**1. Introduction**

252 Current Topics in Tropical Medicine

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Leishmaniasis is a complex disease caused by several species of the *Leishmania* genus ranging in severity from cutaneous and mucocutaneous lesions to the chronic visceral form that if untreated adequately can cause death. It has a worldwide distribution in 98 countries and 85 out of 98 are developing or poor countries. One of the main problems in leishmaniasis is the limited number of drug options along with the adverse effects they can cause including death (Ahasan., et al. 1996; Sundar & Chakravarty 2010; Oliveira., et al. 2011). In addition, there are reports of treatment failures due to increased parasite resistance to the first drug of choice, the antimonials (Faraut-Gambarelli., et al. 1997; Goyeneche-Patino., et al. 2008). Second-choice drugs, such as amphotericin B, pentamidine, paromomycin, and more recently, miltefosine, have also toxic effects that require hospital management (Maltezou 2008; Oliveira., et al. 2011). Miltefosine, the only oral administered drug for leishmaniasis, has not been tested in many *Leishmania* species. Recently, a central nervous system toxicity was reported for liposomal amphotericin B therapy used to treat cutaneous leishmaniasis (Glasser & Murray 2011).

In the search for new drug targets in *Leishmania*, a group of proteins have been proposed based mainly on their known function, the expression level, and localization, or because they are involved in important metabolic processes in the parasite. Topoisomerases (Das., et al. 2008), kinases (de Azevedo & Soares 2009), proteins localized or targeted to lysosomes (Carrero-Lerida., et al. 2009) are some potential *Leishmania* drug targets. However, none of these protein targets have been used to successfully develop new drugs that can substitute the existing therapies.

Currently, the massive genome sequencing of many medically important microorganisms together with protein structure and drug databases and the development of new computational tools, will allow molecular targets and new drugs to be searched in a more rigorous manner. Three *Leishmania* genomes, *L. major*, *L. infantum* and *L. braziliensis*

Current Advances in Computational Strategies for Drug Discovery in Leishmaniasis 255

phenotypic effects of deletion of particular genes have been shown (Giaever., et al. 2002) and more recently the study of genetic interactions on a large scale (Costanzo., et al. 2010). This has been used to elucidate redundancy and possibly some synergistic effects among genes. Therefore, it is possible to find orthologs in the organism of interest that could be essential by comparing its sequences against the list of essential genes in model organisms. The Database of Essential Genes (http://tubic.tju.edu.cn/deg/) (Zhang & Lin 2009) provides information of essential genes in prokaryotes and eukaryotes, and it is also possible to do a BLAST search with the protein of interest. This resource is useful for an exploratory search of essentiality of a particular protein. Another important resource, for drug target data, is the DrugBank database (http://www.drugbank.ca/) (Knox., et al. 2011), which can be used to extract drug-target interactions along with additional pharmacological data. The same strategy can be employed in this case; with the advantage that the homology search will also return possible drug candidates that can be tested on the protein found to have homology to

This methodology has been applied in *Pseudomonas aeruginosa* (Sakharkar., et al. 2004) with the aim of detecting new drug targets, given this bacterium is an important problem in nosocomial settings due to the rapid generation of resistance. In *Leishmania,* drug targets can be also identified by this approach. Tools like BLAST or PSI-BLAST can be employed, with PSI-BLAST being more sensitive for detecting distant relationships among proteins (Altschul., et al. 1997). However, some false positives still can occur due to alignments that are optimal according to the algorithm but not biologically meaningful. The E value helps to detect those alignments that are significant. As an example, running a PSI-BLAST search with the *Leishmania major* proteome against the DrugBank database, one can find among the potential *Leishmania* orthologs to known targets, the protein *LmjF36.2430,* which is similar to the sterol 14- alpha demethylase in fungi. Drugs such as miconazole are known inhibitors of this enzyme. Interestingly, the protein *LmjF19.0450* belongs to the group of protein kinases conserved in other *Leishmania* species; it is constitutively expressed and has significant similarity to other kinase targets in cancer. These are simple cases of how a homology search can generate a list of potential drug targets using existing genomic data. The main advantage of this methodology is that it offers a quick overview of potential targets and second use of drugs. In addition, the STITCH 2 database (http://stitch.embl.de/) (Kuhn., et al. 2010) compiles known and predicted drug-target relationships jointly with biological

Despite its simplicity, the homology search strategy has some caveats. Proteins inside the cell perform specific functions depending on their interactions, and these interactions can vary between species. Even if sequences are highly related, pathway conservation is not necessarily present. In addition, temporal regulation is important, as not all the interactions are active at the same time, which can further complicate the analysis. These problems highlight the importance of detecting targets by incorporating more detailed information

In order to better understand complex pathogens such as *Leishmania* and to improve the efficiency of the drug discovery process, it is crucial to gain deeper knowledge about how protein interactions are established and how these interactions are regulated. This is a central issue for a more accurate definition of essentiality and biological robustness. These interactions can be described as a *network*, a representation commonly used to describe

**2.2 Selection of targets by topological analysis of protein networks** 

the target in DrugBank.

information about targets in a network-based view.

about the molecular interactions.

(Peacock., et al. 2007) have been sequenced and annotated and a fourth species, *L. mexicana* and some *L. major* strains are in the process of being sequenced (GeneDB, http://www.genedb.org; University of Washington Genome Sequencing Center, http://genome.wustl.edu/gsc/gschmpg.html). The availability of these genomes and the annotated proteins can be used in a rational manner to predict novel drug targets and provide a basis to develop new drugs.

The computational prediction of drugs, in addition to the evaluation of drugs already synthesized and used in other diseases, must be coupled with automated in vitro assessment methodologies of these compounds. In this sense and in the case of *Leishmania*, the use of GFP (Varela., et al. 2009) or luciferase transgenic parasites (Lang., et al. 2005) coupled with techniques such as flow cytometry or fluorometry can be used to rapidly evaluate potential anti-leishmanial drugs. The WHO program for training in tropical diseases research has created a network based on reporter gene technology to foster the process of drug search not only against leishmaniasis but also against other diseases with limited therapeutic options.
