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Machine learning-based discovery of GW3965 as a therapeutic compound against invasive emm92-type group A
Lillie M Powell1, Megan E Grund1, Wenxian Shi2
1Department of Microbiology, Immunology, and Cell Biology, West Virginia University School of Medicine, Morgantown, WV, USA.
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 2560 bioactive compounds. The model was used to screen a 6111 FDA-evaluated drug library in silico. Of the 9 validated compounds, GW3965 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 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.
Insights
A machine learning model identified GW3965 as a potential antimicrobial. This drug significantly reduced survival of drug-resistant Group A Streptococcus (GAS) and healed skin infections in preclinical models.
Area of Science:
- Microbiology
- Infectious Diseases
- Drug Discovery
Background:
- Multi-drug resistant emm92-type Group A Streptococcus (GAS) strains cause invasive infections, particularly in people who inject drugs.
- Emergence of these resistant strains necessitates novel therapeutic strategies.
Purpose of the Study:
- To identify and repurpose FDA-investigated compounds as antimicrobials against emm92-type GAS.
- To discover novel therapeutics for invasive GAS infections.
Main Methods:
- A machine learning model was trained on GAS growth response to 2560 compounds.
- The model screened a library of 6111 FDA-evaluated drugs in silico.
- Promising compounds were validated experimentally, including GW3965.
Main Results:
- GW3965 demonstrated a 99% reduction in invasive GAS (iGAS) survival at 6.25 µM.
- GW3965 promoted complete wound closure in a human skin equivalent model.
- GW3965 reduced lesion size and bacterial burden in a mouse model of skin and soft tissue infection.
Conclusions:
- Machine learning expedited the discovery of GW3965 as a therapeutic candidate.
- GW3965 shows promise for treating iGAS skin and soft tissue infections.
- Repurposing FDA-investigated drugs offers a viable strategy for combating antimicrobial resistance.
