These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


Pubmed for Handhelds

PUBMED FOR HANDHELDS

Search MEDLINE/PubMed

  • Title: Ecotoxic interactions between pharmaceuticals in mixtures: Diclofenac and sulfamethoxazole.
    Author: Drzymała J, Kalka J.
    Journal: Chemosphere; 2020 Nov; 259():127407. PubMed ID: 32593821.
    Abstract:
    The objective of this study was to investigate the impact of two pharmaceuticals, diclofenac and sulfamethoxazole, and their binary mixture on aquatic organisms, marine bacteria Aliivibrio fischeri, crustacean Daphnia magna, and vascular plant Lemna minor. The binary mixture of the drugs showed the highest toxicity towards the model organisms. Diclofenac had an average toxicity which posed a high environmental risk to aquatic organisms, while sulfamethoxazole was characterized by a low toxicity with low environmental risk. The organism most sensitive to diclofenac was A. fischeri (IC50 = 8.72 ± 1.14 mg L-1) and for sulfamethoxazole and the binary mixture, it was L. minor (IC50 = 12.56 ± 4.48 and 4.83 ± 0.43 mg L-1, respectively). The toxicity of the mixture was predicted using the Concentration Addition and Independent Action models, and the results were compared with the experimental data. None of the models suitably predicted the real toxicity of the pharmaceutical mixture. Interactions between the mixture components were confirmed by calculating the mixture toxicity index values which showed that the pharmaceuticals displayed synergistic or partial additive effects which depended on the selected test organism and test duration. When added as a complex matrix to wastewater (at a concentration of 2 mg L-1 each), the pharmaceuticals did not display increased toxicity. This observation confirms that the presence of micro-contaminants in aquatic environments may cause interactions between different compounds, the results of which are difficult to predict and model.
    [Abstract] [Full Text] [Related] [New Search]