Multiscale Analysis and Validation of Effective Drug Combinations Targeting Driver Kras Mutations in Non-Small Cell Lung Cancer

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 with 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 proliferation 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. 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.

Effective holistic characterization of small molecule effects using heterogeneous biological networks

“…Here we describe the use of the Computational Analysis of Novel Drug Opportunities (CANDO) platform for both drug indication as well as ADR prediction. CANDO is a shotgun multiscale drug repurposing, discovery, and design platform whose fundamental tenet or paradigm is to assess the biological or therapeutic potential of small molecule chemical compounds based on their interactions to higher scale entities such as proteins, proteomes, and pathways (Minie et al, 2014; Sethi et al, 2015; Chopra and Samudrala, 2016; Falls et al, 2019; Mangione and Samudrala, 2019; Schuler and Samudrala, 2019; Hudson and Samudrala, 2021; Mangione et al, 2020a; Chopra et al, 2016; Mangione et al, 2020b; Overhoff et al, 2021; Schuler et al, 2021; Moukheiber et al, 2022; Mangione et al, 2022; Bruggemann et al, 2022). Our hypotheses are that compound behavior is describable in terms of their interaction signatures, which are real value vectors representing interactions between a given compound and a library of proteins, pathways, cells, etc.…”