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PLoS One. 2015 Oct 30;10(10):e0141076. doi: 10.1371/journal.pone.0141076. eCollection 2015.

Combining Metabolite-Based Pharmacophores with Bayesian Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

Author information

1
Collaborative Drug Discovery Inc., 1633 Bayshore Highway, Suite 342, Burlingame, CA, 94010, United States of America; Collaborations in Chemistry, 5616 Hilltop Needmore Road, Fuquay-Varina, NC, 27526, United States of America.
2
SRI International, 333 Ravenswood Avenue, Menlo Park, CA, 94025, United States of America.
3
Departments of Pharmacology & Physiology and Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University-New Jersey Medical School, 185 South Orange Avenue, Newark, NJ, 07103, United States of America.
4
Department of Medicine, Center for Emerging and Reemerging Pathogens, Rutgers University-New Jersey Medical School, 185 South Orange Avenue, Newark, NJ, 07103, United States of America.
5
Public Health Research Institute, Rutgers University-New Jersey Medical School, Newark, NJ, 07103, United States of America.
6
Collaborative Drug Discovery Inc., 1633 Bayshore Highway, Suite 342, Burlingame, CA, 94010, United States of America.

Abstract

Integrated computational approaches for Mycobacterium tuberculosis (Mtb) are useful to identify new molecules that could lead to future tuberculosis (TB) drugs. Our approach uses information derived from the TBCyc pathway and genome database, the Collaborative Drug Discovery TB database combined with 3D pharmacophores and dual event Bayesian models of whole-cell activity and lack of cytotoxicity. We have prioritized a large number of molecules that may act as mimics of substrates and metabolites in the TB metabolome. We computationally searched over 200,000 commercial molecules using 66 pharmacophores based on substrates and metabolites from Mtb and further filtering with Bayesian models. We ultimately tested 110 compounds in vitro that resulted in two compounds of interest, BAS 04912643 and BAS 00623753 (MIC of 2.5 and 5 μg/mL, respectively). These molecules were used as a starting point for hit-to-lead optimization. The most promising class proved to be the quinoxaline di-N-oxides, evidenced by transcriptional profiling to induce mRNA level perturbations most closely resembling known protonophores. One of these, SRI58 exhibited an MIC = 1.25 μg/mL versus Mtb and a CC50 in Vero cells of >40 μg/mL, while featuring fair Caco-2 A-B permeability (2.3 x 10-6 cm/s), kinetic solubility (125 μM at pH 7.4 in PBS) and mouse metabolic stability (63.6% remaining after 1 h incubation with mouse liver microsomes). Despite demonstration of how a combined bioinformatics/cheminformatics approach afforded a small molecule with promising in vitro profiles, we found that SRI58 did not exhibit quantifiable blood levels in mice.

PMID:
26517557
PMCID:
PMC4627656
DOI:
10.1371/journal.pone.0141076
[Indexed for MEDLINE]
Free PMC Article

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