Speaker
Description
The efficacy of Immune Checkpoint Inhibitors (ICIs) is frequently limited by adaptive immune resistance via the PD-1/PD-L1 pathway. While pairing ICIs with potent immunostimulants (such as NHS-muIL12) offers a strategy to overcome this, foundational mathematical models often struggle to capture complex preclinical behaviors, specifically non-monotonic tumor responses. In this talk, we introduce a refined deterministic model that addresses this limitation by incorporating PD-L1 expression as a fully dynamic variable rather than a static parameter. We demonstrate that this model, using a single parameter set, successfully reproduces experimental data across six distinct treatment arms, including monotherapies and combinations. This framework provides a robust mechanistic explanation for dose-dependent treatment failure driven by adaptive PD-L1 upregulation, while also elucidating the drivers of therapeutic synergy. We will present the systematic development of the model and simulation results.
