Improvements in Estimating Bioaccumulation Metrics in the Light of Toxicokinetic Models and Bayesian Inference
| Autor: | Sandrine CHARLES, Christelle LOPES, Aude RATIER |
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| Přispěvatelé: | Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Institut National de l'Environnement Industriel et des Risques (INERIS), ANR-17-EURE-0018,H2O'LYON,School of Integrated Watershed Sciences(2017), ANR-18-CE34-0013,APPROve,Démarche intégrée pour proposer la protéomique dans la surveillance : accumulation, devenir et multimarqueurs(2018) |
| Rok vydání: | 2022 |
| Předmět: |
Environmental Risk Assessment
[SDV.EE.SANT]Life Sciences [q-bio]/Ecology environment/Health Health Toxicology and Mutagenesis meta-analysis bioaccumulation toxico-kinetics Bayesian inference database Bayes Theorem General Medicine TK models Toxicology Bioaccumulation Risk Assessment Pollution Toxicokinetics Benchmarking [SDV.TOX]Life Sciences [q-bio]/Toxicology classification of chemical substances REACH regulation |
| Zdroj: | Archives of Environmental Contamination and Toxicology Archives of Environmental Contamination and Toxicology, 2022, 83 (4), pp.339-348. ⟨10.1007/s00244-022-00947-2⟩ |
| ISSN: | 1432-0703 0090-4341 |
| DOI: | 10.1007/s00244-022-00947-2 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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