Development of a Combined In Vitro Physiologically Based Kinetic (PBK) and Monte Carlo Modelling Approach to Predict Interindividual Human Variation in Phenol-Induced Developmental Toxicity

Risk assessment of chlorophenols (CPs) exposure in vitro:Inhibition of sulfotransferases (SULTs) activity

Chlorophenols (CPs) are common organic pollutants widely used in many industries. The current study seeks to examine the inhibition of sulfotransferases (SULTs) by CPs. Four SULT isoforms were significantly inhibited by multiply CPs. Furthermore, half inhibition concentration (IC50) was calculated to be 0.31 μM, 0.11 μM and 1.86 μM for the inhibition of PCP (pentachlorophenol) towards SULT1A1, SULT1B1 and SULT1E1. PCP showed competitive inhibition towards SULT1B1 and SULT1E1.The inhibition kinetics (inhibition type and parameters (Ki)) values were calculated to be 0.34 μM and 0.56 μM for the inhibition of PCP towards SULT1B1 and SULT1E1, respectively. In silico docking was used to explain the inhibition difference among CPs. The binding free energy between 4-CP and SULT1A3 was -4.92 kcal/mol, and the binding free energy between 2.4-DCP and SULT1A3 was -5.63 kcal/mol. Therefore, 2.4-DCP exerted stronger inhibition activity towards SULT1A3 compared with 4-CP, which can well explain the experimental result. These results are crucial for exploring the risks associated with CPs exposure from a novel perspective.

Advancing understanding of human variability through toxicokinetic modeling, in vitro-in vivo extrapolation, and new approach methodologies

The merging of physiology and toxicokinetics, or pharmacokinetics, with computational modeling to characterize dosimetry has led to major advances for both the chemical and pharmaceutical research arenas. Driven by the mutual need to estimate internal exposures where in vivo data generation was simply not possible, the application of toxicokinetic modeling has grown exponentially in the past 30 years. In toxicology the need has been the derivation of quantitative estimates of toxicokinetic and toxicodynamic variability to evaluate the suitability of the tenfold uncertainty factor employed in risk assessment decision-making. Consideration of a host of physiologic, ontogenetic, genetic, and exposure factors are all required for comprehensive characterization. Fortunately, the underlying framework of physiologically based toxicokinetic models can accommodate these inputs, in addition to being amenable to capturing time-varying dynamics. Meanwhile, international interest in advancing new approach methodologies has fueled the generation of in vitro toxicity and toxicokinetic data that can be applied in in vitro-in vivo extrapolation approaches to provide human-specific risk-based information for historically data-poor chemicals. This review will provide a brief introduction to the structure and evolution of toxicokinetic and physiologically based toxicokinetic models as they advanced to incorporate variability and a wide range of complex exposure scenarios. This will be followed by a state of the science update describing current and emerging experimental and modeling strategies for population and life-stage variability, including the increasing application of in vitro-in vivo extrapolation with physiologically based toxicokinetic models in pharmaceutical and chemical safety research. The review will conclude with case study examples demonstrating novel applications of physiologically based toxicokinetic modeling and an update on its applications for regulatory decision-making. Physiologically based toxicokinetic modeling provides a sound framework for variability evaluation in chemical risk assessment.

Identification of UDP-glucuronosyltransferase (UGT) isoforms involved in the metabolism of Chlorophenols (CPs)

Chlorophenols (CPs) are a group of pollutants that pose a great threat to the environment, they are widely used in industrial and agricultural wastes, pesticides, herbicides, textiles, pharmaceuticals and plastics. Among CPs, pentachlorophenol was listed as one of the persistent organic pollutants (POPs) by the Stockholm convention. This study aims to identify the UDP-glucosyltransferase (UGT) isoforms involved in the metabolic elimination of CPs. CPs' mono-glucuronide was detected in the human liver microsomes (HLMs) incubation mixture with co-factor uridine-diphosphate glucuronic acid (UDPGA). HLMs-catalyzed glucuronidation metabolism reaction equations followed Michaelis-Menten or substrate inhibition type. Recombinant enzymes and chemical reagents inhibition experiments were utilized to phenotype the main UGT isoforms involved in the glucuronidation of CPs. UGT1A6 might be the major enzyme in the glucuronidation of mono-chlorophenol isomer. UGT1A1, UGT1A6, UGT1A9, UGT2B4 and UGT2B7 were the most important five UGT isoforms for metabolizing the di-chlorophenol and tri-chlorophenol isomers. UGT1A1 and UGT1A3 were the most important UGT isoforms in the catalysis of tetra-chlorophenol and pentachlorophenol isomers. Species differences were investigated using rat liver microsomes (RLMs), pig liver microsomes (PLMs), dog liver microsomes (DLMs), and monkey liver microsomes (MyLMs). All these results were helpful for elucidating the metabolic elimination and toxicity of CPs.

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.

Predicting the in vivo developmental toxicity of fenarimol from in vitro toxicity data using PBTK modelling-facilitated reverse dosimetry approach

In vitro methods are widely used in modern toxicological testing; however, the data cannot be directly employed for risk assessment. In vivo toxicity of chemicals can be predicted from in vitro data using physiologically based toxicokinetic (PBTK) modelling-facilitated reverse dosimetry (PBTK-RD). In this study, a minimal-PBTK model was constructed to predict the in-vivo kinetic profile of fenarimol (FNL) in rats and humans. The model was verified by comparing the observed and predicted pharmacokinetics of FNL for rats (calibrator) and further applied to humans. Using the PBTK-RD approach, the reported in vitro developmental toxicity data for FNL was translated to in vivo dose-response data to predict the assay equivalent oral dose in rats and humans. The predicted assay equivalent rat oral dose (36.46 mg/kg) was comparable to the literature reported in vivo BMD10 value (22.8 mg/kg). The model was also employed to derive the chemical-specific adjustment factor (CSAF) for interspecies toxicokinetics variability of FNL. Further, Monte Carlo simulations were performed to predict the population variability in the plasma concentration of FNL and to derive CSAF for intersubject human kinetic differences. The comparison of CSAF values for interspecies and intersubject toxicokinetic variability with their respective default values revealed that the applied uncertainty factors were adequately protective.

In vitro-in silico-based prediction of inter-individual and inter-ethnic variations in the dose-dependent cardiotoxicity of R- and S-methadone in humans

New approach methodologies predicting human cardiotoxicity are of interest to support or even replace in vivo-based drug safety testing. The present study presents an in vitro–in silico approach to predict the effect of inter-individual and inter-ethnic kinetic variations in the cardiotoxicity of R- and S-methadone in the Caucasian and the Chinese population. In vitro cardiotoxicity data, and metabolic data obtained from two approaches, using either individual human liver microsomes or recombinant cytochrome P450 enzymes (rCYPs), were integrated with physiologically based kinetic (PBK) models and Monte Carlo simulations to predict inter-individual and inter-ethnic variations in methadone-induced cardiotoxicity. Chemical specific adjustment factors were defined and used to derive dose–response curves for the sensitive individuals. Our simulations indicated that Chinese are more sensitive towards methadone-induced cardiotoxicity with Margin of Safety values being generally two-fold lower than those for Caucasians for both methadone enantiomers. Individual PBK models using microsomes and PBK models using rCYPs combined with Monte Carlo simulations predicted similar inter-individual and inter-ethnic variations in methadone-induced cardiotoxicity. The present study illustrates how inter-individual and inter-ethnic variations in cardiotoxicity can be predicted by combining in vitro toxicity and metabolic data, PBK modelling and Monte Carlo simulations. The novel methodology can be used to enhance cardiac safety evaluations and risk assessment of chemicals.

IVIVE: Facilitating the Use of In Vitro Toxicity Data in Risk Assessment and Decision Making

During the past few decades, the science of toxicology has been undergoing a transformation from observational to predictive science. New approach methodologies (NAMs), including in vitro assays, in silico models, read-across, and in vitro to in vivo extrapolation (IVIVE), are being developed to reduce, refine, or replace whole animal testing, encouraging the judicious use of time and resources. Some of these methods have advanced past the exploratory research stage and are beginning to gain acceptance for the risk assessment of chemicals. A review of the recent literature reveals a burst of IVIVE publications over the past decade. In this review, we propose operational definitions for IVIVE, present literature examples for several common toxicity endpoints, and highlight their implications in decision-making processes across various federal agencies, as well as international organizations, including those in the European Union (EU). The current challenges and future needs are also summarized for IVIVE. In addition to refining and reducing the number of animals in traditional toxicity testing protocols and being used for prioritizing chemical testing, the goal to use IVIVE to facilitate the replacement of animal models can be achieved through their continued evolution and development, including a strategic plan to qualify IVIVE methods for regulatory acceptance.

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 (HK_AF). 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 HK_AF values. The variation in metabolism-related kinetic parameters resulted in HK_AF values at the 99th percentile that were slightly higher than the default uncertainty factor of 3.16. While HK_AF 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.

Derivation of a Human In Vivo Benchmark Dose for Bisphenol A from ToxCast In Vitro Concentration Response Data Using a Computational Workflow for Probabilistic Quantitative In Vitro to In Vivo Extrapolation

A computational workflow which integrates physiologically based kinetic (PBK) modelling; global sensitivity analysis (GSA), Approximate Bayesian Computation (ABC), Markov Chain Monte Carlo (MCMC) simulation and the Virtual Cell Based Assay (VCBA) for the estimation of the active, free in vitro concentration of chemical in the reaction medium was developed to facilitate quantitative in vitro to in vivo extrapolation (QIVIVE). The workflow was designed to estimate parameter and model uncertainty within a computationally efficient framework. The workflow was tested using a human PBK model for bisphenol A (BPA) and high throughput screening (HTS) in vitro concentration-response data, for estrogen and pregnane X receptor activation determined in human liver and kidney cell lines, from the ToxCast/Tox21 database. In vivo benchmark dose 10% lower confidence limits (BMDL10) for oral uptake of BPA (ng/kg BW/day) were calculated from the in vivo dose-responses and compared to the human equivalent dose (HED) BMDL10 for relative kidney weight change in the mouse derived by European Food Safety Authority (EFSA). Three from four in vivo BMDL10 values calculated in this study were similar to the EFSA values whereas the fourth was much smaller. The derivation of an uncertainty factor (UF) to accommodate the uncertainties associated with measurements using human cell lines in vitro, extrapolated to in vivo, could be useful for the derivation of Health Based Guidance Values (HBGV).

Variability in Human In Vitro Enzyme Kinetics

There are many factors which are known to cause variability in human in vitro enzyme kinetic data. Factors such as the source of enzyme and how it was prepared, the genetics and background of the donor, how the in vitro studies are designed, and how the data are analyzed contribute to variability in the resulting kinetic parameters. It is important to consider not only the factors which cause variability within an experiment, such as selection of a probe substrate, but also those that cause variability when comparing kinetic data across studies and laboratories. For example, the artificial nature of the microsomal lipid membrane and microenvironment in some recombinantly expressed enzymes, relative to those found in native tissue microsomes, has been shown to influence enzyme activity and thus can be a source of variability when comparing across the two different systems. All of these factors, and several others, are discussed in detail in the chapter below. In addition, approaches which can be used to visualize the uncertainty arising from the use of enzyme kinetic data within the context of predicting human pharmacokinetics are discussed.

Derivation of a Human In Vivo Benchmark Dose for Perfluorooctanoic Acid From ToxCast In Vitro Concentration-Response Data Using a Computational Workflow for Probabilistic Quantitative In Vitro to In Vivo Extrapolation

A computational workflow which integrates physiologically based kinetic (PBK) modeling, global sensitivity analysis (GSA), approximate Bayesian computation (ABC), and Markov Chain Monte Carlo (MCMC) simulation was developed to facilitate quantitative in vitro to in vivo extrapolation (QIVIVE). The workflow accounts for parameter and model uncertainty within a computationally efficient framework. The workflow was tested using a human PBK model for perfluorooctanoic acid (PFOA) and high throughput screening (HTS) in vitro concentration–response data, determined in a human liver cell line, from the ToxCast/Tox21 database. In vivo benchmark doses (BMDs) for PFOA intake (ng/kg BW/day) and drinking water exposure concentrations (µg/L) were calculated from the in vivo dose responses and compared to intake values derived by the European Food Safety Authority (EFSA). The intake benchmark dose lower confidence limit (BMDL₅) of 0.82 was similar to 0.86 ng/kg BW/day for altered serum cholesterol levels derived by EFSA, whereas the intake BMDL₅ of 6.88 was six-fold higher than the value of 1.14 ng/kg BW/day for altered antibody titer also derived by the EFSA. Application of a chemical-specific adjustment factor (CSAF) of 1.4, allowing for inter-individual variability in kinetics, based on biological half-life, gave an intake BMDL₅ of 0.59 for serum cholesterol and 4.91 (ng/kg BW/day), for decreased antibody titer, which were 0.69 and 4.31 the EFSA-derived values, respectively. The corresponding BMDL₅ for drinking water concentrations, for estrogen receptor binding activation associated with breast cancer, pregnane X receptor binding associated with altered serum cholesterol levels, thyroid hormone receptor α binding leading to thyroid disease, and decreased antibody titer (pro-inflammation from cytokines) were 0.883, 0.139, 0.086, and 0.295 ng/ml, respectively, with application of no uncertainty factors. These concentrations are 5.7-, 36-, 58.5-, and 16.9-fold lower than the median measured drinking water level for the general US population which is approximately, 5 ng/ml.

Integrating biokinetics and in vitro studies to evaluate developmental neurotoxicity induced by chlorpyrifos in human iPSC-derived neural stem cells undergoing differentiation towards neuronal and glial cells

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Human iPSC-derived NSCs undergoing differentiation possess some metabolic competence.

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CPF entered the cells and was biotrasformed into its two main metabolites (CPFO and TCP).

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After repeated exposure, very limited bioaccumulation of CPF was observed.

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Treatment with CPF decreased neurite outgrowth, synapse number and electrical activity.

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Treatment with CPF increased BDNF levels and the percentage of astrocytes.

For some complex toxicological endpoints, chemical safety assessment has conventionally relied on animal testing. Apart from the ethical issues, also scientific considerations have been raised concerning the traditional approach, highlighting the importance for considering real life exposure scenario. Implementation of flexible testing strategies, integrating multiple sources of information, including in vitro reliable test methods and in vitro biokinetics, would enhance the relevance of the obtained results. Such an approach could be pivotal in the evaluation of developmental neurotoxicity (DNT), especially when applied to human cell-based models, mimicking key neurodevelopmental processes, relevant to human brain development.

Here, we integrated the kinetic behaviour with the toxicodynamic alterations of chlorpyrifos (CPF), such as in vitro endpoints specific for DNT evaluation, after repeated exposure during differentiation of human neural stem cells into a mixed culture of neurons and astrocytes. The upregulation of some cytochrome P450 and glutathione S-transferase genes during neuronal differentiation and the formation of the two major CPF metabolites (due to bioactivation and detoxification) supported the metabolic competence of the used in vitro model. The alterations in the number of synapses, neurite outgrowth, brain derived neurotrophic factor, the proportion of neurons and astrocytes, as well as spontaneous electrical activity correlated well with the CPF ability to enter the cells and be bioactivated to CPF-oxon. Overall, our results confirm that combining in vitro biokinetics and assays to evaluate effects on neurodevelopmental endpoints in human cells should be regarded as a key strategy for a quantitative characterization of DNT effects.

Use of Physiologically Based Kinetic Modeling to Predict Rat Gut Microbial Metabolism of the Isoflavone Daidzein to S-Equol and Its Consequences for ERα Activation

SCOPE

To predict gut microbial metabolism of xenobiotics and the resulting plasma concentrations of metabolites formed, an in vitro-in silico-based testing strategy is developed using the isoflavone daidzein and its gut microbial metabolite S-equol as model compounds.

METHODS AND RESULTS

Anaerobic rat fecal incubations are optimized and performed to derive the apparent maximum velocities (Vmax ) and Michaelis-Menten constants (Km ) for gut microbial conversion of daidzein to dihydrodaidzein, S-equol, and O-desmethylangolensin, which are input as parameters for a physiologically based kinetic (PBK) model. The inclusion of gut microbiota in the PBK model allows prediction of S-equol concentrations and slightly reduced predicted maximal daidzein concentrations from 2.19 to 2.16 µm. The resulting predicted concentrations of daidzein and S-equol are comparable to in vivo concentrations reported.

CONCLUSION

The optimized in vitro approach to quantify kinetics for gut microbial conversions, and the newly developed PBK model for rats that includes gut microbial metabolism, provide a unique tool to predict the in vivo consequences of daidzein microbial metabolism for systemic exposure of the host to daidzein and its metabolite S-equol. The predictions reveal a dominant role for daidzein in ERα-mediated estrogenicity despite the higher estrogenic potency of its microbial metabolite S-equol.

Integrating physiologically based kinetic (PBK) and Monte Carlo modelling to predict inter-individual and inter-ethnic variation in bioactivation and liver toxicity of lasiocarpine

The aim of the present study was to predict the effect of inter-individual and inter-ethnic human kinetic variation on the sensitivity towards acute liver toxicity of lasiocarpine in the Chinese and the Caucasian population, and to derive chemical specific adjustment factors (CSAFs) by integrating variation in the in vitro kinetic constants V_max and K_m, physiologically based kinetic (PBK) modelling and Monte Carlo simulation. CSAFs were derived covering the 90th and 99th percentile of the population distribution of pyrrole glutathione adduct (7-GS-DHP) formation, reflecting bioactivation. The results revealed that in the Chinese population, as compared to the Caucasian population, the predicted 7-GS-DHP formation at the geometric mean, the 90th and the 99th percentile were 2.1-, 3.3- and 4.3-fold lower respectively. The CSAFs obtained using the 99th percentile values were 8.3, 17.0 and 19.5 in the Chinese, the Caucasian population and the two populations combined, respectively, while the CSAFs were generally 3.0-fold lower at the 90th percentile. These results indicate that when considering the formation of 7-GS-DHP the Caucasian population may be more sensitive towards acute liver toxicity of lasiocarpine, and further point out that the default safety factor of 3.16 for inter-individual human kinetic differences may not be sufficiently protective. Altogether, the results obtained demonstrate that integrating PBK modelling with Monte Carlo simulations using human in vitro data is a powerful strategy to quantify inter-individual variations in kinetics, and can be used to refine the human risk assessment of pyrrolizidine alkaloids.

Challenges Associated With Applying Physiologically Based Pharmacokinetic Modeling for Public Health Decision-Making

The development and application of physiologically based pharmacokinetic (PBPK) models in chemical toxicology have grown steadily since their emergence in the 1980s. However, critical evaluation of PBPK models to support public health decision-making across federal agencies has thus far occurred for only a few environmental chemicals. In order to encourage decision-makers to embrace the critical role of PBPK modeling in risk assessment, several important challenges require immediate attention from the modeling community. The objective of this contemporary review is to highlight 3 of these challenges, including: (1) difficulties in recruiting peer reviewers with appropriate modeling expertise and experience; (2) lack of confidence in PBPK models for which no tissue/plasma concentration data exist for model evaluation; and (3) lack of transferability across modeling platforms. Several recommendations for addressing these 3 issues are provided to initiate dialog among members of the PBPK modeling community, as these issues must be overcome for the field of PBPK modeling to advance and for PBPK models to be more routinely applied in support of public health decision-making.

New insights for risks of chlorophenols (CPs) exposure: Inhibition of UDP-glucuronosyltransferases (UGTs)

Chlorophenols (CPs) are important pollutants extensively utilized in industry, agriculture and forestry. The present study aims to determine the inhibition of CPs on the activity of the important phase II drug-metabolizing enzymes (DMEs) UDP-glucuronosyltransferases (UGTs). 100 μM of fourteen CPs were used for preliminary screening using in vitro incubation. Furthermore, half inhibition concentration (IC₅₀) and inhibition kinetics were determined for CPs with significant inhibition towards UGT isoforms. In silico docking was used to explain the inhibition difference among CPs. Multiple UGT isoforms were inhibited by CPs. In silico docking showed that higher free binding energy due to hydrophobic interactions of 2.4-Dichlorophenol (2.4-DCP) or 4-Chloro-3-methylphenol (4C3MP) with UGT1A9 contributed to stronger inhibition potential of 2.4-Dichlorophenol (2.4-DCP) or 4-Chloro-3-methylphenol (4C3MP) towards UGT1A9 than 4-CP. Pentachlorophenol (PCP) was chosen as the representative CPs to determine the IC₅₀ value towards UGT1A6, UGT1A9 and UGT2B7. IC₅₀ was calculated to be 0.33 μM, 0.24 μM and 31.35 μM for the inhibition of PCP towards UGT1A6, UGT1A9 and UGT2B7. PCP was demonstrated to show competitive inhibition towards UGT1A6, UGT1A9 and UGT2B7, and the inhibition kinetic parameters (Ki) was calculated to be 0.18 μM, 0.01 μM and 5.37 μM for the inhibition of PCP towards UGT1A6, UGT1A9 and UGT2B7. All these information will be beneficial for elucidating the risk of CPs exposure from a new perspective.

A Computational Workflow for Probabilistic Quantitative in Vitro to in Vivo Extrapolation

A computational workflow was developed to facilitate the process of quantitative in vitro to in vivo extrapolation (QIVIVE), specifically the translation of in vitro concentration-response to in vivo dose-response relationships and subsequent derivation of a benchmark dose value (BMD). The workflow integrates physiologically based pharmacokinetic (PBPK) modeling; global sensitivity analysis (GSA), Approximate Bayesian Computation (ABC) and Markov Chain Monte Carlo (MCMC) simulation. For a given set of in vitro concentration and response data the algorithm returns the posterior distribution of the corresponding in vivo, population-based dose-response values, for a given route of exposure. The novel aspect of the workflow is a rigorous statistical framework for accommodating uncertainty in both the parameters of the PBPK model (both parameter uncertainty and population variability) and in the structure of the PBPK model itself recognizing that the model is an approximation to reality. Both these sources of uncertainty propagate through the workflow and are quantified within the posterior distribution of in vivo dose for a fixed representative in vitro concentration. To demonstrate this process and for comparative purposes a similar exercise to previously published work describing the kinetics of ethylene glycol monoethyl ether (EGME) and its embryotoxic metabolite methoxyacetic acid (MAA) in rats was undertaken. The computational algorithm can be used to extrapolate from in vitro data to any organism, including human. Ultimately, this process will be incorporated into a user-friendly, freely available modeling platform, currently under development, that will simplify the process of QIVIVE.