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.

Human biomonitoring and toxicokinetics as key building blocks for next generation risk assessment

Human health risk assessment is historically built upon animal testing, often following Organisation for Economic Co-operation and Development (OECD) test guidelines and exposure assessments. Using combinations of human relevant in vitro models, chemical analysis and computational (in silico) approaches bring advantages compared to animal studies. These include a greater focus on the human species and on molecular mechanisms and kinetics, identification of Adverse Outcome Pathways and downstream Key Events as well as the possibility of addressing susceptible populations and additional endpoints. Much of the advancement and progress made in the Next Generation Risk Assessment (NGRA) have been primarily focused on new approach methodologies (NAMs) and physiologically based kinetic (PBK) modelling without incorporating human biomonitoring (HBM). The integration of toxicokinetics (TK) and PBK modelling is an essential component of NGRA. PBK models are essential for describing in quantitative terms the TK processes with a focus on the effective dose at the expected target site. Furthermore, the need for PBK models is amplified by the increasing scientific and regulatory interest in aggregate and cumulative exposure as well as interactions of chemicals in mixtures. Since incorporating HBM data strengthens approaches and reduces uncertainties in risk assessment, here we elaborate on the integrated use of TK, PBK modelling and HBM in chemical risk assessment highlighting opportunities as well as challenges and limitations. Examples are provided where HBM and TK/PBK modelling can be used in both exposure assessment and hazard characterization shifting from external exposure and animal dose/response assays to animal-free, internal exposure-based NGRA.

Advancing the use of new approach methodologies for assessing teratogenicity: Building a tiered approach

Many New Approach Methodologies (NAMs) have been developed for the safety assessment of new ingredients. Research into reproductive toxicity and teratogenicity is a particularly high priority, especially given their mechanistic complexity. Forty-six non-teratogenic and 39 teratogenic chemicals were screened for teratogenic potential using the in silico DART model from the OECD QSAR Toolbox; the devTox quickPredict™ (devTox assay) test and the Zebrafish Embryotoxicity Test (ZET). The sensitivity and specificity were 94.7% and 84.1%, respectively, for the DART tree (83 chemicals), 86.1% and 35.6% for the devTox (81 chemicals) and 77.8% and 76.7% for the ZET (57 chemicals). Fifty-three chemicals were tested in all three assays and when results were combined and based on a "2 out of 3 rule", the sensitivity and specificity were 96.0% and 71.4%, respectively. The specificity of the devTox assay for a sub-set of 43 chemicals was increased from 26.1% to 82.6% by incorporating human plasma concentrations into the assay interpretation. When all 85 chemicals were assessed in a decision tree approach, there was an excellent predictivity and assay robustness of 90%. In conclusion, all three models exhibited a good sensitivity and specificity, especially when outcomes from all three were combined or used in "2 out of 3" or a tiered decision tree approach. The latter is an interesting predictive approach for evaluating the teratogenic potential of new chemicals. Future investigations will extend the number of chemicals tested, as well as explore ways to refine the results and obtain a robust Integrated Testing Strategy to evaluate teratogenic potential.

DNA adducts as link between in vitro and in vivo carcinogenicity - A case study with benzo[a]pyrene

To reduce the need for animal tests, in vitro assays are often used as alternative methods. To derive toxic doses for higher tier organisms from in vitro assay results, quantitative in vitro-in vivo extrapolation (qIVIVE) based on physiological-based toxicokinetic (PBTK) models is typically the preferred approach. Such PBTK models require many input parameters to address the route from dose to target site concentration. However, respective data is very often not available. Hence, our aim is to call attention to an alternative way to build a link between animal (in vivo) and cell-derived (in vitro) toxicity data. To this end, we selected the carcinogenic chemical benzo[a]pyrene (BaP) for our study. Our approach relates both in vitro assay and in vivo data to a main intermediate marker structure for carcinogenicity on the subcellular level - the BaP-DNA adduct BaP-7,8-dihydrodiol-9,10-epoxide-deoxyguanosine. Thus, BaP dose is directly linked to a measure of the toxicity-initiating event. We used Syrian hamster embryo (SHE) and Balb/c 3T3 cell transformation assay as in vitro data and compared these data to outcomes of in vivo carcinogenicity tests in rodents. In vitro and in vivo DNA adduct levels range within three orders of magnitude. Especially metabolic saturation at higher doses and interspecies variabilities are identified and critically discussed as possible sources of errors in our simplified approach. Finally, our study points out possible routes to overcome limitations of the envisaged approach in order to allow for a reliable qIVIVE in the future.

Advancing New Approach Methodologies (NAMs) for Tobacco Harm Reduction: Synopsis from the 2021 CORESTA SSPT-NAMs Symposium

New approach methodologies (NAMs) are emerging chemical safety assessment tools consisting of in vitro and in silico (computational) methodologies intended to reduce, refine, or replace (3R) various in vivo animal testing methods traditionally used for risk assessment. Significant progress has been made toward the adoption of NAMs for human health and environmental toxicity assessment. However, additional efforts are needed to expand their development and their use in regulatory decision making. A virtual symposium was held during the 2021 Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA) Smoke Science and Product Technology (SSPT) conference (titled "Advancing New Alternative Methods for Tobacco Harm Reduction"), with the goals of introducing the concepts and potential application of NAMs in the evaluation of potentially reduced-risk (PRR) tobacco products. At the symposium, experts from regulatory agencies, research organizations, and NGOs shared insights on the status of available tools, strengths, limitations, and opportunities in the application of NAMs using case examples from safety assessments of chemicals and tobacco products. Following seven presentations providing background and application of NAMs, a discussion was held where the presenters and audience discussed the outlook for extending the NAMs toxicological applications for tobacco products. The symposium, endorsed by the CORESTA In Vitro Tox Subgroup, Biomarker Subgroup, and NextG Tox Task Force, illustrated common ground and interest in science-based engagement across the scientific community and stakeholders in support of tobacco regulatory science. Highlights of the symposium are summarized in this paper.

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.

Prediction of tissue and urine concentrations of 2-phenoxyethanol and its metabolite 2-phenoxyacetic acid in rat and human after oral and dermal exposures via GastroPlusTM physiologically based pharmacokinetic modelling

A physiologically based pharmacokinetic (PBPK) model for the important chemical phenoxyethanol (PhE) and its metabolite phenoxyacetic acid (PhAA) was built via GastroPlus^TM software (version 9.0) using currently available analytically measured plasma and urinary time-courses of both PhE and its metabolite PhAA. This model was validated and used to predict tissue and urine concentrations of PhE and its metabolite PhAA in rats and humans after oral and dermal exposures. The prediction results showed that most predicted tissue concentrations of PhE or PhAA were lower than the experimental tissue concentrations based on total radioactivity. The predicted cumulative excretion of PhAA in both rats and humans fits very well with most experimental data. With this GastroPlus^TM-based model, the margins of exposure (MOE) of PhE and PhAA were also calculated as 194 and 73.7, respectively. The predicted MOE of PhE is two-fold higher than the previous PBPK model built using total radioactivity-based tissue time courses, and the predicted MOE of PhAA was comparable to the previous PBPK model. These data indicate that for chemicals like PhE, GastroPlus^TM can integrate multiple data sets into PBPK models to predict PK parameters for parent and metabolites in both rats and humans following intravenous, dermal, or oral exposures.

Predictive Performance of Next Generation Physiologically Based Kinetic (PBK) Model Predictions in Rats Based on In Vitro and In Silico Input Data

The goal of the present study was to assess the predictive performance of a minimal generic rat physiologically based kinetic (PBK) model based on in vitro and in silico input data to predict peak plasma concentrations (Cmax) upon single oral dosing. To this purpose, a dataset was generated of 3960 Cmax predictions for 44 compounds, applying different combinations of in vitro and in silico approaches for chemical parameterization, and comparison of the predictions to reported in vivo data. Best performance was obtained when (1) the hepatic clearance was parameterized based on in vitro measured intrinsic clearance values, (2) the method of Rodgers and Rowland for calculating partition coefficients, and (3) in silico calculated fraction unbound plasma and Papp values (the latter especially for very lipophilic compounds). Based on these input data, the median Cmax of 32 compounds could be predicted within 10-fold of the observed Cmax, with 22 out of these 32 compounds being predicted within 5-fold, and 8 compounds within 2-fold. Overestimations of more than 10-fold were observed for 12 compounds, whereas no underestimations of more than 10-fold occurred. Median Cmax predictions were frequently found to be within 10-fold of the observed Cmax when the scaled unbound hepatic intrinsic clearance (Clint,u) was either higher than 20 l/h or lower than 1 l/h. Similar findings were obtained with a test set of 5 in-house BASF compounds. Overall, this study provides relevant insights in the predictive performance of a minimal PBK model based on in vitro and in silico input data.

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).

Integrating in vitro chemical transplacental passage into a generic PBK model: A QIVIVE approach

With the increasing application of cell culture models as primary tools for predicting chemical safety, the quantitative extrapolation of the effective dose from in vitro to in vivo (QIVIVE) is of increasing importance. For developmental toxicity this requires scaling the in vitro observed dose-response characteristics to in vivo fetal exposure, while integrating maternal in vivo kinetics during pregnancy, in particular transplacental transfer. Here the transfer of substances across the placental barrier, has been studied using the in vitro BeWo cell assay and six embryotoxic compounds of different kinetic complexity. The BeWo assay results were incorporated in an existing generic Physiologically Based Kinetic (PBK) model which for this purpose was extended with rat pregnancy. Finally, as a "proof of principle", the BeWo PBK model was used to perform a QIVIVE based on developmental toxicity as observed in various different in vitro toxicity assays. The BeWo results illustrated different transport profiles of the chemicals across the BeWo monolayer, allocating the substances into two distinct groups: the 'quickly-transported' and the 'slowly-transported'. BeWo PBK exposure simulations during gestation were compared to experimentally measured maternal blood and fetal concentrations and a reverse dosimetry approach was applied to translate in vitro observed embryotoxicity into equivalent in vivo dose-response curves. This approach allowed for a direct comparison of the in vitro dose-response characteristics as observed in the Whole Embryo Culture (WEC), and the Embryonic Stem Cell test (cardiac:ESTc and neural:ESTn) with in vivo rat developmental toxicity data. Overall, the in vitro to in vivo comparisons suggest a promising future for the application of such QIVIVE methodologies for screening and prioritization purposes of developmental toxicants. Nevertheless, the clear need for further improvements is acknowledged for a wider application of the approach in chemical safety assessment.

An integrated exposure and pharmacokinetic modeling framework for assessing population-scale risks of phthalates and their substitutes

To effectively incorporate in vitro-in silico-based methods into the regulation of consumer product safety, a quantitative connection between product phthalate concentrations and in vitro bioactivity data must be established for the general population. We developed, evaluated, and demonstrated a modeling framework that integrates exposure and pharmacokinetic models to convert product phthalate concentrations into population-scale risks for phthalates and their substitutes. A probabilistic exposure model was developed to generate the distribution of multi-route exposures based on product phthalate concentrations, chemical properties, and human activities. Pharmacokinetic models were developed to simulate population toxicokinetics using Bayesian analysis via the Markov chain Monte Carlo method. Both exposure and pharmacokinetic models demonstrated good predictive capability when compared with worldwide studies. The distributions of exposures and pharmacokinetics were integrated to predict the population distributions of internal dosimetry. The predicted distributions showed reasonable agreement with the U.S. biomonitoring surveys of urinary metabolites. The "source-to-outcome" local sensitivity analysis revealed that food contact materials had the greatest impact on body burden for di(2-ethylhexyl) adipate (DEHA), di-2-ethylhexyl phthalate (DEHP), di(isononyl) cyclohexane-1,2-dicarboxylate (DINCH), and di(2-propylheptyl) phthalate (DPHP), whereas the body burden of diethyl phthalate (DEP) was most sensitive to the concentration in personal care products. The upper bounds of predicted plasma concentrations showed no overlap with ToxCast in vitro bioactivity values. Compared with the in vitro-to-in vivo extrapolation (IVIVE) approach, the integrated modeling framework has significant advantages in mapping product phthalate concentrations to multi-route risks, and thus is of great significance for regulatory use with a relatively low input requirement. Further integration with new approach methodologies will facilitate these in vitro-in silico-based risk assessments for a broad range of products containing an equally broad range of chemicals.

Predicting the in vivo developmental toxicity of benzo[a]pyrene (BaP) in rats by an in vitro-in silico approach

Developmental toxicity testing is an animal-intensive endpoints in toxicity testing and calls for animal-free alternatives. Previous studies showed the applicability of an in vitro–in silico approach for predicting developmental toxicity of a range of compounds, based on data from the mouse embryonic stem cell test (EST) combined with physiologically based kinetic (PBK) modelling facilitated reverse dosimetry. In the current study, the use of this approach for predicting developmental toxicity of polycyclic aromatic hydrocarbons (PAHs) was evaluated, using benzo[a]pyrene (BaP) as a model compound. A rat PBK model of BaP was developed to simulate the kinetics of its main metabolite 3-hydroxybenzo[a]pyrene (3-OHBaP), shown previously to be responsible for the developmental toxicity of BaP. Comparison to in vivo kinetic data showed that the model adequately predicted BaP and 3-OHBaP blood concentrations in the rat. Using this PBK model and reverse dosimetry, a concentration–response curve for 3-OHBaP obtained in the EST was translated into an in vivo dose–response curve for developmental toxicity of BaP in rats upon single or repeated dose exposure. The predicted half maximal effect doses (ED₅₀) amounted to 67 and 45 mg/kg bw being comparable to the ED₅₀ derived from the in vivo dose–response data reported for BaP in the literature, of 29 mg/kg bw. The present study provides a proof of principle of applying this in vitro–in silico approach for evaluating developmental toxicity of BaP and may provide a promising strategy for predicting the developmental toxicity of related PAHs, without the need for extensive animal testing.

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.

Physiologically based kinetic modelling based prediction of in vivo rat and human acetylcholinesterase (AChE) inhibition upon exposure to diazinon

The present study predicts in vivo human and rat red blood cell (RBC) acetylcholinesterase (AChE) inhibition upon diazinon (DZN) exposure using physiological based kinetic (PBK) modelling-facilitated reverse dosimetry. Due to the fact that both DZN and its oxon metabolite diazoxon (DZO) can inhibit AChE, a toxic equivalency factor (TEF) was included in the PBK model to combine the effect of DZN and DZO when predicting in vivo AChE inhibition. The PBK models were defined based on kinetic constants derived from in vitro incubations with liver fractions or plasma of rat and human, and were used to translate in vitro concentration-response curves for AChE inhibition obtained in the current study to predicted in vivo dose-response curves. The predicted dose-response curves for rat matched available in vivo data on AChE inhibition, and the benchmark dose lower confidence limits for 10% inhibition (BMDL10 values) were in line with the reported BMDL10 values. Humans were predicted to be 6-fold more sensitive than rats in terms of AChE inhibition, mainly because of inter-species differences in toxicokinetics. It is concluded that the TEF-coded DZN PBK model combined with quantitative in vitro to in vivo extrapolation (QIVIVE) provides an adequate approach to predict RBC AChE inhibition upon acute oral DZN exposure, and can provide an alternative testing strategy for derivation of a point of departure (POD) in risk assessment.

Novel testing strategy for prediction of rat biliary excretion of intravenously administered estradiol-17β glucuronide

The aim of the present study was to develop a generic rat physiologically based kinetic (PBK) model that includes a novel testing strategy where active biliary excretion is incorporated using estradiol-17β glucuronide (E217βG) as the model substance. A major challenge was the definition of the scaling factor for the in vitro to in vivo conversion of the PBK-model parameter Vmax. In vitro values for the Vmax and Km for transport of E217βG were found in the literature in four different studies based on experiments with primary rat hepatocytes. The required scaling factor was defined based on fitting the PBK model-based predicted values to reported experimental data on E217βG blood levels and cumulative biliary E217βG excretion. This resulted in a scaling factor of 129 mg protein/g liver. With this scaling factor the PBK model predicted the in vivo data for blood and cumulative biliary E217βG levels with on average of less than 1.8-fold deviation. The study provides a proof of principle on how biliary excretion can be included in a generic PBK model using primary hepatocytes to define the kinetic parameters that describe the biliary excretion.

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.

Assessing the impacts on fetal dosimetry of the modelling of the placental transfers of xenobiotics in a pregnancy physiologically based pharmacokinetic model

The developmental origin of health and diseases theory supports the critical role of the fetal exposure to children's health. We developed a physiologically based pharmacokinetic model for human pregnancy (pPBPK) to simulate the maternal and fetal dosimetry throughout pregnancy. Four models of the placental exchanges of chemicals were assessed on ten chemicals for which maternal and fetal data were available. These models were calibrated using non-animal methods: in vitro (InV) or ex vivo (ExV) data, a semi-empirical relationship (SE), or the limitation by the placental perfusion (PL). They did not impact the maternal pharmacokinetics but provided different profiles in the fetus. The PL and InV models performed well even if the PL model overpredicted the fetal exposure for some substances. The SE and ExV models showed the lowest global performance and the SE model a tendency to underprediction. The comparison of the profiles showed that the PL model predicted an increase in the fetal exposure with the pregnancy age, whereas the ExV model predicted a decrease. For the SE and InV models, a small decrease was predicted during the second trimester. All models but the ExV one, presented the highest fetal exposure at the end of the third trimester. Global sensitivity analyses highlighted the predominant influence of the placental transfers on the fetal exposure, as well as the metabolic clearance and the fraction unbound. Finally, the four transfer models could be considered depending on the framework of the use of the pPBPK model and the availability of data or resources to inform their parametrization.

An improved overall risk probability-based method for assessing the combined health risks of chemical mixtures: An example about mixture of aflatoxin B1 and microcystin LR by dietary intake

Previous studies on the risk assessment of chemicals with respect to human health have focused mainly on the safety of individual substances. Recently, public health policy emphasizes the combined effects of mixtures. An overall risk probability (ORP)-based method long with the combined toxicity factor (c_uv) can be used to evaluate the combined toxicity of chemical mixtures from the environment and foods on human health. However, the procedure for calculating the c_uv accurately and quantitatively in the ORP method is yet unclear. In this study, an improved ORP-based method (IORP) was developed by introducing a variable time t, and the c_uv was analyzed quantitatively using simultaneous equations and based on the principle of least squares regression. This phenomenon can be explained based on the example of the mixture of aflatoxin B₁ (AFB₁) and microcystin LR (MC-LR) by dietary intake in order to understand the application of this method. The IORP approach makes it possible for estimating the combined effects of mixtures for human health by dietary pathway.

Predicting the Acute Liver Toxicity of Aflatoxin B1 in Rats and Humans by an In Vitro-In Silico Testing Strategy

SCOPE

High-level exposure to aflatoxin B1 (AFB1) is known to cause acute liver damage and fatality in animals and humans. The intakes actually causing this acute toxicity have so far been estimated based on AFB1 levels in contaminated foods or biomarkers in serum. The aim of the present study is to predict the doses causing acute liver toxicity of AFB1 in rats and humans by an in vitro-in silico testing strategy.

METHODS AND RESULTS

Physiologically based kinetic (PBK) models for AFB1 in rats and humans are developed. The models are used to translate in vitro concentration-response curves for cytotoxicity in primary rat and human hepatocytes to in vivo dose-response curves using reverse dosimetry. From these data, the dose levels at which toxicity would be expected are obtained and compared to toxic dose levels from available rat and human case studies on AFB1 toxicity. The results show that the in vitro-in silico testing strategy can predict dose levels causing acute toxicity of AFB1 in rats and human.

CONCLUSIONS

Quantitative in vitro in vivo extrapolation (QIVIVE) using PBK modeling-based reverse dosimetry can predict AFB1 doses that cause acute liver toxicity in rats and human.

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.

Integrating in vitro testing and physiologically-based pharmacokinetic (PBPK) modelling for chemical liver toxicity assessment-A case study of troglitazone

In vitro to in vivo extrapolation (IVIVE) for next-generation risk assessment (NGRA) of chemicals requires computational modeling and faces unique challenges. Using mitochondria-related toxicity data of troglitazone (TGZ), a prototype drug known for liver toxicity, from HepaRG, HepG2, HC-04, and primary human hepatocytes, we explored inherent uncertainties in IVIVE, including cell models, cellular response endpoints, and dose metrics. A human population physiologically-based pharmacokinetic (PBPK) model for TGZ was developed to predict in vivo doses from in vitro point-of-departure (POD) concentrations. Compared to the 200-800 mg/d dose range of TGZ where liver injury was observed clinically, the predicted POD doses for the mean and top one percentile of the PBPK population were 28-372 and 15-178 mg/d respectively based on C_max dosimetry, and 185-2552 and 83-1010 mg/d respectively based on AUC. In conclusion, although with many uncertainties, integrating in vitro assays and PBPK modeling is promising in informing liver toxicity-inducing TGZ doses.

Combining In Vitro Data and Physiologically Based Kinetic Modeling Facilitates Reverse Dosimetry to Define In Vivo Dose-Response Curves for Bixin- and Crocetin-Induced Activation of PPARγ in Humans

SCOPE

It is investigated whether at realistic dietary intake bixin and crocetin could induce peroxisome proliferator-activated receptor γ (PPARγ)-mediated gene expression in humans using a combined in vitro-in silico approach.

METHODS AND RESULTS

Concentration-response curves obtained from in vitro PPARγ-reporter gene assays are converted to in vivo dose-response curves using physiologically based kinetic modeling-facilitated reverse dosimetry, from which the benchmark dose levels resulting in a 50% effect above background level (BMD50 ) are predicted and subsequently compared to dietary exposure levels. Bixin and crocetin activated PPARγ-mediated gene transcription in a concentration-dependent manner with similar potencies. Due to differences in kinetics, the predicted BMD50 values for in vivo PPARγ activation are about 30-fold different, amounting to 115 and 3505 mg kg bw-1 for crocetin and bixin, respectively. Human dietary and/or supplemental estimated daily intakes may reach these BMD50 values for crocetin but not for bixin, pointing at better possibilities for in vivo PPARγ activation by crocetin.

CONCLUSION

Based on a combined in vitro-in silico approach, it is estimated whether at realistic dietary intakes plasma concentrations of bixin and crocetin are likely to reach concentrations that activate PPARγ-mediated gene expression, without the need for a human intervention study.

Biotransformation and bioactivation reactions - 2018 literature highlights

In the past three decades, ADME sciences have become an integral component of the drug discovery and development process. At the same time, the field has continued to evolve, thus, requiring ADME scientists to be knowledgeable of and engage with diverse aspects of drug assessment: from pharmacology to toxicology, and from in silico modeling to in vitro models and finally in vivo models. Progress in this field requires deliberate exposure to different aspects of ADME; however, this task can seem daunting in the current age of mass information. We hope this review provides a focused and brief summary of a wide array of critical advances over the past year and explains the relevance of this research ( Table 1 ). We divided the articles into categories of (1) drug optimization, (2) metabolites and drug metabolizing enzymes, and (3) bioactivation. This annual review is the fourth of its kind (Baillie et al. 2016 ; Khojasteh et al. 2017 , 2018 ). We have followed the same format we used in previous years in terms of the selection of articles and the authoring of each section. This effort in itself also continues to evolve. I am pleased that Rietjens, Miller, and Mitra have again contributed to this annual review. We would like to welcome Namandjé N. Bumpus, James P. Driscoll, and Donglu Zhang as authors for this year's issue. We strive to maintain a balance of authors from academic and industry settings. We would be pleased to hear your opinions of our commentary, and we extend an invitation to anyone who would like to contribute to a future edition of this review. Cyrus Khojasteh, on behalf of the authors.

The in vivo developmental toxicity of diethylstilbestrol (DES) in rat evaluated by an alternative testing strategy

In the present study, we evaluated an alternative testing strategy to quantitatively predict the in vivo developmental toxicity of the synthetic hormone diethylstilbestrol (DES). To this end, a physiologically based kinetic (PBK) model was defined that was subsequently used to translate concentration-response data for the in vitro developmental toxicity of DES, obtained in the ES-D3 cell differentiation assay, into predicted in vivo dose-response data for developmental toxicity. The previous studies showed that the PBK model-facilitated reverse dosimetry approach is a useful approach to quantitatively predict the developmental toxicity of several developmental toxins. The results obtained in the present study show that the PBK model adequately predicted DES blood concentrations in rats. Further studies revealed that DES tested positive in the ES-D3 differentiation assay and that DES-induced inhibition of the ES-D3 cell differentiation could be counteracted by the estrogen receptor alpha (ERα) antagonist fulvestrant, indicating that the in vitro ES-D3 cell differentiation assay was able to mimic the role of ERα reported in the mode of action underlying the developmental toxicity of DES in vivo. In spite of this, combining these in vitro data with the PBK model did not adequately predict the in vivo developmental toxicity of DES in a quantitative way. It is concluded that although the EST qualifies DES as a developmental toxin and detects the role of ERα in this process, the ES-D3 cell differentiation assay of the EST apparently does not adequately capture the processes underlying DES-induced developmental toxicity in vivo.

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.

Use of physiologically based kinetic modelling-facilitated reverse dosimetry to convert in vitro cytotoxicity data to predicted in vivo liver toxicity of lasiocarpine and riddelliine in rat

Lasiocarpine and riddelliine are pyrrolizidine alkaloids (PAs) present in food and able to cause liver toxicity. The aim of this study was to investigate whether physiologically based kinetic (PBK) modelling-facilitated reverse dosimetry can adequately translate in vitro concentration-response curves for toxicity of lasiocarpine and riddelliine to in vivo liver toxicity data for the rat. To this purpose, PBK models were developed for lasiocarpine and riddelliine, and predicted blood concentrations were compared to available literature data to evaluate the models. Concentration-response curves obtained from in vitro cytotoxicity assays in primary rat hepatocytes were converted to in vivo dose-response curves from which points of departure (PODs) were derived and that were compared to available literature data on in vivo liver toxicity. The results showed that the predicted PODs fall well within the range of PODs derived from available in vivo toxicity data. To conclude, this study shows the proof-of-principle for a method to predict in vivo liver toxicity for PAs by an alternative testing strategy integrating in vitro cytotoxicity assays with in silico PBK modelling-facilitated reverse dosimetry. The approach may facilitate prediction of acute liver toxicity for the large number of PAs for which in vivo toxicity data are lacking.

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

With our recently developed in vitro physiologically based kinetic (PBK) modelling approach, we could extrapolate in vitro toxicity data to human toxicity values applying PBK-based reverse dosimetry. Ideally information on kinetic differences among human individuals within a population should be considered. In the present study, we demonstrated a modelling approach that integrated in vitro toxicity data, PBK modelling and Monte Carlo simulations to obtain insight in interindividual human kinetic variation and derive chemical specific adjustment factors (CSAFs) for phenol-induced developmental toxicity. The present study revealed that UGT1A6 is the primary enzyme responsible for the glucuronidation of phenol in humans followed by UGT1A9. Monte Carlo simulations were performed taking into account interindividual variation in glucuronidation by these specific UGTs and in the oral absorption coefficient. Linking Monte Carlo simulations with PBK modelling, population variability in the maximum plasma concentration of phenol for the human population could be predicted. This approach provided a CSAF for interindividual variation of 2.0 which covers the 99th percentile of the population, which is lower than the default safety factor of 3.16 for interindividual human kinetic differences. Dividing the dose-response curve data obtained with in vitro PBK-based reverse dosimetry, with the CSAF provided a dose-response curve that reflects the consequences of the interindividual variability in phenol kinetics for the developmental toxicity of phenol. The strength of the presented approach is that it provides insight in the effect of interindividual variation in kinetics for phenol-induced developmental toxicity, based on only in vitro and in silico testing.

Towards a generic physiologically based kinetic model to predict in vivo uterotrophic responses in rats by reverse dosimetry of in vitro estrogenicity data

Physiologically based kinetic (PBK) modelling-based reverse dosimetry is a promising tool for the prediction of in vivo developmental toxicity using in vitro concentration–response data. In the present study, the potential of this approach to predict the dose-dependent increase of uterus weight in rats upon exposure to estrogenic chemicals was assessed. In vitro concentration–response data of 17β-estradiol (E2) and bisphenol A (BPA) obtained in the MCF-7/BOS proliferation assay, the U2OS ER-CALUX assay and the yeast estrogen screen (YES) assay, were translated into in vivo dose–response data in rat, using a PBK model with a minimum number of in vitro and in silico determined parameter values. To evaluate the predictions made, benchmark dose (BMD) analysis was performed on the predicted dose–response data and the obtained BMDL₁₀ values were compared with BMDL₁₀ values derived from data on the effects of E2 and BPA in the uterotrophic assay reported in the literature. The results show that predicted dose–response data of E2 and BPA matched with the data from in vivo studies when predictions were made based on YES assay data. The YES assay-based predictions of the BMDL₁₀ values differed 3.9-fold (E2) and 4.7- to 13.4-fold (BPA) from the BMDL₁₀ values obtained from the in vivo data. The present study provides the proof-of-principle that PBK modelling-based reverse dosimetry of YES assay data using a minimum PBK model can predict dose-dependent in vivo uterus growth caused by estrogenic chemicals. In future studies, the approach should be extended to include other estrogens.

Investigating the state of physiologically based kinetic modelling practices and challenges associated with gaining regulatory acceptance of model applications

Physiologically based kinetic (PBK) models are used widely throughout a number of working sectors, including academia and industry, to provide insight into the dosimetry related to observed adverse health effects in humans and other species. Use of these models has increased over the last several decades, especially in conjunction with emerging alternative methods to animal testing, such as in vitro studies and data-driven in silico quantitative-structure-activity-relationship (QSAR) predictions. Experimental information derived from these new approach methods can be used as input for model parameters and allows for increased confidence in models for chemicals that did not have in vivo data for model calibration. Despite significant advancements in good modelling practice (GMP) for model development and evaluation, there remains some reluctance among regulatory agencies to use such models during the risk assessment process. Here, the results of a survey disseminated to the modelling community are presented in order to inform the frequency of use and applications of PBK models in science and regulatory submission. Additionally, the survey was designed to identify a network of investigators involved in PBK modelling and knowledgeable of GMP so that they might be contacted in the future for peer review of PBK models, especially in regards to vetting the models to such a degree as to gain a greater acceptance for regulatory purposes.

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Physiologically Based kinetic (PBK) models are used widely in academia, industry, and government.

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Good modelling practice (GMP) for model development and evaluation continues to expand.

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Further guidance for establishing GMP is called for.

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There remains some reluctance among regulatory agencies to use PBK models.

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The next generation of PBK models could be developed using only data from in vitro and in silico methods.

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

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

Cell cultures in drug discovery and development: The need of reliable in vitro-in vivo extrapolation for pharmacodynamics and pharmacokinetics assessment

For ethical and cost-related reasons, use of animals for the assessment of mode of action, metabolism and/or toxicity of new drug candidates has been increasingly scrutinized in research and industrial applications. Implementation of the 3 "Rs"¹; rule (Reduction, Replacement, Refinement) through development of in silico or in vitro assays has become an essential element of risk assessment. Physiologically based pharmacokinetic (PBPK²) modeling is the most potent in silico tool used for extrapolation of pharmacokinetic parameters to animal or human models from results obtained in vitro. Although, many types of in vitro assays are conducted during drug development, use of cell cultures is the most reliable one. Two-dimensional (2D) cell cultures have been a part of drug development for many years. Nowadays, their role is decreasing in favor of three-dimensional (3D) cell cultures and co-cultures. 3D cultures exhibit protein expression patterns and intercellular junctions that are closer to in vivo states in comparison to classical monolayer cultures. Co-cultures allow for examinations of the mutual influence of different cell lines. However, the complexity and high costs of co-cultures and 3D equipment exclude such methods from high-throughput screening (HTS).³In vitro absorption, distribution, metabolism, and excretion assessment, as well as drug-drug interaction (DDI), are usually performed with the use of various cell culture based assays. Progress in in silico and in vitro methods can lead to better in vitro-in vivo extrapolation (IVIVE⁴) outcomes and have a potential to contribute towards a significant reduction in the number of laboratory animals needed for drug research. As such, concentrated efforts need to be spent towards the development of an HTS in vitro platform with satisfactory IVIVE features.

Use of physiologically based kinetic modeling-facilitated reverse dosimetry of in vitro toxicity data for prediction of in vivo developmental toxicity of tebuconazole in rats

Toxicological hazard and risk assessment largely rely on animal testing. For economic and ethical reasons, the development and validation of reliable alternative methods for these animal studies, such as in vitro assays, are urgently needed. In vitro concentration-response curves, however, need to be translated into in vivo dose-response curves for risk assessment purposes. In the present study, we translated in vitro concentration-response data of the antifungal compound tebuconazole, obtained in the ES-D3 cell differentiation assay, into predicted in vivo dose-response data for developmental toxicity using physiologically based kinetic (PBK) modeling-facilitated reverse dosimetry. Using the predicted in vivo dose-response data BMD(L)10 values for developmental toxicity in rat were calculated and compared with NOAEL values for developmental toxicity data in rats as reported in the literature. The results show that the BMDL10 value from predicted dose-response data are a reasonable approximation of the NOAEL values (ca. 3-fold difference). It is concluded that PBK modeling-facilitated reverse dosimetry of in vitro toxicity data is a promising tool to predict in vivo dose-response curves and may have the potential to define a point of departure for deriving safe exposure limits in risk assessment.

Use of Physiologically Based Kinetic Modeling-Based Reverse Dosimetry to Predict in Vivo Toxicity from in Vitro Data

The development of reliable nonanimal based testing strategies, such as in vitro bioassays, is the holy grail in current human safety testing of chemicals. However, the use of in vitro toxicity data in risk assessment is not straightforward. One of the main issues is that concentration-response curves from in vitro models need to be converted to in vivo dose-response curves. These dose-response curves are needed in toxicological risk assessment to obtain a point of departure to determine safe exposure levels for humans. Recent scientific developments enable this translation of in vitro concentration-response curves to in vivo dose-response curves using physiologically based kinetic (PBK) modeling-based reverse dosimetry. The present review provides an overview of the examples available in the literature on the prediction of in vivo toxicity using PBK modeling-based reverse dosimetry of in vitro toxicity data, showing that proofs-of-principle are available for toxicity end points ranging from developmental toxicity, nephrotoxicity, hepatotoxicity, and neurotoxicity to DNA adduct formation. This review also discusses the promises and pitfalls, and the future perspectives of the approach. Since proofs-of-principle available so far have been provided for the prediction of toxicity in experimental animals, future research should focus on the use of in vitro toxicity data obtained in human models to predict the human situation using human PBK models. This would facilitate human- instead of experimental animal-based approaches in risk assessment.

Integrating in vitro data and physiologically based kinetic (PBK) modelling to assess the in vivo potential developmental toxicity of a series of phenols

Toxicity outcomes derived in vitro do not always reflect in vivo toxicity values, which was previously observed for a series of phenols tested in the embryonic stem cell test (EST). Translation of in vitro data to the in vivo situation is therefore an important, but still limiting step for the use of in vitro toxicity outcomes in the safety assessment of chemicals. The aim of the present study was to translate in vitro embryotoxicity data for a series of phenols to in vivo developmental toxic potency values for the rat by physiologically based kinetic (PBK) modelling-based reverse dosimetry. To this purpose, PBK models were developed for each of the phenols. The models were parameterised with in vitro-derived values defining metabolism and transport of the compounds across the intestinal and placental barrier and with in silico predictions and data from the literature. Using PBK-based reverse dosimetry, in vitro concentration–response curves from the EST were translated into in vivo dose–response curves from which points of departure (PoDs) were derived. The predicted PoDs differed less than 3.6-fold from PoDs derived from in vivo toxicity data for the phenols available in the literature. Moreover, the in vitro PBK-based reverse dosimetry approach could overcome the large disparity that was observed previously between the in vitro and the in vivo relative potency of the series of phenols. In conclusion, this study shows another proof-of-principle that the in vitro PBK approach is a promising strategy for non-animal-based safety assessment of chemicals.

Model-based contextualization of in vitro toxicity data quantitatively predicts in vivo drug response in patients

Understanding central mechanisms underlying drug-induced toxicity plays a crucial role in drug development and drug safety. However, a translation of cellular in vitro findings to an actual in vivo context remains challenging. Here, physiologically based pharmacokinetic (PBPK) modeling was used for in vivo contextualization of in vitro toxicity data (PICD) to quantitatively predict in vivo drug response over time by integrating multiple levels of biological organization. Explicitly, in vitro toxicity data at the cellular level were integrated into whole-body PBPK models at the organism level by coupling in vitro drug exposure with in vivo drug concentration–time profiles simulated in the extracellular environment within the organ. PICD was exemplarily applied on the hepatotoxicant azathioprine to quantitatively predict in vivo drug response of perturbed biological pathways and cellular processes in rats and humans. The predictive accuracy of PICD was assessed by comparing in vivo drug response predicted for rats with observed in vivo measurements. To demonstrate clinical applicability of PICD, in vivo drug responses of a critical toxicity-related pathway were predicted for eight patients following acute azathioprine overdoses. Moreover, acute liver failure after multiple dosing of azathioprine was investigated in a patient case study by use of own clinical data. Simulated pharmacokinetic profiles were therefore related to in vivo drug response predicted for genes associated with observed clinical symptoms and to clinical biomarkers measured in vivo. PICD provides a generic platform to investigate drug-induced toxicity at a patient level and thus may facilitate individualized risk assessment during drug development.

Predicting points of departure for risk assessment based on in vitro cytotoxicity data and physiologically based kinetic (PBK) modeling: The case of kidney toxicity induced by aristolochic acid I

Aristolochic acids are naturally occurring nephrotoxins. This study aims to investigate whether physiologically based kinetic (PBK) model-based reverse dosimetry could convert in vitro concentration-response curves of aristolochic acid I (AAI) to in vivo dose response-curves for nephrotoxicity in rat, mouse and human. To achieve this extrapolation, PBK models were developed for AAI in these different species. Subsequently, concentration-response curves obtained from in vitro cytotoxicity models were translated to in vivo dose-response curves using PBK model-based reverse dosimetry. From the predicted in vivo dose-response curves, points of departure (PODs) for risk assessment could be derived. The PBK models elucidated species differences in the kinetics of AAI with the overall catalytic efficiency for metabolic conversion of AAI to aristolochic acid Ia (AAIa) being 2-fold higher for rat and 64-fold higher for mouse than human. Results show that the predicted PODs generally fall within the range of PODs derived from the available in vivo studies. This study provides proof of principle for a new method to predict a POD for in vivo nephrotoxicity by integrating in vitro toxicity testing with in silico PBK model-based reverse dosimetry.

Considering new methodologies in strategies for safety assessment of foods and food ingredients

Toxicology and safety assessment are changing and require new strategies for evaluating risk that are less depending on apical toxicity endpoints in animal models and relying more on knowledge of the mechanism of toxicity. This manuscript describes a number of developments that could contribute to this change and implement this in a stepwise roadmap that can be applied for the evaluation of food and food ingredients. The roadmap was evaluated in four case studies by using literature and existing data. This preliminary evaluation was shown to be useful. However, this experience should be extended by including examples where experimental work needs to be included. To further implement these new insights in toxicology and safety assessment for the area of food and food ingredients, the recommendation is that stakeholders take action in addressing gaps in our knowledge, e.g. with regard to the applicability of the roadmap for mixtures and food matrices. Further development of the threshold of toxicological concern is needed, as well as cooperation with other sectors where similar schemes are under development. Moreover, a more comprehensive evaluation of the roadmap, also including the identification of the need for in vitro experimental work is recommended.

Advancing In Vitro-In Vivo Extrapolations of Mechanism-Specific Toxicity Data Through Toxicokinetic Modeling

International legislation, such as the European REACH regulation (registration, evaluation, authorization, and restriction of chemicals), mandates the assessment of potential risks of an ever-growing number of chemicals to the environment and human health. Although this legislation is considered one of the most important investments in consumer safety ever, the downside is that the current testing strategies within REACH rely on extensive animal testing. To address the ethical conflicts arising from these increased testing requirements, decision-makers, such as the European Chemicals Agency (ECHA), are committed to Russel and Burch's 3R principle (i.e., reduction, replacement, refinement) by demanding that animal experiments should be substituted with appropriate alternatives whenever possible. A potential solution of this dilemma might be the application of in vitro bioassays to estimate toxic effects using cells or cellular components instead of whole organisms. Although such assays are particularly useful to assess potential mechanisms of toxic action, scientists require appropriate methods to extrapolate results from the in vitro level to the situation in vivo. Toxicokinetic models are a straightforward means of bridging this gap. The present chapter describes different available options for in vitro-in vivo extrapolation (IVIVE) of mechanism-specific effects focused on fish species and also reviews the implications of confounding factors during the conduction of in vitro bioassays and their influence on the optimal choice of different dose metrics.

Biokinetics in repeated-dosing in vitro drug toxicity studies

The aim of the EU FP7 Predict-IV project was to improve the predictivity of in vitro assays for unwanted effects of drugs after repeated dosing. The project assessed the added benefit of integrating long-lived in vitro organotypic cell systems with 'omics' technologies and in silico modelling, including systems biology and pharmacokinetic assessments. RPTEC/TERT1 kidney cells, primary rat and human hepatocytes, HepaRG liver cells and 2D and 3D primary brain cultures were dosed daily or every other day for 14 days to a selection of drugs varying in their mechanism of pharmacological action. Since concentration-effect relationships not only depend on the activity of the drug or the sensitivity of the target, but also on the distribution of compounds in the in vitro system, the concentration of a selection of drugs in cells, microtitre plate plastic and medium was measured over time. Results, reviewed in this paper, indicate that lipophilic drugs bind significantly to plastic labware. A few drugs, including less lipophilic drugs, bind to cell-attachment matrices. Chemicals that reach high concentrations in cells, including cyclosporin A and amiodarone, significantly accumulate over time after repeated dosing, partly explaining their increased toxicity after repeated dosing, compared to a single dose.

Bayesian evaluation of a physiologically-based pharmacokinetic (PBPK) model of long-term kinetics of metal nanoparticles in rats

Biomathematical modeling quantitatively describes the disposition of metal nanoparticles in lungs and other organs of rats. In a preliminary model, adjustable parameters were calibrated to each of three data sets using a deterministic approach, with optimal values varying among the different data sets. In the current effort, Bayesian population analysis using Markov chain Monte Carlo (MCMC) simulation was used to recalibrate the model while improving assessments of parameter variability and uncertainty. The previously-developed model structure and some physiological parameter values were modified to improve physiological realism. The data from one of the three previously-identified studies and from two other studies were used for model calibration. The data from the one study that adequately characterized mass balance were used to generate parameter distributions. When data from a second study of the same nanomaterial (iridium) were added, the level of agreement was still acceptable. Addition of another data set (for silver nanoparticles) led to substantially lower precision in parameter estimates and large discrepancies between the model predictions and experimental data for silver nanoparticles. Additional toxicokinetic data are needed to further evaluate the model structure and performance and to reduce uncertainty in the kinetic processes governing in vivo disposition of metal nanoparticles.

Extended evaluation on the ES-D3 cell differentiation assay combined with the BeWo transport model, to predict relative developmental toxicity of triazole compounds

The mouse embryonic stem D3 (ES-D3) cell differentiation assay is based on the morphometric measurement of cardiomyocyte differentiation and is a promising tool to detect developmental toxicity of compounds. The BeWo transport model, consisting of BeWo b30 cells grown on transwell inserts and mimicking the placental barrier, is useful to determine relative placental transport velocities of compounds. We have previously demonstrated the usefulness of the ES-D3 cell differentiation assay in combination with the in vitro BeWo transport model to predict the relative in vivo developmental toxicity potencies of a set of reference azole compounds. To further evaluate this combined in vitro toxicokinetic and toxicodynamic approach, we combined ES-D3 cell differentiation data of six novel triazoles with relative transport rates obtained from the BeWo model and compared the obtained ranking to the developmental toxicity ranking as derived from in vivo data. The data show that the combined in vitro approach provided a correct prediction for in vivo developmental toxicity, whereas the ES-D3 cell differentiation assay as stand-alone did not. In conclusion, we have validated the combined in vitro approach for developmental toxicity, which we have previously developed with a set of reference azoles, for a set of six novel triazoles. We suggest that this combined model, which takes both toxicodynamic and toxicokinetic aspects into account, should be further validated for other chemical classes of developmental toxicants.