Grouping persistent and mobile substances to expedite assessments and avoid regrettable substitution

1. Introduction There is an interest by regulators and scientists working with chemical hazard assessment to transition from a one-substance-at-a-time approach to a substance-group approach. Two main motivations for this are to expedite hazard assessments related to the large amounts of substances being introduced to the global chemical market, and avoiding regrettable substitution caused by drop-in substitution. The idea of substance grouping based on the relationship between chemical structure and hazard has been applied in several cases already. Many of the very first hazardous organic substances to be regulated globally as pollutants were groups sharing a similar structure, including DDT-derivatives, dioxins, and PBDEs. This presentation will provide an overview of strategies for grouping hazardous substances based on chemical structure and how these substance grouping approaches may be applied in relation to persistent and mobile substances, as as these are a class of substances receiving increasing, global attention. This work will be particularly relevant to identify groups substances with either a common chemical moiety, or common transformation products, that are considered persistent, mobile and toxic (PMT) or very persistent and very mobile (vPvM) [1]. Since approaches are needed now to better assess and regulate them to protect drinking water sources, we explore how grouping persistent and mobiles substances based on specific molecular substructures will support PMT/vPvM substance prioritization for risk governance and assessments. 2. Materials and Methods Two approaches of substance grouping are being considered for persistent and mobile substances: Method 1: common chemcial substructure; and Method 2: precursors leading to common transformation products. Method 1 has some similarities with the concept of read across, where properties of a substance (i.e. of physico-chemical and/or biological properties) are linked to chemical structure [2]. Specifically, the approach will be to first obtain persistency and mobility information for as many substances on the global chemical inventory as possible. Following this, a statistical analysis of common moieties amongst persistent and mobile substances vs non persistent and mobile substances will be conducted. Additional considerations, such as exclusion rules based on molecular volume (for mobility) or percentage of well-known degradable moieties (e.g. alkyl chains), will be included within the statisical analysis. Method 2 is based on “dead-end transformation pathways”, referring to transformation products that can be generated from multiple substances that are found to be persistent, and not prone to further degradation. Transformation products often tend to be more mobile than their precusors. The approach here is to use literature transformation pathways, in combination with digital tools like BioTransformer [3] and enviPath [4] (which predict metabolites through combinations of rule-based and machine learning approaches) to identify transformation products that remain persistent and mobile in the environment. 3. Results and Discussion 1. 2. 3. An early approach to develop these two methods was two consider the case of substances with a 1,2,4-triazole moiety. The Method 1 approach was to consider 1,2,4-triazole as a persistent and mobile moeity because the substance itself is persistent and mobile, and many smaller molecules containing this moeity were also persistent and mobile (e.g. triazole acetic acid, triazole analine). The method 2 approach is based on analysis of transformation pathways in PubChem that many of the larger substances containing this moeity, including ones that themselves are not persistent and mobile, had 1,2,4 triazole as common transformation product. Further many of the substances in Figure 1 are considered to be toxic, including the ultimate degardation product 1,2,4-triazole itself (toxic to reproduction). To identify more substance groups like this, cheminformatics approaches are being developed to streamline the process. Since the availability of reaction data is still hampering prediction quality, an app called “ShinyTPs” [5] was developed to curate and FAIRify existing text-mined transformation information within the PubChem database. This application facilitates the curation of the extracted reactions for upload to open datasets and to provide more data to improve the predictive approaches such as BioTransformer and enviPath. Currently interdisciplinary discussions are ongoing to find the best approaches for the prioritizaiton of persistent and mobile substance groups, towards risk assessment, prevention measures and remova . 4. Conclusions Regulating substances as groups has helped successfully phase out ecotoxic chemicals and persistent organic pollutants through the Montreal Protocol and Stockholm Convention. Thus, there is much potential for successfully developed grouping strategies for regulating PMT/vPvM substances, including those based on transformation products. Further discussions related to priorized persisent and mobile substance groups can be developed around policies relevant to the phase-out of the manufacture, use, and emission of PMT/vPvM substances in the environment. These policies include substituting harmful groups with less harmful ones and the safe and sustainable by-design approach. Such an approach can help guide fossuing alternanatives assessment, regulation, market transition, risk assessment and technological solutions towards green, transformative strategies toward zero pollution of persistent and mobile substances. 5. References [1] Arp, H.P.H. and Hale, S.E. (2022) "Assessing the Persistence and Mobility of Organic Substances to Protect Freshwater Resources", ACS Environ. Au 2022, 2, 6, 482–509. DOI:10.1021/acsenvironau.2c00024. [2] ECHA (2013) “Grouping of substances and read-across approach” . ECHA-13-R-02-EN [3] Wishart Lab (2023) https://biotransformer.ca/ (accessed 24/11/2023) [4] enviPath UG & co (2023) https://envipath.org/ (accessed 24/11/2023) [5] Palm, E. H., Chirsir, P., Krier, J., Thiessen, P. A., Zhang, J., Bolton, E. E., & Schymanski, E. L. (2023). ShinyTPs: Curating transformation products from text mining results. Environ. Sci. Technol. Letters, 10(10), 865-871. DOI: 10.1021/acs.estlett.3c00537

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