Trends in Microbiology
Volume 19, Issue 2, February 2011, Pages 65-74
Journal home page for Trends in Microbiology

Review
Computational databases, pathway and cheminformatics tools for tuberculosis drug discovery

https://doi.org/10.1016/j.tim.2010.10.005Get rights and content

We are witnessing the growing menace of both increasing cases of drug-sensitive and drug-resistant Mycobacterium tuberculosis strains and the challenge to produce the first new tuberculosis (TB) drug in well over 40 years. The TB community, having invested in extensive high-throughput screening efforts, is faced with the question of how to optimally leverage these data to move from a hit to a lead to a clinical candidate and potentially, a new drug. Complementing this approach, yet conducted on a much smaller scale, cheminformatic techniques have been leveraged and are examined in this review. We suggest that these computational approaches should be optimally integrated within a workflow with experimental approaches to accelerate TB drug discovery.

Section snippets

New drugs for tuberculosis

Mycobacterium tuberculosis (Mtb), the causative agent of tuberculosis (TB), infects approximately one-third of the world's population and annually 1.7–1.8 million people die from this disease [1]. The past decade has witnessed the growing menace of both increasing numbers of cases of drug-sensitive and drug-resistant strains and the recognition that fighting this global health pandemic requires a multifaceted research effort from both academia and industry. Infection with drug-sensitive TB can

Databases for TB

We are aware of over 300 000 compounds screened against Mtb in one laboratory alone, so it is likely that several million compounds have been examined cumulatively to date by all groups. It was not until recently that a central location for these screening results was developed. The advantage of collating such data is that it might prevent repetition of screening by different groups, while also allowing large scale analysis of molecular properties of compounds with antitubercular whole cell

Pathway tools and technologies

It has been suggested that an integrated analysis of metabolic pathways, small molecule screening and structural databases will facilitate anti-TB screening efforts [12], which reflects more of a systems biology (see Glossary) and computer aided drug discovery approach. Systems biology approaches based on predictive networks will be increasingly developed at the interface of cheminformatics and bioinformatics, with applications for target selection and discovery 13, 14 alongside other target

Applications of systems biology to TB

One example of TB systems biology research is a study using gene expression data to identify stress response networks before and after treatment with different drugs [17]. The research combined the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCyc metabolic pathway databases with previously published gene expression data and a k-shortest path algorithm. It was found that gene expression networks for isoniazid treatment indicated a generic stress response. This type of approach could

Computational cheminformatic tools and their uses

Computational approaches applied to TB have predominantly implemented standard commercially available cheminformatic methods, as will be described in the following section. These methods have been generally used by specialists focused on a single target or series of compounds, and rarely in combination with other computational tools. Owing to space limitations, we have focused our analysis of cheminformatics tools used in TB research within the past 5 years.

Gap analysis for computational methods in TB drug discovery

The computational methods previously described are widely used in workflows by many project teams in the pharmaceutical industry. We found several gaps when we looked at how computational methods could be used in TB drug discovery (Figure 1) compared with the various reported efforts to date. Beginning with the recent popularity of high-throughput, whole-cell phenotypic screening of large commercial libraries, we noted limited use of filtering of the library input or resulting hit lists for

Conclusions and future perspectives

In the TB community, there appears to be a disparity between the generation and utilization of computational models and the entire drug discovery process. TB models are not well disseminated, shared or even reused, and serve an isolated purpose for publication or comprehending a very limited structure–activity relation. At present, these computational models are in the hands of cheminformatics experts, and insufficient efforts have been made in their dissemination on publicly accessible

Conflicts of interest

S.E. is a consultant for Collaborative Drug Discovery. The other authors have no conflicts of interest.

Acknowledgments

S.E. acknowledges Dr Barry A. Bunin and colleagues for developing the CDD TB database as well as the many TB research collaborators. The CDD TB database along with introductory training was provided freely to Mtb researchers until the end of October 2010 thanks to funding from the Bill and Melinda Gates Foundation (Grant number 49852 Collaborative Drug Discovery for TB Through a Novel Database of SAR Data Optimized to Promote Data Archiving and Sharing). The project described was supported by

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    Present address: Medicinal Chemistry, Institut Pasteur Korea, Bundang-gu, Seongnam-si, Gyeonggi-do, Korea.

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