Quantitative In Vitro To In Vivo Extrapolation Research Articles

Use of quantitative in vitro to in vivo extrapolation (QIVIVE) for the assessment of non-combustible next-generation product aerosols.

With the use of in vitro new approach methodologies (NAMs) for the assessment of non-combustible next-generation nicotine delivery products, new extrapolation methods will also be required to interpret and contextualize the physiological relevance of these results. Quantitative in vitro to in vivo extrapolation (QIVIVE) can translate in vitro concentrations into in-life exposures with physiologically-based pharmacokinetic (PBPK) modelling and provide estimates of the likelihood of harmful effects from expected exposures. A major challenge for evaluating inhalation toxicology is an accurate assessment of the delivered dose to the surface of the cells and the internalized dose. To estimate this, we ran the multiple-path particle dosimetry (MPPD) model to characterize particle deposition in the respiratory tract and developed a PBPK model for nicotine that was validated with human clinical trial data for cigarettes. Finally, we estimated a Human Equivalent Concentration (HEC) and predicted plasma concentrations based on the minimum effective concentration (MEC) derived after acute exposure of BEAS-2B cells to cigarette smoke (1R6F), or heated tobacco product (HTP) aerosol at the air liquid interface (ALI). The MPPD-PBPK model predicted the in vivo data from clinical studies within a factor of two, indicating good agreement as noted by WHO International Programme on Chemical Safety (2010) guidance. We then used QIVIVE to derive the exposure concentration (HEC) that matched the estimated in vitro deposition point of departure (POD) (MEC cigarette = 0.38 puffs or 11.6µg nicotine, HTP = 22.9 puffs or 125.6µg nicotine) and subsequently derived the equivalent human plasma concentrations. Results indicate that for the 1R6F cigarette, inhaling 1/6th of a stick would be required to induce the same effects observed in vitro, in vivo. Whereas, for HTP it would be necessary to consume 3 sticks simultaneously to induce in vivo the effects observed in vitro. This data further demonstrates the reduced physiological potency potential of HTP aerosol compared to cigarette smoke. The QIVIVE approach demonstrates great promise in assisting human health risk assessments, however, further optimization and standardization are required for the substantiation of a meaningful contribution to tobacco harm reduction by alternative nicotine delivery products.

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 0.15ng/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 0.86ng/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.

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

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

Integrating Mechanistic and Toxicokinetic Information in Predictive Models of Cholestasis.

Drug development involves the thorough assessment of the candidate's safety and efficacy. In silico toxicology (IST) methods can contribute to the assessment, complementing in vitro and in vivo experimental methods, since they have many advantages in terms of cost and time. Also, they are less demanding concerning the requirements of product and experimental animals. One of these methods, Quantitative Structure-Activity Relationships (QSAR), has been proven successful in predicting simple toxicity end points but has more difficulties in predicting end points involving more complex phenomena. We hypothesize that QSAR models can produce better predictions of these end points by combining multiple QSAR models describing simpler biological phenomena and incorporating pharmacokinetic (PK) information, using quantitative in vitro to in vivo extrapolation (QIVIVE) models. In this study, we applied our methodology to the prediction of cholestasis and compared it with direct QSAR models. Our results show a clear increase in sensitivity. The predictive quality of the models was further assessed to mimic realistic conditions where the query compounds show low similarity with the training series. Again, our methodology shows clear advantages over direct QSAR models in these situations. We conclude that the proposed methodology could improve existing methodologies and could be suitable for being applied to other toxicity end points.

P14-13: Use of Quantitative In Vitro to In Vivo Extrapolation (QIVIVE) for the assessment of Non-Combustible Next Generation products

P14-13: Use of Quantitative In Vitro to In Vivo Extrapolation (QIVIVE) for the assessment of Non-Combustible Next Generation products

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

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

Linking Transthyretin-Binding Chemicals and Free Thyroid Hormones: In Vitro to In Vivo Extrapolation Based on a Competitive Binding Model.

Large numbers of pollutants competitively inhibit the binding between thyroid hormones and transthyretin (TTR) in vitro. However, the impact of this unintended binding on free thyroid hormones in vivo has not yet been characterized. Herein, we established a quantitative in vitro to in vivo extrapolation (QIVIVE) method based on a competitive binding model to quantify the effect of TTR-binding chemicals on free thyroid hormones in human blood. Twenty-five TTR-binding chemicals including 6 hydroxyl polybromodiphenyl ethers (OH-PDBEs), 6 hydroxyl polychlorobiphenyls (OH-PCBs), 4 halogenphenols, 5 per- and polyfluorinated substances (PFASs), and 4 phenols were selected for investigation. Incorporating the in vitro binding parameters and human exposure data, the QIVIVE model could well predict the in vivo effect on free thyroid hormones. Co-exposure to twenty-five typical TTR-binding chemicals resulted in median increases of 0.080 and 0.060% in circulating levels of free thyroxine (FT4) and free triiodothyronine (FT3) in the general population. Individuals with occupational exposure to TTR-binding chemicals suffered 1.88-32.2% increases in free thyroid hormone levels. This study provides a quantitative tool to evaluate the thyroid-disrupting risks of TTR-binding chemicals and proposes a new framework for assessing the in vivo effects of chemical exposures on endogenous molecules.

Role of bioavailability and protein binding of four anionic perfluoroalkyl substances in cell-based bioassays for quantitative in vitro to in vivo extrapolations

Perfluoroalkyl substances (PFAS) are persistent and pose a risk to human health. High throughput screening (HTS) cell-based bioassays may inform risk assessment of PFAS provided that quantitative in vitro to in vivo extrapolation (QIVIVE) can be developed. The QIVIVE ratio is the ratio of nominal (Cnom) or freely dissolved concentration (Cfree) in human blood to Cnom or Cfree in the bioassays. Considering that the concentrations of PFAS in human plasma and in vitro bioassays may vary by orders of magnitude, we tested the hypothesis that anionic PFAS bind to proteins concentration-dependently and therefore the binding differs substantially between human plasma and bioassays, which has an impact on QIVIVE. Solid phase microextraction (SPME) with C18-coated fibers served to quantify the Cfree of four anionic PFAS (perfluorobutanoate (PFBA), perfluorooctanoate (PFOA), perfluorohexane sulfonate (PFHxS) and perfluorooctane sulfonate (PFOS)) in the presence of proteins and lipid, medium components, cells and human plasma over five orders of magnitude in concentrations. The C18-SPME method was used to quantify the non-linear binding to proteins, human plasma and medium, and the partition constants to cells. These binding parameters were used to predict Cfree of PFAS in cell bioassays and human plasma by a concentration-dependent mass balance model (MBM). The approach was illustrated with a reporter gene assay indicating activation of the peroxisome proliferator-activated receptor gamma (PPARγ-GeneBLAzer). Blood plasma levels were collected from literature for occupational exposure and the general population. The QIVIVEnom ratios were higher than the QIVIVEfree ratios due to the strong affinity to proteins and large differences in protein contents between human blood and bioassays. For human health risk assessment, the QIVIVEfree ratios of many in vitro assays need to be combined to cover all health relevant endpoints. If Cfree cannot be measured, they can be estimated with the MBM and concentration-dependent distribution ratios.

Human health risk assessment of 6:2 Cl-PFESA through quantitative in vitro to in vivo extrapolation by integrating cell-based assays, an epigenetic key event, and physiologically based pharmacokinetic modeling

Human health risk assessment of chemicals is essential but often relies on time-consuming and animal and labor-extensive procedures. Here, we develop a population-based, quantitative in vitro to in vivo extrapolation (QIVIVE) approach which depended on cellular effects monitored by in vitro assays, considered chemical internal concentration determined by LC-MS/MS, extrapolated into in vivo target tissue concentration through physiologically based pharmacokinetic (PBPK) modelling, and assessed populational health risk using in silico modelling. By applying this QIVIVE approach to 6:2 chlorinated polyfluorinated ether sulfonate (6:2 Cl-PFESA), as a representative of the emerging pollutants, we find that 6:2 Cl-PFESA disturbed lipid homeostasis in HepG2 cells through enhancement of lipid accumulation and fatty acid β-oxidation, during which miR-93-5p served as a key event towards toxicity and thus, could serve as an efficient toxicity marker for risk assessment; further, the disruption potency of lipid homeostasis of 6:2 Cl-PFESA for the most of studied populations in China might be of moderate concern. Together, our approach improved the reliability of QIVIVE during human health risk assessment, which can readily be used for other chemicals.

A Pragmatic Framework for the Application of New Approach Methodologies in One Health Toxicological Risk Assessment.

Globally, industries and regulatory authorities are faced with an urgent need to assess the potential adverse effects of chemicals more efficiently by embracing new approach methodologies (NAMs). NAMs include cell and tissue methods (in vitro), structure-based/toxicokinetic models (in silico), methods that assess toxicant interactions with biological macromolecules (in chemico), and alternative models. Increasing knowledge on chemical toxicokinetics (what the body does with chemicals) and toxicodynamics (what the chemicals do with the body) obtained from in silico and in vitro systems continues to provide opportunities for modernizing chemical risk assessments. However, directly leveraging in vitro and in silico data for derivation of human health-based reference values has not received regulatory acceptance due to uncertainties in extrapolating NAM results to human populations, including metabolism, complex biological pathways, multiple exposures, interindividual susceptibility and vulnerable populations. The objective of this article is to provide a standardized pragmatic framework that applies integrated approaches with a focus on quantitative in vitro to in vivo extrapolation (QIVIVE) to extrapolate in vitro cellular exposures to human equivalent doses from which human reference values can be derived. The proposed framework intends to systematically account for the complexities in extrapolation and data interpretation to support sound human health safety decisions in diverse industrial sectors (food systems, cosmetics, industrial chemicals, pharmaceuticals etc.). Case studies of chemical entities, using new and existing data, are presented to demonstrate the utility of the proposed framework while highlighting potential sources of human population bias and uncertainty, and the importance of Good Method and Reporting Practices.

Pharmacotherapeutic and Computational Approaches for Biopharmaceutical Considerations towards Drug Development and Delivery against COVID-19

The novel coronavirus disease (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), affected millions of people worldwide at an alarming rate. Moreover, the development of vaccines is still hope, but its camouflage mutations during transmission are still a challenge. In the dire condition of this pandemic, drug repurposing with the exploitation of computational modeling has become the cynosure to repurpose the already existing drugs such as remdesivir, Favipiravir, dexamethasone, and other drugs at clinical levels. Furthermore, their safety and efficacy against COVID-19 remain a challenge in different age groups and populations with pre-existing conditions like heart disease, hepatic and renal impairment, pregnancy, and immunocompromised states. Moreover, computational modeling allows studying physiological and biochemical parameters on drug transport, delivery, and therapeutic efficacy of dosage forms. This review explicitly provides a comprehensive account of the challenges and opportunities for developing physiologically based pharmacokinetic models (PBPK) and pharmacodynamic(PD) models to establish a therapeutic dosage regimen based on dose selection, safety, and efficacy. We also highlight the pharmacologic targeting strategies for ACE receptors, toxicity concerns, combination therapy, and drug-drug interactions for different repurposed drugs against COVID-19. In dreadful scenarios, PBPK and PD models hold promise for human PK and dose prediction in COVID-19, along with paving new horizons to improve the therapeutic as well as immuno-therapeutic efficacy using nano-drug delivery approaches, computer-aided drug design (CADD), and speed up clinical trials with a better understanding of quantitative in vitro to in vivo extrapolation (QIVIE) and established PK data.

Next Generation Risk Assessment of the Anti-Androgen Flutamide Including the Contribution of Its Active Metabolite Hydroxyflutamide.

In next generation risk assessment (NGRA), non-animal approaches are used to quantify the chemical concentrations required to trigger bioactivity responses, in order to assure safe levels of human exposure. A limitation of many in vitro bioactivity assays, which are used in an NGRA context as new approach methodologies (NAMs), is that toxicokinetics, including biotransformation, are not adequately captured. The present study aimed to include, as a proof of principle, the bioactivity of the metabolite hydroxyflutamide (HF) in an NGRA approach to evaluate the safety of the anti-androgen flutamide (FLU), using the AR-CALUX assay to derive the NAM point of departure (PoD). The NGRA approach applied also included PBK modelling-facilitated quantitative in vitro to in vivo extrapolation (QIVIVE). The PBK model describing FLU and HF kinetics in humans was developed using GastroPlus™ and validated against human pharmacokinetic data. PBK model-facilitated QIVIVE was performed to translate the in vitro AR-CALUX derived concentration-response data to a corresponding in vivo dose-response curve for the anti-androgenicity of FLU, excluding and including the activity of HF (-HF and +HF, respectively). The in vivo benchmark dose 5% lower confidence limits (BMDL05) derived from the predicted in vivo dose-response curves for FLU, revealed a 440-fold lower BMDL05 when taking the bioactivity of HF into account. Subsequent comparison of the predicted BMDL05 values to the human therapeutic doses and historical animal derived PoDs, revealed that PBK modelling-facilitated QIVIVE that includes the bioactivity of the active metabolite is protective and provides a more appropriate PoD to assure human safety via NGRA, whereas excluding this would potentially result in an underestimation of the risk of FLU exposure in humans.

Application of the Adverse Outcome Pathway Concept to In Vitro Nephrotoxicity Assessment: Kidney Injury due to Receptor-Mediated Endocytosis and Lysosomal Overload as a Case Study.

Application of adverse outcome pathways (AOP) and integration of quantitative in vitro to in vivo extrapolation (QIVIVE) may support the paradigm shift in toxicity testing to move from apical endpoints in test animals to more mechanism-based in vitro assays. Here, we developed an AOP of proximal tubule injury linking a molecular initiating event (MIE) to a cascade of key events (KEs) leading to lysosomal overload and ultimately to cell death. This AOP was used as a case study to adopt the AOP concept for systemic toxicity testing and risk assessment based on in vitro data. In this AOP, nephrotoxicity is thought to result from receptor-mediated endocytosis (MIE) of the chemical stressor, disturbance of lysosomal function (KE1), and lysosomal disruption (KE2) associated with release of reactive oxygen species and cytotoxic lysosomal enzymes that induce cell death (KE3). Based on this mechanistic framework, in vitro readouts reflecting each KE were identified. Utilizing polymyxin antibiotics as chemical stressors for this AOP, the dose-response for each in vitro endpoint was recorded in proximal tubule cells from rat (NRK-52E) and human (RPTEC/TERT1) in order to (1) experimentally support the sequence of key events (KEs), to (2) establish quantitative relationships between KEs as a basis for prediction of downstream KEs based on in vitro data reflecting early KEs and to (3) derive suitable in vitro points of departure for human risk assessment. Time-resolved analysis was used to support the temporal sequence of events within this AOP. Quantitative response-response relationships between KEs established from in vitro data on polymyxin B were successfully used to predict in vitro toxicity of other polymyxin derivatives. Finally, a physiologically based kinetic (PBK) model was utilized to transform in vitro effect concentrations to a human equivalent dose for polymyxin B. The predicted in vivo effective doses were in the range of therapeutic doses known to be associated with a risk for nephrotoxicity. Taken together, these data provide proof-of-concept for the feasibility of in vitro based risk assessment through integration of mechanistic endpoints and reverse toxicokinetic modelling.

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).

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.

Quantitative In Vitro-to-In Vivo Extrapolation: Nominal versus Freely Dissolved Concentration.

Discussions are ongoing on which dose metric should be used for quantitative in vitro-to-in vivo extrapolation (QIVIVE) of in vitro bioassay data. The nominal concentration of the test chemicals is most commonly used and easily accessible, while the concentration freely dissolved in the assay medium is considered to better reflect the bioavailable concentration but is tedious to measure. The aim of this study was to elucidate how much QIVIVE results will differ when using either nominal or freely dissolved concentrations. QIVIVEnom and QIVIVEfree ratios, that is, the ratios of plasma concentrations divided by in vitro effect concentrations, were calculated for 10 pharmaceuticals using previously published nominal and freely dissolved effect concentrations for the activation of the peroxisome proliferator-activated receptor gamma (PPARγ) and the activation of oxidative stress response. The QIVIVEnom ratios were higher than QIVIVEfree ratios by up to a factor of 60. The risk of in vivo effects was classified as being high or low for four chemicals using the QIVIVEnom and for three chemicals using QIVIVEfree ratios. Unambiguous classification was possible for nine chemicals by combining the QIVIVEnom or QIVIVEfree ratios with the respective specificity ratios (SRnom or SRfree) of the in vitro effect data, which helps to identify whether the specific effect was influenced by cytotoxicity. QIVIVEfree models should be preferred as they account for differences in bioavailability between in vitro and in vivo, but QIVIVEnom may still be useful for screening the effects of large numbers of chemicals because it is generally more conservative. The use of SR of the in vitro effect data as a second classification factor is recommended for QIVIVEnom and QIVIVEfree models because a clearer picture can be obtained with respect to the likelihood that a biological effect will occur and that it is not caused by nonspecific cytotoxicity.

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.

Incorporation of in vitro metabolism data and physiologically based pharmacokinetic modeling in a risk assessment for chloroprene

Objective: To develop a physiologically based pharmacokinetic (PBPK) model for chloroprene in the mouse, rat and human, relying only on in vitro data to estimate tissue metabolism rates and partitioning, and to apply the model to calculate an inhalation unit risk (IUR) for chloroprene.Materials and methods: Female B6C3F1 mice were the most sensitive species/gender for lung tumors in the 2-year bioassay conducted with chloroprene. The PBPK model included tissue metabolism rate constants for chloroprene estimated from results of in vitro gas uptake studies using liver and lung microsomes. To assess the validity of the PBPK model, a 6-hr, nose-only chloroprene inhalation study was conducted with female B6C3F1 mice in which both chloroprene blood concentrations and ventilation rates were measured. The PBPK model was then used to predict dose measures – amounts of chloroprene metabolized in lungs per unit time – in mice and humans.Results: The mouse PBPK model accurately predicted in vivo pharmacokinetic data from the 6-hr, nose-only chloroprene inhalation study. The PBPK model was used to conduct a cancer risk assessment based on metabolism of chloroprene to reactive epoxides in the lung, the target tissue in mice. The IUR was over100-fold lower than the IUR from the EPA Integrated Risk Information System (IRIS), which was based on inhaled chloroprene concentration. The different result from the PBPK model risk assessment arises from use of the more relevant tissue dose metric, amount metabolized, rather than inhaled concentrationDiscussion and conclusions: The revised chloroprene PBPK model is based on the best available science, including new test animal in vivo validation, updated literature review and a Markov-Chain Monte Carlo analysis to assess parameter uncertainty. Relying on both mouse and human metabolism data also provides an important advancement in the use of quantitative in vitro to in vivo extrapolation (QIVIVE). Inclusion of the best available science is especially important when deriving a toxicity value based on species extrapolation for the potential carcinogenicity of a reactive metabolite.

The role of fit-for-purpose assays within tiered testing approaches: A case study evaluating prioritized estrogen-active compounds in an in vitro human uterotrophic assay

Chemical risk assessment relies on toxicity tests that require significant numbers of animals, time and costs. For the >30,000 chemicals in commerce, the current scale of animal testing is insufficient to address chemical safety concerns as regulatory and product stewardship considerations evolve to require more comprehensive understanding of potential biological effects, conditions of use, and associated exposures. We demonstrate the use of a multi-level new approach methodology (NAMs) strategy for hazard- and risk-based prioritization to reduce animal testing. A Level 1/2 chemical prioritization based on estrogen receptor (ER) activity and metabolic activation using ToxCast data was used to select 112 chemicals for testing in a Level 3 human uterine cell estrogen response assay (IKA assay). The Level 3 data were coupled with quantitative in vitro to in vivo extrapolation (Q-IVIVE) to support bioactivity determination (as a surrogate for hazard) in a tissue-specific context. Assay AC50s and Q-IVIVE were used to estimate human equivalent doses (HEDs), and HEDs were compared to rodent uterotrophic assay in vivo-derived points of departure (PODs). For substances active both in vitro and in vivo, IKA assay-derived HEDs were lower or equivalent to in vivo PODs for 19/23 compounds (83%). Activity exposure relationships were calculated, and the IKA assay was as or more protective of human health than the rodent uterotrophic assay for all IKA-positive compounds. This study demonstrates the utility of biologically relevant fit-for-purpose assays and supports the use of a multi-level strategy for chemical risk assessment.

Inter-ethnic differences in CYP3A4 metabolism: A Bayesian meta-analysis for the refinement of uncertainty factors in chemical risk assessment

CYP3A4 is the major human cytochrome P450 isoform responsible for the metabolism of more than 50% of known xenobiotics. Here, inter-ethnic differences in CYP3A4 metabolism have been investigated through a systematic review of pharmacokinetic data for 15 CYP3A4 probe substrates and parameters reflecting acute (Cmax, oral route) and chronic exposure (clearance and area under the plasma concentration-time curve, oral and intravenous route). All data were extracted in a structured database and meta-analyses were performed using a hierarchical Bayesian model in the R freeware to derive parameter, route and population-specific variability distributions for CYP3A4 metabolism. Two different approaches were applied. 1) Inter-individual differences were quantified using North American healthy adults as a reference group to compare with European, Asian, Middle East, and South-American healthy adults and with ethnicity, elderly, children and neonates. 2) Intra-ethnic–specific variability distributions were derived without comparing to a reference group. Overall, subgroup-specific distributions for CYP3A4-variability provided the basis to derive CYP3A4-related uncertainty factors (UF) to cover 95th or 97.5th centiles of the population and were compared with the human default toxicokinetic UF (3.16). The results indicate that CYP3A4-related UFs in healthy adults were higher for chronic oral exposures (2.5–3.0, UF95 and UF97.5, 10 compounds) than for intravenous exposures (1.7–1.8, 2 compounds). All UFs were within the default TK UF. These distributions allow for: 1) the application of CYP3A4-related UFs in the risk assessment of compounds for which in vitro CYP3A4 metabolism evidence is available without the need for animal data; 2) the integration of CYP3A4-related variability distributions with in vitro metabolism data into physiologically based kinetic (PBK) models for quantitative in vitro to in vivo extrapolation (QIVIVE) and 3) the estimation of UFs in chemical risk assessment using variability distributions of metabolism.