Katharina Wilkins, PhD: No relevant disclosure to display
The state of QSP modeling for ADCs is highly mature with quantitative tools connecting molecular design parameters to pharmacokinetics, efficacy, and toxicity potential.
The theory is established, and modelers have had significant impact over the last decade.
The building blocks of ADCs are modular: one or more ‘payloads’, chemically conjugated via a ‘linker’ to an antibody. Well-constructed, mechanistic QSP models have exploited this modularity, and enabled scientists to simulate combinations of tried-and-true components and test new and improved ones.
Modular QSP platform models integrate a wide variety of data sets, from in-vitro assay through clinical studies, and bridge information from different molecules. There is thus a rich, historical repository of modeling insights for ADCs in oncology, however bounded around a limited set of cytotoxic payloads.
In this talk, we will share some of these insights with an eye towards identifying generalizable properties that can be applied beyond cytotoxic payloads. Deploying a platform QSP model, we demonstrate how ADC design parameters (e.g., payload potency and diffusivity, DAR, antibody affinity) can be optimized in silico to address challenges presented by tumor-specific biology such as target density, expression heterogeneity, turnover rate, and off-tumor target expression in healthy tissues. By extension, platform QSP models developed in oncology are fertile ground for modelers and drug inventors looking to expand beyond the ‘tried-and-true’ with novel payload modalities, new indications or other creative ideas.
