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Improvements in Estimating Bioaccumulation Metrics in the Light of Toxicokinetic Models and Bayesian Inference

Abstract

The surveillance of chemical substances in the scope of Environmental Risk Assessment (ERA) is classically performed through bio-assays from which data are collected and then analysed and/or modelled. Some analysis are based on the fitting of toxicokinetic (TK) models to assess the bioaccumulation capacity of chemical substances via the estimation of bioaccumulation metrics as required by regulatory documents. Given that bio-assays are particularly expensive and time consuming, it is of crucial importance to deeply benefit from all information contained in the data. By revisiting the calculation of bioaccumulation metrics under a Bayesian framework, this paper suggests changes in the way of characterising the bioaccumulation capacity of chemical substances. For this purpose, a meta-analysis of a data-rich TK database was performed, considering uncertainties around bioaccumulation metrics. Our results were statistically robust enough to suggest an additional criterion to the single median estimate of bioaccumulation metrics to assign a chemical substance to a given bioaccumulation capacity. Our proposal is to use the 75th percentile of the uncertainty interval of the bioaccumulation metrics, which revealed an appropriate complement for the classification of chemical substances (e.g. PBT (persistent, bioaccumulative and toxic) and vPvB (very persistent and very bioaccumulative) under the EU chemicals legislation). The 75% quantile proved its efficiency, similarly classifying 90% of the chemical substances as the conventional method.

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Data Availability

Supplementary information is available at https://zenodo.org/record/6634584. All data used in this paper is downloadable from the MOSAICbioacc web tool, directly from the associated TK database freely accessible at http://lbbe-shiny.univ-lyon1.fr/mosaic-bioacc/data/database/TK_database.html.

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Acknowledgements

This work was performed using the computing facilities of the CC LBBE/PRABI.

Funding

The authors are thankful to ANSES for providing the financial support for the development of the MOSAIC\(_\mathrm{bioacc}\) web tool (CNRS contract number 208483). This work is part of the ANR project APPROve (ANR-18-CE34-0013) for an integrated approach to propose proteomics for biomonitoring: accumulation, fate and multi-markers (https://anr.fr/Projet-ANR-18-CE34-0013). A large part of the work benefited from the French GDR “Aquatic Ecotoxicology” framework which aims at fostering stimulating scientific discussions and collaborations for more integrative approaches. At last, this work was made with the financial support of the Graduate School H2O’Lyon (ANR-17-EURE-0018) and “Université de Lyon” (UdL), as part of the program “Investissements d’ Avenir” run by “Agence Nationale de la Recherche” (ANR).

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Contributions

All authors contributed to the investigation of the TK database. Raw data collection and first analyses were performed by Aude Ratier and Sandrine Charles. The first draft of the manuscript was written by Aude Ratier and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Sandrine Charles.

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This manuscript has original research that has not been published previously and is not under consideration for publication elsewhere, in whole or in part.

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Ratier, A., Lopes, C. & Charles, S. Improvements in Estimating Bioaccumulation Metrics in the Light of Toxicokinetic Models and Bayesian Inference. Arch Environ Contam Toxicol (2022). https://doi.org/10.1007/s00244-022-00947-2

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