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

Contribution of ingestive/dietary uptake to bioaccumulation of organics in worms

Ingestive uptake is critical for understanding the accumulation and trophic transfer of chemicals and synthesized particles in general. This study explored the contribution of ingestion in the bioaccumulation of chemicals focusing on worms. Novel theory and equations were developed to derive fractional ingestive contribution, fs, from a broad range of dietary uptake and accumulation studies, and to build a small dataset of fs (n = 43) from relevant toxicokinetic and bioaccumulation measurements. Worm fs could be fitted to log KOW-based sigmoidal models with small errors (RSE < 0.15, RMSE<0.15). The basis and limitations of the applied fs equations were elaborated. These included the assumption that aqueous-based and dietary-based elimination rate constants (kTw and kTS) may be statistically equivalent, as demonstrated using fish and worm data. Bioaccumulation and toxicokinetic parameters obtained at under-exposed conditions can also result in non-sensical, negative fs. The developed fs theory suggested a novel way to model bioaccumulation in the presence of aqueous and solid sources, and the potential to consolidate bioaccumulation data in their variant forms and definitions for assessment, modeling, and benchmarking purposes. While the presented fs-log KOW dependence remained to be explored in other species, the importance of ingestive uptake for high-log KOW chemicals questioned the validity of characterizing and regulating bioaccumulation potential of hydrophobic organics - for which dietary uptake matters - using aqueous-only bioconcentration factor (BCF). This question, along with other less important ones, is yet to be explored in future works.

A proof-of-concept multi-tiered Bayesian approach for the integration of physiochemical properties and toxicokinetic time-course data for Daphnia magna

The use of in silico and in vitro methods, commonly referred to as New Approach Methodologies (NAMs), has been proposed to support environmental (and human) chemical safety decisions, ensuring enhanced environmental protection. Toxicokinetic models developed for environmentally relevant species are fundamental to the deployment of a NAMs-based safety strategy, enabling the conversion between external and internal chemical concentrations, although they require historical toxicokinetic data and robust physical models to be considered a viable solution. Daphnia magna is a key model organism in ecotoxicology albeit with limited and scattered quantitative toxicokinetic data, as for most invertebrates, resulting in a lack of robust toxicokinetic models. Moreover, current D. magna models are chemical specific, which restricts their applicability domain. One aim of this study was to address the current data availability limitations by collecting toxicokinetic time-course data for D. magna covering a broad chemical space and assessing the dataset's uniqueness. The collated toxicokinetic dataset included 48 time-courses for 30 chemicals from 17 studies, which was developed into an R package named AquaTK, with 11 studies unique to our work when compared to existing databases. Subsequently, a proof-of-concept Bayesian analysis was developed to estimate the steady-state concentration ratio (internal concentration / external concentration) from the data at three different levels of precision given three different data availability scenarios for environmental risk assessment. Specifically, an atrazine case study illustrates the multi-level modelling approach providing improvements (uncertainty reductions) in predictions of ratios for increasing amounts of data availability. Our work provides a consistent and self-contained Bayesian framework that irrespective of the hierarchy or resolution of individual experiments, can utilise the available information to generate optimal predictions of steady-state concentration ratios in D. magna. This approach is paramount to supporting the implementation of a NAMs based environmental safety paradigm shift in environmental risk assessment.

Assessing viral freshwater hazard using a toxicokinetic model and Dreissena polymorpha

The detection all pathogenic enteric viruses in water is expensive, time-consuming, and limited by numerous technical difficulties. Consequently, using reliable indicators such as F-specific RNA phages (FRNAPH) can be well adapted to assess the risk of viral contamination of fecal origin in surface waters. However, the variability of results inherent to the water matrix makes it difficult to use them routinely and to interpret viral risk. Spatial and temporal variability of surface waters can lead to underestimate this risk, in particular in the case of low loading. The use of bivalve mollusks as accumulating systems appears as a promising alternative, as recently highlighted with the freshwater mussel Dreissena polymorpha, but its capacity to accumulate and depurate FRNAPH needs to be better understood and described. The purpose of this study is to characterise the kinetics of accumulation and elimination of infectious FRNAPH by D. polymorpha in laboratory conditions, formalised by a toxico-kinetic (TK) mechanistic model. Accumulation and depuration experiments were performed at a laboratory scale to determine the relationship between the concentration of infectious FRNAPH in water and the concentration accumulated by D. polymorpha. The mussels accumulated infectious FRNAPH (3-5.4 × 104 PFU/g) in a fast and concentration-dependent way in only 48 h, as already recently demonstrated. The second exposure demonstrated that the kinetics of infectious FRNAPH depuration by D. polymorpha was independent to the exposure dose, with a T90 (time required to depurate 90 % of the accumulated concentration) of approximately 6 days. These results highlight the capacities of D. polymorpha to detect and reflect the viral pollution in an integrative way and over time, which is not possible with point water sampling. Different TK models were fitted based on the concentrations measured in the digestive tissues (DT) of D. polymorpha. The model has been developed to formalise the kinetics of phage accumulation in mussels tissues through the simultaneous estimation of accumulation and depuration rates. This model showed that accumulation depended on the exposure concentration, while depuration did not. Standardized D. polymorpha could be easily transplanted to the environment to predict viral concentrations using the TK model defined in the present study to predict the level of contamination of bodies of water on the basis of the level of phages accumulated by the organisms. It will be also provide a better understanding of the dynamics of the virus in continental waters at different time and spatial scales, and thereby contribute to the protection of freshwater resources.

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.

Spiking organic chemicals onto sediments for ecotoxicological analyses: an overview of methods and procedures

Laboratory testing with spiked sediments with organic contaminants is a valuable tool for ecotoxicologists to study specific processes such as effects of known concentrations of toxicants, interactions of the toxicants with sediment and biota, and uptake kinetics. Since spiking of the sediment may be performed by using different strategies, a plethora of procedures was proposed in the literature for spiking organic chemicals onto sediments to perform ecotoxicological analyses. In this paper, we reviewed the scientific literature intending to characterise the kind of substrates that were used for spiking (i.e. artificial or field-collected sediment), how the substrates were handled before spiking and amended with the organic chemical, how the spiked sediment was mixed to allow the homogenisation of the chemical on the substrate and finally how long the spiked sediment was allowed to equilibrate before testing. What emerged from this review is that the choice of the test species, the testing procedures and the physicochemical properties of the organic contaminant are the primary driving factors affecting the selection of substrate type, sediment handling procedures, solvent carrier and mixing method. Finally, we provide recommendations concerning storage and characterization of the substrate, equilibrium times and verification of both equilibration and homogeneity.

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.

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.

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 MOSAIC_bioacc (http://mosaic.univ-lyon1.fr/bioacc). MOSAIC_bioacc provides all kinds 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 online interface that automatically fits toxicokinetic models without need 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. Integr Environ Assess Manag 2022;18:10-18. © 2021 SETAC.

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.

One and multi-compartments toxico-kinetic modeling to understand metals' organotropism and fate in Gammarus fossarum

The use of freshwater invertebrates for biomonitoring has been increasing for several decades, but little is known about relations between external exposure concentration of metals and their biodistribution among different tissues. One and multi-compartments toxicokinetic (TK) models are powerful tools to formalize and predict how a contaminant is bioaccumulated. The aim of this study is to develop modeling approaches to improve knowledge on dynamic of accumulation and fate of Cd and Hg in gammarid's organs. Gammarids were exposed to dissolved metals (11.1 ± 1.2 µg.L^-1 of Cd or 0.27 ± 0.13 µg.L^-1 of Hg) before a depuration phase. At each sampling days, their organs (caeca, cephalon, intestine and remaining tissues) were separated by dissection before analyses. Results allowed us to determine that i) G.fossarum takes up Cd as efficiently as the mussel M.galloprovincialis, but eliminates it more rapidly, ii) organs which accumulate and depurate the most, in terms of concentrations, are caeca and intestine for both metals; iii) the one-compartment TK models is the most relevant for Hg, while the multi-compartments TK model allows a better fit to Cd data, demonstrating dynamic transfer of Cd among organs.

Comparison of uptake and elimination kinetics of metallic oxide nanomaterials on the freshwater microcrustacean Daphnia magna

The widespread use and release of nanomaterials (NMs) in aquatic ecosystems is a concerning issue as well as the fate and behavior of the NMs in relation to the aquatic organisms. In this work, the freshwater microcrustacean Daphnia magna was exposed to 12 different and well-known NMs under the same conditions for 24 h and then placed in clean media for 120 h, in order to determine their different uptake and elimination behaviors. The results showed that most of the tested NMs displayed a fast uptake during the first hours arriving to a plateau by the end of the uptake phase. The elimination behavior was determined by a fast loss of NMs during the first hours in the clean media, mainly stimulated by the presence of food. Remaining NMs concentrations can still be found at the end of the elimination phase. Two NMs had a different profile (i) ZnO-NM110 exhibited increase and loss during the uptake phase, and (ii) SiO₂-NM204 did not show any uptake. A toxicokinetic model was applied and the uptake and elimination rates were found along with the dynamic bioconcentration factors. These values allowed to compare the NMs, to cluster them by their similar rates, and to determine that the TiO₂-NM102 is the one that has the fastest uptake and elimination behavior, SiO₂-NM204 has the slowest uptake and CeO₂ <10 nm has the slowest elimination. The present work represents a first attempt to compare different NMs based on their uptake and elimination behaviors from a perspective of the nano-bio interactions influence.

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.

Bayesian population physiologically-based pharmacokinetic model for robustness evaluation of withdrawal time in tilapia aquaculture administrated to florfenicol

The antimicrobial residues of aquacultural production is a growing public concern, leading to reexamine the method for establishing robust withdrawal time and ensuring food safety. Our study aims to develop the optimizing population physiologically-based pharmacokinetic (PBPK) model for assessing florfenicol residues in the tilapia tissues, and for evaluating the robustness of the withdrawal time (WT). Fitting with published pharmacokinetic profiles that experimented under temperatures of 22 and 28 °C, a PBPK model was constructed by applying with the Bayesian Markov chain Monte Carol (MCMC) algorithm to estimate WTs under different physiological, environmental and dosing scenarios. Results show that the MCMC algorithm improves the estimates of uncertainty and variability of PBPK-related parameters, and optimizes the simulation of the PBPK model. It is noteworthy that posterior sets generated from temperature-associated datasets to be respectively used for simulating residues under corresponding temperature conditions. Simulating the residues under regulated regimen and overdosing scenarios for Taiwan, the estimated WTs were 12-16 days at 22 °C and 9-12 days at 28 °C, while for the USA, the estimated WTs were 14-18 and 11-14 days, respectively. Comparison with the regulated WT of 15 days, results indicate that the current WT has well robustness and resilience in the environment of higher temperatures. The optimal Bayesian population PBPK model provides effective analysis for determining WTs under scenario-specific conditions. It is a new insight into the increasing body of literature on developing the Bayesian-PBPK model and has practical implications for improving the regulation of food safety.

Bayesian Data Integration Questions Classic Study on Protease Self-Digest Kinetics

We combine Bayesian data integration with kinetic modeling to rigorously identify reaction mechanisms. This approach forces models to be consistent not only with kinetic measurements but with all available information. We revisit a classic study on trypsin self-digest acceleration by colloidal silica. Bayesian data integration reveals that the mechanism suggested in that study is inconsistent with its presented data. We propose an improved hypothesis. However, the detailed mechanism of the surface reaction cannot be inferred from the available data.

Toxicokinetic modeling of octylphenol bioconcentration in Chlorella vulgaris and its trophic transfer to Daphnia magna

Bioconcentration of 4-tert-Octylphenol (OP) in freshwater algae Chlorella vulgaris was investigated by considering the effects of algal growth and exudate excretion. The OP uptake in algae was approximately 113 mg kg^-1 after 24 h, and the uptake rate constant was estimated as 2.4 × 10⁴ L kg^-1 d^-1. The OP sorption onto exudates reduced OP bioavailability to C. vulgaris to 11% after 24 h, with a sorption coefficient of 9.7 × 10³ L kg^-1. The elimination of OP by algae growth (0.80 d^-1) was dominant over real elimination (0.60 d^-1). The calculated bioconcentration factor of OP in C. vulgaris following uptake and elimination rate constants was 4.0 × 10⁴ L kg^-1. Further, bioaccumulation of OP in Daphnia magna was investigated by considering both aqueous and dietary (C. vulgaris) exposures. Uptake and elimination rates of OP via water were 1.6 × 10⁴ L kg^-1 d^-1 and 0.95 d^-1, respectively, while ingestion rate and assimilation efficiency via diet were 0.41 d^-1 and 58%, respectively. The OP accumulation in D. magna predominantly occurred via water (63%) relative to diet (37%), resulting in a bioaccumulation factor of 2.7 × 10⁴ L kg^-1. The estimated trophic transfer factor was 0.25, suggesting that OP biomagnification was unlikely in the C. vulgaris-D. magna trophic relationship.