Machine learning-based discovery of GW3965 as a therapeutic compound against invasive emm92-type group A Streptococcus

Abstract A multi-drug resistant emm92- type strain of group A Streptococcus (GAS) has emerged as an important causative agent of invasive infections – particularly affecting people who inject drugs - in the United States. To curtail this developing threat, we aimed to identify and repurpose FDA-investigated compounds as antimicrobials. To identify growth-inhibiting compounds, a machine learning-based model was trained on the emm92 -iGAS growth response to 2,560 bioactive compounds. The model was used to screen a 6,111-compound library of FDA-evaluated drugs in silico. The predicted GW3965 compound experimentally exhibited a 99% reduction in iGAS survival at an MIC of 6.25 µM. Treatment with GW3965 aided complete wound closure in a human skin equivalent model, and significantly decreased lesion size and reduced bacterial burden in a mouse model of skin and soft tissue infection. Application of a machine learning model expedited the discovery of GW3965 as a therapeutic for iGAS skin and soft tissue infections.