QSARs for PBPK modelling of environmental contaminants

PBPK Modeling as an Alternative Method of Interspecies Extrapolation that Reduces the Use of Animals: A Systematic Review

INTRODUCTION

Physiologically based pharmacokinetic (PBPK) modeling is a computational approach that simulates the anatomical structure of the studied species and presents the organs and tissues as compartments interconnected by arterial and venous blood flows.

AIM

The aim of this systematic review was to analyze the published articles focused on the development of PBPK models for interspecies extrapolation in the disposition of drugs and health risk assessment, presenting to this modeling an alternative to reduce the use of animals.

METHODS

For this purpose, a systematic search was performed in PubMed using the following search terms: "PBPK" and "Interspecies extrapolation". The revision was performed according to PRISMA guidelines.

RESULTS

In the analysis of the articles, it was found that rats and mice are the most commonly used animal models in the PBPK models; however, most of the physiological and physicochemical information used in the reviewed studies were obtained from previous publications. Additionally, most of the PBPK models were developed to extrapolate pharmacokinetic parameters to humans and the main application of the models was for toxicity testing.

CONCLUSION

PBPK modeling is an alternative that allows the integration of in vitro and in silico data as well as parameters reported in the literature to predict the pharmacokinetics of chemical substances, reducing in large quantity the use of animals that are required in traditional studies.

Pharmacokinetics of Novel Furoxan/Coumarin Hybrids in Rats Using LC-MS/MS Method and Physiologically Based Pharmacokinetic Model

Novel furoxan/coumarin hybrids were synthesized, and pharmacologic studies showed that the compounds displayed potent antiproliferation activities via downregulating both the phosphatidylinositide 3-kinase (PI3K) pathway and the mitogen-activated protein kinase (MAPK) pathway. To investigate the preclinical pharmacokinetic (PK) properties of three candidate compounds (CY-14S-4A83, CY-16S-4A43, and CY-16S-4A93), liquid chromatography, in tandem with the mass spectrometry LC-MS/MS method, was developed and validated for the simultaneous determination of these compounds. The absorption, distribution, metabolism, and excretion (ADME) properties were investigated in in vitro studies and in rats. Meanwhile, physiologically based pharmacokinetic (PBPK) models were constructed using only in vitro data to obtain detailed PK information. Good linearity was observed over the concentration range of 0.01−1.0 μg/mL. The free drug fraction (fu) values of the compounds were less than 3%, and the clearance (CL) values were 414.5 ± 145.7 mL/h/kg, 2624.6 ± 648.4 mL/h/kg, and 500.6 ± 195.2 mL/h/kg, respectively. The predicted peak plasma concentration (Cmax) and the area under the concentration-time curve (AUC) were overestimated for the CY-16S-4A43 PBPK model compared with the experimental ones (fold error > 2), suggesting that tissue accumulation and additional elimination pathways may exist. In conclusion, the LC-MS/MS method was successively applied in the preclinical PK studies, and the detailed information from PBPK modeling may improve decision-making in subsequent new drug development.

Application of an Accessible Interface for Pharmacokinetic Modeling and In Vitro to In Vivo Extrapolation

Regulatory toxicology testing has traditionally relied on in vivo methods to inform decision-making. However, scientific, practical, and ethical considerations have led to an increased interest in the use of in vitro and in silico methods to fill data gaps. While in vitro experiments have the advantage of rapid application across large chemical sets, interpretation of data coming from these non-animal methods can be challenging due to the mechanistic nature of many assays. In vitro to in vivo extrapolation (IVIVE) has emerged as a computational tool to help facilitate this task. Specifically, IVIVE uses physiologically based pharmacokinetic (PBPK) models to estimate tissue-level chemical concentrations based on various dosing parameters. This approach is used to estimate the administered dose needed to achieve in vitro bioactivity concentrations within the body. IVIVE results can be useful to inform on metrics such as margin of exposure or to prioritize potential chemicals of concern, but the PBPK models used in this approach have extensive data requirements. Thus, access to input parameters, as well as the technical requirements of applying and interpreting models, has limited the use of IVIVE as a routine part of in vitro testing. As interest in using non-animal methods for regulatory and research contexts continues to grow, our perspective is that access to computational support tools for PBPK modeling and IVIVE will be essential for facilitating broader application and acceptance of these techniques, as well as for encouraging the most scientifically sound interpretation of in vitro results. We highlight recent developments in two open-access computational support tools for PBPK modeling and IVIVE accessible via the Integrated Chemical Environment (https://ice.ntp.niehs.nih.gov/), demonstrate the types of insights these tools can provide, and discuss how these analyses may inform in vitro-based decision making.

A Novel Method for Predicting the Human Inherent Clearance and Its Application in the Study of the Pharmacokinetics and Drug-Drug Interaction between Azidothymidine and Fluconazole Mediated by UGT Enzyme

In order to improve the benefit–risk ratio of pharmacokinetic (PK) research in the early development of new drugs, in silico and in vitro methods were constructed and improved. Models of intrinsic clearance rate (CL_int) were constructed based on the quantitative structure–activity relationship (QSAR) of 7882 collected compounds. Moreover, a novel in vitro metabolic method, the Bio-PK dynamic metabolic system, was constructed and combined with a physiology-based pharmacokinetic model (PBPK) model to predict the metabolism and the drug–drug interaction (DDI) of azidothymidine (AZT) and fluconazole (FCZ) mediated by the phase II metabolic enzyme UDP-glycosyltransferase (UGT) in humans. Compared with the QSAR models reported previously, the goodness of fit of our CL_int model was slightly improved (determination coefficient (R²) = 0.58 vs. 0.25–0.45). Meanwhile, compared with the predicted clearance of 61.96 L/h (fold error: 2.95–3.13) using CL_int (8 µL/min/mg) from traditional microsomal experiment, the predicted clearance using CL_int (25 μL/min/mg) from Bio-PK system was increased to 143.26 L/h (fold error: 1.27–1.36). The predicted C_max and AUC (the area under the concentration–time curve) ratio were 1.32 and 1.84 (fold error: 1.36 and 1.05) in a DDI study with an inhibition coefficient (Ki) of 13.97 μM from the Bio-PK system. The results indicate that the Bio-PK system more truly reflects the dynamic metabolism and DDI of AZT and FCZ in the body. In summary, the novel in silico and in vitro method may provide new ideas for the optimization of drug metabolism and DDI research methods in early drug development.

Long-acting drugs and formulations for the treatment and prevention of HIV infection

Long-acting and extended-release formulations represent one of the most important approaches to improving the treatment and prevention of chronic HIV infection. Long-acting small molecules and monoclonal antibodies have demonstrated potent anti-HIV activity in early- and late-stage clinical trials. Strategies to manage toxicity and falling drug concentrations after missed doses, as well as primary and secondary resistance to current drugs and monoclonal antibodies are important considerations. Long-acting injectable nanoformulations of the integrase inhibitor cabotegravir and the non-nucleoside reverse transcriptase inhibitor rilpivirine were safe, well tolerated and efficacious in large randomised phase 3 studies. Regulatory approval for this two-drug combination for HIV maintenance therapy was granted in Canada in 2020 and is expected in the USA during 2021. 4'-Ethynyl-2-fluoro-2'-deoxyadenosine (islatravir) is a novel nucleoside reverse transcriptase inhibitor in clinical development as a long-acting oral drug and as a long-acting subcutaneous polymer implant. GS-6207 is a novel HIV capsid inhibitor that is injected subcutaneously every 3 months. Broadly-neutralising monoclonal antibodies have potent antiviral activity in early human trials, however there is substantial baseline resistance and rapid development of resistance to these antibodies if used as monotherapy. Limitations of these antiretroviral approaches include management of toxicities and prevention of drug resistance when these drugs are discontinued and drug concentrations are slowly reduced over time. These approaches appear to be especially attractive for patients complaining of pill fatigue and for those experiencing HIV-associated stigma. As these formulations are shown to be safe, well tolerated and economical, they are likely to gain broader appeal.

Development of a generic lifelong physiologically based biokinetic model for exposome studies

The current study within the frame of the HEALS project aims at the development of a lifelong physiologically based biokinetic (PBBK) model for exposome studies. The aim was to deliver a comprehensive modelling framework for addressing a large chemical space. Towards this aim, the delivered model can easily adapt parameters from existing ad-hoc models or complete the missing compound specific parameters using advanced quantitative structure activity relationship (QSAR). All major human organs are included, as well as arterial, venous, and portal blood compartments. Xenobiotics and their metabolites are linked through the metabolizing tissues. This is mainly the liver, but also other sites of metabolism might be considered (intestine, brain, skin, placenta) based on the presence or not of the enzymes involved in the metabolism of the compound of interest. Each tissue is described by three mass balance equations for (a) red blood cells, (b) plasma and interstitial tissue and (c) cells respectively. The anthropometric parameters of the models are time dependent, so as to provide a lifetime internal dose assessment, as well as to describe the continuously changing physiology of the mother and the developing fetus. An additional component of flexibility is that the biokinetic processes that relate to metabolism are related with either Michaelis-Menten kinetics, as well as intrinsic clearance kinetics. The capability of the model is demonstrated in the assessment of internal exposure and the prediction of expected biomonitored levels in urine for three major compounds within the HEALS project, namely bisphenol A (BPA), Bis(2-ethylhexyl) phthalate (DEHP) and cadmium (Cd). The results indicated that the predicted urinary levels fit very well with the ones from human biomonitoring (HBM) studies; internal exposure to plasticizers is very low (in the range of ng/L), while internal exposure to Cd is in the range of μg/L.

Influence of benzene exposure, fat content, and their interactions on erythroid-related hematologic parameters in petrochemical workers: a cross-sectional study

BACKGROUND

Ubiquitously distributed benzene is a known hematotoxin. Increasing evidence has suggested that erythroid-related hematologic parameters may be sensitive to benzene exposure. Fat content, which is also closely associated with erythroid-related hematologic parameters, may affect the distribution and/or metabolism of benzene, and eventually benzene-induced toxicity.

METHODS

To explore the influence of benzene exposure, fat content, and their interactions on erythroid-related hematologic parameters, we recruited 1669 petrochemical workers and measured their urinary S-phenylmercapturic acid (SPMA) concentration and erythroid-related hematological parameters. Indices for fat content included body fat percentage (BF%), plasma total cholesterol (TC) and triglycerides (TG), and occurrence of fatty liver.

RESULTS

The dose-response curve revealed U-shaped nonlinear relationships of SPMA with hematocrit (HCT) and mean corpuscular hemoglobin concentration (MCHC) (P-overall < 0.001, and P-nonlinear < 0.015), as well as positive linear associations and r-shaped nonlinear relationships of continuous fat content indices with erythroid-related hematological parameters (P-overall ≤0.005). We also observed modification effects of fat content on the associations between benzene exposure and erythroid-related hematological parameters, with workers of lower or higher BF% and TG more sensitive to benzene-induced elevation of MCHC (P_interaction = 0.021) and benzene-induced decrease of HCT (P_interaction = 0.050), respectively. We also found that some erythroid-related hematologic parameters differed between subgroups of workers with different SPMA levels and fat content combination.

CONCLUSIONS

Our study suggested that benzene exposure, fat content, and their interactions may affect erythroid-related hematological parameters in petrochemical workers in a complex manner that are worthy of further investigation.

Physiology-based toxicokinetic modelling in the frame of the European Human Biomonitoring Initiative

Given the opportunities provided by internal dosimetry modelling in the interpretation of human biomonitoring (HBM) data, the assessment of the links between exposure to chemicals and observed HBM data can be effectively supported by PBTK modelling. This paper gives a comprehensive review of available human PBTK models for compounds selected as a priority by the European Human Biomonitoring Initiative (HBM4EU). We highlight their advantages and deficiencies and suggest steps for advanced internal dose modelling. The review of the available PBTK models highlighted the conceptual differences between older models compared to the ones developed recently, reflecting commensurate differences in research questions. Due to the lack of coordinated strategies for deriving useful biomonitoring data for toxicokinetic properties, significant problems in model parameterisation still remain; these are further increased by the lack of human toxicokinetic data due to ethics issues. Finally, questions arise as well as to the extent they are really representative of interindividual variability. QSARs for toxicokinetic properties is a complementary approach for PBTK model parameterisation, especially for data poor chemicals. This approach could be expanded to model chemico-biological interactions such as intestinal absorption and renal clearance; this could serve the development of more complex generic PBTK models that could be applied to newly derived chemicals. Another gap identified is the framework for mixture interaction terms among compounds that could eventually interact in metabolism. From the review it was concluded that efforts should be shifted toward the development of generic multi-compartmental and multi-route models, supported by targeted biomonitoring coupled with parameterisation by both QSAR approach and experimental (in-vivo and in-vitro) data for newly developed and data poor compounds.

A framework for application of quantitative property-property relationships (QPPRs) in physiologically based pharmacokinetic (PBPK) models for high-throughput prediction of internal dose of inhaled organic chemicals

New generation of toxicological tests and assessment strategies require validated toxicokinetic data or models that are lacking for most chemicals. This study aimed at developing a quantitative property-property relationship (QPPR)-based human physiologically based pharmacokinetic (PBPK) modeling framework for high-throughput predictions of inhalation toxicokinetics of organic chemicals. A PBPK model was parameterized with QPPR-derived values for hepatic clearance (CL_h) and partition coefficients (P) [blood:air (P_ba) and tissue:air (P_ta) and tissue:blood (P_tb)]. The model was initially applied to an evaluation dataset of 40 organic chemicals in the applicability domain, and then to an expanded dataset of 249 organic chemicals from diverse chemical classes. 'Batch' analyses were performed for rapid assessments of hundreds of chemicals. The simulations of inhalation toxicokinetics following an 8-h exposure to 1 ppm of each chemical were successful. The mean ratios of their predicted-to-experimental values were within a factor of 1.36-2.36 for P_tb and 1.18 for CL_h, for 80% of the chemicals in the evaluation dataset. The predicted 24-h area under the venous blood concentration-time curve (AUC₂₄) values were within the predicted envelopes obtained while using experimental values of P_ba and considering either no or maximal hepatic extraction. The reliability analysis (based on combined sensitivity and uncertainty analyses) indicated that AUC₂₄ predictions for 55% of the expanded dataset were moderately to highly reliable, with 46% exhibiting highly reliable values. Overall, the modeling framework suggests that molecular structure and chemical properties can together be effectively used to obtain first-cut estimates of the toxicokinetics of data-poor organic chemicals for screening and prioritization purposes.

QSAR modeling of adipose/blood partition coefficients of Alcohols, PCBs, PBDEs, PCDDs and PAHs: A data gap filling approach

Physiologically-based toxicokinetic (PBTK) model has immense role to play in the risk assessment process due to specified mathematical representation of the absorption, Distribution, Metabolism, Excretion and Toxicity (ADMET) of chemicals in diverse environmental compartment. Determination of adipose/blood partition coefficient [logP_{(adipose/blood)}] is regarded as one of the crucial constraints of PBTK models. In respect to the challenge for identifying the chemical-definite parameters for these models, especially within short time frame and with limited resources, quantitative structure-activity relationship (QSAR) models are beneficial for providing the chemical-specific parameters of PBTK models. In the present study, we have developed robust, statistically highly significant (R² = 0.92, Q_LOO² = 0.90, R_Pred² = 0.92) and mechanistically interpretable three descriptors QSAR models for 67 environmental chemicals [Alcohols, polybrominated diphenyl ethers (PBDEs), polychlorinated dibenzodioxins (PCDDs), polychlorinated biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs)] employing the experimental values of adipose/blood partition coefficient for human. The partitioning of chemicals into adipose tissue and blood offers information related to distribution and toxicological effects of these molecules in to the mammal system. The developed models are helpful to understand the mechanistic basis of toxicokinetic processes into the mammal system followed by risk assessment and risk management process. The applicability domain (AD) of the developed model was checked and followed by its employment to predict adipose/blood partition coefficient of 513 environmental contaminants consist of PCBs, PBDEs, PCDDs and PAHs from USA Environmental protection agency (US EPA) site.

A review of quantitative structure-property relationships for the fate of ionizable organic chemicals in water matrices and identification of knowledge gaps

Many organic chemicals are ionizable by nature. After use and release into the environment, various fate processes determine their concentrations, and hence exposure to aquatic organisms. In the absence of suitable data, such fate processes can be estimated using Quantitative Structure-Property Relationships (QSPRs). In this review we compiled available QSPRs from the open literature and assessed their applicability towards ionizable organic chemicals. Using quantitative and qualitative criteria we selected the 'best' QSPRs for sorption, (a)biotic degradation, and bioconcentration. The results indicate that many suitable QSPRs exist, but some critical knowledge gaps remain. Specifically, future focus should be directed towards the development of QSPR models for biodegradation in wastewater and sediment systems, direct photolysis and reaction with singlet oxygen, as well as additional reactive intermediates. Adequate QSPRs for bioconcentration in fish exist, but more accurate assessments can be achieved using pharmacologically based toxicokinetic (PBTK) models. No adequate QSPRs exist for bioconcentration in non-fish species. Due to the high variability of chemical and biological species as well as environmental conditions in QSPR datasets, accurate predictions for specific systems and inter-dataset conversions are problematic, for which standardization is needed. For all QSPR endpoints, additional data requirements involve supplementing the current chemical space covered and accurately characterizing the test systems used.

Informatics and Data Analytics to Support Exposome-Based Discovery for Public Health

The complexity of the human exposome-the totality of environmental exposures encountered from birth to death-motivates systematic, high-throughput approaches to discover new environmental determinants of disease. In this review, we describe the state of science in analyzing the human exposome and provide recommendations for the public health community to consider in dealing with analytic challenges of exposome-based biomedical research. We describe extant and novel analytic methods needed to associate the exposome with critical health outcomes and contextualize the data-centered challenges by drawing parallels to other research endeavors such as human genomics research. We discuss efforts for training scientists who can bridge public health, genomics, and biomedicine in informatics and statistics. If an exposome data ecosystem is brought to fruition, it will likely play a role as central as genomic science has had in molding the current and new generations of biomedical researchers, computational scientists, and public health research programs.

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.

Advances in Inhalation Dosimetry Models and Methods for Occupational Risk Assessment and Exposure Limit Derivation

The purpose of this article is to provide an overview and practical guide to occupational health professionals concerning the derivation and use of dose estimates in risk assessment for development of occupational exposure limits (OELs) for inhaled substances. Dosimetry is the study and practice of measuring or estimating the internal dose of a substance in individuals or a population. Dosimetry thus provides an essential link to understanding the relationship between an external exposure and a biological response. Use of dosimetry principles and tools can improve the accuracy of risk assessment, and reduce the uncertainty, by providing reliable estimates of the internal dose at the target tissue. This is accomplished through specific measurement data or predictive models, when available, or the use of basic dosimetry principles for broad classes of materials. Accurate dose estimation is essential not only for dose-response assessment, but also for interspecies extrapolation and for risk characterization at given exposures. Inhalation dosimetry is the focus of this paper since it is a major route of exposure in the workplace. Practical examples of dose estimation and OEL derivation are provided for inhaled gases and particulates.

PBTK modelling platforms and parameter estimation tools to enable animal-free risk assessment: recommendations from a joint EPAA--EURL ECVAM ADME workshop

Information on toxicokinetics is critical for animal-free human risk assessment. Human external exposure must be translated into human tissue doses and compared with in vitro actual cell exposure associated to effects (in vitro-in vivo comparison). Data on absorption, distribution, metabolism and excretion in humans (ADME) could be generated using in vitro and QSAR tools. Physiologically-based toxicokinetic (PBTK) computer modelling could serve to integrate disparate in vitro and in silico findings. However, there are only few freely-available PBTK platforms currently available. And although some ADME parameters can be reasonably estimated in vitro or in silico, important gaps exist. Examples include unknown or limited applicability domains and lack of (high-throughput) tools to measure penetration of barriers, partitioning between blood and tissues and metabolic clearance. This paper is based on a joint EPAA--EURL ECVAM expert meeting. It provides a state-of-the-art overview of the availability of PBTK platforms as well as the in vitro and in silico methods to parameterise basic (Tier 1) PBTK models. Five high-priority issues are presented that provide the prerequisites for wider use of non-animal based PBTK modelling for animal-free chemical risk assessment.

Incorporating new technologies into toxicity testing and risk assessment: moving from 21st century vision to a data-driven framework

Based on existing data and previous work, a series of studies is proposed as a basis toward a pragmatic early step in transforming toxicity testing. These studies were assembled into a data-driven framework that invokes successive tiers of testing with margin of exposure (MOE) as the primary metric. The first tier of the framework integrates data from high-throughput in vitro assays, in vitro-to-in vivo extrapolation (IVIVE) pharmacokinetic modeling, and exposure modeling. The in vitro assays are used to separate chemicals based on their relative selectivity in interacting with biological targets and identify the concentration at which these interactions occur. The IVIVE modeling converts in vitro concentrations into external dose for calculation of the point of departure (POD) and comparisons to human exposure estimates to yield a MOE. The second tier involves short-term in vivo studies, expanded pharmacokinetic evaluations, and refined human exposure estimates. The results from the second tier studies provide more accurate estimates of the POD and the MOE. The third tier contains the traditional animal studies currently used to assess chemical safety. In each tier, the POD for selective chemicals is based primarily on endpoints associated with a proposed mode of action, whereas the POD for nonselective chemicals is based on potential biological perturbation. Based on the MOE, a significant percentage of chemicals evaluated in the first 2 tiers could be eliminated from further testing. The framework provides a risk-based and animal-sparing approach to evaluate chemical safety, drawing broadly from previous experience but incorporating technological advances to increase efficiency.

Cutting Edge PBPK Models and Analyses: Providing the Basis for Future Modeling Efforts and Bridges to Emerging Toxicology Paradigms

Physiologically based Pharmacokinetic (PBPK) models are used for predictions of internal or target dose from environmental and pharmacologic chemical exposures. Their use in human risk assessment is dependent on the nature of databases (animal or human) used to develop and test them, and includes extrapolations across species, experimental paradigms, and determination of variability of response within human populations. Integration of state-of-the science PBPK modeling with emerging computational toxicology models is critical for extrapolation between in vitro exposures, in vivo physiologic exposure, whole organism responses, and long-term health outcomes. This special issue contains papers that can provide the basis for future modeling efforts and provide bridges to emerging toxicology paradigms. In this overview paper, we present an overview of the field and introduction for these papers that includes discussions of model development, best practices, risk-assessment applications of PBPK models, and limitations and bridges of modeling approaches for future applications. Specifically, issues addressed include: (a) increased understanding of human variability of pharmacokinetics and pharmacodynamics in the population, (b) exploration of mode of action hypotheses (MOA), (c) application of biological modeling in the risk assessment of individual chemicals and chemical mixtures, and (d) identification and discussion of uncertainties in the modeling process.

Toxicokinetics as a key to the integrated toxicity risk assessment based primarily on non-animal approaches

Toxicokinetics (TK) is the endpoint that informs about the penetration into and fate within the body of a toxic substance, including the possible emergence of metabolites. Traditionally, the data needed to understand those phenomena have been obtained in vivo. Currently, with a drive towards non-animal testing approaches, TK has been identified as a key element to integrate the results from in silico, in vitro and already available in vivo studies. TK is needed to estimate the range of target organ doses that can be expected from realistic human external exposure scenarios. This information is crucial for determining the dose/concentration range that should be used for in vitro testing. Vice versa, TK is necessary to convert the in vitro results, generated at tissue/cell or sub-cellular level, into dose response or potency information relating to the entire target organism, i.e. the human body (in vitro-in vivo extrapolation, IVIVE). Physiologically based toxicokinetic modelling (PBTK) is currently regarded as the most adequate approach to simulate human TK and extrapolate between in vitro and in vivo contexts. The fact that PBTK models are mechanism-based which allows them to be 'generic' to a certain extent (various extrapolations possible) has been critical for their success so far. The need for high-quality in vitro and in silico data on absorption, distribution, metabolism as well as excretion (ADME) as input for PBTK models to predict human dose-response curves is currently a bottleneck for integrative risk assessment.

Quantitative Property-Property Relationship for Screening-Level Prediction of Intrinsic Clearance of Volatile Organic Chemicals in Rats and Its Integration within PBPK Models to Predict Inhalation Pharmacokinetics in Humans

The objectives of this study were (i) to develop a screening-level Quantitative property-property relationship (QPPR) for intrinsic clearance (CL_int) obtained from in vivo animal studies and (ii) to incorporate it with human physiology in a PBPK model for predicting the inhalation pharmacokinetics of VOCs. CL_int, calculated as the ratio of the in vivo V_max (μmol/h/kg bw rat) to the K_m (μM), was obtained for 26 VOCs from the literature. The QPPR model resulting from stepwise linear regression analysis passed the validation step (R² = 0.8; leave-one-out cross-validation Q² = 0.75) for CL_int normalized to the phospholipid (PL) affinity of the VOCs. The QPPR facilitated the calculation of CL_int (L PL/h/kg bw rat) from the input data on log P_ow, log blood: water PC and ionization potential. The predictions of the QPPR as lower and upper bounds of the 95% mean confidence intervals (LMCI and UMCI, resp.) were then integrated within a human PBPK model. The ratio of the maximum (using LMCI for CL_int) to minimum (using UMCI for CL_int) AUC predicted by the QPPR-PBPK model was 1.36 ± 0.4 and ranged from 1.06 (1,1-dichloroethylene) to 2.8 (isoprene). Overall, the integrated QPPR-PBPK modeling method developed in this study is a pragmatic way of characterizing the impact of the lack of knowledge of CL_int in predicting human pharmacokinetics of VOCs, as well as the impact of prediction uncertainty of CL_int on human pharmacokinetics of VOCs.

Translational research to develop a human PBPK models tool kit-volatile organic compounds (VOCs)

Toxicity and exposure evaluations remain the two of the key components of human health assessment. While improvement in exposure assessment relies on a better understanding of human behavior patterns, toxicity assessment still relies to a great extent on animal toxicity testing and human epidemiological studies. Recent advances in computer modeling of the dose-response relationship and distribution of xenobiotics in humans to important target tissues have advanced our abilities to assess toxicity. In particular, physiologically based pharmacokinetic (PBPK) models are among the tools than can enhance toxicity assessment accuracy. Many PBPK models are available to the health assessor, but most are so difficult to use that health assessors rarely use them. To encourage their use these models need to have transparent and user-friendly formats. To this end the Agency for Toxic Substances and Disease Registry (ATSDR) is using translational research to increase PBPK model accessibility, understandability, and use in the site-specific health assessment arena. The agency has initiated development of a human PBPK tool-kit for certain high priority pollutants. The tool kit comprises a series of suitable models. The models are recoded in a single computer simulation language and evaluated for use by health assessors. While not necessarily being state-of-the-art code for each chemical, the models will be sufficiently accurate to use for screening purposes. This article presents a generic, seven-compartment PBPK model for six priority volatile organic compounds (VOCs): benzene (BEN), carbon tetrachloride (CCl(4)), dichloromethane (DCM), perchloroethylene (PCE), trichloroethylene (TCE), and vinyl chloride (VC). Limited comparisons of the generic and original model predictions to published kinetic data were conducted. A goodness of fit was determined by calculating the means of the sum of the squared differences (MSSDs) for simulation vs. experimental kinetic data using the generic and original models. Using simplified solvent exposure assumptions for oral ingestion and inhalation, steady-state blood concentrations of each solvent were simulated for exposures equivalent to the ATSDR Minimal Risk Levels (MRLs). The predicted blood levels were then compared to those reported in the National Health and Nutrition Examination Survey (NHANES). With the notable exception of BEN, simulations of combined oral and inhalation MRLs using our generic VOC model yielded blood concentrations well above those reported for the 95th percentile blood concentrations for the U.S. populations, suggesting no health concerns. When the PBPK tool kit is fully developed, risk assessors will have a readily accessible tool for evaluating human exposure to a variety of environmental pollutants.