2. Academic Validation
  3. Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus

Computational drug repositioning identifies niclosamide and tribromsalan as inhibitors of Mycobacterium tuberculosis and Mycobacterium abscessus

  • Tuberculosis (Edinb). 2024 May:146:102500. doi: 10.1016/j.tube.2024.102500.
Jeremy J Yang 1,  Aaron Goff 2,  David J Wild 3,  Ying Ding 4,  Ayano Annis 5,  Randy Kerber 6,  Brian Foote 6,  Anurag Passi 7,  Joel L Duerksen 6,  Shelley London 6,  Ana C Puhl 8,  Thomas R Lane 8,  Miriam Braunstein 5,  Simon J Waddell 2,  Sean Ekins 9
Affiliations

Affiliations

  • 1 School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA; Department of Internal Medicine Translational Informatics Division, University of New Mexico, Albuquerque, NM, USA.
  • 2 Department of Global Health and Infection, Brighton & Sussex Medical School, University of Sussex, UK.
  • 3 School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA.
  • 4 School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN, USA; Data2Discovery, Inc., Bloomington, IN, USA; School of Information, Dell Medical School, University of Texas, Austin, TX, USA.
  • 5 Department of Microbiology and Immunology, School of Medicine, University of North Carolina at Chapel Hill, NC, 27599, USA.
  • 6 Data2Discovery, Inc., Bloomington, IN, USA.
  • 7 Department of Pediatrics, UC San Diego, San Diego, CA, USA.
  • 8 Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA.
  • 9 Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC, 27606, USA. Electronic address: sean@collaborationspharma.com.
PMID: 38432118 DOI: 10.1016/j.tube.2024.102500
Abstract

Tuberculosis (TB) is still a major global health challenge, killing over 1.5 million people each year, and hence, there is a need to identify and develop novel treatments for Mycobacterium tuberculosis (M. tuberculosis). The prevalence of infections caused by nontuberculous mycobacteria (NTM) is also increasing and has overtaken TB cases in the United States and much of the developed world. Mycobacterium abscessus (M. abscessus) is one of the most frequently encountered NTM and is difficult to treat. We describe the use of drug-disease association using a semantic knowledge graph approach combined with machine learning models that has enabled the identification of several molecules for testing anti-mycobacterial activity. We established that niclosamide (M. tuberculosis IC90 2.95 μM; M. abscessus IC90 59.1 μM) and tribromsalan (M. tuberculosis IC90 76.92 μM; M. abscessus IC90 147.4 μM) inhibit M. tuberculosis and M. abscessus in vitro. To investigate the mode of action, we determined the transcriptional response of M. tuberculosis and M. abscessus to both compounds in axenic log phase, demonstrating a broad effect on gene expression that differed from known M. tuberculosis inhibitors. Both compounds elicited transcriptional responses indicative of respiratory pathway stress and the dysregulation of fatty acid metabolism.

Keywords

Drug discovery; Machine learning; Mycobacterium abscessus; Mycobacterium tuberculosis; Transcriptome profiling.

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