Machine Learning-Driven Drug Repurposing for KRAS G12C and KRAS G12D Inhibition.

Area of Science:

• Oncology
• Computational Biology
• Pharmacology

Background:

• KRAS mutations drive numerous cancers and have historically been difficult to target therapeutically.
• While KRAS G12C inhibitors exist, effective treatments for other variants like KRAS G12D, common in pancreatic cancer, are limited.

Purpose of the Study:

• To leverage machine learning to screen FDA-approved compounds for potential KRAS G12D and KRAS G12C inhibitors.
• To establish a data-driven framework for identifying novel KRAS-targeted therapies with allele-specific activity.

Main Methods:

• Machine learning models (Random Forest, Neural Network) were trained on KRAS bioactivity data from BindingDB.
• FDA-approved compounds from the ChEMBL database were screened using the trained predictive models.
• High-confidence candidate compounds were selected for in vitro experimental validation.

Main Results:

• The machine learning models demonstrated strong predictive performance on independent test sets.
• In vitro testing of Cobimetinib and Etrasimod showed measurable activity, with preferential efficacy in KRAS G12D cellular backgrounds.
• The study identified a data-driven approach for prioritizing compounds with allele-specific KRAS activity.

Conclusions:

• Machine learning, combined with experimental validation, provides a viable strategy for discovering targeted KRAS inhibitors.
• The identified compounds warrant further investigation for their potential as KRAS G12D-targeted therapies.
• This approach advances the development of precision oncology treatments for KRAS-mutated cancers.