In silico genome‐scale modeling and analysis for identifying anti‐tubercular drug targets

Abstract

Mycobacterium tuberculosis is the deadly pathogen responsible for causing tuberculosis in humans, continuing to infect and kill millions of people globally. Despite the availability of a number of anti-tuberculosis drugs and advances in high-throughput drug discovery technology there is an urgent need for designing novel anti-tubercular treatments due to growing parasite resistance and compromised immune systems in some patients. Therefore, it is highly necessary to develop systematic approaches that can facilitate the drug discovery by identification of drug targets in effective and efficient ways. In this sense, with the availability of whole genome sequence, application of genome-scale modeling is becoming increasingly important for deriving rational drug target identification. This approach is indeed powerful in unraveling the metabolic behavior of pathogens and helps in identifying most relevant metabolites/genes as drug targets, which are experimentally testable. Herein, we present a review on the application of genome-scale modeling and analysis in the context of identification of anti-tubercular drug targets. Drug Dev Res 72:121-129, 2011.

Metabolic flux analyses of Pseudomonas aeruginosa cystic fibrosis isolates

“…FBA investigations of pathogenic organisms have been used to search for novel drug targets and have pointed to potential metabolic targets not affected by current therapeutics, such as amino acid production or fatty acid metabolism [53][54][55][56][57][58][59][60]. Oberhardt and colleagues have constructed a genome-based model of metabolism and transport in P. aeruginosa [61], and used flux balance analysis (FBA) with transcript data from two CF clinical strains to investigate P. aeruginosa's metabolic capabilities and potential metabolic changes during prolonged infection [62].…”

Fuzzy optimization for identifying anti-cancer targets with few side effects in constraint-based models of head and neck cancer

“…The efficient identification of drug targets and their side effects is a key challenge in drug discovery and development [5][6][7][8][9][10][11][12][13][14][15]. Drug side effects cause substantial clinical and economic burdens.…”

From drugs to targets: Reverse engineering the virtual screening process on a proteomic scale

“…The process, however, is costly and time-consuming and can fail for several reasons, such as the impossibility of obtaining a resistance strain, or the difficulty in correctly identifying the relevant resistance causing mutation (Farha and Brown, 2016). In this context, several bioinformatic approaches have been proposed and applied in order to aid researchers in the identification of an active compound's target (Hasan et al, 2006;Shanmugam and Natarajan, 2010;Lee et al, 2011;Rahman et al, 2014;Mondal et al, 2015;Neelapu et al, 2015;Cloete et al, 2016;Defelipe et al, 2016;Kaur et al, 2017;Mohana and Venugopal, 2017;Wadood et al, 2017;Oany et al, 2018;Ramos et al, 2018;Uddin and Jamil, 2018;Shuvo et al, 2019;Farfán-López et al, 2020;Karim et al, 2020;Lobo-Silva et al, 2020;Aslam et al, 2021;Chakkyarath et al, 2021;Serral et al, 2022).…”