Dataset Information

Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis.

ABSTRACT: We report the construction of a novel Systems Biology based virtual drug discovery model for the prediction of non-toxic metabolic targets in Mycobacterium tuberculosis (Mtb). This is based on a data-intensive genome level analysis and the principle of conservation of the evolutionarily important genes. In the 1623 sequenced Mtb strains, 890 metabolic genes identified through a systems approach in Mtb were evaluated for non-synonymous mutations. The 33 genes showed none or one variation in the entire 1623 strains, including 1084 Russian MDR strains. These invariant targets were further evaluated for their experimental and in silico essentiality as well as availability of their crystal structure in Protein Data Bank (PDB). Along with this, targets for the common existing antibiotics and the new Tb drug candidates were also screened for their variation across 1623 strains of Mtb for understanding the drug resistance. We propose that the reduced set of these reported targets could be a more effective starting point for medicinal chemists in generating new chemical leads. This approach has the potential of fueling the dried up Tuberculosis (Tb) drug discovery pipeline.

SUBMITTER: Kaur D

PROVIDER: S-EPMC5397868 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature

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Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis.

Kaur Divneet D Kutum Rintu R Dash Debasis D Brahmachari Samir K SK

Scientific reports 20170420

We report the construction of a novel Systems Biology based virtual drug discovery model for the prediction of non-toxic metabolic targets in Mycobacterium tuberculosis (Mtb). This is based on a data-intensive genome level analysis and the principle of conservation of the evolutionarily important genes. In the 1623 sequenced Mtb strains, 890 metabolic genes identified through a systems approach in Mtb were evaluated for non-synonymous mutations. The 33 genes showed none or one variation in the e ...[more]

PMID: 28425478

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Project description:BACKGROUND:The problem of drug resistance and bacterial persistence in tuberculosis is a cause of global alarm. Although, the UN's Sustainable Development Goals for 2030 has targeted a Tb free world, the treatment gap exists and only a few new drug candidates are in the pipeline. In spite of large information from medicinal chemistry to 'omics' data, there has been a little effort from pharmaceutical companies to generate pipelines for the development of novel drug candidates against the multi drug resistant Mycobacterium tuberculosis. METHODS:In the present study, we describe an integrated methodology; utilizing systems level information to optimize ligand selection to lower the failure rates at the pre-clinical and clinical levels. In the present study, metabolic targets (Rv2763c, Rv3247c, Rv1094, Rv3607c, Rv3048c, Rv2965c, Rv2361c, Rv0865, Rv0321, Rv0098, Rv0390, Rv3588c, Rv2244, Rv2465c and Rv2607) in M. tuberculosis, identified using our previous Systems Biology and data-intensive genome level analysis, have been used to design potential lead molecules, which are likely to be non-toxic. Various in silico drug discovery tools have been utilized to generate small molecular leads for each of the 15 targets with available crystal structures. RESULTS:The present study resulted in identification of 20 novel lead molecules including 4 FDA approved drugs (droxidropa, tetroxoprim, domperidone and nemonapride) which can be further taken for drug repurposing. This comprehensive integrated methodology, with both experimental and in silico approaches, has the potential to not only tackle the MDR form of Mtb but also the most important persister population of the bacterium, with a potential to reduce the failures in the Tb drug discovery. CONCLUSION:We propose an integrated approach of systems and structural biology for identifying targets that address the high attrition rate issue in lead identification and drug development We expect that this system level analysis will be applicable for identification of drug candidates to other pathogenic organisms as well.

Project description:BackgroundMultidrug drug-resistant tuberculosis (MDR-TB) is a major health problem and seriously threatens TB control and prevention efforts globally. Ethiopia is among the 30th highest TB burden countries for MDR-TB with 14% prevalence among previously treated cases. The focus of this study was on determining drug resistance patterns of Mycobacterium tuberculosis among MDR-TB suspected cases and associated risk factors.MethodsA cross-sectional study was conducted in Addis Ababa from June 2015 to December 2016. Sputum samples and socio-demographic data were collected from 358 MDR-TB suspected cases. Samples were analyzed using Ziehl-Neelsen technique, GeneXpert MTB/RIF assay, and culture using Lowenstein-Jensen and Mycobacterial growth indicator tube. Data were analyzed using SPSS version 23.ResultsA total of 226 the study participants were culture positive for Mycobacterium tuberculosis, among them, 133 (58.8%) participants were males. Moreover, 162 (71.7%) had been previously treated for tuberculosis, while 128 (56.6%) were TB/HIV co-infected. A majority [122 (54%)] of the isolates were resistant to any first-line anti-TB drugs. Among the resistant isolates, 110 (48.7%) were determined to be resistant to isoniazid, 94 (41.6%) to streptomycin, 89 (39.4%) to rifampicin, 72 (31.9%) to ethambutol, and 70 (30.9%) to pyrazinamide. The prevalence of MDR-TB was 89 (39.4%), of which 52/89 (58.4%) isolates were resistance to all five first-line drugs. Risk factors such as TB/HIV co-infection (AOR = 5.59, p = 0.00), cigarette smoking (AOR = 3.52, p = 0.045), alcohol drinking (AOR = 5.14, p = 0.001) hospital admission (AOR = 3.49, p = 0.005) and visiting (AOR = 3.34, p = 0.044) were significantly associated with MDR-TB.ConclusionsThe prevalence of MDR-TB in the study population was of a significantly high level among previously treated patients and age group of 25-34. TB/HIV coinfection, smoking of cigarette, alcohol drinking, hospital admission and health facility visiting were identified as risk factors for developing MDR-TB. Therefore, effective strategies should be designed considering the identified risk factors for control of MDR-TB.

Dataset Information

Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis.

Publications

Data Intensive Genome Level Analysis for Identifying Novel, Non-Toxic Drug Targets for Multi Drug Resistant Mycobacterium tuberculosis.

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