ABSTRACT
Purpose
The search for small molecules with activity against Mycobacterium tuberculosis (Mtb) increasingly uses high throughput screening and computational methods. Several public datasets from the Collaborative Drug Discovery Tuberculosis (CDD TB) database have been evaluated with cheminformatics approaches to validate their utility and suggest compounds for testing.
Methods
Previously reported Bayesian classification models were used to predict a set of 283 Novartis compounds tested against Mtb (containing aerobic and anaerobic hits) and to search FDA approved drugs. The Novartis compounds were also filtered with computational SMARTS alerts to identify potentially undesirable substructures.
Results
Using the Novartis compounds as a test set for the Bayesian models demonstrated a >4.0-fold enrichment over random screening for finding aerobic hits not in the computational models (N = 34). A 10-fold enrichment was observed for finding Mtb active compounds in the FDA drugs database. 85.9% of the Novartis compounds failed the Abbott SMARTS alerts, a value substantially higher than for known TB drugs. Higher levels of failures of SMARTS filters from different groups also correlate with the number of Lipinski violations.
Conclusions
These computational approaches may assist in finding desirable leads for Tuberculosis drug discovery.
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ACKNOWLEDGMENTS
The authors sincerely thank Dr. Jeremy Yang and colleagues (University of New Mexico) for kindly providing access to the SMARTS filter web application. We gratefully acknowledge the many groups that have provided datasets including Novartis and Dr. David Sullivan. S.E. acknowledges colleagues at CDD for developing the software and assistance with large datasets and our collaborators. He also kindly acknowledges Dr. Richard Elliott for stimulating the FDA dataset analysis. The CDD TB database along with introductory training was provided freely to Mtb researchers through October 2010 thanks to 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”).
Competing interests
Sean Ekins is a consultant for Collaborative Drug Discovery Inc.
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Supporting Information Available
Supplemental material is available online. The Bayesian models created in Discovery Studio are available from the authors upon written request.
Supplemental Table I
SMARTS alerts failures at different levels of Lipinski violations for FDA approved drugs in CDD. (DOC 32 kb)
Supplemental Table II
Percentage of FDA approved drugs at different levels of Lipinski violations. (DOC 32 kb)
Supplemental Table III
Number of Novartis compounds (%) that are aerobic active or aerobic inactive at different levels of Lipinski violations. (DOC 31 kb)
Supplemental Table IV
SMARTS filtering number of failures (%) for the Novartis compounds that are aerobic active hits or aerobic inactive. (DOC 30 kb)
Supplemental Fig. 1
Selected compounds from Bayesian model searches and results from searching in PubChem. (DOC 145 kb)
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Ekins, S., Freundlich, J.S. Validating New Tuberculosis Computational Models with Public Whole Cell Screening Aerobic Activity Datasets. Pharm Res 28, 1859–1869 (2011). https://doi.org/10.1007/s11095-011-0413-x
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DOI: https://doi.org/10.1007/s11095-011-0413-x