Dataset Information

A multitarget approach to drug discovery inhibiting Mycobacterium tuberculosis PyrG and PanK.

ABSTRACT: Mycobacterium tuberculosis, the etiological agent of the infectious disease tuberculosis, kills approximately 1.5?million people annually, while the spread of multidrug-resistant strains is of great global concern. Thus, continuous efforts to identify new antitubercular drugs as well as novel targets are crucial. Recently, two prodrugs activated by the monooxygenase EthA, 7947882 and 7904688, which target the CTP synthetase PyrG, were identified and characterized. In this work, microbiological, biochemical, and in silico methodologies were used to demonstrate that both prodrugs possess a second target, the pantothenate kinase PanK. This enzyme is involved in coenzyme A biosynthesis, an essential pathway for M. tuberculosis growth. Moreover, compound 11426026, the active metabolite of 7947882, was demonstrated to directly inhibit PanK, as well. In an independent screen of a compound library against PyrG, two additional inhibitors were also found to be active against PanK. In conclusion, these direct PyrG and PanK inhibitors can be considered as leads for multitarget antitubercular drugs and these two enzymes could be employed as a "double-tool" in order to find additional hit compounds.

SUBMITTER: Chiarelli LR

PROVIDER: S-EPMC5816626 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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A multitarget approach to drug discovery inhibiting Mycobacterium tuberculosis PyrG and PanK.

Chiarelli Laurent R LR Mori Giorgia G Orena Beatrice Silvia BS Esposito Marta M Lane Thomas T de Jesus Lopes Ribeiro Ana Luisa AL Degiacomi Giulia G Zemanová Júlia J Szádocka Sára S Huszár Stanislav S Palčeková Zuzana Z Manfredi Marcello M Gosetti Fabio F Lelièvre Joël J Ballell Lluis L Kazakova Elena E Makarov Vadim V Marengo Emilio E Mikusova Katarina K Cole Stewart T ST Riccardi Giovanna G Ekins Sean S Pasca Maria Rosalia MR

Scientific reports 20180216 1

Mycobacterium tuberculosis, the etiological agent of the infectious disease tuberculosis, kills approximately 1.5 million people annually, while the spread of multidrug-resistant strains is of great global concern. Thus, continuous efforts to identify new antitubercular drugs as well as novel targets are crucial. Recently, two prodrugs activated by the monooxygenase EthA, 7947882 and 7904688, which target the CTP synthetase PyrG, were identified and characterized. In this work, microbiological, ...[more]

PMID: 29453370

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Project description:BackgroundDrug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling.ResultsWe developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%).ConclusionOur work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.

Dataset Information

A multitarget approach to drug discovery inhibiting Mycobacterium tuberculosis PyrG and PanK.

Publications

A multitarget approach to drug discovery inhibiting Mycobacterium tuberculosis PyrG and PanK.

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