The clinical manifestations of COVID-19 are multifaceted and have often proved resistant to existing single drug therapies. Synergistic drug combinations can often achieve greater efficacy at lower doses. We have developed a machine learning classifier that learns from a drug synergy RNAseq dataset to successfully predict viability of drug combinations (AUROC=0.87). We used the classifier to make gene expression predictions for over 700,000 drug combinations using data from the Connectivity Map database. Further study suggested these synergistic gene expression predictions can be used to identify biological pathways and processes that will be altered in each combination. In this study we will expand the database, improve the classifier, and use gene set enrichment analysis to make predictions of drug pairs that synergize for COVID-19 treatment. The computational resource required for this machine-learning based effort is significant. In a preliminary analysis, we performed a limited search of our existing database for drug combinations predicted to synergistically target gene sets relevant to SARS-Cov2 infection, and identified many FDA-approved drugs already under study for COVID-19, as well as novel predictions. Predictions from the expanded database and analysis will be validated in vitro. Repurposing of any FDA-approved drugs identified in synergistic combinations could rapidly impact treatment outcomes. If in vitro validation is successful, we will broaden the scope of the project to search all drug combinations in the massive database (over 2.8 million sets of predictions) generated in this study.
This project is part of the COVID-19 HPC Consortium.
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