In silico Risk Assessment Studies of New Psychoactive Substances Derived from Amphetamines and Cathinones

Abstract

The amount and variety of new psychoactive substances (NPS) are expanding, and there are difficulties in assessing their risks. In this regard, in silico methods are potentially useful to predict NPS properties faster and at a lower cost. In this work a quantitative structure-activity relationship (QSAR) model was used to verify the risk of drugs derived from amphetamines and cathinones. A dataset of 26 derivatives with in vitro affinity for norepinephrine transporter (NET) was selected. To ensure reproducibility of the results, only geometric molecular descriptors (AM1 (Austin model 1) level) obtained from the platform ChemDes and ordered predictors selection (OPS) were used. The model presents good internal statistics (n = 23; coefficient of determination (R2) = 0.914). The small number of samples was divided into seven training sets (n = 17) and seven test sets (n = 6). The average R2pred = 0.754 showed that the model has good predictive capacity. Based on the tests, this model can accurately predict the risk range of three previously selected derivatives: methedrone (low), ethcathinone (medium), and methamphetamine (high), even when only data referring to NET are employed. We used these data to create a simple free program in Java that focuses on the risk assessment of recreational drugs belonging to this class of compounds.