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Published on: July 3, 2013
Multiscale Analysis and Validation of Effective Drug Combinations Targeting Driver KRAS Mutations in Non-Small Cell
Liana Bruggemann1, Zackary Falls1, William Mangione1
1Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA.
Abstract:
Pharmacogenomics is a rapidly growing field with the goal of providing personalized care to every patient. Previously, we developed the Computational Analysis of Novel Drug Opportunities (CANDO) platform for multiscale therapeutic discovery to screen optimal compounds for any indication/disease by performing analytics on their interactions using large protein libraries. We implemented a comprehensive precision medicine drug discovery pipeline within the CANDO platform to determine which drugs are most likely to be effective against mutant phenotypes of non-small cell lung cancer (NSCLC) based on the supposition that drugs with similar interaction profiles (or signatures) will have similar behavior and therefore show synergistic effects. CANDO predicted that osimertinib, an EGFR inhibitor, is most likely to synergize with four KRAS inhibitors.Validation studies with cellular toxicity assays confirmed that osimertinib in combination with ARS-1620, a KRAS G12C inhibitor, and BAY-293, a pan-KRAS inhibitor, showed a synergistic effect on decreasing cellular proliferation by acting on mutant KRAS. Gene expression studies revealed that MAPK expression is strongly correlated with decreased cellular proliferation following treatment with KRAS inhibitor BAY-293, but not treatment with ARS-1620 or osimertinib. These results indicate that our precision medicine pipeline may be used to identify compounds capable of synergizing with inhibitors of KRAS G12C, and to assess their likelihood of becoming drugs by understanding their behavior at the proteomic/interactomic scales.
Insights
A precision medicine pipeline identified synergistic drug combinations for non-small cell lung cancer (NSCLC). Osimertinib combined with KRAS inhibitors ARS-1620 and BAY-293 effectively reduced cancer cell proliferation.
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.
