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Computational Methods for Multi-Target Drug Designing Against Mycobacterium tuberculosis

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Multi-Target Drug Design Using Chem-Bioinformatic Approaches

Part of the book series: Methods in Pharmacology and Toxicology ((MIPT))

Abstract

Despite the availability of several drugs, Mycobacterium tuberculosis is still a big concern for public health. Such situation exists because of continuous emergence of TB-resistant strains. Possible reasons of developing resistance include long therapy and combination therapy. Therefore new potential leads are needed to be identified, and at the same time, the number of drugs in the combination therapy should also be reduced that will make administration of drug doses easier. In the present scenario, developing drug having the ability to interact with multiple targets, simultaneously, is a promising approach to treat the complicated diseases. These multi-target drug therapies have advantage of improved safety profile and high drug efficacy with easier administration over the single-target drug therapies. Many of in silico methods have been applied to reach different polypharmacologically directed drug designing, mainly for multi-target drug designing. In this chapter, we have discussed about the available strategies for computational multi-target drug designing with their advantages and disadvantages. We have also discussed an easy, fast, and equally accurate method for multi-target drug designing against the Mycobacterium tuberculosis.

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Abbreviations

BCG:

Bacillus Calmette-Guerin

CS:

Cycloserine

DDI:

Drug-drug interactions

DOTS:

Directly observed short-course chemotherapy

EMB:

Ethambutol

ETA:

Ethionamide

FEL:

Free energy landscape

IFN:

Interferon

IGRA:

Interferon-gamma release assay

INH:

Isoniazid

MDR:

Multidrug resistant

Mtb:

Mycobacterium tuberculosis

ODE:

Ordinary differential equation

PAS:

Para-amino salicylate

PCA:

Principal component analysis

PZA:

Pyrazinamide

Rg:

Radius of gyration

RIF:

Rifampin

RMSD:

Root mean square deviation

RMSF:

Root mean square fluctuation

SASA:

Solvent-accessible surface area

SMD:

Steered molecular dynamics

TB:

Tuberculosis

TDR:

Totally drug resistant

TST:

Tuberculin skin test

XDR:

Extensively drug resistant

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Acknowledgments

G.S. and A.T. are thankful to ICMR, New Delhi, for their ICMR-SRF and ICMR-RA fellowship. Authors are also thankful to BTISnet program of DBT, New Delhi.

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Authors and Affiliations

  1. Biotechnology Division, CSIR-Central Institute of Medicinal and Aromatic Plants, Lucknow, Uttar Pradesh, India

    Gaurava Srivastava, Ashish Tiwari & Ashok Sharma

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  1. Gaurava Srivastava
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  2. Ashish Tiwari
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  3. Ashok Sharma
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Correspondence to Ashok Sharma .

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Editors and Affiliations

  1. Department of Pharmaceutical Technology, Jadavpur University, Kolkata, West Bengal, India

    Kunal Roy

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Srivastava, G., Tiwari, A., Sharma, A. (2018). Computational Methods for Multi-Target Drug Designing Against Mycobacterium tuberculosis. In: Roy, K. (eds) Multi-Target Drug Design Using Chem-Bioinformatic Approaches. Methods in Pharmacology and Toxicology. Humana Press, New York, NY. https://doi.org/10.1007/7653_2018_19

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  • DOI: https://doi.org/10.1007/7653_2018_19

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-8732-0

  • Online ISBN: 978-1-4939-8733-7

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