Andrographolide: A Diterpenoid from Cymbopogon schoenanthus Identified as a New Hit Compound against Trypanosoma cruzi Using Machine Learning and Experimental Approaches

Abstract

American Trypanosomiasis, also known as Chagas disease, is caused by the protozoan Trypanosoma cruzi and exhibits limited options for treatment. Natural products offer various structurally complex metabolites with biological activities, including those with anti-T. cruzi potential. The discovery and development of prototypes based on natural products frequently display multiple phases that could be facilitated by machine learning techniques to provide a fast and efficient method for selecting new hit candidates. Using Random Forest and k-Nearest Neighbors, two models were constructed to predict the biological activity of natural products from plants against intracellular amastigotes of T. cruzi. The diterpenoid andrographolide was identified from a virtual screening as a promising hit compound. Hereafter, it was isolated from Cymbopogon schoenanthus and chemically characterized by spectral data analysis. Andrographolide was evaluated against trypomastigote and amastigote forms of T. cruzi, showing IC 50 values of 29.4 and 2.9 μM, respectively, while the standard drug benznidazole displayed IC 50 values of 17.7 and 5.0 μM, respectively. Additionally, the isolated compound exhibited a reduced cytotoxicity (CC 50 = 92.8 μM) against mammalian cells and afforded a selectivity index (SI) of 32, similar to that of benznidazole (SI = 39). From the in silico analyses, we can conclude that andrographolide fulfills many requirements implemented by DNDi to be a hit compound. Therefore, this work successfully obtained machine learning models capable of predicting the activity of compounds against intracellular forms of T. cruzi.

Editorial: Machine Learning in Bio-cheminformatics

“…( b ) virtual screening and drug design . Some methods, algorithms, and functional tools were constructed to facilitate the application or improve the performance of the classic virtual screening strategy. − Machine learning methods were also adopted in this collection to identify new hit compounds, discover promising leads for cholestasis, interpret QSAR models, and learn molecular representations. − DiStefano et al and Mao et al conducted research on toxicity prediction and antiviral drug design, respectively. Moreover, ML was also applied to explore the pharmaceutical properties of diverse drug candidates. − A novel knowledge base for nonalcoholic fatty liver disease was developed .…”