Computational and Natural Product Strategies in Anti-leishmanial Drug Discovery.

Abstract

Protozoan parasites from the genus Leishmania cause several neglected tropical diseases globally, which radically affect humans with a wide array of clinical manifestations ranging from cutaneous lesions to potentially fatal disease progression that may lead to death. Conventional methods of treating leishmaniasis using synthetic drugs are failing in major ways due to toxicity, high prices, the need for invasive routes of administration, and increasing reports of drug resistance against currently available medications. This review highlights the promising opportunity of combining two separate fields of natural product chemistry and computer-based drug discovery to develop new treatments for leishmaniasis. The present article provides a critical synthesis of data from both natural product bioactivity catalogues and methodology reviews to evaluate the translational feasibility of selected natural products with respect to their selectivity index and tiered classification for in vivo validation purposes; the identification of priority preclinical natural products based on the characterization of the most promising compound-target pairings will also be included in this article. This review also identifies numerous ongoing methodological limitations, including but not limited to: poor quality of external validation of quantitative structure-activity relationship (QSAR) modelling, activity data not being based on species-specific criteria, and difficulty linking structureactivity relationship (SAR) information with formulation types; and finally, this review provides evidence-based recommendations for enhancing the anti-leishmanial natural product development pipeline.