Tags

Type your tag names separated by a space and hit enter

Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models.
PLoS One. 2013; 8(5):e63240.Plos

Abstract

High-throughput screening (HTS) in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis (TB) drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15-28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery.

Authors+Show Affiliations

Collaborative Drug Discovery, Burlingame, California, United States of America. ekinssean@yahoo.comNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, American Recovery and Reinvestment Act
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

23667592

Citation

Ekins, Sean, et al. "Enhancing Hit Identification in Mycobacterium Tuberculosis Drug Discovery Using Validated Dual-event Bayesian Models." PloS One, vol. 8, no. 5, 2013, pp. e63240.
Ekins S, Reynolds RC, Franzblau SG, et al. Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models. PLoS ONE. 2013;8(5):e63240.
Ekins, S., Reynolds, R. C., Franzblau, S. G., Wan, B., Freundlich, J. S., & Bunin, B. A. (2013). Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models. PloS One, 8(5), e63240. https://doi.org/10.1371/journal.pone.0063240
Ekins S, et al. Enhancing Hit Identification in Mycobacterium Tuberculosis Drug Discovery Using Validated Dual-event Bayesian Models. PLoS ONE. 2013;8(5):e63240. PubMed PMID: 23667592.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models. AU - Ekins,Sean, AU - Reynolds,Robert C, AU - Franzblau,Scott G, AU - Wan,Baojie, AU - Freundlich,Joel S, AU - Bunin,Barry A, Y1 - 2013/05/07/ PY - 2012/11/07/received PY - 2013/03/31/accepted PY - 2013/5/14/entrez PY - 2013/5/15/pubmed PY - 2013/12/18/medline SP - e63240 EP - e63240 JF - PloS one JO - PLoS ONE VL - 8 IS - 5 N2 - High-throughput screening (HTS) in whole cells is widely pursued to find compounds active against Mycobacterium tuberculosis (Mtb) for further development towards new tuberculosis (TB) drugs. Hit rates from these screens, usually conducted at 10 to 25 µM concentrations, typically range from less than 1% to the low single digits. New approaches to increase the efficiency of hit identification are urgently needed to learn from past screening data. The pharmaceutical industry has for many years taken advantage of computational approaches to optimize compound libraries for in vitro testing, a practice not fully embraced by academic laboratories in the search for new TB drugs. Adapting these proven approaches, we have recently built and validated Bayesian machine learning models for predicting compounds with activity against Mtb based on publicly available large-scale HTS data from the Tuberculosis Antimicrobial Acquisition Coordinating Facility. We now demonstrate the largest prospective validation to date in which we computationally screened 82,403 molecules with these Bayesian models, assayed a total of 550 molecules in vitro, and identified 124 actives against Mtb. Individual hit rates for the different datasets varied from 15-28%. We have identified several FDA approved and late stage clinical candidate kinase inhibitors with activity against Mtb which may represent starting points for further optimization. The computational models developed herein and the commercially available molecules derived from them are now available to any group pursuing Mtb drug discovery. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/23667592/Enhancing_hit_identification_in_Mycobacterium_tuberculosis_drug_discovery_using_validated_dual_event_Bayesian_models_ L2 - http://dx.plos.org/10.1371/journal.pone.0063240 DB - PRIME DP - Unbound Medicine ER -