and cancer growth rate to each virtual patient, we studied their impact on tumor exposure to ADC as well as unconjugated payload, and eventually clinical efficacy endpoints such as Objective Response Rate (ORR) and Time to Progression (TTP).

Conclusions:
The developed QSP platform sheds light on the critical influence of tumor heterogeneity on ADC treatment outcomes. By simulating various heterogeneity aspects, we provide a deeper understanding of the factors that promote or inhibit therapeutic responses. Future utilization of this platform aims to guide decision-making in the discovery and development phases, improving compound design and optimizing dosing strategies.

Citations: NA

Keywords: Antibody-Drug Conjugate, heterogeneity, Quantitative Systems Pharmacology