Combining metabolite-based pharmacophores with Bayesian machine learning models for mycobacterium tuberculosis drug discovery

Sean Ekins, Peter B. Madrid, Malabika Sarker, Shao Gang Li, Nisha Mittal, Pradeep Kumar, Xin Wang, Thomas P. Stratton, Matthew Zimmerman, Carolyn Talcott, Pauline Bourbon, Mike Travers, Maneesh Yadav, Joel S. Freundlich

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


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 andaCC50 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 μMat 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.

Original languageEnglish (US)
Article numbere0141076
JournalPloS one
Issue number10
StatePublished - Oct 30 2015

ASJC Scopus subject areas

  • General


Dive into the research topics of 'Combining metabolite-based pharmacophores with Bayesian machine learning models for mycobacterium tuberculosis drug discovery'. Together they form a unique fingerprint.

Cite this