Prediction of synergistic drug combinations for treatment of COVID-19

Hydroxychloroquine is currently in use to treat COVID-19 patients, but its clinical efficacy has been limited, and it has significant side effects. Synergistic drug combinations can often achieve greater efficacy at lower doses. We previously generated a drug synergy RNAseq dataset using a drug combination that includes mefloquine, an antimalarial related to hydroxychloroquine. We developed a machine learning classifier that learns from this data and successfully predicts viability of other 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. We propose to 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 searched 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 and in a clinical COVID-19 database. Repurposing of any FDA-approved drugs identified for synergistic combination with hydroxychloroquine could rapidly impact treatment outcomes. If in vitro validation is successful, we will broaden the scope of the project beyond hydroxychloroquine to search all drug combinations in the massive database (over 2.8 million sets of predictions) generated in this study.