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 (R 2) = 0.914). The small number of samples was divided into seven training sets (n = 17) and seven test sets (n = 6). The average R 2 pred = 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.

SMILES-based 2D-QSAR and similarity search for identification of potential new scaffolds for development of SARS-CoV-2 MPRO inhibitors

“…According to Roy and Ambure [ 49 ], it is difficult to develop robust and predictive models from small datasets because a significant amount of information related to the dependent and independent variables can be lost due to the removal of samples to compose the test set. Therefore, in this study, an alternative approach based on studies conducted by one of the authors was adopted [ 50 – 52 ]: the dataset was randomly divided into 100 different test sets, starting from the initial model obtained (defined as the auxiliary model), with the same number of compounds for each test set (eight derivatives, 25% of the dataset). The average values and standard deviations of external validation metrics (also in Table 1 ) parameters were calculated for each test set.…”

In Silico Infrared Spectroscopy as a Benchmark for Identifying Seized Samples Suspected of Being N-Ethylpentylone

“…LNO and Q 2 LOO must be minimal [70]. For external validation, the computationally obtained datasets were divided into submodels by applying the Kennard-Stone algorithm [71], available in Dataset Division 1.2 software (Jadavpur, Kolkata, West Bengal, India).…”

AddictedChem: A Data-Driven Integrated Platform for New Psychoactive Substance Identification

“…The above efforts to identify and predict NPS are costand time-consuming. From a computational viewpoint, a quantitative structure-activity relationship model was used to verify the analogues of amphetamine cathinone [20]. We still need more computational tools to produce low-cost, rapid predictions of NPS and to pave the way for the earlier identification of emerging drugs.…”