A mechanistic algorithm for predicting blood:air partition coefficients of organic chemicals with the consideration of reversible binding in hemoglobin

Volatile Organic Compounds Analysis as a Potential Novel Screening Tool for Breast Cancer: A Systematic Review

Introduction

An early diagnosis is crucial in reducing mortality among people who have breast cancer (BC). There is a shortfall of characteristic early clinical symptoms in BC patients, highlighting the importance of investigating new methods for its early detection. A promising novel approach is the analysis of volatile organic compounds (VOCs) produced and emitted through the metabolism of cancer cells.

Methods

The purpose of this systematic review is to outline the published research regarding BC-associated VOCs. For this, headspace analysis of VOCs was explored in patient-derived body fluids, animal model-derived fluids, and BC cell lines to identify BC-specific VOCs. A systematic search in PubMed and Web of Science databases was conducted according to the PRISMA guidelines.

Results

Thirty-two studies met the criteria for inclusion in this review. Results highlight that VOC analysis can be promising as a potential novel screening tool. However, results of in vivo, in vitro and case-control studies have delivered inconsistent results leading to a lack of inter-matrix consensus between different VOC sampling methods.

Discussion

Discrepant VOC results among BC studies have been obtained, highly due to methodological discrepancies. Therefore, methodological issues leading to disparities have been reviewed and recommendations have been made on the standardisation of VOC collection and analysis methods for BC screening, thereby improving future VOC clinical validation studies.

Opportunities and challenges related to saturation of toxicokinetic processes: Implications for risk assessment

Top dose selection for repeated dose animal studies has generally focused on identification of apical endpoints, use of the limit dose, or determination of a maximum tolerated dose (MTD). The intent is to optimize the ability of toxicity tests performed in a small number of animals to detect effects for hazard identification. An alternative approach, the kinetically derived maximum dose (KMD), has been proposed as a mechanism to integrate toxicokinetic (TK) data into the dose selection process. The approach refers to the dose above which the systemic exposures depart from being proportional to external doses. This non-linear external-internal dose relationship arises from saturation or limitation of TK process(es), such as absorption or metabolism. The importance of TK information is widely acknowledged when assessing human health risks arising from exposures to environmental chemicals, as TK determines the amount of chemical at potential sites of toxicological responses. However, there have been differing opinions and interpretations within the scientific and regulatory communities related to the validity and application of the KMD concept. A multi-stakeholder working group, led by the Health and Environmental Sciences Institute (HESI), was formed to provide an opportunity for impacted stakeholders to address commonly raised scientific and technical issues related to this topic and, more specifically, a weight of evidence approach is recommended to inform design and dose selection for repeated dose animal studies. Commonly raised challenges related to the use of TK data for dose selection are discussed, recommendations are provided, and illustrative case examples are provided to address these challenges or refute misconceptions.

High-throughput in-silico prediction of ionization equilibria for pharmacokinetic modeling

Chemical ionization plays an important role in many aspects of pharmacokinetic (PK) processes such as protein binding, tissue partitioning, and apparent volume of distribution at steady state (Vdss). Here, estimates of ionization equilibrium constants (i.e., pKa) were analyzed for 8132 pharmaceuticals and 24,281 other compounds to which humans might be exposed in the environment. Results revealed broad differences in the ionization of pharmaceutical chemicals and chemicals with either near-field (in the home) or far-field sources. The utility of these high-throughput ionization predictions was evaluated via a case-study of predicted PK Vdss for 22 compounds monitored in the blood and serum of the U.S. population by the U.S. Centers for Disease Control and Prevention National Health and Nutrition Examination Survey (NHANES). The chemical distribution ratio between water and tissue was estimated using predicted ionization states characterized by pKa. Probability distributions corresponding to ionizable atom types (IATs) were then used to analyze the sensitivity of predicted Vdss on predicted pKa using Monte Carlo methods. 8 of the 22 compounds were predicted to be ionizable. For 5 of the 8 the predictions based upon ionization are significantly different from what would be predicted for a neutral compound. For all but one (foramsulfuron), the probability distribution of predicted Vdss generated by IAT sensitivity analysis spans both the neutral prediction and the prediction using ionization. As new data sets of chemical-specific information on metabolism and excretion for hundreds of chemicals are being made available (e.g., Wetmore et al., 2015), high-throughput methods for calculating Vdss and tissue-specific PK distribution coefficients will allow the rapid construction of PK models to provide context for both biomonitoring data and high-throughput toxicity screening studies such as Tox21 and ToxCast.

Rat Tissue and Blood Partition Coefficients for n-Alkanes (C8 to C12)

Rat tissue:air and blood:air partition coefficients (PCs) for octane, nonane, decane, undecane, and dodecane (n-C8 to n-C12 n-alkanes) were determined by vial equilibration. The blood:air PC values for n-C8 to n-C12 were 3.1, 5.8, 8.1, 20.4, and 24.6, respectively. The lipid solubility of n-alkanes increases with carbon length, suggesting that lipid solubility is an important determinant in describing n-alkane blood:air PC values. The muscle:blood, liver: blood, brain:blood, and fat:blood PC values were octane (1.0, 1.9, 1.4, and 247), nonane (0.8, 1.9, 3.8, and 274), decane (0.9, 2.0, 4.8, and 328), undecane (0.7, 1.5, 1.7, and 529), and dodecane (1.2, 1.9, 19.8, and 671), respectively. The tissue:blood PC values were greatest in fat and the least in muscle. The brain:air PC value for undecane was inconsistent with other n-alkane values. Using the measured partition coefficient values of these n-alkanes, linear regression was used to predict tissue (except brain) and blood:air partition coefficient values for larger n-alkanes, tridecane, tetradecane, pentadecane, hexadecane, and heptadecane (n-C13 to n-C17).Good agreement between measured and predicted tissue:air and blood:air partition coefficient values for n-C8 to n-C12 offer confidence in the partition coefficient predictions for longer chain n-alkanes.

Use of Pharmacokinetic Data Under the FDA's Redbook II Guidelines for Direct Food Additives

Experience with food additive petitions submited after publication of the Food and Drug Administration's Redbook I (U. S. FDA, 1982) guide lines indicated a number of areas in which improvements were needed, and advances in toxicol-ogy testing during the last decade required additional rev is ions. In March 1993, the FDA's Center for Food Safety and Applied Nutrition (CFSAN) distributed copies of a draft of Redbook II for public comment. Since that time, revisions have been made based on comments received on the initial draft. This article describes the rationale for Redbook II guidance on the design of pharm acoki-netic studies and discusses some common problems the FDA has encountered in reviewing pharmacokine tic data submitted as part of food additive petitions. Points emphasized are that (1) pharmaco kinetic information is needed for the interpretation of toxicity studies and is most use ful when conducted before major toxicity studies, (2) the use of whole-body autoradiography is encouraged as a means to select tissues of interest, and as a substitute for dissection and tis-sue sampling, (3) kinetic and mechanistic studies conducted with blood compo-nents, tissue slices, hepatocytes, and othercell types in vitro ofien provide more useful information on the fate of chemicals in specific tissues than information extracted from whole-animal studies. The intention of th e new guide lines for pharmaco kinetic studies is to increase the information content of data gathered and to encourage the use of pharmaco kinetic models and results in the selection of doses for subchronic, chronic, and developmental toxicity studies.

A review of environmental and occupational exposure to xylene and its health concerns

Xylene is a cyclic hydrocarbon, and an environmental pollutant. It is also used in dyes, paints, polishes, medical technology and different industries as a solvent. Xylene easily vaporizes and divides by sunlight into other harmless chemicals. The aim of the present review is to collect the evidence of the xylene toxicity, related to non-cancerous health hazards, as well as to provide possible effective measurement to minimize its risk ratio. For current study a bibliographic search of more than 250 peer-reviewed papers in scientific data including PubMed, and Google Scholar about xylene was done. But approximately 130 peer-reviewed papers relevant to xylene were included (Figure 1(Fig. 1)). All scientific data was reviewed with key words of "xylene toxicity", "xylene toxic health effects", "environmental volatile organic compounds", "human exposure to xylene", "xylene poisoning in laboratory workers", "effects of xylene along with other hydrocarbons", "neurotoxicity of selected hydrocarbons", and "toxic effects of particular xylene isomers in animals". According to these studies, xylene is released into the atmosphere as fugitive emissions from petrochemical industries, fire, cigarette, from different vehicles. Short term exposure to mixed xylene or their individual isomers result in irritation of the nose, eyes and throat subsequently leading toward neurological, gastrointestinal and reproductive harmful effects. In addition long term exposure to xylene may cause hazardous effects on respiratory system, central nervous system, cardiovascular system, and renal system. The health concerns of xylene are well documented in animals and human. It is important to improve health policies, launch xylene related health and toxicity awareness campaigns, to get rid of its dangerous outcomes. Chronic diseases have become a threat to human globally, with special prominence in regions, where xylene is used with other chemicals (benzene, toluene etc.) especially in petroleum and rubber industry. The mechanism of toxicity and interactions with endocrine system should be followed up which is the main threat to human health.

A review of environmental and occupational exposure to xylene and its health concerns

Xylene is a cyclic hydrocarbon, and an environmental pollutant. It is also used in dyes, paints, polishes, medical technology and different industries as a solvent. Xylene easily vaporizes and divides by sunlight into other harmless chemicals. The aim of the present review is to collect the evidence of the xylene toxicity, related to non-cancerous health hazards, as well as to provide possible effective measurement to minimize its risk ratio. For current study a bibliographic search of more than 250 peer-reviewed papers in scientific data including PubMed, and Google Scholar about xylene was done. But approximately 130 peer-reviewed papers relevant to xylene were included (Figure 1(Fig. 1)). All scientific data was reviewed with key words of "xylene toxicity", "xylene toxic health effects", "environmental volatile organic compounds", "human exposure to xylene", "xylene poisoning in laboratory workers", "effects of xylene along with other hydrocarbons", "neurotoxicity of selected hydrocarbons", and "toxic effects of particular xylene isomers in animals". According to these studies, xylene is released into the atmosphere as fugitive emissions from petrochemical industries, fire, cigarette, from different vehicles. Short term exposure to mixed xylene or their individual isomers result in irritation of the nose, eyes and throat subsequently leading toward neurological, gastrointestinal and reproductive harmful effects. In addition long term exposure to xylene may cause hazardous effects on respiratory system, central nervous system, cardiovascular system, and renal system. The health concerns of xylene are well documented in animals and human. It is important to improve health policies, launch xylene related health and toxicity awareness campaigns, to get rid of its dangerous outcomes. Chronic diseases have become a threat to human globally, with special prominence in regions, where xylene is used with other chemicals (benzene, toluene etc.) especially in petroleum and rubber industry. The mechanism of toxicity and interactions with endocrine system should be followed up which is the main threat to human health.

Toxicokinetic Model Development for the Insensitive Munitions Component 2,4-Dinitroanisole

The Armed Forces are developing new explosives that are less susceptible to unintentional detonation (insensitive munitions [IMX]). 2,4-Dinitroanisole (DNAN) is a component of IMX. Toxicokinetic data for DNAN are required to support interpretation of toxicology studies and refinement of dose estimates for human risk assessment. Male Sprague-Dawley rats were dosed by gavage (5, 20, or 80 mg DNAN/kg), and blood and tissue samples were analyzed to determine the levels of DNAN and its metabolite 2,4-dinitrophenol (DNP). These data and data from the literature were used to develop preliminary physiologically based pharmacokinetic (PBPK) models. The model simulations indicated saturable metabolism of DNAN in rats at higher tested doses. The PBPK model was extrapolated to estimate the toxicokinetics of DNAN and DNP in humans, allowing the estimation of human-equivalent no-effect levels of DNAN exposure from no-observed adverse effect levels determined in laboratory animals, which may guide the selection of exposure limits for DNAN.

Toxicokinetic Model Development for the Insensitive Munitions Component 3-Nitro-1,2,4-Triazol-5-One

3-Nitro-1,2,4-triazol-5-one (NTO) is a component of insensitive munitions that are potential replacements for conventional explosives. Toxicokinetic data can aid in the interpretation of toxicity studies and interspecies extrapolation, but only limited data on the toxicokinetics and metabolism of NTO are available. To supplement these limited data, further in vivo studies of NTO in rats were conducted and blood concentrations were measured, tissue distribution of NTO was estimated using an in silico method, and physiologically based pharmacokinetic models of the disposition of NTO in rats and macaques were developed and extrapolated to humans. The model predictions can be used to extrapolate from designated points of departure identified from rat toxicology studies to provide a scientific basis for estimates of acceptable human exposure levels for NTO.

Current Challenges in Volatile Organic Compounds Analysis as Potential Biomarkers of Cancer

An early diagnosis and appropriate treatment are crucial in reducing mortality among people suffering from cancer. There is a lack of characteristic early clinical symptoms in most forms of cancer, which highlights the importance of investigating new methods for its early detection. One of the most promising methods is the analysis of volatile organic compounds (VOCs). VOCs are a diverse group of carbon-based chemicals that are present in exhaled breath and biofluids and may be collected from the headspace of these matrices. Different patterns of VOCs have been correlated with various diseases, cancer among them. Studies have also shown that cancer cells in vitro produce or consume specific VOCs that can serve as potential biomarkers that differentiate them from noncancerous cells. This review identifies the current challenges in the investigation of VOCs as potential cancer biomarkers, by the critical evaluation of available matrices for the in vivo and in vitro approaches in this field and by comparison of the main extraction and detection techniques that have been applied to date in this area of study. It also summarises complementary in vivo, ex vivo, and in vitro studies conducted to date in order to try to identify volatile biomarkers of cancer.

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.

The clinical potential of exhaled breath analysis for diabetes mellitus

Various compounds in present human breath have long been loosely associated with pathological states (including acetone smell in uncontrolled diabetes). Only recently, however, the precise measurement of exhaled volatile organic compounds (VOCs) and aerosolized particles was made possible at extremely low concentrations by advances in several analytical methodologies, described in detail in the international literature and each suitable for specific subsets of exhaled compounds. Exhaled gases may be generated endogenously (in the pulmonary tract, blood, or peripheral tissues), as metabolic by-products of human cells or colonizing micro-organisms, or may be inhaled as atmospheric pollutants; growing evidence indicates that several of these molecules have distinct cell-to-cell signaling functions. Independent of origin and physiological role, exhaled VOCs are attractive candidates as biomarkers of cellular activity/metabolism, and could be incorporated in future non-invasive clinical testing devices. Indeed, several recent studies reported altered exhaled gas profiles in dysmetabolic conditions and relatively accurate predictions of glucose concentrations, at least in controlled experimental conditions, for healthy and diabetic subjects over a broad range of glycemic values. Optimization of this methodology and validation in large-scale trials under a wider range of conditions is needed to determine its true potential to transition into practical clinical use.

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.

Physiologically-based pharmacokinetic (PBPK) models in toxicity testing and risk assessment

Physiologically-based pharmacokinetic (PBPK) modeling offers a scientifically-sound framework for integrating mechanistic data on absorption, distribution, metabolism and elimination to predict the time-course of parent chemical, metabolite(s) or biomarkers in the exposed organism. A major advantage of PBPK models is their ability to forecast the impact of specific mechanistic processes and determinants on the tissue dose. In this regard, they facilitate integration of data obtained with in vitro and in silico methods, for making predictions of the tissue dosimetry in the whole animal, thus reducing and/or refining the use of animals in pharmacokinetic and toxicity studies. This chapter presents the principles and practice of PBPK modeling, as well as the application of these models in toxicity testing and health risk assessments.

Blood/air distribution of volatile organic compounds (VOCs) in a nationally representative sample

Volatile organic compounds (VOCs) in human blood are an effective biomarker of environmental exposure and are closely linked to health outcomes. Unlike VOC concentrations in air, which are routinely collected, blood VOC data are not as readily available. This study aims to develop the quantitative relationship between air and blood VOCs by deriving population-based blood/air distribution coefficients (popKs) of ten common VOCs in the general U.S. population. Air and human blood samples were collected from 364 adults aged 20-59 years in 1999-2000 National Health and Nutrition Examination Survey (NHANES). Determinants of popKs were identified using weighted multivariate regression models. In the non-smoking population, median popKs ranged from 3.1 to 77.3, comparable to values obtained in the laboratory. PopKs decreased with increasing airborne VOC concentrations. Smoking elevated popKs by 1.5-3.5 times for aromatic compounds, but did not affect the popKs for methyl tert-butyl ether (MTBE) or chlorinated compounds. Drinking water concentration was a modifier of MTBE's popK. Age, gender, body composition, nor ethnicity affected popKs. PopKs were predictable using linear models with air concentration as the independent variable for both adults and children. This is the first study to estimate blood/air distribution coefficients using simultaneous environmental and biological monitoring on a national population sample. This study was also the first to determine the blood/air distribution coefficient of p-dichlorobenzene, a compound frequently found in indoor environments. These results have applications in exposure assessment, pharmacokinetic analysis, physiologically-based pharmacokinetic (PBPK) modeling, and uncertainty analysis.

Interspecies extrapolation

Interspecies extrapolation encompasses two related but distinct topic areas that are germane to quantitative extrapolation and hence computational toxicology-dose scaling and parameter scaling. Dose scaling is the process of converting a dose determined in an experimental animal to a toxicologically equivalent dose in humans using simple allometric assumptions and equations. In a hierarchy of quantitative extrapolation approaches, this option is used when minimal information is available for a chemical of interest. Parameter scaling refers to cross-species extrapolation of specific biological processes describing rates associated with pharmacokinetic (PK) or pharmacodynamic (PD) events on the basis of allometric relationships. These parameters are used in biologically based models of various types that are designed for not only cross-species extrapolation but also for exposure route (e.g., inhalation to oral) and exposure scenario (duration) extrapolation. This area also encompasses in vivo scale-up of physiological rates determined in various experimental systems. Results from in vitro metabolism studies are generally most useful for interspecies extrapolation purposes when integrated into a physiologically based pharmacokinetic (PBPK) modeling framework. This is because PBPK models allow consideration and quantitative evaluation of other physiological factors, such as binding to plasma proteins and blood flow to the liver, which may be as or more influential than metabolism in determining relevant dose metrics for risk assessment.

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.

Evaluation of the impact of the exposure route on the human kinetic adjustment factor

The objective of this study was to assess the impact of the exposure route on the human kinetic adjustment factor (HKAF), for which a default value of 3.16 is used in non-cancer risk assessment. A multi-route PBPK model was modified from the literature and used for computing the internal dose metrics in adults, neonates, children, elderly and pregnant women following three route-specific scenarios to chloroform, bromoform, tri- or per-chloroethylene (TCE or PERC). These include 24-h inhalation exposure, body-weight adjusted oral exposure and 30 min dermal exposure to contaminated drinking water. Distributions for body weight (BW), height (BH) and hepatic cytochrome P450 2E1 (CYP2E1) content were obtained from the literature, whereas model parameters (flows, volumes) were calculated from BW and BH. Monte Carlo simulations were performed and the HKAF was calculated as the ratio of the 95th percentile value of internal dose metrics in subpopulation to the 50th percentile value in adults. On the basis of the area under the parent compound's arterial blood concentration vs time curve (AUC(pc)), highest HKAFs were obtained in neonates for every scenario considered, and were the highest for bromoform (range: 3.6-7.4). Exceedance of the default value based on AUC(PC) was also observed for an oral exposure to chloroform in neonates (4.9). In all other cases, HKAFs remained below the default value. Overall, this study has pointed out the dependency of the HKAF on the exposure route, dose metrics and subpopulation considered, as well as characteristics of the chemicals investigated.

A unified algorithm for predicting partition coefficients for PBPK modeling of drugs and environmental chemicals

The algorithms in the literature focusing to predict tissue:blood PC (P(tb)) for environmental chemicals and tissue:plasma PC based on total (K(p)) or unbound concentration (K(pu)) for drugs differ in their consideration of binding to hemoglobin, plasma proteins and charged phospholipids. The objective of the present study was to develop a unified algorithm such that P(tb), K(p) and K(pu) for both drugs and environmental chemicals could be predicted. The development of the unified algorithm was accomplished by integrating all mechanistic algorithms previously published to compute the PCs. Furthermore, the algorithm was structured in such a way as to facilitate predictions of the distribution of organic compounds at the macro (i.e. whole tissue) and micro (i.e. cells and fluids) levels. The resulting unified algorithm was applied to compute the rat P(tb), K(p) or K(pu) of muscle (n=174), liver (n=139) and adipose tissue (n=141) for acidic, neutral, zwitterionic and basic drugs as well as ketones, acetate esters, alcohols, aliphatic hydrocarbons, aromatic hydrocarbons and ethers. The unified algorithm reproduced adequately the values predicted previously by the published algorithms for a total of 142 drugs and chemicals. The sensitivity analysis demonstrated the relative importance of the various compound properties reflective of specific mechanistic determinants relevant to prediction of PC values of drugs and environmental chemicals. Overall, the present unified algorithm uniquely facilitates the computation of macro and micro level PCs for developing organ and cellular-level PBPK models for both chemicals and drugs.

Biopartitioning micellar chromatography to predict blood to lung, blood to liver, blood to fat and blood to skin partition coefficients of drugs

Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases of Brij35 in adequate experimental conditions, has demonstrated to be useful in mimicking the drug partitioning process into biological systems. In this paper, the usefulness of BMC for predicting the partition coefficients from blood to lung, blood to liver, blood to fat and blood to skin is demonstrated. PLS2 and multiple linear regression (MLR) models based on BMC retention data are proposed and compared with other ones reported in bibliography. The proposed models present better or similar descriptive and predictive capability.

Approaches for applications of physiologically based pharmacokinetic models in risk assessment

Physiologically based pharmacokinetic (PBPK) models are particularly useful for simulating exposures to environmental toxicants for which, unlike pharmaceuticals, there is often little or no human data available to estimate the internal dose of a putative toxic moiety in a target tissue or an appropriate surrogate. This article reviews the current state of knowledge and approaches for application of PBPK models in the process of deriving reference dose, reference concentration, and cancer risk estimates. Examples drawn from previous U.S. Environmental Protection Agency (EPA) risk assessments and human health risk assessments in peer-reviewed literature illustrate the ways and means of using PBPK models to quantify the pharmacokinetic component of the interspecies and intraspecies uncertainty factors as well as to conduct route to route, high dose to low dose and duration extrapolations. The choice of the appropriate dose metric is key to the use of the PBPK models for the various applications in risk assessment. Issues related to whether uncertainty factors are most appropriately applied before or after derivation of human equivalent dose (or concentration) continue to be explored. Scientific progress in the understanding of life stage and genetic differences in dosimetry and their impacts on variability in susceptibility, as well as ongoing development of analytical methods to characterize uncertainty in PBPK models, will make their use in risk assessment increasingly likely. As such, it is anticipated that when PBPK models are used to express adverse tissue responses in terms of the internal target tissue dose of the toxic moiety rather than the external concentration, the scientific basis of, and confidence in, risk assessments will be enhanced.

Bayesian calibration of a physiologically based pharmacokinetic/pharmacodynamic model of carbaryl cholinesterase inhibition

Carbaryl, an N-methyl carbamate (NMC), is a common insecticide that reversibly inhibits neuronal cholinesterase activity. The objective of this work was to use a hierarchical Bayesian approach to estimate the parameters in a physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model from experimental measurements of carbaryl in rats. A PBPK/PD model was developed to describe the tissue dosimetry of carbaryl and its metabolites (1-naphthol and "other hydroxylated metabolites") and subsequently to predict the carbaryl-induced inhibition of cholinesterase activity, in particular in the brain and blood. In support of the model parameterization, kinetic tracer studies were undertaken to determine total radioactive tissue levels of carbaryl and metabolites in rats exposed by oral or intravenous routes at doses ranging from 0.8 to 9.2 mg/kg body weight. Inhibition of cholinesterase activity in blood and brain was also measured from the exposed rats. Markov Chain Monte Carlo (MCMC) calibration of the rat model parameters was implemented using prior information from literature for physiological parameter distributions together with kinetic and inhibition data on carbaryl. The posterior estimates of the parameters displayed at most a twofold deviation from the mean. Monte Carlo simulations of the PBPK/PD model with the posterior distribution estimates predicted a 95% credible interval of tissue doses for carbaryl and 1-naphthol within the range of observed data. Similar prediction results were achieved for cholinesterase inhibition by carbaryl. This initial model will be used to determine the experimental studies that may provide the highest added value for model refinement. The Bayesian PBPK/PD modeling approach developed here will serve as a prototype for developing mechanism-based risk models for the other NMCs.

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.

Approaches to acrylamide physiologically based toxicokinetic modeling for exploring child-adult dosimetry differences

Dietary exposure to acrylamide is common as a result of its formation during the cooking of carbohydrate foods. This leads to widespread human exposure in adults and children alike. Acrylamide is neurotoxic and is metabolized by cytochrome P-450 (CYP) 2E1 to a mutagenic epoxide, glycidamide. This article describes a modeling framework for assessing acrylamide and glycidamide dosimetry in rats and human adults and children. The challenges in building a physiologically based toxicokinetic (PBTK) model that is compatible with existing rat and human data are described, with an emphasis on calibration against the hemoglobin adduct database. This exploratory PBTK model was adapted to children by incorporating life-stage-specific parameters consistent with children's changing physiology and metabolic capacity for processes involved in acrylamide disposition in terms of CYP2E1, glutathione conjugation, and epoxide hydrolase. Monte Carlo analysis was used to simulate the distribution of internal doses to gain an initial understanding of the range of child/adult differences possible. This analysis suggests modest dosimetry differences between children and adults, with area-under-the-curve (AUC) doses for the 99th percentile child up to fivefold greater than the median adult for both acrylamide and glycidamide. Early life immaturities tended to exert a greater effect on acrylamide than glycidamide dosimetry because immaturities in CYP2E1 and glutathione counteract one another for glycidamide AUC, but both lead to greater acrylamide dose. The analysis points toward glutathione conjugation parameters as being particularly influential and uncertain in early life, making this a key area for future research.

Physiologically based modeling of the inhalation pharmacokinetics of ethylbenzene in B6C3F1 mice

A physiologically based pharmacokinetic (PBPK) model was developed for inhaled ethylbenzene (EB) in B6C3F1 mice. The mouse physiological parameters were obtained from the literature, but the blood:air and tissue:air partition coefficients were determined by vial equilibration technique. The maximal velocity for hepatic metabolism (Vmax) obtained from a previously published rat study was increased by a factor of approximately 3 to account for enzyme induction during repeated exposures. The Michaelis affinity constant (Km) for hepatic metabolism of EB, obtained from a previously published rat PBPK modeling study, was kept unchanged during single and repeated exposure scenarios. Hepatic metabolism alone could not adequately describe the clearance of EB from mouse blood. Additional metabolism was assumed to be localized in the lung. The parameters for pulmonary metabolism were obtained by optimization of PBPK model fits to kinetic data collected following exposures to 75-1000 ppm. The PBPK model successfully predicted all available blood and tissue concentration data in mice exposed to 75 or 750 ppm EB. Overall, the results indicate that the rate of EB clearance is markedly higher in B6C3F1 mice than rats or humans and exceeds the hepatic metabolism capacity. Available biochemical evidence is consistent with a significant role for pulmonary metabolism; however, the extent to which the extrahepatic metabolism is localized in the lung is unclear. Overall, the PBPK model developed for the mouse adequately simulated the blood and tissue kinetics of EB by accounting for its high rate of clearance.

Mechanistic Approaches to Volume of Distribution Predictions: Understanding the Processes

PURPOSE

To use recently developed mechanistic equations to predict tissue-to-plasma water partition coefficients (Kpus), apply these predictions to whole body unbound volume of distribution at steady state (Vu(ss)) determinations, and explain the differences in the extent of drug distribution both within and across the various compound classes.

MATERIALS AND METHODS

Vu(ss) values were predicted for 92 structurally diverse compounds in rats and 140 in humans by two approaches. The first approach incorporated Kpu values predicted for 13 tissues whereas the second was restricted to muscle.

RESULTS

The prediction accuracy was good for both approaches in rats and humans, with 64-78% and 82-92% of the predicted Vu(ss) values agreeing with in vivo data to within factors of +/-2 and 3, respectively.

CONCLUSIONS

Generic distribution processes were identified as lipid partitioning and dissolution where the former is higher for lipophilic unionised drugs. In addition, electrostatic interactions with acidic phospholipids can predominate for ionised bases when affinities (reflected by binding to constituents within blood) are high. For acidic drugs albumin binding dominates when plasma protein binding is high. This ability to explain drug distribution and link it to physicochemical properties can help guide the compound selection process.

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.

Physiologically-Based Toxicokinetic Modeling of Durene (1,2,3,5-Tetramethylbenzene) and Isodurene (1,2,4,5-Tetramethylbenzene) in Humans

OBJECTIVES

Physiologically-based toxicokinetic (PB-TK) models are developed to simulate absorption, distribution and excretion of xenobiotics. PB-TK models consist of several groups of compartments, where tissues are grouped together according to physiological parameters (tissue blood flows, tissue group volumes) and physicochemical properties (partition coefficients, metabolic constants). Tetramethylbenzene (TETMB), a mixture of its three isomers: prenitene (1,2,3,4-TETMB), isodurene (1,2,3,5-TETMB), and durene (1,2,4,5-TETMB) is an essential component of numerous commercial preparations of organic solvents. The aim of the study was to develop the PB-TK model for two TETMB isomers, durene and isodurene, in humans.

MATERIALS AND METHODS

The assumed PB-TK model groups organs and tissues into five physiological compartments: fat tissue, muscles, organs, liver, and brain. The brain has been considered as a separate compartment due to the potential neurotoxicity of TETMB. Water/air, oil/air and blood/air partition coefficients for durene and isodurene were measured in vitro. Tissue/air partition coefficients were calculated from values of olive/air and water/air partition coefficients and the average fat and water content in different tissues. Tissue/blood partition coefficients were calculated as a tissue/air quotient and the blood/air partition coefficient measured in vitro. The Michaelis-Menten constant (KM) values and maximum metabolism rate constant (VMAX) for selected metabolites of durene and isodurene were obtained in vitro using microsomal fraction of the human liver.

RESULTS

The developed model was validated against experimental data obtained earlier as a result of an 8-h exposure of volunteers to durene and isodurene vapors of 10 and 25 mg/m3. The prediction of both TETMB isomers concentration in blood as well as of the elimination rates of 2,4,5-TMBA and 2,3,5-TMBA were close to the results of experimental exposures.

CONCLUSIONS

Simulations of one working week inhalation exposure to aromatic hydrocarbons indicate that the elaborated PB-TK model may be used to predict the chemical distribution in different body compartments, based on physicochemical properties.

Analysis of algorithms predicting blood:air and tissue:blood partition coefficients from solvent partition coefficients for prevalent components of JP-8 jet fuel

Algorithms predicting tissue and blood partition coefficients (PCs) from solvent properties were compared to assess their usefulness in a petroleum mixture physiologically based pharmacokinetic/pharmacodynamic model. Measured blood:air and tissue:blood PCs for rat and human tissues were sought from literature resources for 14 prevalent jet fuel (JP-8) components. Average experimental PCs were compared with predicted PCs calculated using algorithms from 9 published sources. Algorithms chosen used solvent PCs (octanol:water, saline or water:air, oil:air coefficients) due to the relative accessibility of these parameters. Tissue:blood PCs were calculated from ratios of predicted tissue:air and experimental blood:air values (PCEB). Of the 231 calculated values, 27% performed within +/- 20% of the experimental PC values. Physiologically based equations (based on water and lipid components of a tissue type) did not perform as well as empirical equations (derived from linear regression of experimental PC data) and hybrid equations (physiological parameters and empirical factors combined) for the jet fuel components. The major limitation encountered in this analysis was the lack of experimental data for the selected JP-8 constituents. PCEB values were compared with tissue:blood PCs calculated from ratios of predicted tissue:air and predicted blood:air values (PCPB). Overall, 68% of PCEB values had smaller absolute % errors than PCPB values. If calculated PC values must be used in models, a comparison of experimental and predicted PCs for chemically similar compounds would estimate the expected error level in calculated values.

Human exposure and internal dose assessments of acrylamide in food

This review provides a framework contributing to the risk assessment of acrylamide in food. It is based on the outcome of the ILSI Europe FOSIE process, a risk assessment framework for chemicals in foods and adds to the overall framework by focusing especially on exposure assessment and internal dose assessment of acrylamide in food. Since the finding that acrylamide is formed in food during heat processing and preparation of food, much effort has been (and still is being) put into understanding its mechanism of formation, on developing analytical methods and determination of levels in food, and on evaluation of its toxicity and potential toxicity and potential human health consequences. Although several exposure estimations have been proposed, a systematic review of key information relevant to exposure assessment is currently lacking. The European and North American branches of the International Life Sciences Institute, ILSI, discussed critical aspects of exposure assessment, parameters influencing the outcome of exposure assessment and summarised data relevant to the acrylamide exposure assessment to aid the risk characterisation process. This paper reviews the data on acrylamide levels in food including its formation and analytical methods, the determination of human consumption patterns, dietary intake of the general population, estimation of maximum intake levels and identification of groups of potentially high intakes. Possible options and consequences of mitigation efforts to reduce exposure are discussed. Furthermore the association of intake levels with biomarkers of exposure and internal dose, considering aspects of bioavailability, is reviewed, and a physiologically-based toxicokinetic (PBTK) model is described that provides a good description of the kinetics of acrylamide in the rat. Each of the sections concludes with a summary of remaining gaps and uncertainties.

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.

QSPR treatment of rat blood:air, saline:air and olive oil:air partition coefficients using theoretical molecular descriptors

A QSPR treatment has been applied to a data set that consists of 100 diverse organic compounds to relate the logarithmic function of rat blood:air, saline:air and olive oil:air partition coefficients (denoted by log K(b:a), log K(s:a), and log K(o:a), respectively), with theoretical molecular and fragment descriptors. Three QSPR models with squared correlation coefficients of 0.881, 0.926, and 0.922, respectively, were obtained. The verification of the predictive power of these models on a test set of 33 organic chemicals that were not included in the training set gave satisfactory squared correlation coefficients: 0.791 for rat blood:air, 0.794 for saline:air and 0.846 for olive oil:air.

Predicting blood:air partition coefficients using theoretical molecular descriptors

Three regression methods, namely ridge regression (RR), partial least squares (PLS), and principal components regression (PCR), were used to develop models for the prediction of rat blood:air partition coefficient for increasingly diverse data sets. Initially, modeling was performed for a set of 13 chlorocarbons. To this set, 10 additional hydrophobic compounds were added, including aromatic and non-aromatic hydrocarbons. A set of 16 hydrophilic compounds was also modeled separately. Finally, all 39 compounds were combined into one data set for which comprehensive models were developed. A large set of diverse, theoretical molecular descriptors was calculated for use in the current study. The topostructural (TS), topochemical (TC), and geometrical or 3-dimensional (3D) indices were used hierarchically in model development. In addition, single-class models were developed using the TS, TC, and 3D descriptors. In most cases, RR outperformed PLS and PCR, and the models developed using TC indices were superior to those developed using other classes of descriptors.

Prediction of human blood: air partition coefficient: a comparison of structure-based and property-based methods

In recent years, there has been increased interest in the development and use of quantitative structure-activity/property relationship (QSAR/QSPR) models. For the most part, this is due to the fact that experimental data is sparse and obtaining such data is costly, while theoretical structural descriptors can be obtained quickly and inexpensively. In this study, three linear regression methods, viz. principal component regression (PCR), partial least squares (PLS), and ridge regression (RR), were used to develop QSPR models for the estimation of human blood:air partition coefficient (logPblood:air) for a group of 31 diverse low-molecular weight volatile chemicals from their computed molecular descriptors. In general, RR was found to be superior to PCR or PLS. Comparisons were made between models developed using parameters based solely on molecular structure and linear regression (LR) models developed using experimental properties, including saline:air partition coefficient (logPsaline:air) and olive oil:air partition coefficient (logPolive oil:air), as independent variables, indicating that the structure-property correlations are comparable to the property-property correlations. The best models, however, were those that used rat logPblood:air as the independent variable. Haloalkane subgroups were modeled separately for comparative purposes and, although models based on the congeneric compounds were superior, the models developed on the complete set of diverse compounds were of acceptable quality. The structural descriptors were placed into one of three classes based on level of complexity: topostructural (TS), topochemical (TC), or three-dimensional/geometrical (3D). Modeling was performed using the structural descriptor classes both in a hierarchical fashion and separately. The results indicate that highest quality structure-based models, in terms of descriptor classes, were those derived using TC descriptors.

Relationship between olive oil:air, saline:air, and rat brain:air partition coefficients of organic solvents in vitro

Partition coefficients of 28 volatile organic solvents (13 alkylbenzenes, 10 chlorinated hydrocarbons, and 5 ketones) in olive oil, saline, and rat brain tissue homogenates were measured by equilibration in a closed vial and subsequent gas-chromatographic analysis of headspace air. The values of oil and saline partition coefficients correlate well with previously reported data. Brain partition coefficients were fit to a bilinear equation of the form P(brain:air) = alpha(o)P(oil:air) + alpha(s)P(saline:air) + c. The regression coefficients accurately predicted previously reported rat brain partition coefficients of 19 solvents with distinct physicochemical properties within a factor of 2.5. The combined data set of presently determined and previously reported brain partition coefficients (n = 46) yields tissue-specific regression coefficients for solvent partitioning in rat brain of 0.028 for alpha(O'), 0.845 for alpha(S'), and 0.90 for the intercept, with coefficients of variation amounting to 11%, 4%, and 463%, respectively. The generalized empirical relationship predicts the brain partition coefficients within a factor of 2.5 accurately for 95% of the compounds. The ratios of rat brain concentrations calculated from predicted and measured P(brain:air) and P(blood:air) values were within a factor of 4 for 95% of the compounds. It was concluded that the enlargement of the empirical data set leads to more reliable predictions of rat brain partition coefficients, particularly for the lipophilic volatile organic compounds.