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Combining Cheminformatics Methods and Pathway Analysis to Identify Molecules with Whole-Cell Activity Against Mycobacterium Tuberculosis

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Abstract

Purpose

New strategies for developing inhibitors of Mycobacterium tuberculosis (Mtb) are required in order to identify the next generation of tuberculosis (TB) drugs. Our approach leverages the integration of intensive data mining and curation and computational approaches, including cheminformatics combined with bioinformatics, to suggest biological targets and their small molecule modulators.

Methods

We now describe an approach that uses the TBCyc pathway and genome database, the Collaborative Drug Discovery database of molecules with activity against Mtb and their associated targets, a 3D pharmacophore approach and Bayesian models of TB activity in order to select pathways and metabolites and ultimately prioritize molecules that may be acting as substrate mimics and exhibit activity against TB.

Results

In this study we combined the TB cheminformatics and pathways databases that enabled us to computationally search >80,000 vendor available molecules and ultimately test 23 compounds in vitro that resulted in two compounds (N-(2-furylmethyl)-N′-[(5-nitro-3-thienyl)carbonyl]thiourea and N-[(5-nitro-3-thienyl)carbonyl]-N′-(2-thienylmethyl)thiourea) proposed as mimics of D-fructose 1,6 bisphosphate, (MIC of 20 and 40 μg/ml, respectively).

Conclusion

This is a simple yet novel approach that has the potential to identify inhibitors of bacterial growth as illustrated by compounds identified in this study that have activity against Mtb.

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Acknowledgments & DISCLOSURES

S.E. kindly acknowledges CDD colleagues for developing the CDD TB database as well as the many TB research collaborators. M.S. and C.T acknowledge the Biocyc group and TBDB for access to tools and data. J.S.F. acknowledges generous start-up funding from UMDNJ-New Jersey Medical School. The CDD TB database was made possible with funding from the Bill and Melinda Gates Foundation (Grant#49852 “Collaborative drug discovery for TB through a novel database of SAR data optimized to promote data archiving and sharing”). The project described was supported by Award Number R41AI088893 from the National Institute of Allergy And Infectious Diseases. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute Of Allergy And Infectious Diseases or the National Institutes of Health.

S.E. is a consultant for Collaborative Drug Discovery.

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Authors and Affiliations

  1. SRI International, 333 Ravenswood Avenue, Menlo Park, California, 94025, USA

    Malabika Sarker, Carolyn Talcott, Peter Madrid & Sidharth Chopra

  2. Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California, 94010, USA

    Barry A. Bunin & Sean Ekins

  3. Department of Medicine, Johns Hopkins School of Medicine, 1550 Orleans St, Room 103, Baltimore, Maryland, 21287, USA

    Gyanu Lamichhane

  4. Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, UMDNJ—New Jersey Medical School, 185 South Orange Avenue, Newark, New Jersey, 07103, USA

    Joel S. Freundlich

  5. Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, North Carolina, 27526, USA

    Sean Ekins

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  1. Malabika Sarker
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Correspondence to Sean Ekins.

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Sarker, M., Talcott, C., Madrid, P. et al. Combining Cheminformatics Methods and Pathway Analysis to Identify Molecules with Whole-Cell Activity Against Mycobacterium Tuberculosis . Pharm Res 29, 2115–2127 (2012). https://doi.org/10.1007/s11095-012-0741-5

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