A prioritization strategy for functional alternatives to bisphenol A in food contact materials.

The use of bisphenol A (BPA), a substance of very high concern, is proposed to be banned in food contact materials (FCMs) in the European Union. To prevent regrettable substitution of BPA by alternatives with similar or unknown hazardous properties, it is of importance to gain the relevant toxicological information on potential BPA alternative substances and monitor them adequately. We created an inventory of over 300 substances mentioned as potential BPA alternatives in regulatory reports and scientific literature. This study presents a prioritization strategy to identify substances that may be used as an alternative to BPA in FCMs. We prioritized 20 potential BPA alternatives of which 10 are less familiar. We subsequently reviewed the available information on the 10 prioritized less familiar substances regarding hazard profiles and migration potential obtained from scientific literature and in silico screening tools to identify a possible risk of the substances. Major data gaps regarding the hazard profiles of the prioritized substances exist, although the scarce available data give some indications on the possible hazard for some of the substances (like bisphenol TMC, 4,4-dihydroxybenzophenone, and tetrachlorobisphenol A). In addition, very little is known about the actual use and exposure to these substances. More toxicological research and monitoring of these substances in FCMs are, therefore, required to avoid regrettable substitution of BPA in FCM.

New Approach Methodologies (NAMs) for ad hoc human health risk assessment of food and non-food products - Proceedings of a workshop.

RIVM convened a workshop on the use of New Approach Methodologies (NAMs) for the ad hoc human health risk assessment of food and non-food products. Central to the workshop were two case studies of marketed products with a potential health concern: the botanical Tabernanthe iboga which is used to facilitate mental or spiritual insight or to (illegally) treat drug addiction and is associated with cardiotoxicity, and dermal creams containing female sex hormones, intended for use by perimenopausal women to reduce menopause symptoms without medical supervision. The workshop participants recognized that data from NAM approaches added valuable information for the ad hoc risk assessment of these products, although the available approaches were inadequate to derive health-based guidance values. Recommendations were provided on how to further enhance and implement NAM approaches in regulatory risk assessment, specifying both scientific and technical aspects as well as stakeholder engagement aspects.

The Bigger Fish: A Comparison of Meta-Learning QSAR Models on Low-Resourced Aquatic Toxicity Regression Tasks.

Toxicological information as needed for risk assessments of chemical compounds is often sparse. Unfortunately, gathering new toxicological information experimentally often involves animal testing. Simulated alternatives, e.g., quantitative structure-activity relationship (QSAR) models, are preferred to infer the toxicity of new compounds. Aquatic toxicity data collections consist of many related tasks─each predicting the toxicity of new compounds on a given species. Since many of these tasks are inherently low-resource, i.e., involve few associated compounds, this is challenging. Meta-learning is a subfield of artificial intelligence that can lead to more accurate models by enabling the utilization of information across tasks. In our work, we benchmark various state-of-the-art meta-learning techniques for building QSAR models, focusing on knowledge sharing between species. Specifically, we employ and compare transformational machine learning, model-agnostic meta-learning, fine-tuning, and multi-task models. Our experiments show that established knowledge-sharing techniques outperform single-task approaches. We recommend the use of multi-task random forest models for aquatic toxicity modeling, which matched or exceeded the performance of other approaches and robustly produced good results in the low-resource settings we studied. This model functions on a species level, predicting toxicity for multiple species across various phyla, with flexible exposure duration and on a large chemical applicability domain.

Prioritization of chemicals in food for risk assessment by integrating exposure estimates and new approach methodologies: A next generation risk assessment case study.

Next generation risk assessment is defined as a knowledge-driven system that allows for cost-efficient assessment of human health risk related to chemical exposure, without animal experimentation. One of the key features of next generation risk assessment is to facilitate prioritization of chemical substances that need a more extensive toxicological evaluation, in order to address the need to assess an increasing number of substances. In this case study focusing on chemicals in food, we explored how exposure data combined with the Threshold of Toxicological Concern (TTC) concept could be used to prioritize chemicals, both for existing substances and new substances entering the market. Using a database of existing chemicals relevant for dietary exposure we calculated exposure estimates, followed by application of the TTC concept to identify substances of higher concern. Subsequently, a selected set of these priority substances was screened for toxicological potential using high-throughput screening (HTS) approaches. Remarkably, this approach resulted in alerts for a selection of substances that are already on the market and represent relevant exposure in consumers. Taken together, the case study provides proof-of-principle for the approach taken to identify substances of concern, and this approach can therefore be considered a supportive element to a next generation risk assessment strategy.

ZZS similarity tool: The online tool for similarity screening to identify chemicals of potential concern.

Screening and prioritization of chemicals is essential to ensure that available evaluation capacity is invested in those substances that are of highest concern. We, therefore, recently developed structural similarity models that evaluate the structural similarity of substances with unknown properties to known Substances of Very High Concern (SVHC), which could be an indication of comparable effects. In the current study the performance of these models is improved by (1) separating known SVHCs in more specific subgroups, (2) (re-)optimizing similarity models for the various SVHC-subgroups, and (3) improving interpretability of the predicted outcomes by providing a confidence score. The improvements are directly incorporated in a freely accessible web-based tool, named the ZZS similarity tool: https://rvszoeksysteem.rivm.nl/ZzsSimilarityTool. Accordingly, this tool can be used by risk assessors, academia and industrial partners to screen and prioritize chemicals for further action and evaluation within varying frameworks, and could support the identification of tomorrow's substances of concern.

Dose Addition in the Induction of Craniofacial Malformations in Zebrafish Embryos Exposed to a Complex Mixture of Food-Relevant Chemicals with Dissimilar Modes of Action.

BACKGROUND: Humans are exposed to combinations of chemicals. In cumulative risk assessment (CRA), regulatory bodies such as the European Food Safety Authority consider dose addition as a default and sufficiently conservative approach. The principle of dose addition was confirmed previously for inducing craniofacial malformations in zebrafish embryos in binary mixtures of chemicals with either similar or dissimilar modes of action (MOAs). OBJECTIVES: In this study, we explored a workflow to select and experimentally test multiple compounds as a complex mixture with each of the compounds at or below its no observed adverse effect level (NOAEL), in the same zebrafish embryo model. METHODS: Selection of candidate compounds that potentially induce craniofacial malformations was done using in silico methods-structural similarity, molecular docking, and quantitative structure-activity relationships-applied to a database of chemicals relevant for oral exposure in humans via food (EuroMix inventory, n=1,598). A final subselection was made manually to represent different regulatory fields (e.g., food additives, industrial chemicals, plant protection products), different chemical families, and different MOAs. RESULTS: A final selection of eight compounds was examined in the zebrafish embryo model, and craniofacial malformations were observed in embryos exposed to each of the compounds, thus confirming the developmental toxicity as predicted by the in silico methods. When exposed to a mixture of the eight compounds, each at its NOAEL, substantial craniofacial malformations were observed; according to a dose-response analysis, even embryos exposed to a 7-fold dilution of this mixture still exhibited a slight abnormal phenotype. The cumulative effect of the compounds in the mixture was in accordance with dose addition (added doses of the individual compounds after adjustment for relative potencies), despite different MOAs of the compounds involved. DISCUSSION: This case study of a complex mixture inducing craniofacial malformations in zebrafish embryos shows that dose addition can adequately predicted the cumulative effect of a mixture of multiple substances at low doses, irrespective of the (expected) MOA. The applied workflow may be useful as an approach for CRA in general. https://doi.org/10.1289/EHP9888.

Characterization of ecotoxicological risks from unintentional mixture exposures calculated from European freshwater monitoring data: Forwarding prospective chemical risk management.

Current regulatory chemical safety assessments do not acknowledge that ambient exposures are to multiple chemicals at the same time. As a result, potentially harmful exposures to unintentional mixtures may occur, leading to potential insufficient protection of the environment. The present study describes cumulative environmental risk assessment results for European fresh water ecosystems, based on the NORMAN chemical surface water monitoring database (1998-2016). It aims to characterize the magnitude of the mixture problem and the relative contribution of chemicals to the mixture risk, and evaluates how cumulative risks reduce when the acceptable risk per single chemical is fractionally lowered. Available monitoring data were curated and aggregated to 26,631 place-time combinations with at least two chemicals, of which 376 place-time combinations had at least 25 chemicals identified above the Limit of Detection. Various risk metrics were based on measured environmental concentrations (MECs). Mixture risk characterization ratio's (ΣRCRs) ≥ 1 were found for 39% of the place-time combinations, with few chemicals dominating the ΣRCR. Analyses of mixture toxic pressures, expressed as multi-substance Potentially Affected Fractions of species based on No Observed Effect Concentrations (msPAFNOEC), showed similar outcomes. Small fractional reductions of the ambient chemical concentrations give a steep increase of the percentage of sufficiently protected water bodies (i.e. ΣRCR < 1 and msPAFNOEC < 5%). Scientific and regulatory aspects of these results are discussed, especially with reference to the representativeness of the monitoring data for characterizing ambient mixtures, the robustness of the findings, and the possible regulatory implementation of the concept of a Mixture Allocation Factor (MAF) for prospective chemicals risk management. Although the monitoring data do not represent the full spectrum of ambient mixture exposures in Europe, results show the need for adapting policies to reach European Union goals for a toxic-free environment and underpin the utility and possible magnitude of a MAF.

Model development for evidence-based prioritisation of policy action on emerging chemical and microbial drinking water risks.

While the burden of disease from well-studied drinking water contaminants is declining, risks from emerging chemical and microbial contaminants arise because of social, technological, demographic and climatological developments. At present, emerging chemical and microbial drinking water contaminants are not assessed in a systematic way, but reactively and incidence based. Furthermore, they are assessed separately despite similar pollution sources. As a result, risks might be addressed ineffectively. Integrated risk assessment approaches are thus needed that elucidate the uncertainties in the risk evaluation of emerging drinking water contaminants, while considering risk assessors' values. This study therefore aimed to (1) construct an assessment hierarchy for the integrated evaluation of the potential risks from emerging chemical and microbial contaminants in drinking water and (2) develop a decision support tool, based on the agreed assessment hierarchy, to quantify (uncertain) risk scores. A multi-actor approach was used to construct the assessment hierarchy, involving chemical and microbial risk assessors, drinking water experts and members of responsible authorities. The concept of value-focused thinking was applied to guide the problem-structuring and model-building process. The development of the decision support tool was done using Decisi-o-rama, an open-source Python library. With the developed decision support tool (uncertain) risk scores can be calculated for emerging chemical and microbial drinking water contaminants, which can be used for the evidence-based prioritisation of actions on emerging chemical and microbial drinking water risks. The decision support tool improves existing prioritisation approaches as it combines uncertain indicator levels with a multi-stakeholder approach and integrated the risk assessment of chemical and microbial contaminants. By applying the concept of value-focused thinking, this study addressed difficulties in evidence-based decision-making related to emerging drinking water contaminants. Suggestions to improve the model were made to guide future research in assisting policy makers to effectively protect public health from emerging drinking water risks.

Relevance of In Vitro Transcriptomics for In Vivo Mode of Action Assessment.

Recently, we reported an in vitro toxicogenomics comparison approach to categorize chemical substances according to similarities in their proposed toxicological modes of action. Use of such an approach for regulatory purposes requires, among others, insight into the extent of biological concordance between in vitro and in vivo findings. To that end, we applied the comparison approach to transcriptomics data from the Open TG-GATEs database for 137 substances with diverging modes of action and evaluated the outcomes obtained for rat primary hepatocytes and for rat liver. The results showed that a relatively small number of matches observed in vitro were also observed in vivo, whereas quite a large number of matches between substances were found to be relevant solely in vivo or in vitro. The latter could not be explained by physicochemical properties, leading to insufficient bioavailability or poor water solubility. Nevertheless, pathway analyses indicated that for relevant matches the mechanisms perturbed in vitro are consistent with those perturbed in vivo. These findings support the utility of the comparison approach as tool in mechanism-based risk assessment.

Evaluating chemical similarity as a measure to identify potential substances of very high concern.

Due to the large amount of chemical substances on the market, fast and reproducible screening is essential to prioritize chemicals for further evaluation according to highest concern. We here evaluate the performance of structural similarity models that are developed to identify potential substances of very high concern (SVHC) based on structural similarity to known SVHCs. These models were developed following a systematic analysis of the performance of 112 different similarity measures for varying SVHC-subgroups. The final models consist of the best combinations of fingerprint, similarity coefficient and similarity threshold, and suggested a high predictive performance (≥80%) on an internal dataset consisting of SVHC and non-SVHC substances. However, the application performance on an external dataset was not evaluated. Here, we evaluated the application performance of the developed similarity models with a 'pseudo-external assessment' on a set of substances (n = 60-100 for the varying SVHC-subgroups) that were putatively assessed as SVHC or non-SVHC based upon consensus scoring using expert elicitations (n = 30 experts). Expert scores were direct evaluations based on structural similarity to the most similar SVHCs according to the similarity models, and did not consider an extensive evaluation of available data. The use of expert opinions is particularly suitable as this is exactly the intended purpose of the chemical similarity models: a quick, reproducible and automated screening tool that mimics the expert judgement that is frequently applied in various screening applications. In addition, model predictions were analyzed via qualitative approaches and discussed via specific examples, to identify the model's strengths and limitations. The results indicate a good statistical performance for carcinogenic, mutagenic or reprotoxic (CMR) and endocrine disrupting (ED) substances, whereas a moderate performance was observed for (very) persistent, (very) bioaccumulative and toxic (PBT/vPvB) substances when compared to expert opinions. For the PBT/vPvB model, particularly false positive substances were identified, indicating the necessity of outcome interpretation. The developed similarity models are made available as a freely-accessible online tool. In general, the structural similarity models showed great potential for screening and prioritization purposes. The models proved to be effective in identifying groups of substances of potential concern, and could be used to identify follow-up directions for substances of potential concern.

The effects of aliphatic alcohols and related acid metabolites in zebrafish embryos - correlations with rat developmental toxicity and with effects in advanced life stages in fish.

The zebrafish embryo toxicity test (ZFET) is a simple medium-throughput test to inform about (sub)acute lethal effects in embryos. Enhanced analysis through morphological and teratological scoring, and through gene expression analysis, detects developmental effects and the underlying toxicological pathways. Altogether, the ZFET may inform about hazard of chemical exposure for embryonal development in humans, as well as for lethal effects in juvenile and adult fish. In this study, we compared the effects within a series of 12 aliphatic alcohols and related carboxylic acid derivatives (ethanol, acetic acid, 2-methoxyethanol, 2-methoxyacetic acid, 2-butoxyethanol, 2-butoxyacetic acid, 2-hydroxyacetic acid, 2-ethylhexan-1-ol, 2-ethylhexanoic acid, valproic acid, 2-aminoethanol, 2-(2-hydroxyethylamino)ethanol) in ZFET and early life stage (ELS, 28d) exposures, and compared ZFET results with existing results of rat developmental studies and LC50s in adult fish. High correlation scores were observed between compound potencies in ZFET with either ELS, LC50 in fish and developmental toxicity in rats, indicating similar potency ranking among the models. Compounds could be mapped to specific pathways in an adverse outcome pathway (AOP) network through morphological scoring and gene expression analysis in ZFET. Similarity of morphological effects and gene expression profiles in pairs of alcohols with their acid metabolites suggested metabolic activation of the parent alcohols, although with additional, metabolite-independent activity independent for ethanol and 2-ethylhexanol. Overall, phenotypical and gene expression analysis with these compounds indicates that the ZFET can potentially contribute to the AOP for developmental effects in rodents, and to predict toxicity of acute and chronic exposure in advanced life stages in fish.

Distinguishing mode of action of compounds inducing craniofacial malformations in zebrafish embryos to support dose-response modeling in combined exposures.

Knowledge on mode-of-action (MOA) is required to understand toxicological effects of compounds, notably in the context of risk assessment of mixtures. Such information is generally scarce, and often complicated by the existence of multiple MOAs per compound. Here, MOAs related to developmental craniofacial malformations were derived from literature, and assembled in a MOA network. A selection of gene expression markers was based on these MOAs. Next, these markers were verified by qPCR in zebrafish embryos, after exposure to reference compounds. These were: triazoles for inhibition of retinoic acid (RA) metabolism, AM580 and CD3254 for selective activation of respectively RA-receptor (RAR) and retinoid-X-receptor (RXR), dithiocarbamates for inhibition of lysyl oxidase, TCDD for activation of the aryl-hydrocarbon-receptor (AhR), VPA for inhibition of histone deacetylase (HDAC), and PFOS for activation of peroxisome proliferator-activated receptor-alpha (PPARα). Next, marker gene profiles for these reference compounds were used to map the profiles of test compounds to known MOAs. In this way, 2,4-dinitrophenol matched with the TCDD and RAR profiles, boric acid with RAR, endosulfan with PFOS, fenpropimorph with dithiocarbamates, PCB126 with AhR, and RA with triazoles and RAR profiles. Prochloraz showed no match. Activities of these compounds in ToxCast assays, and in silico analysis of binding affinity to the respective targets showed limited concordance with the marker gene expression profiles, but still confirmed the complex MOA profiles of reference and test compounds. Ultimately, this approach could be used to support modeling of mixture effects based on upfront knowledge of (dis)similarity of MOAs.

A generic PBTK model implemented in the MCRA platform: Predictive performance and uses in risk assessment of chemicals.

Physiologically-based toxicokinetic (PBTK) models are important tools for in vitro to in vivo or inter-species extrapolations in health risk assessment of foodborne and non-foodborne chemicals. Here we present a generic PBTK model implemented in the EuroMix toolbox, MCRA 9 and predict internal kinetics of nine chemicals (three endocrine disrupters, three liver steatosis inducers, and three developmental toxicants), in data-rich and data-poor conditions, when increasingly complex levels of parametrization are applied. At the first stage, only QSAR models were used to determine substance-specific parameters, then some parameter values were refined by estimates from substance-specific or high-throughput in vitro experiments. At the last stage, elimination or absorption parameters were calibrated based on available in vivo kinetic data. The results illustrate that parametrization plays a capital role in the output of the PBTK model, as it can change how chemicals are prioritized based on internal concentration factors. In data-poor situations, estimates can be far from observed values. In many cases of chronic exposure, the PBTK model can be summarized by an external to internal dose factor, and interspecies concentration factors can be used to perform interspecies extrapolation. We finally discuss the implementation and use of the model in the MCRA risk assessment platform.

A retain and refine approach to cumulative risk assessment.

Mixtures of substances to which humans are exposed may lead to cumulative exposure and health effects. To study their effects, it is first necessary to identify a cumulative assessment group (CAG) of substances for risk assessment or hazard testing. Excluding substances from consideration before there is sufficient evidence may underestimate the risk. Conversely, including everything and treating the inevitable uncertainties using conservative assumptions is inefficient and may overestimate the risk, with an unknown level of protection. An efficient, transparent strategy is described to retain a large group, quantifying the uncertainty of group membership and other uncertainties. Iterative refinement of the CAG then focuses on adding information for the substances with high probability of contributing significantly to the risk. Probabilities can be estimated using expert opinion or derived from data on substance properties. An example is presented with 100 pesticides, in which the retain step identified a single substance to target refinement. Using an updated hazard characterisation for this substance reduced the mean exposure estimate from 0.43 to 0.28 µg kg-bw-1 day-1 and reduced the 99.99th percentile exposure from 24.9 to 5.1 µg kg-bw-1 day-1. Other retained substances contributed little to the risk estimates, even after accounting for uncertainty.

A Case Study with Triazole Fungicides to Explore Practical Application of Next-Generation Hazard Assessment Methods for Human Health.

The ongoing developments in chemical risk assessment have led to new concepts building on integration of sophisticated nonanimal models for hazard characterization. Here we explore a pragmatic approach for implementing such concepts, using a case study of three triazole fungicides, namely, flusilazole, propiconazole, and cyproconazole. The strategy applied starts with evaluating the overall level of concern by comparing exposure estimates to toxicological potential, followed by a combination of in silico tools and literature-derived high-throughput screening assays and computational elaborations to obtain insight into potential toxicological mechanisms and targets in the organism. Additionally, some targeted in vitro tests were evaluated for their utility to confirm suspected mechanisms of toxicity and to generate points of departure. Toxicological mechanisms instead of the current "end point-by-end point" approach should guide the selection of methods and assays that constitute a toolbox for next-generation risk assessment. Comparison of the obtained in silico and in vitro results with data from traditional in vivo testing revealed that, overall, nonanimal methods for hazard identification can produce adequate qualitative hazard information for risk assessment. Follow-up studies are needed to further refine the proposed approach, including the composition of the toolbox, toxicokinetics models, and models for exposure assessment.

The MCRA toolbox of models and data to support chemical mixture risk assessment.

A model and data toolbox is presented to assess risks from combined exposure to multiple chemicals using probabilistic methods. The Monte Carlo Risk Assessment (MCRA) toolbox, also known as the EuroMix toolbox, has more than 40 modules addressing all areas of risk assessment, and includes a data repository with data collected in the EuroMix project. This paper gives an introduction to the toolbox and illustrates its use with examples from the EuroMix project. The toolbox can be used for hazard identification, hazard characterisation, exposure assessment and risk characterisation. Examples for hazard identification are selection of substances relevant for a specific adverse outcome based on adverse outcome pathways and QSAR models. Examples for hazard characterisation are calculation of benchmark doses and relative potency factors with uncertainty from dose response data, and use of kinetic models to perform in vitro to in vivo extrapolation. Examples for exposure assessment are assessing cumulative exposure at external or internal level, where the latter option is needed when dietary and non-dietary routes have to be aggregated. Finally, risk characterisation is illustrated by calculation and display of the margin of exposure for single substances and for the cumulation, including uncertainties derived from exposure and hazard characterisation estimates.

Testing developmental toxicity in a second species: are the differences due to species or replication error?

Developmental toxicity studies for chemical and pharmaceutical safety are primarily performed in rats. Regulatory frameworks may require testing in a second, non-rodent species, for which the rabbit is usually chosen. This study shows that differences in NOAELs or LOAELs (N(L)OAELs) observed between rat and rabbit developmental toxicity studies performed according to OECD guidelines could just as well be caused by study replication errors, and not necessarily by differences in species sensitivity. This conclusion follows from an analysis of a database with rat and rabbit developmental toxicity studies for over 1000 industrial chemicals, pesticides, veterinary drugs and human pharmaceuticals, which included 143 compounds with multiple oral rat studies and 124 compounds with multiple oral rabbit studies. Our analysis confirms earlier findings that, on average over all compounds, rat and rabbit do not differ in sensitivity to developmental effects. There is substantial scatter in the correlation plots comparing rat and rabbit developmental N(L)OAELs, which is easily interpreted as species differences for individual compounds. However, for compounds tested twice in the same species, these N(L)OAELs may differ up to a factor of 25. Thus, potential interspecies differences in developmental N(L)OAEL will be overwhelmed by the reproducibility error, rendering the added value of a second species study questionable. As N(L)OAELs serve as point of departure (POD) for setting health-based guidance values in risk assessment, the large reproducibility error of N(L)OAELs should be taken into account by the introduction of an additional uncertainty factor. It is recommended to aim for reducing the reproducibility error by applying dose-response (BMD) analysis, optimize study designs and harmonize study protocols.

Is current risk assessment of non-genotoxic carcinogens protective?

Non-genotoxic carcinogens (NGTXCs) do not cause direct DNA damage but induce cancer via other mechanisms. In risk assessment of chemicals and pharmaceuticals, carcinogenic risks are determined using carcinogenicity studies in rodents. With the aim to reduce animal testing, REACH legislation states that carcinogenicity studies are only allowed when specific concerns are present; risk assessment of compounds that are potentially carcinogenic by a non-genotoxic mode of action is usually based on subchronic toxicity studies. Health-based guidance values (HBGVs) of NGTXCs may therefore be based on data from carcinogenicity or subchronic toxicity studies depending on the legal framework that applies. HBGVs are usually derived from No-Observed-Adverse-Effect-Levels (NOAELs). Here, we investigate whether current risk assessment of NGTXCs based on NOAELs is protective against cancer. To answer this question, we estimated Benchmark doses (BMDs) for carcinogenicity data of 44 known NGTXCs. These BMDs were compared to the NOAELs derived from the same carcinogenicity studies, as well as to the NOAELs derived from the associated subchronic studies. The results lead to two main conclusions. First, a NOAEL derived from a subchronic study is similar to a NOAEL based on cancer effects from a carcinogenicity study, supporting the current practice in REACH. Second, both the subchronic and cancer NOAELs are, on average, associated with a cancer risk of around 1% in rodents. This implies that for those chemicals that are potentially carcinogenic in humans, current risk assessment of NGTXCs may not be completely protective against cancer. Our results call for a broader discussion within the scientific community, followed by discussions among risk assessors, policy makers, and other stakeholders as to whether or not the potential cancer risk levels that appear to be associated with currently derived HBGVs of NGXTCs are acceptable.

In vitro to in vivo extrapolation of effective dosimetry in developmental toxicity testing: Application of a generic PBK modelling approach.

Incorporation of kinetics to quantitative in vitro to in vivo extrapolations (QIVIVE) is a key step for the realization of a non-animal testing paradigm, in the sphere of regulatory toxicology. The use of Physiologically-Based Kinetic (PBK) modelling for determining systemic doses of chemicals at the target site is accepted to be an indispensable element for such purposes. Nonetheless, PBK models are usually designed for a single or a group of compounds and are considered demanding, with respect to experimental data needed for model parameterization. Alternatively, we evaluate here the use of a more generic approach, i.e. the so-called IndusChemFate model, which is based on incorporated QSAR model parametrization. The model was used to simulate the in vivo kinetics of three diverse classes of developmental toxicants: triazoles, glycol ethers' alkoxyacetic acid metabolites and phthalate primary metabolites. The model required specific input per each class of compounds. These compounds were previously tested in three alternative assays: the whole-embryo culture (WEC), the zebrafish embryo test (ZET), and the mouse embryonic stem cell test (EST). Thereafter, the PBK-simulated blood levels at toxic in vivo doses were compared to the respective in vitro effective concentrations. Comparisons pertaining to relative potency and potency ranking with integration of kinetics were similar to previously obtained comparisons. Additionally, all three in vitro systems produced quite comparable results, and hence, a combination of alternative tests is still preferable for predicting the endpoint of developmental toxicity in vivo. This approach is put forward as biologically more plausible since plasma concentrations, rather than external administered doses, constitute the most direct in vivo dose metric.