J Korean Soc Environ Anal Search


Journal of the Korean Society for Environmental Analysis 2018;21(4):243-251.
Qualitative Analysis for Metabolites of Pharmaceuticals Formed in Daphnia magna and Gammarus pulex Using Liquid Chromatogram-High Resolution Mass Spectrometry (LC-HRMS)
LC-HRMS를 이용한 Daphnia magna 및 Gammarus pulex 생체내 의약품 대사체 정성분석
Pharmaceuticals in wastewater effluents have been recognized as emerging pollutants threatening freshwater organisms. To extend understanding for bioaccumulation and toxicity in those organisms, information on biotransformation products (or metabolites) and their metabolic pathway are crucial. The aim of the present study is to identify and elucidate metabolites of pharmaceuticals formed in exposed organisms using suspect and nontarget screening approach using LC-HRMS. As the target pharmaceuticals, carbamazepine, ketoprofen, metoprolol, propranolol, and verapamil were selected whereas Daphnia magna and Gammarus pulex were used as test organisms. After 24h exposure, metabolites formed in the organisms were identified using LC-HRMS. The structures of metabolites were elucidated via analysis of MS/MS fragment pattern and the comparison with fragment database. As the results, a total of 10 metabolites were identified for 5 parent compounds (C253/C356 for carbamazepine, K211 for ketoprofen, M256 for metoprolol, P218/P276/P306 for propranolol, V196/V291/ V441 for verapamil). Among them, the presence of C253 and V291 was confirmed using standard materials. Most of the identified metabolites were formed through oxidative reactions such as hydroxylation, N-demethylation, and dealkylation. Cysteine conjugation (phase II reaction) metabolite (C356) for carbamazepine was found in daphnia. The metabolic pathway of verapamil showed similar metabolic pathways and metabolic pathways for both species. Although the toxicological information on the identified metabolites could not be confirmed, the molecular structure information of the proposed metabolites can be used for future evaluation and prediction of toxicity.
Key Words: Pharmaceuticals, Metabolite, Structure elucidation, LC-HRMS, Suspect and nontarget screening


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