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<p>A multi-drug resistant <i>emm92-</i>type strain of group A <i>Streptococcus</i> (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 <i>emm92</i>-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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月30日

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  • Lillie M. Powell,
  • Megan E. Grund,
  • Wenxian Shi,
  • Soo Jeon Choi,
  • Keilly Zelaya,
  • Sharan Bobbala,
  • Werner Geldenhuys,
  • Mariette Barbier,
  • P. Rocco LaSala,
  • Slawomir Lukomski

摘要

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.