Literature DB >> 33221013

Antidepressant drugs as emerging contaminants: Occurrence in urban and non-urban waters and analytical methods for their detection.

Carlos Castillo-Zacarías1, Mario E Barocio1, Enrique Hidalgo-Vázquez1, Juan Eduardo Sosa-Hernández1, Lizeth Parra-Arroyo1, Itzel Y López-Pacheco1, Damià Barceló2, Hafiz N M Iqbal3, Roberto Parra-Saldívar4.   

Abstract

Antidepressants are drugs with a direct action on the brain's biochemistry through their interaction with the neurotransmitters, such as dopamine, norepinephrine, and serotonin. The increasing worldwide contamination from these drugs may be witnessed through their increasing presence in the urban water cycle. Furthermore, their occurrence has been detected in non-urban water, such as rivers and oceans. Some endemic aquatic animals, such as certain fish and mollusks, have bioaccumulated different antidepressant drugs in their tissues. This problem will increase in the years to come because the present COVID-19 pandemic has increased the general worldwide occurrence of depression and anxiety, triggering the consumption of antidepressants and, consequently, their presence in the environment. This work provides information on the occurrence of the most administrated antidepressants in urban waters, wastewater treatment plants, rivers, and oceans. Furthermore, it provides an overview of the analytical approaches currently used to detect each antidepressant presented. Finally, the ecotoxicological effect of antidepressants on several in vivo models are listed. Considering the information provided in this review, there is an urgent need to test the presence of antidepressant members of the MAOI and TCA groups. Furthermore, incorporating new degradation/immobilization technologies in WWTPs will be useful to stop the increasing occurrence of these drugs in the environment.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Antidepressants; Emerging contaminants; Liquid chromatography; Mass spectrometry; Toxicity; Wastewater

Mesh:

Substances:

Year:  2020        PMID: 33221013     DOI: 10.1016/j.scitotenv.2020.143722

Source DB:  PubMed          Journal:  Sci Total Environ        ISSN: 0048-9697            Impact factor:   7.963


  9 in total

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5.  Computational Modelling and Sustainable Synthesis of a Highly Selective Electrochemical MIP-Based Sensor for Citalopram Detection.

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  9 in total

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