2. Academic Validation
  3. Linking High-Throughput Screens to Identify MoAs and Novel Inhibitors of Mycobacterium tuberculosis Dihydrofolate Reductase

Linking High-Throughput Screens to Identify MoAs and Novel Inhibitors of Mycobacterium tuberculosis Dihydrofolate Reductase

  • ACS Chem Biol. 2017 Sep 15;12(9):2448-2456. doi: 10.1021/acschembio.7b00468.
John P Santa Maria Jr 1,  Yumi Park 2,  Lihu Yang 3,  Nicholas Murgolo 4,  Michael D Altman 1,  Paul Zuck 5,  Greg Adam 6,  Chad Chamberlin 7,  Peter Saradjian 7,  Peter Dandliker 7,  Helena I M Boshoff 2,  Clifton E Barry 3rd 2,  Charles Garlisi 8,  David B Olsen 9,  Katherine Young 9,  Meir Glick 1,  Elliott Nickbarg 7,  Peter S Kutchukian 1
Affiliations

Affiliations

  • 1 Modeling & Informatics, Merck Research Laboratories , Boston, Massachusetts, United States.
  • 2 National Institute of Allergy and Infectious Diseases , Bethesda, Maryland, United States.
  • 3 Department of Chemistry, Merck Sharp & Dohme Corp. , Kenilworth, New Jersey, United States.
  • 4 Department of Information & Analytics, Merck Sharp & Dohme Corp. , Kenilworth, New Jersey, United States.
  • 5 Research Science, Merck Sharp & Dohme Corp. , North Wales, Pennsylvania, United States.
  • 6 Department of Pharmacology, Merck Sharp & Dohme Corp. , North Wales, Pennsylvania, United States.
  • 7 Department of Pharmacology, Merck Sharp & Dohme Corp. , Boston, Massachusetts, United States.
  • 8 Department of Pharmacology, Merck Sharp & Dohme Corp. , Kenilworth, New Jersey, United States.
  • 9 Neglected Tropical Disease Discovery, Merck Sharp & Dohme Corp. , West Point, Pennsylvania, United States.
PMID: 28806050 DOI: 10.1021/acschembio.7b00468
Abstract

Though phenotypic and target-based high-throughput screening approaches have been employed to discover new Antibiotics, the identification of promising therapeutic candidates remains challenging. Each approach provides different information, and understanding their results can provide hypotheses for a mechanism of action (MoA) and reveal actionable chemical matter. Here, we describe a framework for identifying efficacy targets of bioactive compounds. High throughput biophysical profiling against a broad range of targets coupled with machine learning was employed to identify chemical features with predicted efficacy targets for a given phenotypic screen. We validate the approach on data from a set of 55 000 compounds in 24 historical internal Antibacterial phenotypic screens and 636 Bacterial targets screened in high-throughput biophysical binding assays. Models were built to reveal the relationships between phenotype, target, and chemotype, which recapitulated mechanisms for known antibacterials. We also prospectively identified novel inhibitors of dihydrofolate reductase with nanomolar Antibacterial efficacy against Mycobacterium tuberculosis. Molecular modeling provided structural insight into target-ligand interactions underlying selective killing activity toward mycobacteria over human cells.

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