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Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets.
Pharm Res. 2011 Aug; 28(8):1859-69.PR

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.

Authors+Show Affiliations

Collaborative Drug Discovery, 1633 Bayshore Highway, Suite 342, Burlingame, California 94010, USA. sekins@collaborativedrug.comNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

21547522

Citation

Ekins, Sean, and Joel S. Freundlich. "Validating New Tuberculosis Computational Models With Public Whole Cell Screening Aerobic Activity Datasets." Pharmaceutical Research, vol. 28, no. 8, 2011, pp. 1859-69.
Ekins S, Freundlich JS. Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets. Pharm Res. 2011;28(8):1859-69.
Ekins, S., & Freundlich, J. S. (2011). Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets. Pharmaceutical Research, 28(8), 1859-69. https://doi.org/10.1007/s11095-011-0413-x
Ekins S, Freundlich JS. Validating New Tuberculosis Computational Models With Public Whole Cell Screening Aerobic Activity Datasets. Pharm Res. 2011;28(8):1859-69. PubMed PMID: 21547522.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Validating new tuberculosis computational models with public whole cell screening aerobic activity datasets. AU - Ekins,Sean, AU - Freundlich,Joel S, Y1 - 2011/03/10/ PY - 2010/12/22/received PY - 2011/02/25/accepted PY - 2011/5/7/entrez PY - 2011/5/7/pubmed PY - 2011/12/13/medline SP - 1859 EP - 69 JF - Pharmaceutical research JO - Pharm. Res. VL - 28 IS - 8 N2 - 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. SN - 1573-904X UR - https://www.unboundmedicine.com/medline/citation/21547522/Validating_new_tuberculosis_computational_models_with_public_whole_cell_screening_aerobic_activity_datasets_ L2 - https://doi.org/10.1007/s11095-011-0413-x DB - PRIME DP - Unbound Medicine ER -