Natural Products from Annonaceae as Potential Antichagasic Agents. (1st July 2022)

• Record Type: Journal Article
• Authors: Barros de Menezes, Renata Priscila; Fechine Tavares, Josean; Kato, Massuo Jorge; da Rocha Coelho, Francisco Alex; Sousa dos Santos, Airton Lucas; da Franca Rodrigues, Klinger Antonio; Sessions, Zoe L.; Muratov, Eugene N.; Scotti, Luciana; Tullius Scotti, Marcus
• Is Part Of: ChemMedChem. Volume 17:Number 15(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-01
• Subjects: Natural Products -- Annonaceae -- Ligand-based Virtual Screening -- Machine learning -- Antichagasic activity; Pharmaceutical chemistry -- Periodicals; 615.19005
• DOI: 10.1002/cmdc.202200196
• Languages: English
• ISSNs: 1860-7179

Abstract

Abstract: Chagas disease, a neglected tropical disease, is endemic in 21 Latin American countries and particularly prevalent in Brazil. Chagas disease has drawn more attention in recent years due to its expansion into non‐endemic areas. The aim of this work was to computationally identify and experimentally validate the natural products from an Annonaceae family as antichagasic agents. Through the ligand‐based virtual screening, we identified 57 molecules with potential activity against the epimastigote form of T . cruzi . Then, 16 molecules were analyzed in the in vitro study, of which, six molecules displayed previously unknown antiepimastigote activity. We also evaluated these six molecules for trypanocidal activity. We observed that all six molecules have potential activity against the amastigote form, but no molecules were active against the trypomastigote form. 13‐Epicupressic acid seems to be the most promising, as it was predicted as an active compound in the in silico study against the amastigote form of T . cruzi, in addition to having in vitro activity against the epimastigote form. Abstract : Chagas disease is a neglected disease, considered endemic in 21 Latin American countries, especially in Brazil. Using a Annonaceae dataset obtained from SistematX, ligand‐based virtual screening was performed using two different algorithms. After the models have been built the predictions of the molecules were combined to select potential antiepimastigote molecules. The best molecules were evaluated in vitro . Molecules that obtained in vitro activity were evaluated in silico against the amastigote and trypomastigote forms in search of multitarget molecules.