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Computational Methods for Multi-Target Drug Designing Against Mycobacterium tuberculosis

  • Gaurava Srivastava
  • Ashish Tiwari
  • Ashok Sharma
Protocol
Part of the Methods in Pharmacology and Toxicology book series

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.

Keywords

De novo methods Docking FEL MDR-TB MM-PBSA Molecular dynamics simulation Multi-target drug designing Mycobacterium tuberculosis PCA Pharmacophore 

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

Notes

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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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Gaurava Srivastava
    • 1
  • Ashish Tiwari
    • 1
  • Ashok Sharma
    • 1
  1. 1.Biotechnology DivisionCSIR-Central Institute of Medicinal and Aromatic PlantsLucknowIndia

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