Development of a Generic Physiologically Based Kinetic Model for the Prediction of Internal Exposure to Organophosphate Pesticides.

Since their introduction into agriculture, the toxicity of organophosphate (OP) pesticides has been widely studied in animal models. However, next generation risk assessment (NGRA) intends to maximize the use of novel approach methodologies based on in vitro and in silico methods. Therefore, this study describes the development and evaluation of a generic physiologically based kinetic (PBK) model for acute exposure to OP pesticides in rats and humans using quantitative structure property relationships and data from published in vitro studies. The models were evaluated using in vivo studies from the literature for chlorpyrifos, diazinon, fenitrothion, methyl-parathion, ethyl-parathion, dimethoate, chlorfenvinphos, and profenofos. Evaluation was performed by comparing simulated and in vivo observed time profiles for blood, plasma, or urinary concentrations and other toxicokinetic parameters. Of simulated concentration-time profiles, 87 and 91% were within a 5-fold difference from observed toxicokinetic data from rat and human studies, respectively. Only for dimethyl-organophosphates further refinement of the model is required. It is concluded that the developed generic PBK model provides a new tool to assess species differences in rat and human kinetics of OP pesticides. This approach provides a means to perform NGRA for these compounds and could also be adopted for other classes of compounds.

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.

A dynamic inventory database for assessing age-, gender-, and route-specific chronic internal exposure to chemicals in support of human exposome research.

In this study, we proposed a dynamic inventory database to evaluate chronic internal exposure to chemicals at a population level, which enables users to perform modeling exercises specific to a particular chemical, route of exposure, age group, and gender. The database was built based on the steady-state solution of physiologically based kinetic (PBK) models. The biotransfer factors [BTF, the steady-state ratio between the chemical concentration in human tissues and the average daily dose (ADD) of the chemical] of 931 organic chemicals in major organs and tissues were simulated for a total of 14 population age groups for males and females. The results indicated that infants and children had the highest simulated BTFs of chemicals, and middle-aged adults had the lowest simulated BTFs. The route-specific analysis of the simulated BTFs indicated that the biotransformation half-life and octanol-water partition coefficient of chemicals had a profound impact on the BTFs. Organ- and chemical-specific results indicated that the biotransfer potential of chemicals in human bodies was primarily determined by bio-thermodynamic variables (e.g., lipid contents). In conclusion, the proposed inventory database can be conveniently used to access chronic internal exposure doses of chemicals by multiplying the route-specific ADD values for different population groups. In future studies, we recommend incorporating human biotransformation data, partition coefficients of ionizable chemicals, age-specific vulnerable indicators (e.g., the degree of maturation of immune systems), physiological variations within the same age group (e.g., intensity of daily physical activities), growth rates (i.e., the dilution effect on chemical biotransfer), and all possible target organs of carcinogenicity (e.g., bladder) into the proposed dynamic inventory database to help promote human exposome research.

Population pharmacokinetic model to generate mechanistic insights in bile acid homeostasis and drug-induced cholestasis.

Bile acids (BA) fulfill a wide range of physiological functions, but are also involved in pathologies, such as cholestasis. Cholestasis is characterized by an intrahepatic accumulation of BAs and subsequent spillage to the systemic circulation. The aim of the present study was to develop physiologically based kinetic (PBK) models that would provide a tool to predict dose-dependent BA accumulation in humans upon treatment with a Bile Salt Export Pump (BSEP) inhibitor. We developed a PBK model describing the BA homeostasis using glycochenodeoxycholic acid as an exemplary BA. Population wide distributions of BSEP abundances were incorporated in the PBK model using Markov Chain Monte Carlo simulations, and alternatively the total amount of BAs was scaled empirically to describe interindividual differences in plasma BA levels. Next, the effects of the BSEP inhibitor bosentan on the BA levels were simulated. The PBK model developed adequately predicted the in vivo BA dynamics. Both the Markov Chain Monte Carlo simulations based on a distribution of BSEP abundances and empirical scaling of the total BA pool readily described the variations within and between data in human volunteers. Bosentan treatment disproportionally increased the maximum BA concentration in individuals with a large total BA pool or low BSEP abundance. Especially individuals having a large total BA pool size and a low BSEP abundance were predicted to be at risk for rapid saturation of BSEP and subsequent intrahepatic BA accumulation. This model provides a first estimate of personalized safe therapeutic external dose levels of compounds with BSEP-inhibitory properties.

Inter-individual variation in chlorpyrifos toxicokinetics characterized by physiologically based kinetic (PBK) and Monte Carlo simulation comparing human liver microsome and Supersome™ cytochromes P450 (CYP)-specific kinetic data as model input.

The present study compares two approaches to evaluate the effects of inter-individual differences in the biotransformation of chlorpyrifos (CPF) on the sensitivity towards in vivo red blood cell (RBC) acetylcholinesterase (AChE) inhibition and to calculate a chemical-specific adjustment factor (CSAF) to account for inter-individual differences in kinetics (HKAF). These approaches included use of a Supersome™ cytochromes P450 (CYP)-based and a human liver microsome (HLM)-based physiologically based kinetic (PBK) model, both combined with Monte Carlo simulations. The results revealed that bioactivation of CPF exhibits biphasic kinetics caused by distinct differences in the Km of CYPs involved, which was elucidated by Supersome™ CYP rather than by HLM. Use of Supersome™ CYP-derived kinetic data was influenced by the accuracy of the intersystem extrapolation factors (ISEFs) required to scale CYP isoform activity of Supersome™ to HLMs. The predicted dose-response curves for average, 99th percentile and 1st percentile sensitive individuals were found to be similar in the two approaches when biphasic kinetics was included in the HLM-based approach, resulting in similar benchmark dose lower confidence limits for 10% inhibition (BMDL10) and HKAF values. The variation in metabolism-related kinetic parameters resulted in HKAF values at the 99th percentile that were slightly higher than the default uncertainty factor of 3.16. While HKAF values up to 6.9 were obtained when including also the variability in other influential PBK model parameters. It is concluded that the Supersome™ CYP-based approach appeared most adequate for identifying inter-individual variation in biotransformation of CPF and its resulting RBC AChE inhibition.

Modeling of clearance, retention, and translocation of inhaled gold nanoparticles in rats.

Objective: The increasing exposure to gold nanoparticles (AuNPs), due to their wide range of applications, has led to the need for thorough understanding of their biodistribution, following exposure. The objective of this paper is to develop a PBK model in order to study the clearance, retention and translocation of inhaled gold nanoparticles in rats, providing a basis for the understanding of the absorption, distribution, metabolism and elimination (ADME) mechanisms of AuNPs in various organs.Materials and methods: A rat PBK computational model was developed, connected to a detailed respiratory model, including the olfactory, tracheobronchial, and alveolar regions. This model was coupled with a Multiple Path Particle Dosimetry (MPPD) model to appropriately simulate the exposure to AuNPs. Three existing in vivo experimental datasets from scientific literature for the biodistribution of inhaled AuNPs for different AuNP sizes and exposure scenarios were utilized for model calibration and validation.Results and Discussion: The model was calibrated using two individual datasets for nose only inhaled and intratracheally instilled AuNPs, while an independent dataset for nose only inhaled AuNPs was used as external validation. The overall fitting over the three datasets was proved acceptable as shown by the relevant statistical metrics. The influence of several physiological parameters is also studied via a sensitivity analysis, providing useful insights into the mechanisms of NP pharmacokinetics. The key aspects of the inhaled AuNPs biodistribution are discussed, revealing the key mechanisms for the AuNPs absorption routes, the AuNP uptake by secondary organs and the influence of the AuNP size on the translocation from the lungs to blood circulation.Conclusions: The model results together with the model sensitivity analysis clarified the key mechanisms for the inhaled AuNPs biodistribution to secondary organs. It was observed that nose-only inhaled AuNPs of smaller size can enter the blood circulation through secondary routes, such as absorption through the gastrointestinal (GI) lumen, showing that such translocations should not be underestimated in biodistribution modelling. Finally, the computational framework presented in this study can be used as a basis for a more wide investigation of inhaled nanoparticles biodistribution, including interspecies extrapolation of the resulting PBK model for the inhalation and subsequent biodistribution of AuNPs in humans.

Prediction of dose-dependent in vivo acetylcholinesterase inhibition by profenofos in rats and humans using physiologically based kinetic (PBK) modeling-facilitated reverse dosimetry.

Organophosphate pesticides (OPs) are known to inhibit acetylcholine esterase (AChE), a critical effect used to establish health-based guidance values. This study developed a combined in vitro-in silico approach to predict AChE inhibition by the OP profenofos in rats and humans. A physiologically based kinetic (PBK) model was developed for both species. Parameter values for profenofos conversion to 4-bromo-2-chlorophenol (BCP) were derived from in vitro incubations with liver microsomes, liver cytosol, and plasma from rats (catalytic efficiencies of 1.1, 2.8, and 0.19 ml/min/mg protein, respectively) and humans (catalytic efficiencies of 0.17, 0.79, and 0.063 ml/min/mg protein, respectively), whereas other chemical-related parameter values were derived using in silico calculations. The rat PBK model was evaluated against literature data on urinary excretion of conjugated BCP. Concentration-dependent inhibition of rat and human AChE was determined in vitro and these data were translated with the PBK models to predicted dose-dependent AChE inhibition in rats and humans in vivo. Comparing predicted dose-dependent AChE inhibition in rats to literature data on profenofos-induced AChE inhibition revealed an accurate prediction of in vivo effect levels. Comparison of rat predictions (BMDL10 of predicted dose-response data of 0.45 mg/kg bw) and human predictions (BMDL10 of predicted dose-response data of 0.01 mg/kg bw) suggests that humans are more sensitive than rats, being mainly due to differences in kinetics. Altogether, the results demonstrate that in vivo AChE inhibition upon acute exposure to profenofos was closely predicted in rats, indicating the potential of this novel approach method in chemical hazard assessment.

Monocrotaline-induced liver toxicity in rat predicted by a combined in vitro physiologically based kinetic modeling approach.

The aim of the present study was to use an in vitro-in silico approach to predict the in vivo acute liver toxicity of monocrotaline and to characterize the influence of its metabolism on its relative toxic potency compared to lasiocarpine and riddelliine. In the absence of data on acute liver toxicity of monocrotaline upon oral exposure, the predicted dose-response curve for acute liver toxicity in rats and the resulting benchmark dose lower and upper confidence limits for 10% effect (BMDL10 and BMDU10) were compared to data obtained in studies with intraperitoneal or subcutaneous dosing regimens. This indicated the predicted BMDL10 value to be in line with the no-observed-adverse-effect levels (NOAELs) derived from availabe in vivo studies. The predicted BMDL10-BMDU10 of 1.1-4.9 mg/kg bw/day also matched the oral dose range of 1-3 mg PA/kg bw/day at which adverse effects in human are reported. A comparison to the oral toxicity of the related pyrrolizidine alkaloids (PAs) lasiocarpine and riddelliine revealed that, although in the rat hepatocytes monocrotaline was less toxic than lasiocarpine and riddelliine, due to its relatively inefficient clearance, its in vivo acute liver toxicity was predicted to be comparable. It is concluded that the combined in vitro-PBK modeling approach can provide insight in monocrotaline-induced acute liver toxicity in rats, thereby filling existing gaps in the database on PA toxicity. Furthermore, the results reveal that the kinetic and metabolic properties of PAs can vary substantially and should be taken into account when considering differences in relative potency between different PAs.

Integrating in vitro data and physiologically based kinetic modeling-facilitated reverse dosimetry to predict human cardiotoxicity of methadone.

Development of novel testing strategies to detect adverse human health effects is of interest to replace in vivo-based drug and chemical safety testing. The aim of the present study was to investigate whether physiologically based kinetic (PBK) modeling-facilitated conversion of in vitro toxicity data is an adequate approach to predict in vivo cardiotoxicity in humans. To enable evaluation of predictions made, methadone was selected as the model compound, being a compound for which data on both kinetics and cardiotoxicity in humans are available. A PBK model for methadone in humans was developed and evaluated against available kinetic data presenting an adequate match. Use of the developed PBK model to convert concentration-response curves for the effect of methadone on human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM) in the so-called multi electrode array (MEA) assay resulted in predictions for in vivo dose-response curves for methadone-induced cardiotoxicity that matched the available in vivo data. The results also revealed differences in protein plasma binding of methadone to be a potential factor underlying variation between individuals with respect to sensitivity towards the cardiotoxic effects of methadone. The present study provides a proof-of-principle of using PBK modeling-based reverse dosimetry of in vitro data for the prediction of cardiotoxicity in humans, providing a novel testing strategy in cardiac safety studies.

Defining in vivo dose-response curves for kidney DNA adduct formation of aristolochic acid I in rat, mouse and human by an in vitro and physiologically based kinetic modeling approach.

Aristolochic acid I (AAI) is a well-known genotoxic kidney carcinogen. Metabolic conversion of AAI into the DNA-reactive aristolactam-nitrenium ion is involved in the mode of action of tumor formation. This study aims to predict in vivo AAI-DNA adduct formation in the kidney of rat, mouse and human by translating the in vitro concentration-response curves for AAI-DNA adduct formation to the in vivo situation using physiologically based kinetic (PBK) modeling-based reverse dosimetry. DNA adduct formation in kidney proximal tubular LLC-PK1 cells exposed to AAI was quantified by liquid chromatography-electrospray ionization-tandem mass spectrometry. Subsequently, the in vitro concentration-response curves were converted to predicted in vivo dose-response curves in rat, mouse and human kidney using PBK models. Results obtained revealed a dose-dependent increase in AAI-DNA adduct formation in the rat, mouse and human kidney and the predicted DNA adduct levels were generally within an order of magnitude compared with values reported in the literature. It is concluded that the combined in vitro PBK modeling approach provides a novel way to define in vivo dose-response curves for kidney DNA adduct formation in rat, mouse and human and contributes to the reduction, refinement and replacement of animal testing.

Physiologically based kinetic modeling of the bioactivation of myristicin.

The present study describes physiologically based kinetic (PBK) models for the alkenylbenzene myristicin that were developed by extension of the PBK models for the structurally related alkenylbenzene safrole in rat and human. The newly developed myristicin models revealed that the formation of the proximate carcinogenic metabolite 1'-hydroxymyristicin in liver is at most 1.8 fold higher in rat than in human and limited for the ultimate carcinogenic metabolite 1'-sulfoxymyristicin to (2.8-4.0)-fold higher in human. In addition, a comparison was made between the relative importance of bioactivation for myristicin and safrole. Model predictions indicate that for these related compounds, the formation of the 1'-sulfoxy metabolites in rat and human liver is comparable with a difference of <2.2-fold over a wide dose range. The results from this PBK analysis support that risk assessment of myristicin may be based on the BMDL10 derived for safrole of 1.9-5.1 mg/kg bw per day. Using an estimated daily intake of myristicin of 0.0019 mg/kg bw per day resulting from the use of herbs and spices, this results in MOE values for myristicin that amount to 1000-2700, indicating a priority for risk management. The results obtained illustrate that PBK modeling provides insight into possible species differences in the metabolic activation of myristicin. Moreover, they provide an example of how PBK modeling can facilitate a read-across in risk assessment from a compound for which in vivo toxicity studies are available to a related compound for which tumor data are not reported, thus contributing to alternatives in animal testing.

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.

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.

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.

Impact of in vitro experimental variation in kinetic parameters on physiologically based kinetic (PBK) model simulations.

In vitro toxicokinetic data are critical in meeting an increased regulatory need to improve chemical safety evaluations towards a better understanding of internal human chemical exposure and toxicity. In vitro intrinsic hepatic clearance (CLint), the fraction unbound in plasma (fup), and the intestinal apparent permeability (Papp) are important parameters as input in a physiologically based kinetic (PBK) model to make first estimates of internal exposure after oral dosing. In the present study we explored the experimental variation in the values for these parameters as reported in the literature. Furthermore, the impact that this experimental variation has on PBK model predictions of maximum plasma concentration (Cmax) and the area under the concentration time curve (AUC0-24h) was determined. As a result of the experimental variation in CLint, Papp, and fup, the predicted variation in Cmax for individual compounds ranged between 1.4- to 28-fold, and the predicted variation in AUC0-24h ranged between 1.4- and 23-fold. These results indicate that there are still some important steps to take to achieve robust data that can be used in regulatory applications. To gain regulatory acceptance of in vitro kinetic data and PBK models based on in vitro input data, the boundaries in experimental conditions as well as the applicability domain and the use of different in vitro kinetic models need to be described in guidance documents.

Development of physiologically-based gut absorption model for probabilistic prediction of environmental chemical bioavailability.

Absorption in the gastrointestinal tract is a key factor determining the bioavailability of chemicals after oral exposure but is frequently assumed to have a conservative value of 100% for environmental chemicals, particularly in the context of high-throughput toxicokinetics for in vitro-to-in vivo extrapolation (IVIVE). For pharmaceutical compounds, the physiologically based advanced compartmental absorption and transit (ACAT) model has been used extensively to predict gut absorption but has not generally been applied to environmental chemicals. Here we develop a probabilistic environmental compart­mental absorption and transit (PECAT) model, adapting the ACAT model to environmental chemicals. We calibrated the model parameters to human in vivo, ex vivo, and in vitro datasets of drug permeability and fractional absorption by con­sidering two key factors: (1) differences between permeability in Caco-2 cells and in vivo permeability in the jejunum, and (2) differences in in vivo permeability across different gut segments. Incorporating these factors probabilistically, we found that given Caco-2 permeability measurements, predictions of the PECAT model are consistent with the (limited) available gut absorption data for environmental chemicals. However, the substantial chemical-to-chemical variability observed in the cal­ibration data often led to wide probabilistic confidence bounds in the predicted fraction absorbed and resulting steady state blood concentration. Thus, while the PECAT model provides a statistically rigorous, physiologically based approach for incor­porating in vitro data on gut absorption into toxicokinetic modeling and IVIVE, it also highlights the need for more accurate in vitro models and data for measuring gut segment-specific in vivo permeability of environmental chemicals.

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.

A physiologically based kinetic modeling of ethyl tert-butyl ether in humans-An illustrative application of quantitative structure-property relationship and Monte Carlo simulation.

Although physiologically based kinetic (PBK) modeling is informative for the risk assessment of industrial chemicals, chemical-specific input values for partition coefficients and metabolic parameters, including Vmax and Km are mostly unavailable; however, in silico methods, such as quantitative structure-property relationship (QSPR) could fill the absence. To assess the PBK model validity using necessary toxicokinetic (TK) parameters predicted by QSPR, the PBK model of ethyl tert-butyl ether (ETBE) as a model substance was constructed, in which the values of the partition coefficients, Vmax, and Km of ETBE were predicted using those of the related chemicals previously reported in the literature, and toxicokinetics of inhaled ETBE were stochastically estimated using the Monte Carlo simulation. The calculated ETBE concentrations in venous blood were comparable to the measured values in humans, implying that the reproducibility of ETBE toxicokinetics in humans was established in this PBK model. The Monte Carlo simulation was used to conduct uncertainty and sensitivity analyses of the dose metrics in terms of maximum blood concentration (Cmax) and area under the blood concentration-time curve (AUC) and the estimated Cmax and AUC were highly and moderately reliable, respectively. Conclusively, the PBK model validity combined with in silico methods of QSPR was demonstrated in an ETBE model substance. QSPR-PBK modeling coupled with the Monte Carlo simulation is effective for estimating chemical toxicokinetics for which input values are unavailable and for evaluating the estimation validity.

An integrative approach to define chemical exposure threshold limits for endangered sea turtles.

Environmental contaminants pose serious health threats to marine megafauna species, yet methods defining exposure threshold limits are lacking. Here, a three-pillar chemical risk assessment framework is presented based on (1) species- and chemical-specific lifetime bioaccumulation modelling, (2) non-destructive in vitro and in vivo toxicity threshold assessment, and (3) chemical risk quantification. We used the effects of cadmium (Cd) in green sea turtles (Chelonia mydas) as a proof of concept to evaluate the quantitative mechanistic modelling approach. A physiologically-based kinetic (PBK) model simulated Cd tissue concentrations (liver, kidney, muscle, fat, brain, scute, and 'rest of the body') in C.mydas. The validated PBK model then translated species-specific in vitro results to in vivo effects. The results showed that the resilience of C.mydas towards Cd kidney toxicity is age-dependent and differs with changing physiology and feeding ecology. Using the model in reverse mode, a steady-state exposure threshold of 0.1 µg/g dry weight Cd in forage was derived and compared to real-world exposure scenarios. Three out of the four globally distinct C.mydas populations assessed are exposed to Cd levels above this threshold limit. This approach can be adapted to other marine species and chemicals to prioritize measures for managing potentially harmful chemical exposures.

A Scoping Review of Technologies and Their Applicability for Exposome-Based Risk Assessment in the Oil and Gas Industry.

INTRODUCTION: Oil and gas workers have been shown to be at increased risk of chronic diseases including cancer, asthma, chronic obstructive pulmonary disease, and hearing loss, among others. Technological advances may be used to assess the external (e.g. personal sensors, smartphone apps and online platforms, exposure models) and internal exposome (e.g. physiologically based kinetic modeling (PBK), biomonitoring, omics), offering numerous possibilities for chronic disease prevention strategies and risk management measures. The objective of this study was to review the literature on these technologies, by focusing on: (i) evaluating their applicability for exposome research in the oil and gas industry, and (ii) identifying key challenges that may hamper the successful application of such technologies in the oil and gas industry. METHOD: A scoping review was conducted by identifying peer-reviewed literature with searches in MEDLINE/PubMed and SciVerse Scopus. Two assessors trained on the search strategy screened retrieved articles on title and abstract. The inclusion criteria used for this review were: application of the aforementioned technologies at a workplace in the oil and gas industry or, application of these technologies for an exposure relevant to the oil and gas industry but in another occupational sector, English language and publication period 2005-end of 2019. RESULTS: In total, 72 articles were included in this scoping review with most articles focused on omics and bioinformatics (N = 22), followed by biomonitoring and biomarkers (N = 20), external exposure modeling (N = 11), PBK modeling (N = 10), and personal sensors (N = 9). Several studies were identified in the oil and gas industry on the application of PBK models and biomarkers, mainly focusing on workers exposed to benzene. The application of personal sensors, new types of exposure models, and omics technology are still in their infancy with respect to the oil and gas industry. Nevertheless, applications of these technologies in other occupational sectors showed the potential for application in this sector. DISCUSSION AND CONCLUSION: New exposome technologies offer great promise for personal monitoring of workers in the oil and gas industry, but more applied research is needed in collaboration with the industry. Current challenges hindering a successful application of such technologies include (i) the technological readiness of sensors, (ii) the availability of data, (iii) the absence of standardized and validated methods, and (iv) the need for new study designs to study the development of disease during working life.