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Computational modeling (in silico) methods combined with ecotoxicological experiments (in vivo) to predict the environmental risks of an antihistamine drug (loratadine)

Authors :
Roveri, Vinicius
Guimarães, Luciana Lopes
Correia, Alberto Teodorico
Publication Year :
2023
Publisher :
Taylor & Francis, 2023.

Abstract

This study employed computational modeling (in silico) methods, combined with ecotoxicological experiments (in vivo) to predict the persistence/biodegradability, bioaccumulation, mobility, and ecological risks of an antihistamine drug (Loratadine: LOR) in the aquatic compartment. To achieve these goals, four endpoints of the LOR were obtained from different open-source computational tools, namely: (i) “STP total removal”; (ii) Predicted ready biodegradability; (iii) Octanol-water partition coefficient (KOW); and (iv) Soil organic adsorption coefficient (KOC). Moreover, acute and chronic, ecotoxicological assays using non-target freshwater organisms of different trophic levels (namely, algae Pseudokirchneriella subcapitata; microcrustaceans Daphnia similis and Ceriodaphnia dubia; and fish Danio rerio), were used to predict the ecological risks of LOR. The main results showed that LOR: (i) is considered persistent (after a weight-of-evidence assessment) and highly resistant to biodegradation; (ii) is hydrophobic (LOG KOW = 5.20), immobile (LOG KOC = 5.63), and thus, it can potentially bioaccumulate and/or can cause numerous deleterious effects in aquatic species; and (iii) after ecotoxicological evaluation is considered “toxic” and/or “highly toxic” to the three trophic levels tested. Moreover, both the ecotoxicological assays and risk assessment (RQ), showed that LOR is more harmful for the crustaceans (RQcrustaceans = moderate to high risks) than for algae and fish. Ultimately, this study reinforces the ecological concern due to the indiscriminate disposal of this antihistamine drug in worldwide aquatic ecosystems.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....338394cd38803d28b03ac5f636cfee94
Full Text :
https://doi.org/10.6084/m9.figshare.23666386.v1