Development of classification models for predicting chronic toxicity of chemicals to Daphnia magna and Pseudokirchneriella subcapitata

ABSTRACT

Both the acute toxicity and chronic toxicity data on aquatic organisms are indispensable parameters in the ecological risk assessment priority chemical screening process (e.g. persistent, bioaccumulative and toxic chemicals). However, most of the present modelling actions are focused on developing predictive models for the acute toxicity of chemicals to aquatic organisms. As regards chronic aquatic toxicity, considerable work is needed. The major objective of the present study was to construct in silico models for predicting chronic toxicity data for Daphnia magna and Pseudokirchneriella subcapitata. In the modelling, a set of chronic toxicity data was collected for D. magna (21 days no observed effect concentration (NOEC)) and P. subcapitata (72 h NOEC), respectively. Then, binary classification models were developed for D. magna and P. subcapitata by employing the k-nearest neighbour method (k-NN). The model assessment results indicated that the obtained optimum models had high accuracy, sensitivity and specificity. The model application domain was characterized by the Euclidean distance-based method. In the future, the data gap for other chemicals within the application domain on their chronic toxicity for D. magna and P. subcapitata could be filled using the models developed here.

KEYWORDS:

• chronic toxicity
• aquatic organisms
• Daphnia magna
• Pseudokirchneriella subcapitata
• classification model
• k-nearest neighbour method

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

The study was supported by the National Natural Science Foundation of China (No. 21507038; 41671489; 201507061), Natural Science Foundation of Jiangsu Province (No. BK20150771), 2017 Specialized Fund for the Basic Research Operating Expenses Program of Central Public Welfare Research Institutes (GYZX170202, 2017).