Estimating the dynamic early life exposure to PFOA and PFOS of the HELIX children: Emerging profiles via prenatal exposure, breastfeeding, and diet.

In utero and children's exposure to per- and polyfluoroalkyl substances (PFAS) is a major concern in health risk assessment as early life exposures are suspected to induce adverse health effects. Our work aims to estimate children's exposure (from birth to 12 years old) to PFOA and PFOS, using a Physiologically-Based Pharmacokinetic (PBPK) modelling approach. A model for PFAS was updated to simulate the internal PFAS exposures during the in utero life and childhood, and including individual characteristics and exposure scenarios (e.g., duration of breastfeeding, weight at birth, etc.). Our approach was applied to the HELIX cohort, involving 1,239 mother-child pairs with measured PFOA and PFOS plasma concentrations at two sampling times: maternal and child plasma concentrations (6 to 12 y.o). Our model predicted an increase in plasma concentrations during fetal development and childhood until 2 y.o when the maximum concentrations were reached. Higher plasma concentrations of PFOA than PFOS were predicted until 2 y.o, and then PFOS concentrations gradually became higher than PFOA concentrations. From 2 to 8 y.o, mean concentrations decreased from 3.1 to 1.88 µg/L or ng/mL (PFOA) and from 4.77 to 3.56 µg/L (PFOS). The concentration-time profiles vary with the age and were mostly influenced by in utero exposure (on the first 4 months after birth), breastfeeding (from 5 months to 2 (PFOA) or 5 (PFOS) y.o of the children), and food intake (after 3 (PFOA) or 6 (PFOS) y.o of the children). Similar measured biomarker levels can correspond to large differences in the simulated internal exposures, highlighting the importance to investigate the children's exposure over the early life to improve exposure classification. Our approach demonstrates the possibility to simulate individual internal exposures using PBPK models when measured biomarkers are scarce, helping risk assessors in gaining insight into internal exposure during critical windows, such as early life.

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

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.

rbioacc: An R-package to analyze toxicokinetic data.

The R-package rbioacc allows to analyse experimental data from bioaccumulation tests where organisms are exposed to a chemical (exposure) then put into clean media (depuration). Internal concentrations are measured over time during the experiment. rbioacc provides turnkey functions to visualise and analyse such data. Under a Bayesian framework, rbioacc fits a generic one-compartment toxicokinetic model built from the data. It provides TK parameter estimates (uptake and elimination rates) and standard bioaccumulation metrics. All parameter estimates, bioaccumulation metrics and predictions of internal concentrations are delivered with their uncertainty. Bioaccumulation metrics are provided in support of environmental risk assessment, in full compliance with regulatory requirements required to approve market release of chemical substances. This paper provides worked examples of the use of rbioacc from data collected through standard bioaccumulation tests, publicly available within the scientific literature. These examples constitute step-by-step user-guides to analyse any new data set, uploaded in the right format.

Accumulation-depuration data collection in support of toxicokinetic modelling.

Regulatory bodies require bioaccumulation evaluation of chemicals within organisms to better assess toxic risks. Toxicokinetic (TK) data are particularly useful in relating the chemical exposure to the accumulation and depuration processes happening within organisms. TK models are used to predict internal concentrations when experimental data are lacking or difficult to access, such as within target tissues. The bioaccumulative property of chemicals is quantified by metrics calculated from TK model parameters after fitting to data collected via bioaccumulation tests. In bioaccumulation tests, internal concentrations of chemicals are measured within organisms at regular time points during accumulation and depuration phases. The time course is captured by TK model parameters thus providing bioaccumulation metrics. But raw TK data remain difficult to access, most often provided within papers as plots. To increase availability of TK data, we developed an innovative database from data extracted in the scientific literature to support TK modelling. Freely available, our database can dynamically evolve thanks to any researcher interested in sharing data to be findable, accessible, interoperable and reusable.

In Silico Methods for Environmental Risk Assessment: Principles, Tiered Approaches, Applications, and Future Perspectives.

This chapter aims to introduce the reader to the basic principles of environmental risk assessment of chemicals and highlights the usefulness of tiered approaches within weight of evidence approaches in relation to problem formulation i.e., data availability, time and resource availability. In silico models are then introduced and include quantitative structure-activity relationship (QSAR) models, which support filling data gaps when no chemical property or ecotoxicological data are available. In addition, biologically-based models can be applied in more data rich situations and these include generic or species-specific models such as toxicokinetic-toxicodynamic models, dynamic energy budget models, physiologically based models, and models for ecosystem hazard assessment i.e. species sensitivity distributions and ultimately for landscape assessment i.e. landscape-based modeling approaches. Throughout this chapter, particular attention is given to provide practical examples supporting the application of such in silico models in real-world settings. Future perspectives are discussed to address environmental risk assessment in a more holistic manner particularly for relevant complex questions, such as the risk assessment of multiple stressors and the development of harmonized approaches to ultimately quantify the relative contribution and impact of single chemicals, multiple chemicals and multiple stressors on living organisms.

Taking full advantage of modelling to better assess environmental risk due to xenobiotics-the all-in-one facility MOSAIC.

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.

New perspectives on the calculation of bioaccumulation metrics for active substances in living organisms.

Today, only few ready-to-use and convenient decision-making tools are available in ecotoxicology concerning accumulation and effects of chemical substances on organisms, accounting for exposure situations that are known to be complex (routes of exposure, metabolism, mixtures, etc.). This paper presents new perspectives on the generic calculation of bioaccumulation metrics via the innovative web tool MOSAICbioacc (http://mosaic.univ-lyon1.fr/bioacc). MOSAICbioacc provides all kinds of bioaccumulation metrics associated with their uncertainty whatever the species-compound combination. MOSAICbioacc expects accumulation-depuration data as inputs, even with complex exposure and clearance patterns, to quickly perform their relevant analysis. MOSAICbioacc intends to facilitate the daily work of regulators, or any ecotoxicologist, who will freely benefit from a user-friendly online interface that automatically fits toxicokinetic models without need for users to invest in the technical aspects to get bioaccumulation metrics estimates. MOSAICbioacc also provides all results in a fully transparent way to ensure reproducibility. Integr Environ Assess Manag 2022;18:10-18. © 2021 SETAC.

Effects of meteorological conditions and topography on the bioaccumulation of PAHs and metal elements by native lichen (Xanthoria parietina).

The bioaccumulation of PAHs and metal elements in the indigenous lichens Xanthoria parietina was monitored during two years at a quarterly frequency, in 3 sites of contrasted anthropic influence. The impact of the meteorological factors (temperature, relative humidity, rainfall, wind speed) was first estimated through principal component analysis, and then by stepwise multilinear regressions to include wind directions. The pollutants levels reflected the proximity of atmospheric emissions, in particular from a large industrial harbor. High humidity and mild temperatures, and in a lower extent low wind speed and rainfall, also favored higher concentration levels. The contributions of these meteorological aspects became minor when including wind direction, especially when approaching major emission sources. The bioaccumulation integration time towards meteorological variations was on a seasonal basis (1-2 months) but the wind direction and thus local emissions also relied on a longer time scale (12 months). This showed that the contribution of meteorological conditions may be prevalent in remote places, while secondary in polluted areas, and should be definitely taken into account regarding long-term lichen biomonitoring and inter-annual comparisons. In the same time, a quadruple sampling in each site revealed a high homogeneity among supporting tree species and topography. The resulting uncertainty, including sampling, preparation and analysis was below 30% when comfortable analytical conditions were achieved. Finally, the occurrence of unexpected events such as a major forest fire, permitted to evaluate that this type of short, although intense, events did not have a strong influence on PAH and metals bioaccumulation by lichen.

The added value of Bayesian inference for estimating biotransformation rates of organic contaminants in aquatic invertebrates.

Toxicokinetic (TK) models refer to the process of contaminant bioaccumulation as a balance between rate of uptake from different sources (e.g., water or food), and rate of elimination via different processes such as excretion, growth and/or biotransformation. Biotransformation can considerably modify the fate of chemicals in an organism, especially their bioavailability, residence time, and toxicity. Invertebrate models generally neglect this process as they assume a low metabolic activity. However, some species such as Gammarus sp. amphipods are able to metabolize a vast range of organic compounds. Some recent TK models include biotransformation, but they prove limited for estimating related parameters by giving negative values and/or large uncertainties for biotransformation rate(s). Here we propose a generic TK model accounting for biotransformation using a Bayesian framework for simultaneously estimating the parameters. We illustrated the added value of our method by fitting this generic TK model to 22 published datasets of several benthic invertebrate species exposed to different chemicals. All parameters are estimated simultaneously for all datasets and showed narrow estimates. Furthermore, the median model predictions and their 95% credibility intervals showed that the model confidently fitted the data. In most cases the uncertainties around biotransformation rate(s) were reduced in comparison to the original studies. From a methodology standpoint, this paper reflects that Bayesian inference has real added value for simultaneously estimating all TK parameters for parent chemicals and their metabolite(s) based on all available data, while accounting for different types of data and the correlation between parameters. Bayesian inference was able to overcome the limits of previous methods, since no parameters were fixed and no irrelevant negative values were obtained. Moreover, the 95% credibility intervals around model predictions, which are core uncertainties for Environmental Risk Assessment, were easily acquired.

A Bayesian framework for estimating parameters of a generic toxicokinetic model for the bioaccumulation of organic chemicals by benthic invertebrates: Proof of concept with PCB153 and two freshwater species.

Toxicokinetic (TK) models are relevant and widely used to predict chemical concentrations in biological organisms. The importance of dietary uptake for aquatic invertebrates has been increasingly assessed in recent years. However, the model parameters are estimated on limited specific laboratory data sets that are bounded by several uncertainties. The aim of this study was to implement a Bayesian framework for simultaneously estimating the parameters of a generic TK model for benthic invertebrate species from all data collected. We illustrate our approach on the bioaccumulation of PCB153 by two species with different life traits and therefore exposure routes: Chironomus riparius larvae exposed to spiked sediment for 7 days and Gammarus fossarum exposed to spiked sediment and/or leaves for 7 days and then transferred to a clean media for 7 more days. The TK models assuming first-order kinetics were fitted to the data using Bayesian inference. The median model predictions and their 95% credibility intervals showed that the model fit the data well. From a methodological point of view, this paper illustrates that simultaneously estimating all model parameters from all available data by Bayesian inference, while considering the correlation between parameters and different types of data, is a real added value for TK modeling. Moreover, we demonstrated the ability of a generic TK model considering uptake and elimination routes as modules to add according to the availability of the data measured. From an ecotoxicological point of view, we show differences in PCB153 bioaccumulation between chironomids and gammarids, explained by the different life traits of these two organisms.

Characterization of atmospheric emission sources in lichen from metal and organic contaminant patterns.

Lichen samples from contrasted environments, influenced by various anthropic activities, were investigated focusing on the contaminant signatures according to the atmospheric exposure typologies. Most of the contaminant concentrations measured in the 27 lichen samples, collected around the industrial harbor of Fos-sur-Mer (France), were moderate in rural and urban environments, and reached extreme levels in industrial areas and neighboring cities (Al up to 6567 mg kg-1, Fe 42,398 mg kg-1, or ΣPAH 1417 µg kg-1 for example). At the same time, a strong heterogeneity was noticed in industrial samples while urban and rural ones were relatively homogeneous. Several metals could be associated to steel industry (Fe, Mn, Cd), road traffic, and agriculture (Sb, Cu, Sn), or to a distinct chemical installation (Mo). As well, PCDFs dominated in industrial samples while PCDDs prevailed in urban areas. The particularities observed supported the purpose of this work and discriminated the contributions of various atmospheric pollution emission sources in lichen samples. A statistical approach based on principal component analysis (PCA) was applied and resolved these potential singularities into specific component factors. Even if a certain degree of mixing of the factors is pointed out, relevant relationships were observed with several atmospheric emission sources. By this methodology, the contribution of industrial emissions to the atmospheric metal, PAH, PCB, and PCDD/F levels was roughly estimated to be 60.2%, before biomass burning (10.2%) and road traffic (3.8%). These results demonstrate that lichen biomonitoring offers an encouraging perspective of spatially resolved source apportionment studies.