Integrative machine learning approach for identification of new molecular scaffold and prediction of inhibition responses in cancer cells using multi-omics data.

Abstract

MDM2 (Mouse Double Minute 2), a fundamental governor of the p53 tumor suppressor pathway, has garnered significant attention as a favorable target for cancer therapy. Recent years have witnessed the development and synthesis of potent MDM2 inhibitors. Despite the fact that numerous MDM2 inhibitors and degraders have been assessed in clinical studies for various human cancers, no FDA-approved drug targeting MDM2 is presently available in the market. Researchers have investigated the effects of various drugs, which are involved in cancer therapies with known mechanisms, on well-characterized cancer cell lines. The prediction of drug inhibition responses becomes crucial to enhance the effectiveness and personalization of cancer treatments. Such findings can provide new perceptions aimed at designing new drugs for targeted cancer therapies. In our current insilico work, a robust response was observed for Idasanutlin in cancer cell lines, indicating the drug's significant impact on gene expression. We also identified transcriptional response signatures, which were informative about the drug's mechanism of action and potential clinical application. Further, we applied a similarity search approach for the identification of potential lead compounds from the ChEMBL database and validated them by molecular docking and dynamics studies. The study highlights the potential of incorporating machine learning with omics and single-cell RNA-seq data for predicting drug responses in cancer cells. Our findings could provide valuable insights for improving cancer treatment in the future, particularly in developing effective therapies.