Article

Environmental Monitoring and Assessment

, Volume 185, Issue 6, pp 4513-4527

A docking-based receptor library of antibiotics and its novel application in predicting chronic mixture toxicity for environmental risk assessment

  • Xiaoming ZouAffiliated withState Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji UniversityCollege of Life Science, Jinggangshan University
  • , Xianghong ZhouAffiliated withDepartment of Public Management, Tongji University
  • , Zhifen LinAffiliated withState Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University Email author 
  • , Ziqing DengAffiliated withState Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji University
  • , Daqiang YinAffiliated withState Key Laboratory of Pollution Control and Resource Reuse, College of Environmental Science and Engineering, Tongji UniversityKey Laboratory of Yangtze River Water Environment, Ministry of Education, College of Environmental Science and Engineering, Tongji University

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Abstract

As organisms are typically exposed to chemical mixtures over long periods of time, chronic mixture toxicity is the best way to perform an environmental risk assessment (ERA). However, it is difficult to obtain the chronic mixture toxicity data due to the high expense and the complexity of the data acquisition method. Therefore, an approach was proposed in this study to predict chronic mixture toxicity. The acute (15 min exposure) and chronic (24 h exposure) toxicity of eight antibiotics and trimethoprim to Vibrio fischeri were determined in both single and binary mixtures. The results indicated that the risk quotients (RQs) of antibiotics should be based on the chronic mixture toxicity. To predict the chronic mixture toxicity, a docking-based receptor library of antibiotics and the receptor-library-based quantitative structure–activity relationship (QSAR) model were developed. Application of the developed QSAR model to the ERA of antibiotic mixtures demonstrated that there was a close affinity between RQs based on the observed chronic toxicity and the corresponding RQs based on the predicted data. The average coefficients of variations were 46.26 and 34.93 % and the determination coefficients (R 2) were 0.999 and 0.998 for the low concentration group and the high concentration group, respectively. This result convinced us that the receptor library would be a promising tool for predicting the chronic mixture toxicity of antibiotics and that it can be further applied in ERA.

Keywords

Chronic mixture toxicity Predicting Molecular docking QSAR