Literature DB >> 15809316

Search of chemical scaffolds for novel antituberculosis agents.

Angeles García-García1, Jorge Gálvez, Jesus Vicente de Julián-Ortiz, Ramón García-Domenech, Carlos Muñoz, Remedios Guna, Rafael Borrás.   

Abstract

A method to identify chemical scaffolds potentially active against Mycobacterium tuberculosis is presented. The molecular features of a set of structurally heterogeneous antituberculosis drugs were coded by means of structural invariants. Three techniques were used to obtain equations able to model the antituberculosis activity: linear discriminant analysis, multilinear regression, and shrinkage estimation-ridge regression. The model obtained was statistically validated through leave-n-out test, and an external set and was applied to a database for the search of new active agents. The selected compounds were assayed in vitro, and among those identified as active stand reserpine, N,N,N',N'-tetrakis-(2-pyridylmethyl)-ethylenediamine (TPEN), trifluoperazine, pentamidine, and 2-methyl-4,6-dinitro-phenol (DNOC). They show activity comparable to or superior to ethambutol, used in combination with other drugs for the prevention and treatment of Mycobacterium avium complex and drug-resistant tuberculosis.

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Year:  2005        PMID: 15809316     DOI: 10.1177/1087057104273486

Source DB:  PubMed          Journal:  J Biomol Screen        ISSN: 1087-0571


  8 in total

1.  True prediction of lowest observed adverse effect levels.

Authors:  R García-Domenech; J V de Julián-Ortiz; E Besalú
Journal:  Mol Divers       Date:  2006-05-24       Impact factor: 2.943

2.  Design of novel antituberculosis compounds using graph-theoretical and substructural approaches.

Authors:  Alejandro Speck Planche; Marcus Tulius Scotti; América García López; Vicente de Paulo Emerenciano; Enrique Molina Pérez; Eugenio Uriarte
Journal:  Mol Divers       Date:  2009-04-02       Impact factor: 2.943

Review 3.  Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery.

Authors:  Sean Ekins; Joel S Freundlich; Inhee Choi; Malabika Sarker; Carolyn Talcott
Journal:  Trends Microbiol       Date:  2010-12-02       Impact factor: 17.079

4.  Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks.

Authors:  Lázaro Guillermo Pérez-Montoto; María Auxiliadora Dea-Ayuela; Francisco J Prado-Prado; Francisco Bolas-Fernández; Florencio M Ubeira; Humberto González-Díaz
Journal:  Polymer (Guildf)       Date:  2009-06-03       Impact factor: 4.430

5.  Bigger data, collaborative tools and the future of predictive drug discovery.

Authors:  Sean Ekins; Alex M Clark; S Joshua Swamidass; Nadia Litterman; Antony J Williams
Journal:  J Comput Aided Mol Des       Date:  2014-06-19       Impact factor: 3.686

6.  Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation.

Authors:  Sean Ekins; Joel S Freundlich; Robert C Reynolds
Journal:  J Chem Inf Model       Date:  2013-10-30       Impact factor: 4.956

Review 7.  Early Drug Development and Evaluation of Putative Antitubercular Compounds in the -Omics Era.

Authors:  Alina Minias; Lidia Żukowska; Ewelina Lechowicz; Filip Gąsior; Agnieszka Knast; Sabina Podlewska; Daria Zygała; Jarosław Dziadek
Journal:  Front Microbiol       Date:  2021-02-02       Impact factor: 5.640

8.  Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated dual-event Bayesian models.

Authors:  Sean Ekins; Robert C Reynolds; Scott G Franzblau; Baojie Wan; Joel S Freundlich; Barry A Bunin
Journal:  PLoS One       Date:  2013-05-07       Impact factor: 3.240

  8 in total

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