Computational Biology in Anti-Tuberculosis Drug Discovery

Author(s): Dennis J. Murphy, James R. Brown

Journal Name: Infectious Disorders - Drug Targets
(Formerly Current Drug Targets - Infectious Disorders)

Volume 9 , Issue 3 , 2009

Become EABM
Become Reviewer


The resurgence of drug resistant tuberculosis (TB) is a major global healthcare problem. Mycobacterium tuberculosis (MTB), TBs causative agent, evades the host immune system and drug regimes by entering prolonged periods of nonproliferation or dormancy. The identification of genes essential to the bacterium in its dormancy phase infections is a key strategy in the development of new anti-TB therapeutics. The rapid expansion of TB-related genomic data sources including DNA sequences, transcriptomic and proteomic profiles, and genome-wide essentiality data, present considerable opportunities to apply advanced computational analyses to predict potential drug targets. However, the translation of in silico predictions to effective clinical therapies remains a significant challenge.

Keywords: Computational Biology, Drug Discovery, Anti-Tuberculosis, Mycobacterium tuberculosis (MTB), immune system, DNA sequences

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2009
Page: [319 - 326]
Pages: 8
DOI: 10.2174/1871526510909030319
Price: $65

Article Metrics

PDF: 6