Predicting the potential risks posed by antidepressants as emerging contaminants in fish based on network pharmacological analysis.

This study conducted a network pharmacology-based analysis to simultaneously discern a broad spectrum of potential environmental risks and health effects of antidepressants, a common class of pharmaceutical emerging contaminants (PECs) possessing a complex pharmacological profile, and in silico predict the adverse phenotypes potentially occurring in fish associated with exposure to antidepressants and their mixtures under realistic exposure scenarios. Results showed that 24 of the included 39 antidepressants had been detected worldwide in water environment across 50 countries. Using the environmentally realistic exposure scenario for China as an example, the predicted blood concentrations of antidepressant residues that were generated based on the Fish Plasma Model ranged from 37.89 (Alprazolam) to 16,772.05 (Sertraline) ng/L in exposed fish. Hazard-based bioactivity network without regard to concentration data was composed of 148 potential targets and 701 antidepressant-target interactions. After filtering each antidepressant-target interaction node using the predicted drug concentrations in the blood of fish under realistic exposure scenarios in China, an environmental risk-based network was refined and showed that 11 targets, including muscarinic acetylcholine receptor M1, alpha-2B adrenergic receptor, serotonin 2 A receptor, etc. might be modulated by antidepressants at concentrations equal to or below the environmental exposure levels and their mixtures in fish. Environmentally relevant concentrations of antidepressants in water samples from China might perturb the behavior, stress response, phototaxis, and development in exposed fish.

OCCURRENCE OF METFORMIN IN ENVIRONMENTAL WATER SAMPLES AND COMPARISON WITH CONSUMPTION DATA FROM A SURROUNDING HOSPITAL OVER 5 YEARS: A RETROSPECTIVE CASE STUDY.

Context: The environmental occurrence of metformin has been frequently world-widely reported. Despite the diabetes susceptibility in the Chinese population, the studies on occurrence of metformin as environment disruptor in China are insufficient. Objective: To determine the occurrence trends and possible environmental pollution sources of metformin as an emerging micropollutant. Methods: High-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system was used to detect the metformin levels in water samples collected from the Xi'an city Moat, China once a year from 2017 to 2021. Correlations among the metformin levels in moat water, in surrounding hospital wastewater, and hospital metformin consumption data were assessed using Pearson, Spearman and Kendall's tau-b correlation coefficients. Results: Occurrence of metformin was found in Xi'an city Moat water with levels in the range of 304-793 ng/L. Significant correlations were found between the metformin levels in city moat water and the total (or outpatient) metformin utilization data of the hospital. Conclusion: Data suggested the potential environmental issues posed by metformin in Xi'an city in China. The metformin consumption volume in the surrounding hospitals, especially at the outpatient services, could be used to predict the metformin concentrations in the moat water.