The therapeutic effect of targeted kinase inhibitors can be significantly reduced by intrinsic or acquired resistance mutations that modulate the affinity of the drug for kinase. In cancer, the majority of missense mutations are rare, making it difficult to predict their impact on inhibitor affinity. This complicates the practice of precision medicine, pairing of patients with clinical trials, and development of next-generation inhibitors. Here, we examine the potential for alchemical free-energy calculations to predict how kinase mutations modulate inhibitor affinities to Abl, a major target in chronic myelogenous leukemia. We find these calculations can achieve useful accuracy in predicting resistance for a set of eight FDA-approved kinase inhibitors across 144 clinically-identified point mutations, achieving a mean unsigned error and root mean square error in binding free energy changes of 0.8 kcal/mol and 1.1 kcal/mol respectively, and correctly classifying mutations as resistant or susceptible with 90% accuracy. This benchmark establishes the potential for physical modeling to collaboratively support the assessment and anticipation of whether mutations might lead to drug resistance.

Predicting targeted kinase inhibitor resistance to clinical Abl mutations using alchemical free-energy calculations

255th National Meeting (2018)

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