(509c) Identifying Determinants of EGFR-Targeted Therapeutic Efficacy Using Computational Modeling | AIChE

(509c) Identifying Determinants of EGFR-Targeted Therapeutic Efficacy Using Computational Modeling

Authors 

Lazzara, M. J. - Presenter, University of Virginia
Monast, C. S., University of Pennsylvania



For decades, clinicians have attempted to antagonize aberrant growth factor receptor-mediated signaling in cancer using antibodies and small-molecule inhibitors designed to interfere with specific mechanistic steps leading to receptor activation and phosphorylation.  Significant dose-response variability is observed for these drugs among cells and tumors even in the absence of receptor mutations, but the basis for this is not fully understood.  Here, using the epidermal growth factor receptor (EGFR) as a model system, we implement a computational model of EGFR phosphorylation dynamics to identify the factors that determine the efficacy of EGFR-targeted kinase inhibitors and antibodies that compete with EGFR ligands.  Our results: 1. identify different kinetic processes as preferentially controlling the efficacy of antibodies versus inhibitors; 2. suggest that the efficacy of inhibitors and antibodies may be favored by the expression of certain naturally expressed EGFR ligands versus others; and 3. suggest new therapeutic design principles.  As an example, the model predicts that the efficacy of kinase inhibitors is more sensitive than that of antibodies to perturbations in the activity of protein tyrosine phosphatases (PTPs) regulating EGFR because the time scale for drug-bound receptors to eventually become phosphorylated after drug dissociation is closer to the time scale of receptor dephosphorylation for inhibitors than for antibodies.  This result suggests that variation in PTP expression or activity among cells is more likely to explain differential cellular response to kinase inhibitors than to antibodies.  Conversely, antibody efficacy is more sensitive to perturbations in ligand binding kinetics, suggesting that local variation in the expression of EGFR ligands is more likely to explain differential cellular response to antibodies than to kinase inhibitors.  Our results also specifically highlight that kinetic considerations beyond those reflected by equilibrium binding affinities determine therapeutic efficacy.  For example, for a constant kinase inhibitor binding affinity, decreasing rates of inhibitor binding and unbinding is predicted to promote inhibitor efficacy by increasing the time scale with which the receptor must remain unphosphorylated.  Conversely, for constant antibody affinity, decreasing rates of antibody binding and unbinding impair antibody efficacy as the antibody becomes increasingly less able to compete with ligand binding.  Finally, by integrating these considerations for kinase inhibitors and antibodies, the model identifies minimum concentrations of the two drugs that can be combined to maximally reduce receptor phosphorylation.