Discovery of Alternative Chemotherapy Options for Leishmaniasis through Computational Studies of Asteraceae

Abstract

Leishmaniasis is a complex disease caused by over 20Leishmania species that primarily affects populations with poor socioeconomic conditions. Currently available drugs for treating leishmaniasis include amphotericin B, paromomycin, and pentavalent antimonials, which have been associated with several limitations, such as low efficacy, the development of drug resistance, and high toxicity. Natural products are an interesting source of new drug candidates. The Asteraceae family includes more than 23 000 species worldwide. Secondary metabolites that can be found in species from this family have been widely explored as potential new treatments for leishmaniasis. Recently, computational tools have become more popular in medicinal chemistry to establish experimental designs, identify new drugs, and compare the molecular structures and activities of novel compounds. Herein, we review various studies that have used computational tools to examine various compounds identified in the Asteraceae family in the search for potential drug candidates against Leishmania.

Identification of Kaurane-Type Diterpenes as Inhibitors of Leishmania Pteridine Reductase I

“…Computational studies using natural products have been reported in the continuous search for new leishmanicidal drugs or lead compounds. In particular, machine learning and molecular docking calculations have been used to identify new structures with potential anti- Leishmania activities, based on secondary metabolites found in Asteraceae species [ 17 , 18 ], especially sesquiterpenoids [ 19 , 20 ], triterpenes [ 21 ], and phytosterols [ 22 ].…”

Development of Novel Anti-Leishmanials: The Case for Structure-Based Approaches

“…AI in anti-leishmanial drug discovery is still an emerging field and at an early phase that certainly requires extensive exploration at various levels such as predicting the protein structure, studying the interaction with inhibitors, projecting the favorable structure-activity relationship, mining the possible pharmacological features, suggesting the facile chemical synthesis processes, modeling the future cost analysis for actual drug discoveries, prediction of clinical trial failure, listing the adverse effects of the future drug, suggesting post-treatment prognosis situations, and advising of alternative medical treatments. AI in leishmaniasis research has contributed to critical aspects including infection diagnosis from microscopic images, peptide-fingerprints-based prediction and modeling of enzyme classes, pyruvate kinase inhibitors designing, prognosis features of unresponsive patients, and alternative chemotherapy prediction [56][57][58][59][60].…”

Artificial intelligence and machine learning in Leishmania drug discovery

“…Of these, nine research articles, ten reviews, and one editorial from 18 sources, the journal Current Topics in Medicinal Chemistry was the only one that submitted more than one document (three documents). Finally, all the documents found, after excluding the repeated ones, were as follows: [4,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. After reading the abstracts, the articles presented in Table 1 were selected.…”