Predicting Resistance to Small Molecule Kinase Inhibitors.

Area of Science:

• Computational biology
• Drug discovery
• Molecular modeling

Background:

• Drug resistance, particularly to small molecule inhibitors (SMIs), poses a significant challenge in treating cancers and infectious diseases.
• Predicting on-target resistance mutations is crucial for developing effective and durable therapies.

Purpose of the Study:

• To develop and validate a novel computational workflow for predicting on-target resistance mutations to small molecule inhibitors.
• To integrate genetic models with physics-based calculations for enhanced prediction accuracy.

Main Methods:

• Developed a computational workflow integrating the RECODE genetic model with alchemical free energy perturbation (FEP+) calculations.
• RECODE prioritizes probable amino acid changes based on cancer-specific mutation patterns.
• Physics-based calculations assessed the impact of mutations on protein stability, substrate binding, and inhibitor binding.

Main Results:

• The workflow accurately predicted known binding site mutations for gefitinib (4/11) and osimertinib (7/19), including clinically relevant T790M and C797S mutations.
• Successfully identified key resistance mutations in epidermal growth factor receptor (EGFR) inhibitors used for non-small cell lung cancer (NSCLC).

Conclusions:

• The integrated computational approach demonstrates significant potential for predicting small molecule inhibitor resistance mutations.
• This methodology can be extended to other kinases and target classes, facilitating the design of next-generation inhibitors with improved clinical durability.