Systems toxicology to advance human and environmental hazard assessment: A roadmap for advanced materials

Ideally, a Systems Toxicology (ST) approach is aimed at by (eco)toxicologists, i.e. a multidisciplinary area incorporating classical toxicological concepts with omics technologies, and the understanding of this through computational data sciences, chemistry, mathematics, and physics modelling. As outlined in sev-eral public reports (e.g. from ECHA-European Chemical Agency and EFSA-European Food Safety Authority), the way forward in the coming years in Europe is to integrate New Approach Methodologies (NAMs) (in-cluding omics technologies) into hazard and hence risk assessment (RA). Adverse Outcome Pathways (AOPs) describe a sequence of events in response to stress, from the molecular initiating event until an adverse outcome, which is relevant to RA or regulatory decision-making. AOPs are one of the facilitators to integrate mechanistic data into RA, but it is urgent to increase the inclusion of the vast mechanistic knowledge available, especially for the RA of novel smart and advanced materials (AdMa) with multi-functional characteristics. There are still many challenges to the routine usage of NAMs, e.g. omics-based information. Here, we summarise the current state of the art of ST, the benefits of human and environ-mental health cross knowledge and the available methods and output. The importance of this area has been highlighted for many years but is even more pressing in the context of AdMa. Furthermore, we outline the challenges and suggest recommendations for future implementation.(c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Automated summary

A Systems Toxicology (ST) approach integrates toxicological concepts with omics technologies and computational data sciences, aiming to integrate New Approach Methodologies (NAMs) into hazard and risk assessment. Adverse Outcome Pathways (AOPs) are important for mechanistic data integration in risk assessment, especially for novel smart and advanced materials. However, there are challenges in the routine usage of NAMs, including omics-based information.