Prediction of bioconcentration factors in fish and invertebrates using machine learning

Miller, Thomas, Gallidabino, Matteo, MacRae, James, Owen, Stewart, Bury, Nicolas and Barron, Leon (2019) Prediction of bioconcentration factors in fish and invertebrates using machine learning. Science of the Total Environment, 648. pp. 80-89. ISSN 0048-9697

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Abstract

The application of machine learning has recently gained interest from ecotoxicological fields for its ability to model and predict chemical and/or biological processes, such as the prediction of bioconcentration. However, comparison of different models and the prediction of bioconcentration in invertebrates has not been previously evaluated. A comparison of 24 linear and machine learning models is presented herein for the prediction of bioconcentration in fish and important factors that influenced accumulation identified. R2 and rootmean square error (RMSE) for the test data (n=110 cases) ranged from 0.23–0.73 and 0.34–1.20, respectively. Model performance was critically assessed with neural networks and tree-based learners showing the best performance. An optimised 4-layer multi-layer perceptron (14 descriptors) was selected for further testing. The model was applied for cross-species prediction of bioconcentration in a freshwater invertebrate, Gammarus pulex. The model for G. pulex showed good performance with R2 of 0.99 and 0.93 for the verification and test data, respectively. Important molecular descriptors determined to influence bioconcentration were molecular mass (MW), octanol-water distribution coefficient (logD), topological polar surface area (TPSA) and number of nitrogen atoms (nN) among others. Modelling of hazard criteria such as PBT, showed potential to replace the need for animal testing. However, the use of machine learning models in the regulatory context has been minimal to date and is critically discussed herein. The movement away from experimental estimations of accumulation to in silico modelling would enable rapid prioritisation of contaminants that may pose a risk to environmental health and the food chain.

Item Type: Article
Uncontrolled Keywords: Modelling; PBT; Pharmaceutical; Bioconcentration; BCF; Machine learning
Subjects: F100 Chemistry
F800 Physical and Terrestrial Geographical and Environmental Sciences
G300 Statistics
G900 Others in Mathematical and Computing Sciences
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: Matteo Gallidabino
Date Deposited: 14 Sep 2018 08:19
Last Modified: 20 Dec 2018 11:45
URI: http://nrl.northumbria.ac.uk/id/eprint/35711

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