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.

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.

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 MOSAIC _bioacc ( http://mosaic.univ-lyon1.fr/bioacc ). MOSAIC _bioacc provides all kind of bioaccumulation metrics associated with their uncertainty whatever the species-compound combination. MOSAIC _bioacc expects accumulation-depuration data as inputs, even with complex exposure and clearance patterns, to quickly perform their relevant analysis. MOSAIC _bioacc intends to facilitate the daily work of regulators, or any ecotoxicologist, who will freely benefit from a user-friendly on-line interface that automatically fits toxicokinetic models without needs for users to invest in the technical aspects to get bioaccumulation metrics estimates. MOSAIC _bioacc also provides all results in a fully transparent way to ensure reproducibility.

Dietary risk assessment of food contaminants requires a well-established understanding of the exposure in a heterogeneous population. There are many methods for estimating human exposure to food contaminants, such as intake calculations and internal biomarkers of exposure measured in individuals. However, those methods are expensive, partly invasive, and often provide a momentary exposure snapshot. Physiologically Based Kinetic (PBK) modelling is increasingly used to overcome those challenges that traditional human exposure methods encounter. Still, PBK models are often restricted to certain life stages (e.g., children, adolescents, adults). This study outlines a strategy for implementing nonlinear organ growths in age-specific PBK models to enhance dietary risk assessment from lifetime exposure. To this end, lifetime physiological equations calculating organ growth for both sexes were inventoried from literature and a library was established for 24 organs. We then assessed total lifelong mercury exposure via foodstuff by combining two existing age-specific PBK models for methylmercury (MeHg) and inorganic mercury (iHg) that simulated internal exposure to total mercury, the speciation typically measured in hair and urine. We implemented a set of physiological equations in the PBK model that fitted best the total mercury measured in individuals' organs, hair, and urine from heterogeneous populations. For refined dietary risk assessment, we ultimately estimated total mercury concentration in hair and urine based on i) maximum limits defined by the regulation for MeHg in seafood, ii) the health-based guidance values for MeHg and iHg, and iii) realistic intakes considering French demographic parameters and food consumption data. These exposure scenarios demonstrated that total mercury concentrations in hair and urine estimated from realistic intakes are below critical effect level measures at all ages. The result of this study is the creation of easily accessible tools in Excel and R that facilitate the implementation of physiological equations in Next Generation PBK models.