QSAR approach for mixture toxicity prediction using independent latent descriptors and fuzzy membership functions†

†Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).

Abstract

The principle of using a singe model to predict the toxicity of mixtures of chemicals based on the characterisation of the degrees of similarity and dissimilarity of the constituent chemicals using descriptors has been demonstrated in a previous work. The current study introduces a feature extraction technique, independent component analysis, to the method to remove the correlations and dependencies between descriptors and reduce the dimension prior to similarity and dissimilarity calculations. In addition, a goal attainment multi-objective optimisation technique is used for the determination of the fuzzy membership function parameters. For three mixtures, which include a new mixture and two previously studied mixtures that all inhibit reproduction (via different mechanisms of action) in green freshwater algae scenedesmus vacuolatus, the approach showed better or equivalent prediction performance than either concentration addition or independent action models. Unlike QSARs for pure compounds that require large collections of data, the new approach for mixtures only requires one mixture at a particular composition to determine the necessary fuzzy membership function parameter values. These values can then be used to predict the toxicity of the mixture at any other compositions. This could potentially lead to a reduction in the frequency of bioassay tests. Use of the fuzzy membership functions and parameter values obtained for one mixture when used to predict the toxicity of a completely different mixture is also tested and it is found that the approach also gives prediction results with good accuracy.

†Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).

Keywords:

• Mixture toxicity
• Mixture QSAR
• Ecotoxicity
• Environmental risk assessment

Acknowledgements

The work is funded by the EPSRC Crystal Faraday Partnership on Green Technology (Ref: 01306000), Brixham Environmental Laboratory of AstraZeneca and the Centre of Ecology and Hydrology. The first author would like to thank the Keyworth Institute of Manufacturing and Information Systems of the University of Leeds for providing a PhD scholarship. The corresponding author would like to thank Malvern Instruments Ltd for sponsoring him the readership position of Malvern Reader in Intelligent Measurement and Control.

Notes

†Presented at CMTPI 2005: Computational Methods in Toxicology and Pharmacology Integrating Internet Resources (Shanghai, China, October 29–November 1, 2005).