QSAR and read-across models for water quality

In silico models for predicting the toxicity of contaminants deliver added value for water-quality issues. The chemical structure of a contaminant and data about similar substances provide insight in the toxicological properties and possible adverse effects on human health and the environment of contaminants for which toxicological data are not available. This helps with the prioritisation of follow-up research, hazard and risk assessment of contaminants, and the targeted deployment of measures and decision-making procedures to mitigate potential risks.

Challenges for the assessment of chemical water quality

Around the world, the number of contaminants that may be found in water is increasing. Non-target screening (NTS) methods, in which water samples are subjected to broad chemical analyses for the presence of contaminants, are revealing the chemical structures of more and more unknown contaminants. It is important to know whether contaminants are dangerous for the environment. This knowledge is needed not only by the companies that produce these substances but also by drinking water companies and other water management authorities that come across these substances in water. With new, emerging substances and substances that may be produced in the environment or during water treatment (i.e. transformation products), it is often the case that hardly any information is available about toxicological properties and potentially adverse effects on human health and the environment.

In silico toxicology

In silico models for predicting the toxicity of contaminants provide a possible solution here. (Quantitative) structure-activity relationships – (Q)SARs – are used to investigate which chemical substructures and/or physico-chemical properties are predictors of adverse effects on health and to determine whether a new contaminant comes with a ‘structural alert’ for those effects. In addition, a ‘read-across’ can be used to compare the contaminant with substances that have a similar chemical structure and physico-chemical properties. In the case of contaminants for which almost no toxicological information is available, an in silico approach is faster and more cost-effective than conducting experiments in a laboratory.

In silico models for water quality

A range of computer programs that operate with in silico models to predict toxicity are used by KWR for different purposes, such as prioritising substances for studies of treatment efficiency, acquiring insights into the possible health effects of unknown contaminants found in sources of water and drinking water in particular, and for toxicological risk assessments (sometimes in combination with bioassays). In silico models are not limited to predicting the toxicity of contaminants; they also make it possible to predict the physico-chemical properties of a contaminant and how it behaves in the environment and during treatment. For this purpose, KWR has developed the Aqua Priori tool to predict the rate of removal of organic micropollutants in treatment on the basis of QSARs.

Most in silico tools are relatively user-friendly, but expert judgement is needed to review and interpret the results. In silico tools make it clear which micropollutants in water pose a potential risk to human health and the environment. As a result, they have a clear added value in terms of solving a range of water-quality issues with respect to the prioritisation of follow-up research (Figure 1) and the hazard and risk assessment of contaminants (Figure 2). Measures and decision-making processes to mitigate any risks can therefore be selected and deployed in targeted ways.

Schematic representation of applications of in silico models of toxicity for the prioritisation of contaminants for follow-up testing and interpretation of chemical analyses as part of water-quality monitoring.

Schematic representation of the application of in silico models for the assessment of safety and risks associated with individual, water-relevant, contaminants.