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多尺度分析和验证针对非小细胞肺癌中驱动 KRAS 突变的有效药物组合。

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

机构信息

Department of Biomedical Informatics, University at Buffalo, Buffalo, NY 14260, USA.

Department of Medicine, University at Buffalo, Buffalo, NY 14260, USA.

出版信息

Int J Mol Sci. 2023 Jan 5;24(2):997. doi: 10.3390/ijms24020997.

DOI:10.3390/ijms24020997
PMID:36674513
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9867122/
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.

摘要

药物基因组学是一个快速发展的领域,其目标是为每位患者提供个性化的护理。此前,我们开发了用于多尺度治疗发现的新型药物机会计算分析(CANDO)平台,通过使用大型蛋白质库对其相互作用进行分析,为任何适应症/疾病筛选最佳化合物。我们在 CANDO 平台中实施了全面的精准医学药物发现管道,以确定哪些药物最有可能针对非小细胞肺癌(NSCLC)的突变表型有效,假设具有相似相互作用谱(或特征)的药物将具有相似的行为,因此会产生协同作用。CANDO 预测表皮生长因子受体抑制剂奥希替尼最有可能与四种 KRAS 抑制剂协同作用。细胞毒性测定的验证研究证实,奥希替尼与 KRAS G12C 抑制剂 ARS-1620 和泛 KRAS 抑制剂 BAY-293 联合使用对降低细胞增殖具有协同作用,作用于突变的 KRAS。基因表达研究表明,MAPK 表达与用 KRAS 抑制剂 BAY-293 处理后细胞增殖减少强烈相关,但与用 ARS-1620 或奥希替尼处理无关。这些结果表明,我们的精准医学管道可用于识别能够与 KRAS G12C 抑制剂协同作用的化合物,并通过了解它们在蛋白质组/相互作用组尺度上的行为来评估它们成为药物的可能性。

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