Citation:

Astuto, M., M. Di Nicola, J. Tarazona, A. Rortais, Y. Devos, A. Liem, G. Kass, M. Bastaki, R. Schoonjans, A. Maggiore, S. Charles, A. Ratier, C. Lopes, O. Gestin, T. Robinson, A. Williams, N. Kramer, E. Carnesecchi, AND J. Dorne. In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives. Chapter 23, Emilio Benfenati (ed.), In Silico Methods for Predicting Drug Toxicity. Methods in Molecular Biology. Humana, New York, NY, 2425:589-636, (2022). https://doi.org/10.1007/978-1-0716-1960-5_23

Impact/Purpose:

In the modern world the environment is exposed to a multitude of chemicals of anthropogenic and natural origin from different sources (1). Such man-made and natural chemicals include regulated products (e.g. pesticides, feed additives, pharmaceuticals) and contaminants and may alter the delicate equilibrium of environmental compartments and actors in addition to other stressors such as climate change and biological stressors (fungi, virus, bacteria etc.) (2). Environmental or Ecological Risk Assessment (ERA) provides methodologies to determine the risk(s) posed by chemicals on the environment by combining information on the hazard and the exposure. The purpose of ERA is to evaluate the likelihood and extent of harmful effects resulting from exposure to a chemical on different organisms (e.g. animals, microbes or plants) and different levels of biological organisation (e.g. single cells, individuals, populations, ecosystems, or landscapes). This process has extensive data requirements and entails a multidisciplinary effort, including toxicology, chemistry, biology, physiology, geology, engineering, statistics, and mathematical modelling.   

Description:

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure–activity relationship (QSAR) models, which support filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic–toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e. species sensitivity distributions and ultimately for landscape assessment i.e. landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application of such in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impact of single chemicals, multiple chemicals and multiple stressors on living organisms.

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