Predicting toxicity of triazole fungicide: a molecular-based insight into triadimefon and triadimenol

Abstract

Background: Triazole fungicides, such as triadimefon (TF) and its stereoisomeric metabolite, triadimenol (TN), remain cornerstone agrochemicals. However, their stereochemistry may affect shape reactivity, intermolecular recognition, and ecotoxicity in different ways, with potential impacts on non-target species. Objective: This study employs computational methods to describe structural and electronic descriptors, quantify supramolecular interactions, and predict toxicological endpoints in silico for TF and its individual stereoisomer, TN. Methods: Density functional theory calculations were employed to optimize the molecular geometries and calculate reactivity descriptors at the M06-2×/6–311++G(d,p) level of theory. The intermolecular interactions were compared using Hirshfeld surface analysis, quantum theory of atoms in molecules, and natural bond orbital analysis. Chemical reactivity descriptors were calculated to assess reactive sites. Pharmacophore modeling was performed to verify the potential of the six isomers to interact with the lanosterol 14α-demethylase target. Toxicity was predicted using computational models available online. Results: Differences were found between the electronic properties and intermolecular interactions of TF and TN, due to the presence of –OH or –CO– functional groups. These variations influence the molecular electrostatic potential, reactivity descriptors, and interaction energies, affecting their biological behavior. The results of the pharmacophore modeling suggest that only the 1R-TF, 1S,2R-TN, and 1S,2S-TN isomers demonstrated potential for interaction with the lanosterol 14α- demethylase target. Toxicity predictions revealed that some triadimenol stereoisomers exhibit a greater potential for adverse effects on non-target organisms when compared to TF. Conclusion: The findings highlight the crucial role of stereochemistry in modulating the chemical behavior and toxicity of triazole fungicides. Additionally, the use of in silico methods proves effective in predicting the ecotoxicological profiles of such compounds and supports the development of safer and more sustainable agrochemical practices.