Crawling into the Unknown: Unveiling Novel Psychoactive Substances (NPSs) in the Post Pandemic Era Utilizing the...

Novel Psychoactive Substances (NPSs) and other psychoactive or misuse-relevant compounds present a persistent global public health threat, underscoring the critical need for real-time crawling and monitoring tools to investigate and provide valuable information assisting in the management and preparedness against that dynamic and evolving threat. This study utilized the NPSfinder^® web crawler to analyze NPS trends during the post-pandemic period, with data collected from selected highly trafficked psychonaut websites continuously monitored between January and September 2023. Following a comprehensive filtering process of screening, assessment, and evaluation, the dataset provided insights into the availability and characteristics of recently reported NPSs. Three hundred and sixty-eight molecules were analyzed, of which 158 were newly captured NPSs, suitable for inclusion in the NPSfinder^® database. Those substances were classified based on their chemical structure, pharmacological mechanism of action, therapeutic indication, if any, and abuse liability. The main NPS classes identified were natural origin/herbal substances (25; 16%), cannabinoids (23; 15%), prescribed medications (23; 15%), synthetic opioids (20; 13%), phenethylamines (14; 9%), cathinones (11; 7%), and anabolic substances (8; 5%). The findings highlight ongoing diversification in substances identified across selected online sources during the post-pandemic period. The study benefits from a timely focus, a substantial and systematically curated dataset, and the application of a continuous web-crawling methodology enabling near real-time detection of emerging substances. The integration of pharmacological and chemical classification further enhances the interpretability and potential clinical relevance of the findings. However, results should be interpreted cautiously. The reliance on predominantly English-language, open-web sources may introduce selection bias and limit geographic generalizability, and the descriptive design does not allow inference regarding prevalence, user behavior, or causal trends. Within these constraints, web-crawling approaches may serve as valuable complementary tools to established early warning systems, supporting early signal detection and informing future toxicological and public health responses.