Taking full advantage of modelling to better assess environmental risk due to xenobiotics-the all-in-one facility MOSAIC.
Accessibility
Bioaccumulation metrics
Dose-response models
Toxicokinetic-toxicodynamic model
Uncertainty
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
Apr 2022
Apr 2022
Historique:
received:
09
04
2021
accepted:
17
06
2021
pubmed:
14
7
2021
medline:
14
4
2022
entrez:
13
7
2021
Statut:
ppublish
Résumé
In the European Union, more than 100,000 man-made chemical substances are awaiting an environmental risk assessment (ERA). Simultaneously, ERA of these chemicals has now entered a new era requiring determination of risks for physiologically diverse species exposed to several chemicals, often in mixtures. Additionally, recent recommendations from regulatory bodies underline a crucial need for the use of mechanistic effect models, allowing assessments that are not only ecologically relevant, but also more integrative, consistent and efficient. At the individual level, toxicokinetic-toxicodynamic (TKTD) models are particularly encouraged for the regulatory assessment of pesticide-related risks on aquatic organisms. In this paper, we first briefly present a classical dose-response model to showcase the on-line MOSAIC tool, which offers all necessary services in a turnkey web platform, whatever the type of data analyzed. Secondly, we focus on the necessity to account for the time-dimension of the exposure by illustrating how MOSAIC can support a robust calculation of bioaccumulation metrics. Finally, we show how MOSAIC can be of valuable help to fully complete the EFSA workflow regarding the use of TKTD models, especially with GUTS models, providing a user-friendly interface for calibrating, validating and predicting survival over time under any time-variable exposure scenario of interest. Our conclusion proposes a few lines of thought for an easier use of modelling in ERA.
Identifiants
pubmed: 34255258
doi: 10.1007/s11356-021-15042-7
pii: 10.1007/s11356-021-15042-7
doi:
Substances chimiques
Pesticides
0
Xenobiotics
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
29244-29257Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Ankley G, Bennett R, Erickson R, Hoff D, Hornung M, Johnson R, Mount D, Nichols J, Russom C, Schmieder P (2010) Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment. Environ Toxicol Chem 29(3):730–741
doi: 10.1002/etc.34
Ashauer R, Jager T (2018) Physiological modes of action across species and toxicants: the key to predictive ecotoxicology. Environ Sci Process Impacts 00:1–10
Baudrot V, Charles S (2019) Recommendations to address uncertainties in environmental risk assessment using toxicokinetics-toxicodynamics models. Sci Rep Nat Res 9:11432
doi: 10.1038/s41598-019-47698-0
Baudrot V, Preux S, Ducrot V, Pave A, Charles S (2018a) New insights to compare and choose tktd models for survival based on an interlaboratory study for lymnaea stagnalis exposed to cd. Environ Sci Tech 52(3):1582–1590
doi: 10.1021/acs.est.7b05464
Baudrot V, Veber P, Gence G, Charles S (2018b) Fit reduced GUTS models online: from theory to practice. Integr Environ Assess Manag 14(5):625–630
doi: 10.1002/ieam.4061
Baudrot V, Charles S, Delignette-Muller ML, Duchemin W, Goussen B, Kehrein N, Kon-Kam-King G, Lopes C, Ruiz P, Singer A, Veber P (2021) morse: modelling tools for reproduction and survival data in ecotoxicology. https://CRAN.R-project.org/package=morse , r package version 3.3.0
Brock T, Arena M, Cedergreen N, Charles S, Duquesne S, Ippolito A, Klein M, Reed M, Teodorovic I, Van den Brink P J, Focks A (2020) Application of GUTS models for regulatory aquatic pesticide risk assessment illustrated with an example for the insecticide chlorpyrifos. Integr Environ Assess Manag 17:243–258
doi: 10.1002/ieam.4327
Chang W, Cheng J, Allaire J, Sievert C, Schloerke B, Xie Y, Allen J, McPherson J, Dipert A, Borges B (2021) shiny: Web Application Framework for R. https://CRAN.R-project.org/package=shiny , r package version 1.6.0
Charles S, Veber P, Delignette-Muller ML (2018) MOSAIC: a web-interface for statistical analyses in ecotoxicology. Environ Sci Pollut Res 25:11295–11302
doi: 10.1007/s11356-017-9809-4
Charles S, Wu D, Ducrot V (2021) How to account for the uncertainty from standard toxicity tests in species sensitivity distributions: an example in non-target plants. PLOS ONE 16(1):e0245071
doi: 10.1371/journal.pone.0245071
Clements W (2000) Integrating effects of contaminants across levels of biological organization: an overview. J Aquat Ecosyst Stress Recover Formerly J Aquat Ecosyst Health 7(2):113–116
doi: 10.1023/A:1009927612391
EFSA Scientific Committee (2018) Guidance on uncertainty analysis in scientific assessments. EFSA J 16(1):1–39
European Commission (2013) European Commission (EU) No 283/2013 of 1 March 2013 setting out the data requirements for active substances, in accordance with Regulation (EC) No 1107/2009 of the European Parliament and of the Council concerning the placing of plant protection produc
European Food Safety Authority (2017) EFSA Guidance Document for predicting environmental concentrations of active substances of plant protection products and transformation products of these active substances in soil. EFSA J 15(178):1–50
Forbes V E, Calow P (2002) Species sensitivity distributions revisited: a critical appraisal. Hum Ecol Risk Assess 8(3):473–492
doi: 10.1080/10807030290879781
Forbes V E, Galic N (2016) Next-generation ecological risk assessment: predicting risk from molecular initiation to ecosystem service delivery. Environ Int 91:215–219
doi: 10.1016/j.envint.2016.03.002
Forfait-Dubuc C, Charles S, Billoir E, Delignette-Muller M (2012) Survival data analyses in ecotoxicology: critical effect concentrations, methods and models. What should we use? Ecotoxicology 12(4):1072–1083
doi: 10.1007/s10646-012-0860-0
Grech A, Brochot C, Dorne J L, Quignot N, Bois F Y, Beaudouin R (2017) Toxicokinetic models and related tools in environmental risk assessment of chemicals. Sci Total Environ 578:1–15
doi: 10.1016/j.scitotenv.2016.10.146
Jager T (2020) Robust likelihood-based approach for automated optimization and uncertainty analysis of toxicokinetic-toxicodynamic models. Integr Environ Assess Manag 17(2):388–397. https://doi.org/10.1002/ieam.4333
doi: 10.1002/ieam.4333
Jager T, Ashauer R (2018) Modelling survival under chemical stress. A comprehensive guide to the GUTS framework, leanpub edn. Leanpub. https://leanpub.com/guts_book
Kon Kam King G, Veber P, Charles S, Delignette-Muller ML (2014) MOSAIC_SSD: a new web tool for species sensitivity distribution to include censored data by maximum likelihood. Environ Toxicol Chem 33(9):2133–9
doi: 10.1002/etc.2644
MOSAIC (2013). https://mosaic.univ-lyon1.fr/ . Accessed 03 Mar 2021
MOSAICbioacc (2020). https://mosaic.univ-lyon1.fr/bioacc/ . Accessed 03 Mar 2021
MOSAICgrowth (2020). https://mosaic.univ-lyon1.fr/growth/ . Accessed 03 Mar 2021
MOSAICguts-fit (2018). https://mosaic.univ-lyon1.fr/guts/ . Accessed 03 Mar 2021
MOSAICguts-predict (2018). http://lbbe-shiny.univ-lyon1.fr/guts-predict/ http://lbbe-shiny.univ-lyon1.fr/guts-predict/ . Accessed 03 Mar 2021
MOSAICrepro (2014). https://mosaic.univ-lyon1.fr/repro/ . Accessed 03 Mar 2021
MOSAICssd (2013). https://mosaic.univ-lyon1.fr/ssd/ . Accessed 03 Mar 2021
MOSAICsurv (2014). https://mosaic.univ-lyon1.fr/surv/ . Accessed 03 Mar 2021
Ockleford C, Adriaanse P, Berny P, Brock T, Duquesne S, Grilli S, Hernandez-Jerez AF, Bennekou SH, Klein M, Kuhl T, Laskowski R, Machera K, Pelkonen O, Pieper S, Smith RH, Stemmer M, Sundh I, Tiktak A, Topping CJ, Wolterink G, Cedergreen N, Charles S, Focks A, Reed M, Arena M, Ippolito A, Byers H, Teodorovic I (2018) Scientific opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms. EFSA J 16(8):5377
OECD (2012) Test No. 305: Bioaccumulation in fish: aqueous and dietary exposure vol section 3. OECD Publishing, Paris. https://doi.org/10.1787/9789264185296-en
doi: 10.1787/9789264185296-en
Park R, Clough J, Wellman M (2008) AQUATOX: modeling environmental fate and ecological effects in aquatic ecosystems. Ecol Model 213(1):1–15
doi: 10.1016/j.ecolmodel.2008.01.015
Preuss T, Hommen U, Alix A, Ashauer R, van den Brink P, Chapman P, Ducrot V, Forbes V, Grimm V, Schäfer D (2009) Mechanistic effect models for ecological risk assessment of chemicals (MEMoRisk)—a new SETAC-Europe Advisory Group. Environ Sci Pollut Res 16(3):250–252
doi: 10.1007/s11356-009-0124-6
R Core Team (2021) R: A Language and environment for statistical computing r foundation for statistical computing. Vienna, Austria. https://www.R-project.org/
Ratier A, Charles S (2021) Accumulation-depuration data collection in support of toxicokinetic modelling. Sci Data Nat submitted. https://doi.org/10.1101/2021.04.15.439942
Ratier A, Lopes C, Labadie P, Budzinski Hèlène Delorme N, Quéau H, Peluhet L, Geffard O, Babut M (2019) A unified Bayesian framework for estimating model parameters for the bioaccumulation of organic chemicals by benthic invertebrates: proof of concept with PCB153 and two freshwater species. Ecotoxicol Environ Saf 180:33–42
doi: 10.1016/j.ecoenv.2019.04.080
Ratier A, Lopes C, Multari G, Mazerolles V, Carpentier P, Charles S (2021) New perspectives on the calculation of bioaccumulation metrics for active substances in living organisms. Integrated environmental assessment and management accepted. https://doi.org/10.1101/2020.07.07.185835
Ritz C (2010) Toward a unified approach to dose–response modeling in ecotoxicology. Environ Toxicol Chem 29(1):220–229
doi: 10.1002/etc.7
Rubach MN, Baird DJ, Boerwinkel MC, Maund SJ, Roessink I, Van den Brink PJ (2012) Species traits as predictors for intrinsic sensitivity of aquatic invertebrates to the insecticide chlorpyrifos. Ecotoxicology 21(7):2088–101
doi: 10.1007/s10646-012-0962-8
Schmolke A, Thorbek P, Chapman P, Grimm V (2010) Ecological models and pesticide risk assessment: current modeling practice. Environ Toxicol Chem 29(4):1006–1012
doi: 10.1002/etc.120
Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A (2002) Bayesian measures of model complexity and fit. J R Stat Society Ser B Stat Methodol 64(4):583–639. https://doi.org/10.1111/1467-9868.00353
doi: 10.1111/1467-9868.00353
Watanabe S (2010) Asymptotic equivalence of Bayes cross validation and widely applicable information criterion in singular learning theory. J Mach Learn Res 11:3571–3594. arXiv: 1004.2316
Wollenberger L, Halling-Sorensen B, Kusk KO (2000) Acute and chronic toxicity of veterinary antibiotics to Daphnia magna. Chemosphere 40(7):723–730
doi: 10.1016/S0045-6535(99)00443-9