Imazalil and prochloraz toxicokinetics in fish probed by a physiologically based kinetic (PBK) model.

Azole fungicides are highly suspected endocrine disruptors (EDs) and are frequently detected in surface water. Among them, there are prochloraz (PCZ), a commonly used  molecule for ED studies, and imazalil (IMZ), a highly suspected ED. Little is known about their toxicokinetic (TK) behavior in fish. Hence, research suggested that an improved risk assessment could be achieved by gaining insight into their TK behavior. The aim of this study is to understand and model the TK of both substances in different fish species, irrespective of the scheme of exposure. TK data from the literature were retrieved including different modes of exposure (per os and waterborne). In addition, two experiments on zebrafish exposed to either IMZ or PCZ were performed to address the lack of in vivo TK data. A physiologically based kinetic (PBK) model applied to IMZ and PCZ was developed, capable of modeling different exposure scenarios. The parameters of the PBK model were simultaneously calibrated on datasets reporting internal concentration in several organs in three fish species (original and literature datasets) by Bayesian methods (Monte Carlo Markov Chain). Model predictions were then compared to other experimental data (i.e., excluded from the calibration step) to assess the predictive performance of the model. The results strongly suggest that PCZ and IMZ are actively transported across the gills, resulting in a small fraction being effectively absorbed by the fish. The model's results also confirm that both molecules are extensively metabolized by the liver into mainly glucuronate conjugates. Overall, the model performances were satisfying, predicting internal concentrations in several key organs. On average, 90% of experimental data were predicted within a two-fold range. The PBK model allows the understanding of IMZ and PCZ kinetics profiles by accurately predicting internal concentrations in three different fish species regardless of the exposure scenario. This enables a proper understanding of the mechanism of action of EDs at the molecular initiating event (MIE) by predicting bioaccumulation in target organs, thus linking this MIE to a possible adverse outcome.

Physiologically based Kinetic Modeling-Facilitated Quantitative In Vitro to In Vivo Extrapolation to Predict the Effects of Aloe-Emodin in Rats and Humans.

Aloe-emodin, a natural hydroxyanthraquinone, exerts both adverse and protective effects. This study aimed at investigating these potential effects of aloe-emodin in humans upon the use of food supplements and herbal medicines using a physiologically based kinetic (PBK) modeling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE) approach. For this, PBK models in rats and humans were established for aloe-emodin including its active metabolite rhein and used to convert in vitro data on hepatotoxicity, nephrotoxicity, reactive oxidative species (ROS) generation, and Nrf2 induction to corresponding in vivo dose-response curves, from which points of departure (PODs) were derived by BMD analysis. The derived PODs were subsequently compared to the estimated daily intakes (EDIs) resulting from the use of food supplements or herbal medicines. It is concluded that the dose levels of aloe-emodin from food supplements or herbal medicines are unlikely to induce toxicity, ROS generation, or Nrf2 activation in liver and kidney.

Next generation risk assessment for occupational chemical safety - A real world example with sodium-2-hydroxyethane sulfonate.

Next Generation Risk Assessment (NGRA) is an exposure-led approach to safety assessment that uses New Approach Methodologies (NAMs). Application of NGRA has been largely restricted to assessments of consumer use of cosmetics and is not currently implemented in occupational safety assessments, e.g. under EU REACH. By contrast, a large proportion of regulatory worker safety assessments are underpinned by toxicological studies using experimental animals. Consequently, occupational safety assessment represents an area that would benefit from increasing application of NGRA to safety decision making. Here, a workflow for conducting NGRA under an occupational safety context was developed, which is illustrated with a case study chemical; sodium 2-hydroxyethane sulphonate (sodium isethionate or SI). Exposures were estimated using a standard occupational exposure model following a comprehensive life cycle assessment of SI and considering factory-specific data. Outputs of this model were then used to estimate internal exposures using a Physiologically Based Kinetic (PBK) model, which was constructed with SI specific Absorption, Distribution, Metabolism and Excretion (ADME) data. PBK modelling indicated a worst-case plasma maximum concentration (Cmax) of 0.8 µM across the SI life cycle. SI bioactivity was assessed in a battery of NAMs relevant to systemic, reproductive, and developmental toxicity; a cell stress panel, high throughput transcriptomics in three cell lines (HepG2, HepaRG and MCF-7 cells), pharmacological profiling and specific assays relating to developmental toxicity (Reprotracker and devTOX quickPredict). Points of Departure (PoDs) for SI ranged from 104 to 5044 µM. Cmax values obtained from PBK modelling of occupational exposures to SI were compared with PoDs from the bioactivity assays to derive Bioactivity Exposure Ratios (BERs) which demonstrated the safety for workers exposed to SI under current levels of factory specific risk management. In summary, the tiered and iterative workflow developed here represents an opportunity for integrating non animal approaches for a large subset of substances for which systemic worker safety assessment is required. Such an approach could be followed to ensure that animal testing is only conducted as a "last resort" e.g. under EU REACH.

Application of partition coefficient methods to predict tissue:plasma affinities in common farm animals: Influence of ionisation state.

Tissue affinities are conventionally determined from in vivo steady-state tissue and plasma or plasma-water chemical concentration data. In silico approaches were initially developed for preclinical species but standardly applied and tested in human physiologically-based kinetic (PBK) models. Recently, generic PBK models for farm animals have been made available and require partition coefficients as input parameters. In the current investigation, data for species-specific tissue compositions have been collected, and prediction of chemical distribution in various tissues of livestock species for cattle, chicken, sheep and swine have been performed. Overall, tissue composition was very similar across the four farm animal species. However, small differences were observed in moisture, fat and protein content in the various organs within each species. Such differences could be attributed to factors such as variations in age, breed, and weight of the animals and general conditions of the animal itself. With regards to the predictions of tissue:plasma partition coefficients, 80 %, 71 %, 77 % of the model predictions were within a factor 10 using the methods of Berezhkovskiy (2004), Rodgers and Rowland (2006) and Schmitt (2008). The method of Berezhkovskiy (2004) was often providing the most reliable predictions except for swine, where the method of Schmitt (2008) performed best. In addition, investigation of the impact of chemical classes on prediction performance, all methods had very similar reliability. Notwithstanding, no clear pattern regarding specific chemicals or tissues could be detected for the values predicted outside a 10-fold change in certain chemicals or specific tissues. This manuscript concludes with the need for future research, particularly focusing on lipophilicity and species differences in protein binding.

Systematic evaluation of high-throughput PBK modelling strategies for the prediction of intravenous and oral pharmacokinetics in humans.

Physiologically based kinetic (PBK) modelling offers a mechanistic basis for predicting the pharmaco-/toxicokinetics of compounds and thereby provides critical information for integrating toxicity and exposure data to replace animal testing with in vitro or in silico methods. However, traditional PBK modelling depends on animal and human data, which limits its usefulness for non-animal methods. To address this limitation, high-throughput PBK modelling aims to rely exclusively on in vitro and in silico data for model generation. Here, we evaluate a variety of in silico tools and different strategies to parameterise PBK models with input values from various sources in a high-throughput manner. We gather 2000 + publicly available human in vivo concentration-time profiles of 200 + compounds (IV and oral administration), as well as in silico, in vitro and in vivo determined compound-specific parameters required for the PBK modelling of these compounds. Then, we systematically evaluate all possible PBK model parametrisation strategies in PK-Sim and quantify their prediction accuracy against the collected in vivo concentration-time profiles. Our results show that even simple, generic high-throughput PBK modelling can provide accurate predictions of the pharmacokinetics of most compounds (87% of Cmax and 84% of AUC within tenfold). Nevertheless, we also observe major differences in prediction accuracies between the different parameterisation strategies, as well as between different compounds. Finally, we outline a strategy for high-throughput PBK modelling that relies exclusively on freely available tools. Our findings contribute to a more robust understanding of the reliability of high-throughput PBK modelling, which is essential to establish the confidence necessary for its utilisation in Next-Generation Risk Assessment.

From hazard to risk prioritization: a case study to predict drug-induced cholestasis using physiologically based kinetic modeling.

Cholestasis is characterized by hepatic accumulation of bile acids. Clinical manifestation of cholestasis only occurs in a small proportion of exposed individuals. The present study aims to develop a new approach methodology (NAM) to predict drug-induced cholestasis as a result of drug-induced hepatic bile acid efflux inhibition and the resulting bile acid accumulation. To this end, hepatic concentrations of a panel of drugs were predicted by a generic physiologically based kinetic (PBK) drug model. Their effects on hepatic bile acid efflux were incorporated in a PBK model for bile acids. The predicted bile acid accumulation was used as a measure for a drug's cholestatic potency. The selected drugs were known to inhibit hepatic bile acid efflux in an assay with primary suspension-cultured hepatocytes and classified as common, rare, or no for cholestasis incidence. Common cholestasis drugs included were atorvastatin, chlorpromazine, cyclosporine, glimepiride, ketoconazole, and ritonavir. The cholestasis incidence of the drugs appeared not to be adequately predicted by their Ki for inhibition of hepatic bile acid efflux, but rather by the AUC of the PBK model predicted internal hepatic drug concentration at therapeutic dose level above this Ki. People with slower drug clearance, a larger bile acid pool, reduced bile salt export pump (BSEP) abundance, or given higher than therapeutic dose levels were predicted to be at higher risk to develop drug-induced cholestasis. The results provide a proof-of-principle of using a PBK-based NAM for cholestasis risk prioritization as a result of transporter inhibition and identification of individual risk factors.

Exploring the potential and challenges of developing physiologically-based toxicokinetic models to support human health risk assessment of microplastic and nanoplastic particles.

Microplastics (MPs) and nanoplastics (NPs) pollution has emerged as a significant and widespread environmental issue. Humans are inevitably exposed to MPs and NPs via ingestion, inhalation, and dermal contacts from various sources. However, mechanistic knowledge of their distribution, interaction, and potency in the body is still lacking. To address this knowledge gap, we have undertaken the task of elucidating the toxicokinetic (TK) behaviors of MPs and NPs, aiming to provide mechanistic information for constructing a conceptual physiologically based toxicokinetic (PBTK) model to support in silico modeling approaches. Our effort involved a thorough examination of the existing literature and data collation on the presence of MPs in the human body and in vitro/ex vivo/in vivo biodistribution across various cells and tissues. By comprehending the absorption, distribution, metabolism, and excretion mechanisms of MPs and NPs in relation to their physicochemical attributes, we established a foundational understanding of the link between external exposure and internal tissue dosimetry. We observed that particle size and surface chemistry have been thoroughly explored in previous experimental studies. However, certain attributes, such as polymer type, shape, and biofilm/biocorona, warrant attention and further examination. We discussed the fundamental disparities in TK properties of MPs/NPs from those of engineered nanoparticles. We proposed a preliminary PBTK framework with several possible modeling approaches and discussed existing challenges for further investigation. Overall, this article provides a comprehensive compilation of existing TK data of MPs/NPs, a critical overview of TK processes and mechanisms, and proposes potential PBTK modeling approaches, particularly regarding their applicability to the human system, and outlines future perspectives for developing PBTK models and their integration into human health risk assessment of MPs and NPs.

Parameter grouping and co-estimation in physiologically based kinetic models using genetic algorithms.

Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.

A novel method to derive a human safety limit for PFOA by gene expression profiling and modelling.

Perfluorooctanoic acid (PFOA) is a persistent environmental contaminant that can accumulate in the human body due to its long half-life. This substance has been associated with liver, pancreatic, testicular and breast cancers, liver steatosis and endocrine disruption. PFOA is a member of a large group of substances also known as "forever chemicals" and the vast majority of substances of this group lack toxicological data that would enable their effective risk assessment in terms of human health hazards. This study aimed to derive a health-based guidance value for PFOA intake (ng/kg BW/day) from in vitro transcriptomics data. To this end, we developed an in silico workflow comprising five components: (i) sourcing in vitro hepatic transcriptomics concentration-response data; (ii) deriving molecular points of departure using BMDExpress3 and performing pathway analysis using gene set enrichment analysis (GSEA) to identify the most sensitive molecular pathways to PFOA exposure; (iii) estimating freely-dissolved PFOA concentrations in vitro using a mass balance model; (iv) estimating in vivo doses by reverse dosimetry using a PBK model for PFOA as part of a quantitative in vitro to in vivo extrapolation (QIVIVE) algorithm; and (v) calculating a tolerable daily intake (TDI) for PFOA. Fourteen percent of interrogated genes exhibited in vitro concentration-response relationships. GSEA pathway enrichment analysis revealed that "fatty acid metabolism" was the most sensitive pathway to PFOA exposure. In vitro free PFOA concentrations were calculated to be 2.9% of the nominal applied concentrations, and these free concentrations were input into the QIVIVE workflow. Exposure doses for a virtual population of 3,000 individuals were estimated, from which a TDI of 0.15 ng/kg BW/day for PFOA was calculated using the benchmark dose modelling software, PROAST. This TDI is comparable to previously published values of 1.16, 0.69, and 0.86 ng/kg BW/day by the European Food Safety Authority. In conclusion, this study demonstrates the combined utility of an "omics"-derived molecular point of departure and in silico QIVIVE workflow for setting health-based guidance values in anticipation of the acceptance of in vitro concentration-response molecular measurements in chemical risk assessment.

The role and mechanism of PDZ binding kinase in hypobaric and hypoxic acute lung injury.

Background: Acute lung injury (ALI) caused by hypobaric hypoxia (HH) is frequently observed in high-altitude areas, and it is one of the leading causes of death in high-altitude-related diseases due to its rapid onset and progression. However, the pathogenesis of HH-related ALI (HHALI) remains unclear, and effective treatment approaches are currently lacking. Methods: A new mouse model of HHALI developed by our laboratory was used as the study subject (Chinese patent No. ZL 2021 1 1517241 X). Real-time quantitative polymerase chain reaction (RT-qPCR) was used to detect the messenger RNA (mRNA) expression levels of PDZ-binding kinase (PBK), sirtuin 1 (SIRT1), and PTEN-induced kinase 1 (PINK1) in mouse lung tissue. Hematoxylin and eosin staining was used to observe the main types of damage and damaged cells in lung tissue, and the lung injury score was used for quantification. The wet-dry (W/D) ratio was used to measure lung water content. Enzyme-linked immunosorbent assay was used to detect changes in inflammatory factors and oxidative stress markers in the lungs. Western blotting verified the expression of various mitochondrial autophagy-related proteins. The 5,5',6,6'-tetrachloro-1,1',3,3'-tetraethylbenzimi-dazoylcarbocyanine iodide (JC-1) method was used determined the health status of mitochondria based on changes in mitochondrial membrane potential. Transmission electron microscopy was used to directly observe the morphology of mitochondria. Multicolor immunofluorescence was used to observe the levels of mitochondrial autophagy markers. Other signaling pathways and molecular mechanisms that may play a role in epithelial cells were analyzed via through RNA sequencing. Results: Low pressure and hypoxia caused pathological changes in mouse lung tissue, mainly ALI, leading to increased levels of inflammatory factors and intensified oxidative stress response in the lungs. Overexpression of PBK was found to alleviate HHALI, and activation of the p53 protein was shown to abrogate this therapeutic effect, while activation of SIRT1 protein reactivated this therapeutic effect. The therapeutic effect of PBK on HHALI is achieved via the activation of mitochondrial autophagy. Finally, RNA sequencing demonstrated that besides mitochondrial autophagy, PBK also exerts other functions in HHALI. Conclusions: Overexpression of PBK inhibits the expression of p53 and activates SIRT1-PINK1 axis mediated mitochondrial autophagy to alleviate HHALI.

Nano- and microplastic PBK modeling in the context of human exposure and risk assessment.

Insufficient data on nano- and microplastics (NMP) hinder robust evaluation of their potential health risks. Methodological disparities and the absence of established toxicity thresholds impede the comparability and practical application of research findings. The diverse attributes of NMP, such as variations in sizes, shapes, and compositions, complicate human health risk assessment. Although probability density functions (PDFs) show promise in capturing this diversity, their integration into risk assessment frameworks is limited. Physiologically based kinetic (PBK) models offer a potential solution to bridge the gap between external exposure and internal dosimetry for risk evaluation. However, the heterogeneity of NMP poses challenges for accurate biodistribution modeling. A literature review, encompassing both experimental and modeling studies, was conducted to examine biodistribution studies of monodisperse micro- and nanoparticles. The literature search in PubMed and Scopus databases yielded 39 studies that met the inclusion criteria. Evaluation criteria were adapted from previous Quality Assurance and Quality Control (QA-QC) studies, best practice guidelines from WHO (2010), OECD guidance (2021), and additional criteria specific to NMP risk assessment. Subsequently, a conceptual framework for a comprehensive NMP-PBK model was developed, addressing the multidimensionality of NMP particles. Parameters for an NMP-PBK model are presented. QA-QC evaluations revealed that most experimental studies scored relatively well (>0) in particle characterizations and environmental settings but fell short in criteria application for biodistribution modeling. The evaluation of modeling studies revealed that information regarding the model type and allometric scaling requires improvement. Three potential applications of PDFs in PBK modeling of NMP are identified: capturing the multidimensionality of the NMP continuum, quantifying the probabilistic definition of external exposure, and calculating the bio-accessibility fraction of NMP in the human body. A framework for an NMP-PBK model is proposed, integrating PDFs to enhance the assessment of NMP's impact on human health.

Characterizing the risk related to the exposure to methylmercury over a lifetime: A global approach using population internal exposure.

Seafood products accumulate methylmercury throughout the food chain and are the main source of methylmercury exposure. Methylmercury may trigger a number of adverse health effects, such as neurodevelopmental or nephrotoxic effects, the risk of which cannot be ruled out for the French high consumers of seafood. The characterisation of methylmercury-related risks is generally based on short-term dietary exposure without considering changes in consumption and exposure over the lifetime. Additionally, focusing on short-term dietary exposure, the fate of methylmercury (especially its accumulation) in the organism is not considered. The present study proposes a methodology basing risk characterization on estimates of body burden over a lifetime. First, trajectories of dietary exposures throughout lifetime were constructed based on the actual concentrations of total diet studies for a fictive representative French population, taking into account the social, economic and demographic parameters of individuals. Next, the fate of methylmercury in the body was estimated, based on these trajectories, using a specific physiologically-based kinetic (PBK) model that generated a representative pool of body burden trajectories. Simulated hair mercury concentrations were closed to previously reported French representative human biomonitoring data. Results showed that at certain stages of life, concentrations of methylmercury in hair were higher than the human biomonitoring guidance value set at 2.5 µg/g of hair by JECFA. This study showed the added value, in the case of substances accumulating in the body, of estimating dietary exposure over a lifetime and using exposure biomarkers estimated by a PBK model characterize the risk.

Phosphorylation of PBK at Thr9 by CDK5 correlates with invasion of prolactinomas.

CONTEXT: Prolactinomas are the most prevalent functional pituitary neuroendocrine tumors (PitNETs), and they are invasive to surrounding anatomic structures. The detailed mechanisms of invasion are not yet clear. OBJECTIVE: We explored the role of PBK phosphorylation in the proliferation and invasion of prolactinomas and its possible mechanism. RESULTS: We report that PBK directly binds to and is phosphorylated at Thr9 by cyclin-dependent kinase 5 (CDK5), which promotes GH3 cell EMT progression and proliferation. Phosphorylation of PBK at Thr9 (pPBK-T9) by CDK5 enhances the stability of PBK. p38 is one of the downstream targets of PBK, and its phosphorylation is reduced as pPBK-T9 increases in vivo and in vitro. Furthermore, we found that pPBK-T9 is highly expressed in invasive PitNETs and was significantly correlated with invasion by univariate and multivariate analyses. CONCLUSIONS: Phosphorylation of PBK at Thr9 by CDK5 promotes cell proliferation and EMT progression in prolactinomas.

A systems toxicology approach for identification of disruptions in cholesterol homeostasis after aggregated exposure to mixtures of perfluorinated compounds in humans.

Per- and polyfluoroalkyl substances (PFAS) are used in various household and industrial products. In humans, positive associations were reported between PFAS, including perfluorsulfonic acid and perfluorooctanoic acid, and cholesterol, a cardiometabolic risk factor. Animal studies show the opposite. Human-centered approaches are needed to better understand the effects of PFAS mixtures on cholesterol. Here, a systems toxicology approach is described, using a gene-centered cholesterol biokinetic model. PFAS exposure-gene expression relations from published data were introduced into the model. An existing PFAS physiologically based kinetic model was augmented with lung and dermal compartments and integrated with the cholesterol model to enable exposure-effect modeling. The final model was populated with data reflecting lifetime mixture exposure from: tolerable weekly intake values; the environment; high occupational exposures (ski waxing, PFAS industry). Results indicate that low level exposures (tolerable weekly intake, environmental) did not change cholesterol. In contrast, occupational exposures clearly resulted in internal PFAS exposure and disruption of cholesterol homeostasis, largely in line with epidemiological observations. Despite model limitations (eg, dynamic range, directionality), changes in cholesterol homeostasis were predicted for ski waxers, hitherto unknown from epidemiological studies. Here, future studies involving lipid metabolism could improve risk assessment.

Assessing chronic gestational exposure to environmental chemicals in pregnant women: Advancing the co-PBK model.

Vulnerable populations, such as pregnant women and their fetuses, confront potential health risks due to exposure to environmental toxic compounds. Computational methods have been popular in assessing chemical exposure to populations, contrasting with traditional cohort studies for human biomonitoring. This study proposes a screening-level approach based on physiologically based kinetic (PBK) modeling to evaluate the steady-state exposure of pregnant women to environmental chemicals throughout pregnancy. To exemplify the modeling application, naphthalene was chosen. Simulation results indicated that maternal fat exhibited significant bioaccumulation potential, with the log-transformed BTF of naphthalene at 0.51 mg kg-1 per mg d-1 in the steady state. The placenta was primarily exposed to 0.83 mg/d naphthalene for a 75.2 kg pregnant woman, considering all exposure routes. In the fetal structure, single-organ fetal PBK modeling estimated a naphthalene exposure of 123.64 mg/d to the entire fetus, while multiple-organ fetal PBK modeling further revealed the bioaccumulation highest in fat tissue. The liver identified as the vital organ for metabolism, kBioT,LiverM was demonstrated with the highest sensitivity among rate constants in the maternal body. Furthermore, the first-order kinetic rate constants related to the placenta and blood were found to impact the distribution process of naphthalene in the fetus, influencing gestational exposure. In conclusion, urgent attention is needed to develop a computational biomonitoring tool for assessing toxic chemical exposure in vulnerable populations.

Acetylcholinesterase Inhibition in Rats and Humans Following Acute Fenitrothion Exposure Predicted by Physiologically Based Kinetic Modeling-Facilitated Quantitative In Vitro to In Vivo Extrapolation.

Worldwide use of organophosphate pesticides as agricultural chemicals aims to maintain a stable food supply, while their toxicity remains a major public health concern. A common mechanism of acute neurotoxicity following organophosphate pesticide exposure is the inhibition of acetylcholinesterase (AChE). To support Next Generation Risk Assessment for public health upon acute neurotoxicity induced by organophosphate pesticides, physiologically based kinetic (PBK) modeling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE) approach was employed in this study, with fenitrothion (FNT) as an exemplary organophosphate pesticide. Rat and human PBK models were parametrized with data derived from in silico predictions and in vitro incubations. Then, PBK model-based QIVIVE was performed to convert species-specific concentration-dependent AChE inhibition obtained from in vitro blood assays to corresponding in vivo dose-response curves, from which points of departure (PODs) were derived. The obtained values for rats and humans were comparable with reported no-observed-adverse-effect levels (NOAELs). Humans were found to be more susceptible than rats toward erythrocyte AChE inhibition induced by acute FNT exposure due to interspecies differences in toxicokinetics and toxicodynamics. The described approach adequately predicts toxicokinetics and acute toxicity of FNT, providing a proof-of-principle for applying this approach in a 3R-based chemical risk assessment paradigm.

Predicting acute paraquat toxicity using physiologically based kinetic modelling incorporating in vitro active renal excretion via the OCT2 transporter.

Including active renal excretion in physiologically based kinetic (PBK) models can improve their use in quantitative in vitro- in vivo extrapolation (QIVIVE) as a new approach methodology (NAM) for predicting the acute toxicity of organic cation transporter 2 (OCT2) substrates like paraquat (PQ). To realise this NAM, kinetic parameters Vmax and Km for in vitro OCT2 transport of PQ were obtained from the literature. Appropriate scaling factors were applied to translate the in vitro Vmax to an in vivo Vmax. in vitro cytotoxicity data were defined in the rat RLE-6TN and L2 cell lines and the human A549 cell line. The developed PQ PBK model was used to apply reverse dosimetry for QIVIVE translating the in vitro cytotoxicity concentration-response curves to predicted in vivo toxicity dose-response curves after which the lower and upper bound benchmark dose (BMD) for 50% lethality (BMDL50 and BMDU50) were derived by applying BMD analysis. Comparing the predictions to the in vivo reported LD50 values resulted in a conservative prediction for rat and a comparable prediction for human showing proof of principle on the inclusion of active renal excretion and prediction of PQ acute toxicity for the developed NAM.

Development of a Generic PBK Model for Human Biomonitoring with an Application to Deoxynivalenol.

Toxicokinetic modelling provides a powerful tool in relating internal human exposure (i.e., assessed through urinary biomarker levels) to external exposure. Chemical specific toxicokinetic models are available; however, this specificity prevents their application to similar contaminants or to other routes of exposure. For this reason, we investigated whether a generic physiological-based kinetic (PBK) model might be a suitable alternative for a biokinetic model of deoxynivalenol (DON). IndusChemFate (ICF) was selected as a generic PBK model, which could be fit for purpose. Being suited for simulating multiple routes of exposure, ICF has particularly been used to relate the inhalation and dermal exposure of industrial chemicals to their urinary excretion. For the first time, the ICF model was adapted as a generic model for the human biomonitoring of mycotoxins, thereby extending its applicability domain. For this purpose, chemical-specific data for DON and its metabolites were collected directly from the literature (distribution and metabolism) or indirectly (absorption and excretion) by fitting the ICF model to previously described urinary excretion data. The obtained results indicate that this generic model can be used to model the urinary excretion of DON and its glucuronidated metabolites following dietary exposure to DON. Additionally, the present study establishes the basis for further development of the model to include an inhalation exposure route alongside the oral exposure route.

ADME characterization and PBK model development of 3 highly protein-bound UV filters through topical application.

Estimating human exposure in the safety assessment of chemicals is crucial. Physiologically based kinetic (PBK) models which combine information on exposure, physiology, and chemical properties, describing the absorption, distribution, metabolism, and excretion (ADME) processes of a chemical, can be used to calculate internal exposure metrics such as maximum concentration and area under the concentration-time curve in plasma or tissues of a test chemical in next-generation risk assessment. This article demonstrates the development of PBK models for 3 UV filters, specifically octyl methoxycinnamate, octocrylene, and 4-methylbenzylidene camphor. The models were parameterized entirely based on data obtained from in vitro and/or in silico methods in a bottom-up modeling approach and then validated based on human dermal pharmacokinetic (PK) data. The 3 UV filters are "difficult to test" in in vitro test systems due to high lipophilicity, high binding affinity for proteins, and nonspecific binding, for example, toward plastic. This research work presents critical considerations in ADME data generation, interpretation, and parameterization to assure valid PBK model development to increase confidence in using PBK modeling to help make safety decisions in the absence of human PK data. The developed PBK models of the 3 chemicals successfully simulated the plasma concentration profiles of clinical PK data following dermal application, indicating the reliability of the ADME data generated and the parameters determined. The study also provides insights and lessons learned for characterizing ADME and developing PBK models for highly lipophilic and protein-bound chemicals in the future.

Integrating In Vitro Data and Physiologically Based Kinetic Modeling to Predict and Compare Acute Neurotoxic Doses of Saxitoxin in Rats, Mice, and Humans.

Current climate trends are likely to expand the geographic distribution of the toxigenic microalgae and concomitant phycotoxins, making intoxications by such toxins a global phenomenon. Among various phycotoxins, saxitoxin (STX) acts as a neurotoxin that might cause severe neurological symptoms in mammals following consumptions of contaminated seafood. To derive a point of departure (POD) for human health risk assessment upon acute neurotoxicity induced by oral STX exposure, a physiologically based kinetic (PBK) modeling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE) approach was employed. The PBK models for rats, mice, and humans were built using parameters from the literature, in vitro experiments, and in silico predictions. Available in vitro toxicity data for STX were converted to in vivo dose-response curves via the PBK models established for these three species, and POD values were derived from the predicted curves and compared to reported in vivo toxicity data. Interspecies differences in acute STX toxicity between rodents and humans were found, and they appeared to be mainly due to differences in toxicokinetics. The described approach resulted in adequate predictions for acute oral STX exposure, indicating that new approach methodologies, when appropriately integrated, can be used in a 3R-based chemical risk assessment paradigm.