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Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis.
Mol Biosyst. 2010 Nov; 6(11):2316-2324.MB

Abstract

There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity.

Authors+Show Affiliations

Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010. sekins@collaborativedrug.com ekinssean@yahoo.com and Collaborations In Chemistry, 601 Runnymede Avenue, Jenkintown, PA 19046, USA and Department of Pharmaceutical Sciences, University of Maryland, Baltimore, MD, USA and Department of Pharmacology, Robert Wood Johnson Medical School, University of Medicine & Dentistry of New Jersey, Piscataway, New Jersey 08854, USA.Global Alliance for TB Drug Development, 40 Wall Street, 24th floor, New York, NY 10005, USA.10 Connshire Drive, Waterford, Connecticut, 06385, USA.Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010. sekins@collaborativedrug.com ekinssean@yahoo.com.Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010. sekins@collaborativedrug.com ekinssean@yahoo.com.Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010. sekins@collaborativedrug.com ekinssean@yahoo.com.Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010. sekins@collaborativedrug.com ekinssean@yahoo.com.Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010. sekins@collaborativedrug.com ekinssean@yahoo.com.Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010. sekins@collaborativedrug.com ekinssean@yahoo.com.Division of Biocomputing, University of New Mexico, Albuquerque, NM 87131.SureChem, The Macmillan Building, 4 Crinan Street, London, UKN1 9XW.Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010. sekins@collaborativedrug.com ekinssean@yahoo.com.Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, CA 94010. sekins@collaborativedrug.com ekinssean@yahoo.com.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

20835433

Citation

Ekins, Sean, et al. "Analysis and Hit Filtering of a Very Large Library of Compounds Screened Against Mycobacterium Tuberculosis." Molecular BioSystems, vol. 6, no. 11, 2010, pp. 2316-2324.
Ekins S, Kaneko T, Lipinski CA, et al. Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis. Mol Biosyst. 2010;6(11):2316-2324.
Ekins, S., Kaneko, T., Lipinski, C. A., Bradford, J., Dole, K., Spektor, A., Gregory, K., Blondeau, D., Ernst, S., Yang, J., Goncharoff, N., Hohman, M. M., & Bunin, B. A. (2010). Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis. Molecular BioSystems, 6(11), 2316-2324. https://doi.org/10.1039/c0mb00104j
Ekins S, et al. Analysis and Hit Filtering of a Very Large Library of Compounds Screened Against Mycobacterium Tuberculosis. Mol Biosyst. 2010;6(11):2316-2324. PubMed PMID: 20835433.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Analysis and hit filtering of a very large library of compounds screened against Mycobacterium tuberculosis. AU - Ekins,Sean, AU - Kaneko,Takushi, AU - Lipinski,Christopher A, AU - Bradford,Justin, AU - Dole,Krishna, AU - Spektor,Anna, AU - Gregory,Kellan, AU - Blondeau,David, AU - Ernst,Sylvia, AU - Yang,Jeremy, AU - Goncharoff,Nicko, AU - Hohman,Moses M, AU - Bunin,Barry A, Y1 - 2010/09/08/ PY - 2010/9/14/entrez PY - 2010/9/14/pubmed PY - 2011/1/25/medline SP - 2316 EP - 2324 JF - Molecular bioSystems JO - Mol Biosyst VL - 6 IS - 11 N2 - There is an urgent need for new drugs against tuberculosis which annually claims 1.7-1.8 million lives. One approach to identify potential leads is to screen in vitro small molecules against Mycobacterium tuberculosis (Mtb). Until recently there was no central repository to collect information on compounds screened. Consequently, it has been difficult to analyze molecular properties of compounds that inhibit the growth of Mtb in vitro. We have collected data from publically available sources on over 300 000 small molecules deposited in the Collaborative Drug Discovery TB Database. A cheminformatics analysis on these compounds indicates that inhibitors of the growth of Mtb have statistically higher mean logP, rule of 5 alerts, while also having lower HBD count, atom count and lower PSA (ChemAxon descriptors), compared to compounds that are classed as inactive. Additionally, Bayesian models for selecting Mtb active compounds were evaluated with over 100 000 compounds and, they demonstrated 10 fold enrichment over random for the top ranked 600 compounds. This represents a promising approach for finding compounds active against Mtb in whole cells screened under the same in vitro conditions. Various sets of Mtb hit molecules were also examined by various filtering rules used widely in the pharmaceutical industry to identify compounds with potentially reactive moieties. We found differences between the number of compounds flagged by these rules in Mtb datasets, malaria hits, FDA approved drugs and antibiotics. Combining these approaches may enable selection of compounds with increased probability of inhibition of whole cell Mtb activity. SN - 1742-2051 UR - https://www.unboundmedicine.com/medline/citation/20835433/Analysis_and_hit_filtering_of_a_very_large_library_of_compounds_screened_against_Mycobacterium_tuberculosis_ L2 - https://doi.org/10.1039/c0mb00104j DB - PRIME DP - Unbound Medicine ER -