Drug development is urgently needed to combat infectious diseases in the developing world, such as malaria, tuberculosis, African trypanosomiasis, Chagas disease, leishmaniasis, onchocerciasis, lymphatic filariasis and schistosomiasis. Even in cases where drugs for such diseases are available, their use is often limited by factors including high cost, low efficacy, toxicity issues and the emergence of resistance1
Despite this pressing need, the development of new therapeutics to combat these diseases has been inadequate for reasons ranging from a limited understanding of targets that might be amenable to drug development, to anticipated low return on investment. However, recent trends are encouraging1
. Philanthropic organizations have helped to kindle interest in tropical disease research, and the advent of Public Private Partnerships has stimulated collaborations between academia and the pharmaceutical industry. On the basic science front, the genome sequences of the disease-causing pathogens are now becoming available, and new technologies are aiding the evaluation of gene function, essentiality and suitability for drug development.
To facilitate the assimilation, integration and mining of data emerging from such studies, and the identification and prioritization of candidate drug targets, we have established a global network of public and private sector partners, to develop an open-access database for tropical disease pathogens: the TDR Targets database (http://tdrtargets.org
). This resource seeks to bring together data and annotation emerging from genome sequencing and functional genomics projects, protein structural data, manual curation of inhibitors and targets, and information on target essentiality and druggability. We do not propose that this (or any) in silico
strategy will be able to identify targets for successful drug development through computational methods alone. Rather, our goal is to facilitate the translation of biological questions into a computationally tractable format, enabling individual researchers to query the database, scan the vast quantity of genomic-scale datasets that are now available, filter out and prioritize a short list of candidate targets suitable for further investigation.
As of July 2008, the TDR Targets database provides resources for the exploration of drug targets in the tuberculosis pathogen Mycobacterium tuberculosis
, the leprosy pathogen Mycobacterium leprae
, the malaria parasites Plasmodium falciparum
and P. vivax
; the intracellular protozoan parasite Toxoplasma gondii
; the filariasis helminth pathogen Brugia malayi
and its intracellular symbiont bacterium Wolbachia bancrofti
; and the kinetoplastid parasites Leishmania major, Trypanosoma brucei
, and T. cruzi
, responsible for kala-azar and other forms of leishmaniasis, sleeping sickness, and Chagas disease, respectively (see and Supplementary Methods
Selected lines of evidence accessible via the TDR Targets database
Key features of the TDR Targets database include:
- Incorporation of a wide range of genetic, genomic, biochemical, structural and pharmacological data from diverse sources.
- Computational assessment of target druggability and compound desirability.
- Orthology-based inference of relevant information for genes lacking direct functional evidence in particular species of interest.
- Integration of these large-scale datasets with manually curated information on genetic and chemical validation, collected from the primary literature and community surveys.
- The ability to weight results so as to assemble a ranked list of candidate targets. These results may be saved for future reference, modified as new information becomes available, downloaded for integration with locally held datasets, or posted for others to view, modify, or download.
To date, virtually all approved anti-infective drugs have been discovered and developed via non
-target-based approaches, i.e., without optimization for specific targets. Notable exceptions include alpha-difluoromethylornithine (DFMO), which inhibits ornithine decarboxylase, for African sleeping sickness2
; HIV protease inhibitors3
; and zanamivir and oseltamivir, which inhibit the neuraminidase enzymes of influenza virus4
. The ability to rapidly and effectively locate, capture, integrate, query and retrieve genomic-scale datasets should greatly expedite target-based drug discovery efforts against tropical infectious diseases. In this article, we describe the characteristics of the TDR Targets database and discuss how we have approached the associated challenges for data integration and application.