Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches

Resistance
to small molecule drugs often emerges in cancer cells,
viruses, and bacteria as a result of the evolutionary pressure exerted
by the therapy. Protein mutations that directly impair drug binding
are frequently involved in resistance, and the ability to anticipate
these mutations would be beneficial in drug development and clinical
practice. Here, we evaluate the ability of three distinct computational
methods to predict ligand binding affinity changes upon protein mutation
for the cancer target Abl kinase. These structure-based approaches
rely on first-principle statistical mechanics, mixed physics- and
knowledge-based potentials, and machine learning, and were able to
estimate binding affinity changes and identify resistant mutations
with remarkable accuracy. We expect that these complementary approaches
will enable the routine prediction of resistance-causing mutations
in a variety of other target proteins.