Modeling the toxicity of pollutants mixtures for risk assessment: a review

The occurrence of contaminants in natural waters is a potential threat to the environment. Since contaminants are commonly present as mixtures, numerous interactions may occur, resulting in lower or, more dangerously, higher toxicity, by comparison with single substances. The toxicity of multicomponent systems can be determined experimentally, but toxicity prediction by suitable models is faster, environmentally friendly and less expensive. Here we review approaches and models, which can be utilized in assessing toxicity of chemical mixtures. In the first part, the assessment of toxicity of chemical mixtures and possible interactions between mixture constituents are discussed. The second part covers conventional modeling, including the simplest, and most common toxicity models, namely concentration addition and independent action models, and derived integrated models. The third part presents advanced toxicity modeling. We review the quantitative structure-activity relationship (QSAR) approach and its elements: calculation of molecular descriptors and their selection with principal component analysis and genetic algorithm. Modeling with artificial neural networks is also discussed. We present hybrid models which combine the fuzzy set theory approach with the conventional concentration addition and independent action models. We conclude that conventional models: concentration addition and independent action model, are still most commonly used; integrated models are more accurate compared to conventional ones, even though their application requires more data; advanced numerical methods such as genetic algorithm, neural networks, and fuzzy set theory give a new perspective on toxicity prediction, and no universal tool for toxicity assessment has been developed so far.

Automated summary

The presence of contaminants in natural waters poses a potential threat to the environment, especially in multi-component systems where interactions can lead to varying levels of toxicity. While experimental determination of toxicity in multi-component systems is possible, utilizing predictive models is faster, more environmentally friendly, and cost-effective. Conventional models like concentration addition and independent action models are still widely used, but integrated models offer greater accuracy despite requiring more data. Advanced numerical methods like genetic algorithms, neural networks, and fuzzy set theory are providing new perspectives on toxicity prediction, although a universal tool for toxicity assessment has not yet been developed.