Identifying metabolites of new psychoactive substances using in silico prediction tools
Romain Pelletier, Dina Nahle, Mareme Sarr, Alexis Bourdais, Isabelle Morel, Brendan Le Daré, Thomas Gicquel
Abstract
New psychoactive substances (NPS) pose an increasing challenge for clinical and forensic toxicology due to the initial lack of analytical and metabolic data. This study evaluates the performance of four in silico prediction tools (GLORYx, BioTransformer 3.0, SyGMa, and MetaTrans) in predicting the metabolism of seven NPS from five major chemical families (cathinones, synthetic cannabinoids, synthetic opioids, designer benzodiazepines, and dissociative anesthetics). The predicted metabolites were compared to those reported in the literature. The results revealed that SyGMa was the most exhaustive tool, predicting 437 metabolites, whereas MetaTrans predicted the fewest (61). GLORYx uniquely identified glutathione conjugation, while BioTransformer was particularly effective in predicting phase I reactions. However, no single tool provided complete predictions. Combining the four tools enabled the identification of several key biomarkers consistent with experimental data, such as m/z 238.1443 for eutylone and m/z 381.1926 for etonitazepipne. These findings highlight the need for integrated approaches to optimize metabolite prediction. Future advancements in artificial intelligence-based models could reduce false positives and enhance the accuracy of predictions, thus reinforcing the role of in silico tools in toxicological investigations.