Data-driven risk assessment of antibiotic mixtures against gram-negative and gram-positive bacteria: QSAR, joint toxic action, and component contribution
Rattachement africain : cn. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
The increasing demand and use of antibiotics due to epidemic outbreaks under climate change leads to more antibacterial substances entering the environment. The residual drugs, commonly found as mixtures, can specially target bacteria, posing a threat to the ecosystem and human health. Therefore, it is necessary to assess the risk of antibiotic mixtures using bacteria as the model organisms. In this study, selecting sulfonamides, sulfonamides potentiators, and tetracyclines as the representative antibiotics, the individual and combined toxicity of these agents were tested against Gram-positive ( Bacillus subtilis ) and Gram-negative bacteria ( Aliivibrio fischeri and Escherichia coli ). The quantitative structure–activity relationship models were constructed by setting E bind (lowest interaction energy between antibiotic and target protein) and K ow (octanol–water partition coefficient) as the structural descriptors, which provide reliable and robust tools for predicting the toxicity of single agents and binary mixtures. Furthermore, the hormetic effects, characterized by low-dose stimulation and high-dose inhibition, were observed in Gram-negative bacteria, and the dose-responses in Gram-positive bacteria all exhibited S-shaped. Within the hormetic phenomena of antibiotic mixtures, the pattern of joint toxic action and component contribution generally changed with the transition from stimulatory to inhibitory actions. These results not only demonstrate the influence of bacterial species and test endpoints on the toxicity of antibiotics, but also clarify the pivotal role of hormesis in the joint effects of antibiotics. This study provides a data-driven methodology for evaluating the combined toxicity of mixtures, which will promote the development of the risk assessment of antibiotics and other environmental pollutants.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Data-driven risk assessment of antibiotic mixtures against gram-negative and gram-positive bacteria: QSAR, joint toxic action, and component contribution
- Date Crossref
- 01/08/2025
- Éditeur
- Elsevier BV
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Shanghai University pays non établi dans la noticeUniversité ou école supérieure
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Tongji University pays non établi dans la noticeUniversité ou école supérieure
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State Key Laboratory of Pollution Control and Resource Reuse pays non établi dans la noticeStructure de recherche
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Fuzhou University pays non établi dans la noticeUniversité ou école supérieure
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Fujian University of Technology pays non établi dans la noticeUniversité ou école supérieure
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School of Environmental and Chemical Engineering Key Laboratory of Organic Compound Pollution Control Engineering (MOE) pays non établi dans la noticeUniversité ou école supérieure
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College of Environmental Science and Engineering State Key Laboratory of Pollution Control and Resource Reuse pays non établi dans la noticeUniversité ou école supérieure
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School of Future Membrane Technology pays non établi dans la noticeUniversité ou école supérieure
Shanghai University, Tongji University et State Key Laboratory of Pollution Control and Resource Reuse, avec 5 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.