Predicting Resistance to Small Molecule Kinase Inhibitors

Drug resistance is a critical challenge
in treating diseases
like
cancer and infectious disease. This study presents a novel computational
workflow for predicting on-target resistance mutations to small molecule
inhibitors (SMIs). The approach integrates genetic models with alchemical
free energy perturbation (FEP+) calculations to identify likely resistance
mutations. Specifically, a genetic model, RECODE, leverages cancer-specific
mutation patterns to prioritize probable amino acid changes. Physics-based
calculations assess the impact of these mutations on protein stability,
endogenous substrate binding, and inhibitor binding. We apply this
approach retrospectively to gefitinib and osimertinib, two clinical
epidermal growth factor receptor (EGFR) inhibitors used to treat nonsmall
cell lung cancer (NSCLC). Among hundreds of possible mutations, the
pipeline accurately predicted 4 out of 11 and 7 out of 19 known binding
site mutations for gefitinib and osimertinib, respectively, including
the clinically relevant T790M and C797S resistance mutations. This
study demonstrates the potential of integrating genetic models and
physics-based calculations to predict SMI resistance mutations. This
approach can be applied to other kinases and target classes, potentially
enabling the design of next-generation inhibitors with improved durability
of response in patients.