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Review Article

Unveiling the Targets Involved in the Quest of Antileishmanial Leads Using In silico Methods

Author(s): Pone K. Boniface*, Cinthya M. Sano and Ferreira I. Elizabeth

Volume 21, Issue 7, 2020

Page: [681 - 712] Pages: 32

DOI: 10.2174/1389450121666200128112948

Price: $65

Abstract

Background: Leishmaniasis is a neglected tropical disease associated with several clinical manifestations, including cutaneous, mucocutaneous, and visceral forms. As currently available drugs have some limitations (toxicity, resistance, among others), the target-based identification has been an important approach to develop new leads against leishmaniasis. The present study aims to identify targets involved in the pharmacological action of potent antileishmanial compounds.

Methods: The literature information regarding molecular interactions of antileishmanial compounds studied over the past half-decade is discussed. The information was obtained from databases such as Wiley, SciFinder, Science Direct, National Library of Medicine, American Chemical Society, Scientific Electronic Library Online, Scopus, Springer, Google Scholar, Web of Science, etc.

Results: Numerous in vitro antileishmanial compounds showed affinity and selective interactions with enzymes such as arginase, pteridine reductase 1, trypanothione reductase, pyruvate kinase, among others, which are crucial for the survival and virulence of the Leishmania parasite.

Conclusion: The in-silico activity of small molecules (enzymes, proteins, among others) might be used as pharmacological tools to develop candidate compounds for the treatment of leishmaniasis. As some pharmacologically active compounds may act on more than one target, additional studies of the mechanism (s) of action of potent antileishmanial compounds might help to better understand their pharmacological action. Also, the optimization of promising antileishmanial compounds might improve their biological activity.

Keywords: Leishmaniasis, bioinformatics, in silico, pharmacology, drug targets, molecular modelling.

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