Computational predictive toxicology modeling for assessing human health risks of novel psychoactive substances (NPS): a case study

Abstract

The study aimed to identify the increasing health challenges posed by the rapid emergence of novel psychoactive substances (NPS) compounds. The research employed a multifaceted approach, integrating advanced computational techniques such as the Maestro Schrödinger 12.8 software suite, admeSAR, Protox II, Guasar toxicity modeling and molecular docking. A diverse dataset comprising chemical structures, hysicochemical properties, and toxicity data for known NPS compounds was curated and used to develop predictive models for various adverse health effects, including neurotoxicity, cardiotoxicity, hepatotoxicity, and reproductive toxicity. Key findings from the study revealed significant correlations between chemical structural features and toxicological endpoints, enabling the identification of structural alerts and toxicophores associated with NPS-induced adverse effects. Moreover, the study investigated the impact of metabolic pathways and bioactivation processes on NPS toxicity, providing insights into the potential formation of reactive metabolites and their contribution to adverse health outcomes. Overall, this research contributes to advancing the field of predictive toxicology and provides valuable tools for assessing the health risks associated with NPS consumption. The findings underscore the importance of integrating computational approaches into regulatory decision-making processes and public health policies to effectively mitigate the adverse effects of NPS on individuals and communities.