Multiscale Analysis and Validation of Effective Drug Combinations Targeting Driver KRAS Mutations in Non-Small Cell...

Area of Science:

• Pharmacogenomics and computational drug discovery
• Precision medicine for oncology
• Proteomics and interactomics analysis

Background:

• Personalized medicine aims to tailor treatments to individual patients.
• The Computational Analysis of Novel Drug Opportunities (CANDO) platform screens compounds for therapeutic potential.
• Understanding drug interaction profiles can predict synergistic effects.

Purpose of the Study:

• To implement a precision medicine drug discovery pipeline within the CANDO platform.
• To identify drugs that synergize with KRAS inhibitors for non-small cell lung cancer (NSCLC) treatment.
• To validate predicted synergistic drug combinations experimentally.

Main Methods:

• Utilized the CANDO platform for multiscale therapeutic discovery and interaction analytics.
• Developed a precision medicine pipeline to screen drugs against mutant NSCLC phenotypes.
• Conducted cellular toxicity assays and gene expression studies to validate predictions.

Main Results:

• CANDO predicted synergy between osimertinib (EGFR inhibitor) and four KRAS inhibitors.
• Experimental validation confirmed synergy between osimertinib and ARS-1620 (KRAS G12C inhibitor) or BAY-293 (pan-KRAS inhibitor) in reducing NSCLC cell proliferation.
• MAPK expression correlated with decreased proliferation upon BAY-293 treatment, but not ARS-1620 or osimertinib.

Conclusions:

• The developed precision medicine pipeline can identify synergistic drug combinations for KRAS-mutant NSCLC.
• The CANDO platform aids in assessing drug potential by analyzing proteomic and interactomic behavior.
• This approach supports the discovery of novel therapeutic strategies for challenging cancers.