A QSAR-based application for the prediction of lethal blood concentration of new psychoactive substances

Abstract

The rapid development and introduction of new psychoactive substances (NPS) into illegal markets present an enormous challenge for forensic toxicologists, as there is limited knowledge about their toxicity in humans. To strengthen forensic interpretation of NPS intoxication cases, we have developed a predictive model for estimating human lethal blood concentrations (LBC) of various NPS. This quantitative structure-activity relationship (QSAR) model focuses on opioids, designer benzodiazepines, synthetic cathinones, synthetic cannabinoids, and phenethylamines. Utilising linear regression and multilayer perceptron algorithms, the models was trained using data from the existing literature. A toxicological significance-based approach have been applied to refine the selection of training data. The model demonstrated satisfactory performance metrics through cross-validation (R ≈ 0.8, MAE ≈ 0.6) and comparison with experimental data (R ≈ 0.9). A Python-based web application have been developed to facilite the use of the created model in predicting LBC of NPS. Despite the model's reliability, limitations due to data availability, quality and the complexities of post-mortem toxicology mean that its predictions should be interpreted with caution.