Quantitative structure-property relationships for physiologically based pharmacokinetic modeling of volatile organic chemicals in rats

Comparison of in vivo derived and scaled in vitro metabolic rate constants for several volatile organic compounds (VOCs)

Metabolic rate parameters estimation using in vitro data is necessary due to numbers of chemicals for which data are needed, trend towards minimizing laboratory animal use, and limited opportunity to collect data in human subjects. We evaluated how well metabolic rate parameters derived from in vitro data predict overall in vivo metabolism for a set of environmental chemicals for which well validated and established methods exist. We compared values of VmaxC derived from in vivo vapor uptake studies with estimates of VmaxC scaled up from in vitro hepatic microsomal metabolism studies for VOCs for which data were available in male F344 rats. For 6 of 7 VOCs, differences between the in vivo and scaled up in vitro VmaxC estimates were less than 2.6-fold. For bromodichloromethane (BDCM), the in vivo derived VmaxC was approximately 4.4-fold higher than the in vitro derived and scaled up VmaxC. The more rapid rate of BDCM metabolism estimated based in vivo studies suggests other factors such as extrahepatic metabolism, binding or other non-specific losses making a significant contribution to overall clearance. Systematic and reliable utilization of scaled up in vitro biotransformation rate parameters in PBPK models will require development of methods to predict cases in which extrahepatic metabolism and binding as well as other factors are likely to be significant contributors.

Physiologically Based Pharmacokinetic Models Predicting Renal and Hepatic Concentrations of Industrial Chemicals after Virtual Oral Doses in Rats

Recently developed high-throughput in vitro assays in combination with computational models could provide alternatives to animal testing. The purpose of the present study was to model the plasma, hepatic, and renal pharmacokinetics of approximately 150 structurally varied types of drugs, food components, and industrial chemicals after virtual external oral dosing in rats and to determine the relationship between the simulated internal concentrations in tissue/plasma and their lowest-observed-effect levels. The model parameters were based on rat plasma data from the literature and empirically determined pharmacokinetics measured after oral administrations to rats carried out to evaluate hepatotoxic or nephrotic potentials. To ensure that the analyzed substances exhibited a broad diversity of chemical structures, their structure-based location in the chemical space underwent projection onto a two-dimensional plane, as reported previously, using generative topographic mapping. A high-throughput in silico one-compartment model and a physiologically based pharmacokinetic (PBPK) model consisting of chemical receptor (gut), metabolizing (liver), central (main), and excreting (kidney) compartments were developed in parallel. For 159 disparate chemicals, the maximum plasma concentrations and the areas under the concentration-time curves obtained by one-compartment models and modified simple PBPK models were closely correlated. However, there were differences between the PBPK modeled and empirically obtained hepatic/renal concentrations and plasma maximal concentrations/areas under the concentration-time curves of the 159 chemicals. For a few compounds, the lowest-observed-effect levels were available for hepatotoxicity and nephrotoxicity in the Hazard Evaluation Support System Integrated Platform in Japan. The areas under the renal or hepatic concentration-time curves estimated using PBPK modeling were inversely associated with these lowest-observed-effect levels. Using PBPK forward dosimetry could provide the plasma/tissue concentrations of drugs and chemicals after oral dosing, thereby facilitating estimates of nephrotoxic or hepatotoxic potential as a part of the risk assessment.

Role of Physiologically Based Kinetic modelling in addressing environmental chemical mixtures - A review

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The availability and applicability of Physiologically Based Kinetic (PBK) models for mixtures is reviewed.

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PBK models can support risk assessment of mixtures by incorporating the toxicokinetic processes.

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Quantitative structure-activity relationship (QSAR) models can be used to fill data gaps in PBK modelling.

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PBK models for mixtures can be improved by including various types of interactions.

The role of Physiologically Based Kinetic (PBK) modelling in assessing mixture toxicology has been growing for the last three decades. It has been widely used to investigate and address interactions in mixtures. This review describes the current state-of-the-art of PBK models for chemical mixtures and to evaluate the applications of PBK modelling for mixtures with emphasis on their role in chemical risk assessment. A total of 35 mixture PBK models were included after searching web resources (Scopus, PubMed, Web of Science, and Google Scholar), screening for duplicates, and excluding articles based on eligibility criteria. Binary mixtures and volatile organic compounds accounted for two-thirds of the chemical mixtures identified. The most common exposure route and modelled system were found to be inhalation and rats respectively. Twenty two (22) models were for binary mixtures, 5 for ternary mixtures, 3 for quaternary mixtures, and 5 for complex mixtures. Both bottom-up and top-down PBK modelling approaches are described. Whereas bottom-up approaches are based on a series of binary interactions, top-down approaches are based on the lumping of mixture components. Competitive inhibition is the most common type of interaction among the various types of mixtures, and usually becomes a concern at concentrations higher than environmental exposure levels. It leads to reduced biotransformation that either means a decrease in the amount of toxic metabolite formation or an increase in toxic parent chemical accumulation. The consequence is either lower or higher toxicity compared to that estimated for the mixture based on the additivity principle. Therefore, PBK modelling can play a central role in predicting interactions in chemical mixture risk assessment.

Plasma and Hepatic Concentrations of Chemicals after Virtual Oral Administrations Extrapolated Using Rat Plasma Data and Simple Physiologically Based Pharmacokinetic Models

Only a small fraction of chemicals possesses adequate in vivo toxicokinetic data for assessing potential hazards. The aim of the present study was to model the plasma and hepatic pharmacokinetics of more than 50 disparate types of chemicals and drugs after virtual oral administrations in rats. The models were based on reported pharmacokinetics determined after oral administration to rats. An inverse relationship was observed between no-observed-effect levels after oral administration and chemical absorbance rates evaluated for cell permeability ( r = -0.98, p < 0.001, n = 17). For a varied selection of more than 30 chemicals, the plasma concentration curves and the maximum concentrations obtained using a simple one-compartment model (recently recommended as a high-throughput toxicokinetic model) and a simple physiologically based pharmacokinetic (PBPK) model (consisting of chemical receptor, metabolizing, and central compartments) were highly consistent. The hepatic and plasma concentrations and the hepatic and plasma areas under the concentration-time curves of more than 50 chemicals were roughly correlated; however, differences were evident between the PBPK-modeled values in livers and empirically obtained values in plasma. Of the compounds selected for analysis, only seven had the lowest observed effect level (LOEL) values for hepatoxicity listed in the Hazard Evaluation Support System Integrated Platform in Japan. For these seven compounds, the LOEL values and the areas under the hepatic concentration-time curves estimated using PBPK modeling were inversely correlated ( r = -0.78, p < 0.05, n = 7). This study provides important information to help simulate the high hepatic levels of potent hepatotoxic compounds. Using suitable PBPK parameters, the present models could estimate the plasma/hepatic concentrations of chemicals and drugs after oral doses using both PBPK forward and reverse dosimetry, thereby indicating the potential value of this modeling approach in predicting hepatic toxicity as a part of risk assessments of chemicals absorbed in the human body.

Review of laboratory-based terrestrial bioaccumulation assessment approaches for organic chemicals: Current status and future possibilities

In the last decade, interest has been renewed in approaches for the assessment of the bioaccumulation potential of chemicals, principally driven by the need to evaluate large numbers of chemicals as part of new chemical legislation, while reducing vertebrate test organism use called for in animal welfare legislation. This renewed interest has inspired research activities and advances in bioaccumulation science for neutral organic chemicals in aquatic environments. In January 2013, ILSI Health and Environmental Sciences Institute convened experts to identify the state of the science and existing shortcomings in terrestrial bioaccumulation assessment of neutral organic chemicals. Potential modifications to existing laboratory methods were identified, including areas in which new laboratory approaches or test methods could be developed to address terrestrial bioaccumulation. The utility of "non-ecotoxicity" data (e.g., mammalian laboratory data) was also discussed. The highlights of the workshop discussions are presented along with potential modifications in laboratory approaches and new test guidelines that could be used for assessing the bioaccumulation of chemicals in terrestrial organisms.

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.

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.

An in silico approach for evaluating a fraction-based, risk assessment method for total petroleum hydrocarbon mixtures

Both the Massachusetts Department of Environmental Protection (MADEP) and the Total Petroleum Hydrocarbon Criteria Working Group (TPHCWG) developed fraction-based approaches for assessing human health risks posed by total petroleum hydrocarbon (TPH) mixtures in the environment. Both organizations defined TPH fractions based on their expected environmental fate and by analytical chemical methods. They derived toxicity values for selected compounds within each fraction and used these as surrogates to assess hazard or risk of exposure to the whole fractions. Membership in a TPH fraction is generally defined by the number of carbon atoms in a compound and by a compound's equivalent carbon (EC) number index, which can predict its environmental fate. Here, we systematically and objectively re-evaluate the assignment of TPH to specific fractions using comparative molecular field analysis and hierarchical clustering. The approach is transparent and reproducible, reducing inherent reliance on judgment when toxicity information is limited. Our evaluation of membership in these fractions is highly consistent (˜80% on average across various fractions) with the empirical approach of MADEP and TPHCWG. Furthermore, the results support the general methodology of mixture risk assessment to assess both cancer and noncancer risk values after the application of fractionation.

Uptake and disposition of 1,1-difluoroethane (HFC-152a) in humans

The aim of this study was to determine the toxicokinetics of inhaled 1,1-difluoroethane (HFC-152a) in humans. Healthy volunteers were exposed to 0, 200 or 1000 ppm 1,1-difluoroethane for 2h at light exercise in an exposure chamber. Capillary blood, urine and exhaled air were sampled up to 22 h post-exposure and analyzed for 1,1-difluoroethane. Fluoride and other potential metabolites were analyzed in urine. Symptoms of irritation and central nervous system effects were rated and inflammatory markers were analyzed in blood. Within a few minutes of exposure to 200 and 1000 ppm, 1,1-difluoroethane increased rapidly in blood and reached average levels of 7.4 and 34.3 μM, respectively. The post-exposure decreases in blood were fast and parallel to those in exhaled air. The observed time courses in blood and breath agreed well with those obtained with the PBPK model. The PBPK simulations indicate a net uptake during exposure to 1000 ppm of 6.6 mmol (6.7%) which corresponds to the amount exhaled post-exposure. About 20 μmol excess fluoride (0.013% of inhaled 1,1-difluoroethane on a molar basis) was excreted in urine after exposure to 1000 ppm, compared to control. No fluorine-containing metabolites were detected in urine. Symptom ratings and changes in inflammatory markers revealed no exposure-related effects.

An integrated QSAR-PBPK modelling approach for predicting the inhalation toxicokinetics of mixtures of volatile organic chemicals in the rat

The objective of this study was to predict the inhalation toxicokinetics of chemicals in mixtures using an integrated QSAR-PBPK modelling approach. The approach involved: (1) the determination of partition coefficients as well as V(max) and K(m) based solely on chemical structure for 53 volatile organic compounds, according to the group contribution approach; and (2) using the QSAR-driven coefficients as input in interaction-based PBPK models in the rat to predict the pharmacokinetics of chemicals in mixtures of up to 10 components (benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene, and styrene). QSAR-estimated values of V(max) varied compared with experimental results by a factor of three for 43 out of 53 studied volatile organic compounds (VOCs). K(m) values were within a factor of three compared with experimental values for 43 out of 53 VOCs. Cross-validation performed as a ratio of predicted residual sum of squares and sum of squares of the response value indicates a value of 0.108 for V(max) and 0.208 for K(m). The integration of QSARs for partition coefficients, V(max) and K(m), as well as setting the K(m) equal to K(i) (metabolic inhibition constant) within the mixture PBPK model allowed to generate simulations of the inhalation pharmacokinetics of benzene, toluene, m-xylene, o-xylene, p-xylene, ethylbenzene, dichloromethane, trichloroethylene, tetrachloroethylene and styrene in various mixtures. Overall, the present study indicates the potential usefulness of the QSAR-PBPK modelling approach to provide first-cut evaluations of the kinetics of chemicals in mixtures of increasing complexity, on the basis of chemical structure.

QSARs for PBPK modelling of environmental contaminants

Physiologically-based pharmacokinetic (PBPK) models are increasingly finding use in risk assessment applications of data-rich compounds. However, it is a challenge to determine the chemical-specific parameters for these models, particularly in time- and resource-limiting situations. In this regard, SARs, QSARs and QPPRs are potentially useful for computing the chemical-specific input parameters of PBPK models. Based on the frequency of occurrence of molecular fragments (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure) and exposure conditions, the available QSAR-PBPK models facilitate the simulation of tissue and blood concentrations for some inhaled volatile organic chemicals. The application domain of existing QSARs for developing PBPK models is limited, due to lack of relevant data for diverse chemicals and mechanisms. Even though this approach is conceptually applicable to non-volatile and high molecular weight organics as well, it is more challenging to predict the other PBPK model parameters required for modelling the kinetics of these chemicals (particularly tissue diffusion coefficients, association constants for binding and oral absorption rates). As the level of our understanding of the mechanistic basis of toxicokinetic processes improves, QSARs to provide a priori predictions of key chemical-specific PBPK parameters can be developed to expedite the internal dose-based health risk assessments in data-poor situations.

QSPR analysis of air-to-blood distribution of volatile organic compounds

Quantitative structure property relationship (QSPR) models for the prediction of human blood:air partition coefficient (log K(blood)) of volatile organic compounds (VOCs) has been developed based on the linear heuristic method (HM) and non-linear radial basis function neural networks (RBFNNs). Molecular descriptors that are calculated from the structures alone were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. RBFNN was performed to obtain more accurate models. Both the linear and the non-linear models can give very satisfactory prediction results: the correlation coefficient R was 0.964 and 0.979, and the root-mean-square (RMS) error was 0.3303 and 0.2542 for the whole data set, respectively. The prediction result of the non-linear model is better than that obtained by the linear model. In addition, this paper provides an effective method for predicting log K(blood) from its structures and gives some insight into the structural features related to the solubility of VOCs in human blood.

An integrated QSPR-PBPK modelling approach for in vitro-in vivo extrapolation of pharmacokinetics in rats

In vitro data on metabolism and partitioning may be integrated within physiologically-based pharmacokinetic (PBPK) models to provide simulations of the kinetics and bioaccumulation of chemicals in intact organisms. Quantitative structure-property relationship (QSPR) modelling of available in vitro data may be performed to predict metabolism rates and partition coefficients (PCs) for developing in vivo PBPK models. The objective of the present study was to develop an integrated QSPR-PBPK modelling approach for the conduct of in vitro to in vivo extrapolation. For this purpose, data on rat blood:air (P(b)) and fat:air (P(f)) PCs, as well as intrinsic metabolic clearance (CL(int)) obtained using rat liver slices for some C(5)-C(10) volatile organic compounds (VOCs) were compiled from the literature. Multilinear additive QSPR models for P(f), P(b) and CL(int) were developed based on the number and nature of molecular fragments in these VOCs (CH(3), CH(2), CH, C, C=C, H, benzene ring and H in benzene ring structure). The mean estimated/experimental (est/exp) ratios (+/-SD; range) were 1.0 (+/-0.04; 0.93 - 1.06) for log P(f), 1.08 (+/-0.26; 0.70 - 1.62) for log P(b), and 1.07 (+/- 0.21; 0.80 - 1.44) for CL(int). By accounting for the difference in the content of neutral lipids in fat and other tissues, the liver : air and muscle : air PCs of the compounds investigated in this study, with the excerption of n-decane, were adequately predicted from P(f). Integrating the QSPRs for P(f), P(b) and CL(int) within a rat PBPK model, simulations of inhalation pharmacokinetics of several VOCs were generated on the basis of molecular structure, for a given exposure scenario. The integrated QSPR-PBPK model developed in this study is a potentially useful tool for predicting in vivo kinetics and bioaccumulation of chemicals in rats under poor data situations.

Whole-body physiologically based pharmacokinetic models

This review summarizes the most recent developments in and applications of physiologically based pharmacokinetic (PBPK) modeling methodology originating from both the pharmaceutical and environmental toxicology areas. It focuses on works published in the last 5 years, although older seminal papers have also been referenced. After a brief introduction to the field and several essential definitions, the main body of the text is structured to follow the major steps of a typical PBPK modeling exercise. Various applications of the methodology are briefly described. The major future trends and perspectives are outlined. The main conclusion from the review of the available literature is that PBPK modeling, despite its obvious potential and recent incremental developments, has not taken the place it deserves, especially in pharmaceutical and drug development sciences.

PBTK modeling demonstrates contribution of dermal and inhalation exposure components to end-exhaled breath concentrations of naphthalene

BACKGROUND

Dermal and inhalation exposure to jet propulsion fuel 8 (JP-8) have been measured in a few occupational exposure studies. However, a quantitative understanding of the relationship between external exposures and end-exhaled air concentrations has not been described for occupational and environmental exposure scenarios.

OBJECTIVE

Our goal was to construct a physiologically based toxicokinetic (PBTK) model that quantitatively describes the relative contribution of dermal and inhalation exposures to the end-exhaled air concentrations of naphthalene among U.S. Air Force personnel.

METHODS

The PBTK model comprised five compartments representing the stratum corneum, viable epidermis, blood, fat, and other tissues. The parameters were optimized using exclusively human exposure and biological monitoring data.

RESULTS

The optimized values of parameters for naphthalene were a) permeability coefficient for the stratum corneum 6.8 x 10(-5) cm/hr, b) permeability coefficient for the viable epidermis 3.0 x 10(-3) cm/hr, c) fat:blood partition coefficient 25.6, and d) other tissue:blood partition coefficient 5.2. The skin permeability coefficient was comparable to the values estimated from in vitro studies. Based on simulations of workers' exposures to JP-8 during aircraft fuel-cell maintenance operations, the median relative contribution of dermal exposure to the end-exhaled breath concentration of naphthalene was 4% (10th percentile 1% and 90th percentile 11%).

CONCLUSIONS

PBTK modeling allowed contributions of the end-exhaled air concentration of naphthalene to be partitioned between dermal and inhalation routes of exposure. Further study of inter- and intraindividual variations in exposure assessment is required to better characterize the toxicokinetic behavior of JP-8 components after occupational and/or environmental exposures.

Air to liver partition coefficients for volatile organic compounds and blood to liver partition coefficients for volatile organic compounds and drugs

Values of in vitro air to liver partition coefficients, K(liver), of VOCs have been collected from the literature. For 124 VOCs, application of the Abraham solvation equation to logK(liver) yielded a correlation equation with R(2)=0.927 and SD=0.26 log units. Combination of the logK(liver) values with logK(blood) values leads to in vitro blood to liver partition coefficients, as logP(liver) for VOCs; an Abraham solvation equation can correlate 125 such values with R(2)=0.583 and SD=0.23 log units. Values of in vivo logP(liver) for 85 drugs were collected, and were correlated with R(2)=0.522 and SD=0.42 log units. When the logP(liver) values for VOCs and drugs were combined, an Abraham solvation equation could correlate the 210 compounds with R(2)=0.544 and SD=0.32 log units. Division of the 210 compounds into a training set and a test set, each of 105 compounds, showed that the training equation could predict logP(liver) values with an average error of 0.05 and a standard deviation of 0.34 log units.

Evaluation of physiologically based pharmacokinetic models for use in risk assessment

Physiologically based pharmacokinetic (PBPK) models are sophisticated dosimetry models that offer great flexibility in modeling exposure scenarios for which there are limited data. This is particularly of relevance to assessing human exposure to environmental toxicants, which often requires a number of extrapolations across species, route, or dose levels. The continued development of PBPK models ensures that regulatory agencies will increasingly experience the need to evaluate available models for their application in risk assessment. To date, there are few published criteria or well-defined standards for evaluating these models. Herein, important considerations for evaluating such models are described. The evaluation of PBPK models intended for risk assessment applications should include a consideration of: model purpose, model structure, mathematical representation, parameter estimation, computer implementation, predictive capacity and statistical analyses. Model purpose and structure require qualitative checks on the biological plausibility of a model. Mathematical representation, parameter estimation, computer implementation involve an assessment of the coding of the model, as well as the selection and justification of the physical, physicochemical and biochemical parameters chosen to represent a biological organism. Finally, the predictive capacity and sensitivity, variability and uncertainty of the model are analysed so that the applicability of a model for risk assessment can be determined. Published in 2007 by John Wiley & Sons, Ltd.

Simple stochastic fingerprints towards mathematical modeling in biology and medicine. 3. ocular irritability classification model

MARCH-INSIDE methodology and a statistical classification method--linear discriminant analysis (LDA)--is proposed as an alternative method to the Draize eye irritation test. This methodology has been successfully applied to a set of 46 neutral organic chemicals, which have been defined as ocular irritant or nonirritant. The model allow to categorize correctly 37 out of 46 compounds, showing an accuracy of 80.46%. Specifically, this model demonstrates the existence of a good categorization average of 91.67 and 76.47% for irritant and nonirritant compounds, respectively. Validation of the model was carried out using two cross-validation tools: Leave-one-out (LOO) and leave-group-out (LGO), showing a global predictability of the model of 71.7 and 70%, respectively. The average of coincidence of the predictions between leave-one-out/leave-group-out studies and train set were 91.3% (42 out of 46 cases)/89.1% (41 out of 46 cases) proving the robustness of the model obtained. Ocular irritancy distribution diagram is carried out in order to determine the intervals of the property where the probability of finding an irritant compound is maximal relating to the choice of find a false nonirritant one. It seems that, until today, the present model may be the first predictive linear discriminant equation able to discriminate between eye irritant and nonirritant chemicals.

Air to brain, blood to brain and plasma to brain distribution of volatile organic compounds: linear free energy analyses

Partition coefficients, K(brain), for volatile organic compounds, VOCs, from air to brain have been collected for 81 compounds (air to human brain and air to rat brain). For the 81 VOCs a linear free energy equation (LFER) correlates log K(brain) with R(2) = 0.923 and S.D. = 0.346 log units. Use of training and test sets gives a predictive assessment of 0.35-0.40 log units. Combination of log K(brain) with our previously listed values of log K(blood) enables blood to brain partition, as log P(b-brain), to be obtained for 78 VOCs. These values can be correlated with R(2) = 0.725 and S.D. = 0.203 log units; use of training and test sets allows a predictive assessment for log P(b-brain) of 0.16-0.20 log units. Values for air to plasma were available for 21 VOCs. When these data were combined with the data on air to blood and air to brain, values for partition between (blood or plasma) to brain, P(bp-brain), were available for 99 VOCs. A LFER correlates this data with R(2) = 0.703 and S.D.=0.197 log units; use of training and test sets allows a predictive assessment for log P(bp-brain) of 0.15-0.20 log units.

A spreadsheet program for modeling quantitative structure-pharmacokinetic relationships for inhaled volatile organics in humans

The extent and profile of target tissue exposure to toxicants depend upon the pharmacokinetic processes, namely, absorption, distribution, metabolism and excretion. The present study developed a spreadsheet program to simulate the pharmacokinetics of inhaled volatile organic chemicals (VOCs) in humans based on information from molecular structure. The approach involved the construction of a human physiologically-based pharmacokinetic (PBPK) model, and the estimation of its parameters based on quantitative structure-property relationships (QSPRs) in an Excel spreadsheet. The compartments of the PBPK model consisted of liver, adipose tissue, poorly perfused tissues and richly perfused tissues connected by circulating blood. The parameters required were: human physiological parameters such as cardiac output, breathing rate, tissue volumes and tissue blood flow rates (obtained from the biomedical literature), tissue/air partition coefficients (obtained using QSPRs developed with rat data), blood/air partition coefficients (Pb) and hepatic clearance (CL). Using literature data on human Pb and CL for several VOCs (alkanes, alkenes, haloalkanes and aromatic hydrocarbons), multi-linear additive QSPR models were developed. The numerical contributions to human Pb and CL were obtained for eleven structural fragments (CH3, CH2, CH, C, C [double bond] C, H, Cl, Br, F, benzene ring, and H in the benzene ring structure). Using these data as input, the PBPK model written in an Excel spreadsheet simulated the inhalation pharmacokinetics of ethylbenzene (33 ppm, 7 h) and dichloromethane (100 ppm, 6 h) in humans exposed to these chemicals. The QSPRs developed in this study should be useful for predicting the inhalation pharmacokinetics of VOCs in humans, prior to testing and experimentation.

Framework for evaluation of physiologically-based pharmacokinetic models for use in safety or risk assessment

Proposed applications of increasingly sophisticated biologically-based computational models, such as physiologically-based pharmacokinetic models, raise the issue of how to evaluate whether the models are adequate for proposed uses, including safety or risk assessment. A six-step process for model evaluation is described. It relies on multidisciplinary expertise to address the biological, toxicological, mathematical, statistical, and risk assessment aspects of the modeling and its application. The first step is to have a clear definition of the purpose(s) of the model in the particular assessment; this provides critical perspectives on all subsequent steps. The second step is to evaluate the biological characterization described by the model structure based on the intended uses of the model and available information on the compound being modeled or related compounds. The next two steps review the mathematical equations used to describe the biology and their implementation in an appropriate computer program. At this point, the values selected for the model parameters (i.e., model calibration) must be evaluated. Thus, the fifth step is a combination of evaluating the model parameterization and calibration against data and evaluating the uncertainty in the model outputs. The final step is to evaluate specialized analyses that were done using the model, such as modeling of population distributions of parameters leading to population estimates for model outcomes or inclusion of early pharmacodynamic events. The process also helps to define the kinds of documentation that would be needed for a model to facilitate its evaluation and implementation.