Showing papers on "Physiologically based pharmacokinetic modelling published in 2005"

Prediction of drug disposition in infants and children by means of physiologically based pharmacokinetic (PBPK) modelling: theophylline and midazolam as model drugs

Abstract: Aims To create a general physiologically based pharmacokinetic (PBPK) model for drug disposition in infants and children, covering the age range from birth to adulthood, and to evaluate it with theophylline and midazolam as model drugs.

Physiologically Based Pharmacokinetic Modeling: Science and Applications

Abstract: Preface. Acknowledgments. Contributors. Chapter 1. Introduction: A Historical Perspective of the Development and Applications of PBPK Models. 1. Introduction. 2. A Historical Perspective. 2-1. Responses to Inhaled Compounds. 2-2. Pharmaceutical Applications. 2-3. Occupational and Environmental Applications. 2-4. Digital Computation and PBPK Modeling. 3. Expansion of PBPK Model Applications. 3-1. PBPK Models for Tissue Dosimetry from Secondary Data. 3-2. Biological Mechanisms Underlying PK Behaviors. 3-3. Chemicals as Probes of Biological Processes. 3-4. Risk Assessment Applications. 3-5. PBPK Models as Repository of Mechanistic Data on Distribution and Response. 4. Summary. PART I: PBPK MODELING FOR VOLATILE ORGANIC COMPOUNDS. Chapter 2. Halogenated Alkanes. 1. Introduction. 2. PBPK Model Development for Volatile Organics. 2-1. Model Formulation. 2-2. Model Equations. 2-3. Model Parameterization. 2-4. Model Calculations. 3. Advances in Experimental Methods Demonstrated for Groups of Chemicals. 4. PBPK Models for Halogenated Alkanes. 4-1. Anesthetic Gases. 4-2. Chlorofluorocarbons (CFCs), Refrigerants and Halons. 4-3. Halogenated Alkanes. 5. Summary. Chapter 3. Halogenated Alkenes. 1. Introduction. 2. The Chloroethylenes: Background. 3. Review of PBPK Models. 3-1. Vinyl Chloride (VC). 3-2. Vinyl Fluoride (VF). 3-3. cis-1,2-Dichloroethylene (cDCE) and trans-1,2-Dichloroethylene (tDCE). 3-4. Vinylidene Chloride (VDC). 3-5. Trichloroethylene (TCE). 3-6. Tetrachloroethylene (PERC). 3-7. Allyl Chloride (AC). 3-8. b-Chloroprene (CD). 3-9. Hexachlorobutadiene, HCB. 4. Summary. Chapter 4. Alkene and Aromatic Compounds. 1. Introduction. 2. PK and Pharmacodynamic Properties Important in PBPK Model Development for Aromatic and Alkene Compounds. 2-1. Metabolism and Mode of Action. 2-2. Model Structures. 2-3. PK Differences. 2-4. Extrahepatic Metabolism and Transport of Metabolites. 2-5. GSH Conjugation. 2-6. Endogenous Production. 2-7. Reactivity with DNA and Protein. 2-8. Inhibition of Second Oxidative Steps. 2-9. Variability and PK Differences. 2-10. Subcompartments in PBPK Models. 2-11. "Privileged Access" of Epoxide Hydratase to Epoxide Substrates. 3. Review of Aromatic and Alkene PBPK Models. 3-1. Benzene-A Known Human Carcinogen with an Uncertain Mode of Action. 3-2. Styrene- Early PBPK Models. 3-3. 1,3-Butadiene. 3-4. Isoprene. 3-5. Ethylene, Propylene and their Oxides. 3-6. Naphthalene and Other PAHs. 3-7. Halobenzenes. 3-8. Miscellaneous Related Compounds. 4. Summary. Chapter 5. Reactive Vapors in the Nasal Cavity. Introduction. 1-1. Nasal Effects and Risk Assessment. 1-2. General Models for Nasal Uptake. 1-2-1. Air Phase. 1-2-2. Specific Nasal Regions. 1-2-3. Air Phase Mass Transfer Coefficients. 1-2-4. Interfacial Mass Transfer Coefficient. 1-2-5. Tissue Diffusion. 2. No Air-Phase Models. 2-1. The "Perfused Nose" Model. 2-2. Vinyl Acetate. 3. Creating the Air Phase Compartments. 3-1. Computational Fluid Dynamics. 3-2. Estimating the Air Phase Mass Transfer Coefficient. 3-3. Estimating Air Phase Mass Transfer Coefficients - Acrylic Acid. 4. Other Models for Vapors Affecting Nasal Tissues. 4-1. Vinyl Acetate. 4-2. Ethyl Acrylate and its Metabolite, Acrylic Acid. 4-2. Epichlorohydrin. 5. Methyl Methacrylate. 6. Formaldehyde. 7. Hydrogen Sulfide. 10. Summary. Chapter 6. Alkanes, Oxyhydrocarbons, and Related Compounds. 1. Introduction. 2. Purposes for PBPK Model Development. 3. PBPK Models for Four Classes of Compounds. 3-1. Alkanes. 3-2. Oxyhydrocarbons. 3-3. Alkylbenzenes. 3-4. Siloxanes. 4. Summary. PARK II: PBPK MODEL DEVELOPMENT FOR ENVIRONMENTAL POLLUTANTS. Chapter 7. Pesticides and Persistent Organic Pollutants (POPs). 1. Introduction. 2. Pesticides. 2-1. Chemical Classes of Pesticides. 2-2. Modeling Tissue Distribution. 2-3. Modeling Metabolism. 2.4 Summary of Individual Models. 3. Polychlorinated and Polybrominated Biphenyls, PCBs and PBBs. 3-1. Modeling in Mammals. 3-2. Modeling in Nonmammalian Species. 4. Summary. Chapter 8. Dioxin and Related Compounds. 1. Introduction. 2. Toxicity. 3. Mode of Action. 4. Pharmacokinetics. 4-1. Absorption, Metabolism, and Excretion. 4-2. Distribution. 5. PBPK Models of TCDD. 5-1. PBPK Models of TCDD in Rodents. 5-2. PBPK Models of TCDD in Humans. 6. Summary. Chapter 9. Metals and Inorganic Compounds. 1. Introduction. 2. Physiologically Based Modeling of Metals. 2-1. Arsenic. 2-2. Nickel. 2-3. Lead. 2-4. Chromium. 3. PBPK Models for Non-Metals. 3-1. A PBPK Model for Fluoride, a Bone-Seeking Non-Metal. 3-2. PBPK Models for Other Non-Metals. 4. Compartmental Models for Miscellaneous Inorganic and/or Endogenous Chemicals. 5. Research Needs. 5-1. The Need for Physiologically Based Modeling for Essential Metals. 5-2. Other Research Needs. 6. Summary. PART III: PHARMACEUTICAL APPLICATIONS OF PBPK MODELS. Chapter 10. Drugs. 1. Introduction. 2. Describing the Tissue Distribution of Drugs. 3. Describing Metabolism and Other Clearance Processes of Drugs. 4. Other Issues in Model Development for Drugs. 4-1. Altered Physiological States. 4-2. Drug Stereospecificity. 4-3. Non Steady-State Dynamics. 4-4. Drug Interactions. 4-5. Utilization of In Vitro Data. 5. Future Perspectives. 6. Summary. Chapter 11. Antineoplastic Agents. 1. Introduction. 2. PBPK Models for Antineoplastic Agents. 2-1. Methotrexate. 2-2. cis-Dichlorodiammine-platinum. 2-3. Actinomycin D. 2-4. 2'-Deoxycoformycin (Pentostatin). 2-5. 5-Fluorouracil. 2-6. 2-Amino-1,3,4-thiadiazole. 2-7. 1-beta-D-Arabinofuranosylcytosine. 2-8. Adriamycin. 2-9. Melphalan. 2-10. Topotecan. 2-11. 17-(Allylamino)-17-demethoxygeldanamycin. 3. Summary. PART IV: PBPK MODELING APPROACHES FOR SPECIAL APPLICATIONS. Chapter 12. Perinatal Pharmacokinetics. 1. Introduction. 2. Physiological and Biochemical Changes During Pregnancy. 2-1. Body Weight Changes and Organ Growth. 2-2. Physiological and Biochemical Changes in Pregnant Females. 2-3. Physiological Changes in Fetuses. 2-4. Mechanisms of Chemicals Transfer through Placenta. 2-5. Mechanisms of Chemical Transfer through Breast Milk. 3. Physiological Factors Incorporated into PBPK Models for Perinatal Pharmacokinetics. 3-1. Body Weight in the Mother. 3-2. Organ Volume and Cardiac Output in the Mother. 3-3. Chemical Transfer through the Placenta and Mammary Gland. 4. PBPK Models for Perinatal Transfer. 4-1. Tetracycline. 4-2. Morphine. 4-3. Theophylline. 4-4. Methadone. 4-5. Pethidine. 4-6. Trichloroethylene. 4-7. 5,5'-Dimethyloxazolidine-2,4-dione (DMO). 4-8. Tetrachloroethylene. 4-9. 2-Methoxyethanol and Methoxyacetic Acid. 4-10. Methylmercury (MeHg). 4-11. 2,4-Dichlorophenoxyacetic Acid (2,4-D). 4-12. Methanol. 4-13. Vitamin A Acid. 4-14. Organic Solvents. 4-15. p-Phenylbenzoic Acid (PPBA). 4-16. p,p'-Dichloro-2,2-bis(p-chlorophenyl)ethylene (DDE). 4-17. 2-Ethoxyethanol and Ethoxyacetic Acid. 4-18.Perchlorate. 5. Risk Assessment Dosimetry Models. 6. Summary. Chapter 13. Mixtures. 1. Introduction. 2. PBPK Modeling Of Chemical Mixtures. 2-1. Earlier Days: PBPK Modeling of Binary Mixtures. 2-2. More Recent Endeavors: PBPK Modeling of Higher Order Mixtures. 3. Future Perspectives: Second Generation PBPK/PD modeling. 4. Summary. Chapter 14. Dermal Exposure Models. 1. Introduction. 2. Factors to Consider in Modeling Dermal Absorption. 3. Dermal Absorption Models. 3-1. Membrane Models. 3-2. Compartment Models. 4. Experimental Methods. 5. Summary. Chapter 15. Conclusions and Future Directions. 1. Introduction. 2. A Systems Approach for Pharmacokinetics. 3. Modeling Both Dose and Response. 4. Opportunities for PBPK Modeling in Pharmaceutical Industry. 5. Reaction Network Modeling with Xenobiotics. 6. Systems Biology and Dose-Response. 7. Summary. Index.

Physiologically based pharmacokinetic modelling: a sound mechanistic basis is needed.

Abstract: Pharmacokinetics is the science of drug absorption, distribution, and elimination, or more specifically the quantification of those processes, leading to the understanding, interpretation, and prediction of blood concentration-time profiles. Occasionally, concentration-time data from other physiological fluids or tissues are available, but it is the lack of data in relevant tissues and organs that limits one's ability to get at the underlying mechanisms determining the blood profile. For example, blood concentration data are used to evaluate drug absorption for orally administered drugs, even though absorption is a multistep erratic process under the control of many factors, and measurements within the gut are not usually available. Nevertheless, blood concentration can be a useful biomarker, and sometimes a surrogate [1], to guide therapy. The idea is that pharmacokinetics accounts for some of the variability in the dose-response relationship. However, in order to transform dose into concentration, a pharmacokinetic model is required, based on an analysis of concentration-time data and preferably incorporating relevant patient characteristics, allowing it to be used for individualized therapy [2]. The complexity of a pharmacokinetic model depends on the level and quality of information available and on its purpose.

Human physiologically based pharmacokinetic model for propofol.

Abstract: Background Propofol is widely used for both short-term anesthesia and long-term sedation. It has unusual pharmacokinetics because of its high lipid solubility. The standard approach to describing the pharmacokinetics is by a multi-compartmental model. This paper presents the first detailed human physiologically based pharmacokinetic (PBPK) model for propofol.

Comparison of the use of a physiologically based pharmacokinetic model and a classical pharmacokinetic model for dioxin exposure assessments

Abstract: In epidemiologic studies, exposure assessments of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) assume a fixed elimination rate. Recent data suggest a dose-dependent elimination rate for TCDD. A physiologically based pharmacokinetic (PBPK) model, which uses a body-burden-dependent elimination rate, was developed previously in rodents to describe the pharmacokinetics of TCDD and has been extrapolated to human exposure for this study. Optimizations were performed using data from a random selection of veterans from the Ranch Hand cohort and data from a human volunteer who was exposed to TCDD. Assessment of this PBPK model used additional data from the Ranch Hand cohort and a clinical report of two women exposed to TCDD. This PBPK model suggests that previous exposure assessments may have significantly underestimated peak blood concentrations, resulting in potential exposure misclassifications. Application of a PBPK model that incorporates an inducible elimination of TCDD may improve the exposure assessments in epidemiologic studies of TCDD.

Development and application of physiologically based pharmacokinetic-modeling tools to support drug discovery.

Abstract: Physiologically based pharmacokinetic (PBPK) modeling integrates physicochemical (PC) and in vitro pharmacokinetic (PK) data using a mechanistic framework of principal ADME (absorption, distribution, metabolism, and excretion) processes into a physiologically based whole-body model. Absorption, distribution, and clearance are modeled by combining compound-specific PC and PK properties with physiological processes. Thereby, isolated in vitro data can be upgraded by means of predicting full concentration ± time profiles prior to animal experiments. The integrative process of PBPK modeling leads to a better understanding of the specific ADME processes driving the PK behavior in vivo, and has the power to rationally select experiments for a more focussed PK project support. This article presents a generic disposition model based on tissue-compositionbased distribution and directly scaled hepatic clearance. This model can be used in drug discovery to identify the critical PK issues of compound classes and to rationally guide the optimization path of the compounds toward a viable development candidate. Starting with a generic PBPK model, which is empirically based on the most common PK processes, the model will be gradually tailored to the specifics of drug candidates as more and more experimental data become available. This will lead to a growing understanding of the −drug in the making×, allowing a range of predictions to be made for various purposes and conditions. The stage is set for a wide penetration of PK modeling and simulations to form an intrinsic part of a project starting from lead discovery, to lead optimization and candidate selection, to preclinical profiling and clinical trials. 1. Purpose and Principles of Physiologically Based Pharmacokinetic Modeling 1 ). ± Appropriate drug concentrations must be achieved in the patient in order to ensure efficacious and safe treatment. Therefore, principal pharmacokinetic (PK) processes such as absorption, distribution, metabolism, and excretion (ADME) which determine the concentration ± time profile of the drug in the body tissues must be understood. To examine these complex processes in detail, a broad range of drug properties has to be investigated from early on in the drug-discovery process, when the final drug compound is still hidden among thousands of potential molecules. Physicochemical (PC) data like lipophilicity (log D), ionization (pKa) and water solubility (Sw) are measured to this

Evaluation of Physiologically Based Pharmacokinetic Models in Risk Assessment: An Example with Perchloroethylene

Abstract: One of the more problematic aspects of the application of physiologically based pharmacokinetic (PBPK) models in risk assessment is the question of whether the model has been adequately validated to provide confidence in the dose metrics calculated with it. A number of PBPK models have been developed for perchloroethylene (PCE), differing primarily in the parameters estimated for metabolism. All of the models provide reasonably accurate simulations of selected kinetic data for PCE in mice and humans and could thus be considered to be "validated" to some extent. However, quantitative estimates of PCE cancer risk are critically dependent on the prediction of the rate of metabolism at low environmental exposures. Recent data on the urinary excretion of trichloroacetic acid (TCA), the major metabolite of PCE, for human subjects exposed to lower concentrations than those used in previous studies, make it possible to compare the high- to low-dose extrapolation capability of the various published human models. The model of Gearhart et al., which is the only model to include a description of TCA kinetics, provided the closest predictions of the urinary excretion observed in these low-concentration exposures. Other models overestimated metabolite excretion in this study by 5- to 15-fold. A systematic discrepancy between model predictions and experimental data for the time course of the urinary excretion of TCA suggested a contribution from TCA formed by metabolism of PCE in the kidney and excreted directly into the urine. A modification of the model of Gearhart et al. to include metabolism of PCE to TCA in the kidney at 10% of the capacity of the liver, with direct excretion of the TCA formed in the kidney into the urine, markedly improved agreement with the experimental time-course data, without altering predictions of liver metabolism. This case study with PCE demonstrates the danger of relying on parent chemical kinetic data to validate a model that will be used for the prediction of metabolism.

Evaluation of potential toxicity from co-exposure to three CNS depressants (toluene, ethylbenzene, and xylene) under resting and working conditions using PBPK modeling.

Abstract: Under OSHA and American Conference of Governmental Industrial Hygienists (ACGIH®) guidelines, the mixture formula (unity calculation) provides a method for evaluating exposures to mixtures of chemicals that cause similar toxicities. According to the formula, if exposures are reduced in proportion to the number of chemicals and their respective exposure limits, the overall exposure is acceptable. This approach assumes that responses are additive, which is not the case when pharmacokinetic interactions occur. To determine the validity of the additivity assumption, we performed unity calculations for a variety of exposures to toluene, ethylbenzene, and/or xylene using the concentration of each chemical in blood in the calculation instead of the inhaled concentration. The blood concentrations were predicted using a validated physiologically based pharmacokinetic (PBPK) model to allow exploration of a variety of exposure scenarios. In addition, the Occupational Safety and Health Administration and ACGIH® occup...

Physiologically-Based Pharmacokinetic and Toxicokinetic Models in Cancer Risk Assessment

Abstract: Physiologically-based pharmacokinetic (PBPK) and toxicokinetic models are increasingly being used for the conduct of high dose to low dose and interspecies extrapolations required in cancer risk assessment. These models, by simulating tissue dose of toxic chemicals, help address the uncertainty associated with the default approaches for interspecies and high dose to low dose extrapolations. The applicability of PBPK models in cancer risk assessment has been demonstrated with a number of chemicals (e.g., acrylonitrile, 2-butoxyethanol, chloroform, 1,4-dioxane, methyl chloroform, methylene chloride, styrene, trichloroethylene, tetrachloroethylene, vinyl chloride, vinyl acetate). Recent advances in PBPK modeling facilitate the consideration of population distribution of parameter values, age-dependent changes in physiology and metabolism, multi-route exposures as well as multichemical interactions for application in cancer risk assessment. Whereas the average values for various input parameters have been used to evaluate the age-dependency of tissue dose, the Markov Chain Monte Carlo technique can be applied to address variability and uncertainty in parameter estimates, thus facilitating a more accurate estimation of cancer risk in the population. The PBPK models also uniquely facilitate the simulation of tissue dose, and thereby cancer risks, associated with multi-route and multichemical exposure situations. Overall, the recent advances reviewed in this article point to the continued enhancement of the scientific basis and applicability of PBPK models in cancer risk assessment.

Moving From External Exposure Concentration to Internal Dose: Duration Extrapolation Based on Physiologically Based Pharmacokinetic Derived Estimates of Internal Dose

Abstract: The potential human health risk(s) from chemical exposure must frequently be assessed under conditions for which adequate human or animal data are not available. The default method for exposure-duration adjustment, based on Haber's rule, C (external exposure concentration) or C(n) (the ten Berge modification) x t (exposure duration) = K (a constant toxic effect), has been criticized for prediction errors. A promising alternative approach to duration adjustment is based on equivalence of internal dose, that is, target-tissue dose levels, across different exposure durations. A proposed methodology for dose-duration adjustments for acute exposure guideline levels (AEGLs) based on physiologically based pharmacokinetic (PBPK) estimates of dose is illustrated with trichloroethylene (TCE). Steps in this methodology include: (1) selection and evaluation, or development and evaluation, of an appropriate PBPK model; (2) determination of an appropriate measure of internal dose; (3) estimation with the PBPK model of the tissue dose (the target tissue dose) resulting from the external exposure conditions (concentration, duration) of the critical effect; (4) estimation of the external exposure concentrations required to achieve tissue doses equivalent to the target tissue dose at exposure durations of interest; and (5) evaluation of sources of variability and uncertainty. For TCE, this PBPK modeling approach has allowed determination of dose metrics predictive of the acute neurotoxic effects of TCE and dose-duration adjustments based on estimates of internal dose.

Duration adjustment of acute exposure guideline level values for trichloroethylene using a physiologically-based pharmacokinetic model.

Abstract: Acute Exposure Guideline Level (AEGL) recommendations are developed for 10-minute, 30-minute, 1-hour, 4-hours, and 8-hours exposure durations and are designated for three levels of severity: AEGL-1 represents concentrations above which acute exposures may cause noticeable discomfort including irritation; AEGL-2 represents concentrations above which acute exposure may cause irreversible health effects or impaired ability to escape; and AEGL-3 represents concentrations above which exposure may cause life-threatening health effects or death. The default procedure for setting AEGL values across durations when applicable data are unavailable involves estimation based on Haber's rule, which has an underlying assumption that cumulative exposure is the determinant of toxicity. For acute exposure to trichloroethylene (TCE), however, experimental data indicate that momentary tissue concentration, and not the cumulative amount of exposure, is important. We employed an alternative approach to duration adjustments in which a physiologically-based pharmacokinetic (PBPK) model was used to predict the arterial blood concentrations [TCE a ] associated with adverse outcomes appropriate for AEGL-1, -2, or -3-level effects. The PBPK model was then used to estimate the atmospheric concentration that produces equivalent [TCE a ] at each of the AEGL-specific exposure durations. This approach yielded [TCE a ] values of 4.89 mg/l for AEGL-1, 18.7 mg/l for AEGL-2, and 310 mg/l for AEGL-3. Duration adjustments based on equivalent target tissue doses should provide similar degrees of toxicity protection at different exposure durations.

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

Abstract: 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.

Improving predictive modeling in pediatric drug development: pharmacokinetics, pharmacodynamics, and mechanistic modeling.

Abstract: A workshop was conducted on November 18?19, 2004, to address the issue of improving predictive models for drug delivery to developing humans. Although considerable progress has been made for adult humans, large gaps remain for predicting pharmacokinetic/pharmacodynamic (PK/PD) outcome in children because most adult models have not been tested during development. The goals of the meeting included a description of when, during development, infants/children become adultlike in handling drugs. The issue of incorporating the most recent advances into the predictive models was also addressed: both the use of imaging approaches and genomic information were considered. Disease state, as exemplified by obesity, was addressed as a modifier of drug pharmacokinetics and pharmacodynamics during development. Issues addressed in this workshop should be considered in the development of new predictive and mechanistic models of drug kinetics and dynamics in the developing human.

Using physiologically-based pharmacokinetic modeling to address nonlinear kinetics and changes in rodent physiology and metabolism due to aging and adaptation in deriving reference values for propylene glycol methyl ether and propylene glycol methyl ether acetate.

Abstract: Reference values, including an oral reference dose (RfD) and an inhalation reference concentration (RfC), were derived for propylene glycol methyl ether (PGME), and an oral RfD was derived for its acetate (PGMEA). These values were based on transient sedation observed in F344 rats and B6C3F1 mice during a two-year inhalation study. The dose-response relationship for sedation was characterized using internal dose measures as predicted by a physiologically-based pharmacokinetic (PBPK) model for PGME and its acetate. PBPK modeling was used to account for changes in rodent physiology and metabolism due to aging and adaptation, based on data collected during Weeks 1, 2, 26, 52, and 78 of a chronic inhalation study. The peak concentration of PGME in richly perfused tissues (i.e., brain) was selected as the most appropriate internal dose measure based on a consideration of the mode of action for sedation and similarities in tissue partitioning between brain and other richly perfused tissues. Internal doses (peak tissue concentrations of PGME) were designated as either no-observed-adverse-effect levels (NOAELs) or lowest-observed-adverse-effect levels (LOAELs) based on the presence or the absence of sedation at each time point, species, and sex in the two-year study. Distributions of the NOAEL and LOAEL values expressed in terms of internal dose were characterized using an arithmetic mean and standard deviation, with the mean internal NOAEL serving as the basis for the reference values, which was then divided by appropriate uncertainty factors. Where data were permitting, chemical-specific adjustment factors were derived to replace default uncertainty factor values of 10. Nonlinear kinetics, which was predicted by the model in all species at PGME concentrations exceeding 100 ppm, complicate interspecies, and low-dose extrapolations. To address this complication, reference values were derived using two approaches that differ with respect to the order in which these extrapolations were performed: (1) default approach of interspecies extrapolation to determine the human equivalent concentration (PBPK modeling) followed by uncertainty factor application, and (2) uncertainty factor application followed by interspecies extrapolation (PBPK modeling). The resulting reference values for these two approaches are substantially different, with values from the latter approach being seven-fold higher than those from the former approach. Such a striking difference between the two approaches reveals an underlying issue that has received little attention in the literature regarding the application of uncertainty factors and interspecies extrapolations to compounds where saturable kinetics occur in the range of the NOAEL. Until such discussions have taken place, reference values based on the former approach are recommended for risk assessments involving human exposures to PGME and PGMEA.

Pitfalls and related improvements of in vivo gas uptake pharmacokinetic experimental systems.

Abstract: Gas uptake chamber studies have been widely used to study inhalation pharmacokinetics (PKs) in rodents, often for the ultimate purpose of developing physiologically-based pharmacokinetic (PBPK) models that can be used to describe human PKs and to support risk assessment for the chemical. In the course of our studies of gasoline PKs, we revisited several important issues heretofore not thoroughly addressed. Here, we report several refinements which will significantly improve future studies with this type of system, relating to the understanding of loss rates, the importance of carbon dioxide removal, and sampling of blood and chamber air at the same time. Losses of chemicals in gas uptake systems consist of leakage, adsorption to system components, and adsorption to the hair and skin (fur) of experimental animals. The loss rates were experimentally determined for a series of chemicals and mixtures including n-hexane, benzene, toluene, ethylbenzene, o-xylene, gasoline, and other gasoline components. The rat...

Physiologically Based Pharmacokinetic Modeling Using Microsoft Excel and Visual Basic for Applications

Abstract: Physiologically based pharmacokinetic (PBPK) models are mathematical descriptions depicting the relationship between external exposure and internal dose. These models have found great utility for interspecies extrapolation. However, specialized computer software packages, which are not widely distributed, have typically been used for model development and utilization. A few physiological models have been reported using more widely available software packages (e.g., Microsoft Excel), but these tend to include less complex processes and dose metrics. To ascertain the capability of Microsoft Excel and Visual Basis for Applications (VBA) for PBPK modeling, models for styrene, vinyl chloride, and methylene chloride were coded in Advanced Continuous Simulation Language (ACSL), Excel, and VBA, and simulation results were compared. For styrene, differences between ACSL and Excel or VBA compartment concentrations and rates of change were less than ±7.5E−10 using the same numerical integration technique and...

Physiologically Based Pharmacokinetic Modeling as a Tool to Interpret Human Biomonitoring Data

Abstract: Biomonitoring, the measurement of chemicals in human tissues and fluids, is becoming commonplace and reveals the presence of chemicals, both natural and synthetic, in many human populations. The amount detected in human samples is generally very low, typically in the parts-per-billion (ppb) or parts-per-trillion (ppt) range. To assist in the interpretation of biomonitoring results for various classes of chemicals, CIIT research efforts are currently exploring the use of physiologically based pharmacokinetic (PBPK) modeling approaches. Scientists are developing generic PBPK models for chemicals with differences in persistence and physico-chemical characteristics. They use these models to incorporate mechanistic information on chemical disposition and individual variability to conduct reverse dosimetry, which estimates the range of exposure levels in the environment that could give rise to specific biomarker concentrations in blood, breath, urine or other media. The degree of risk posed by these chemicals depends on whether the exposure level approaches those known to cause toxicity in test animals or people.

Integration of PBPK and Reaction Network Modelling: Predictive Xenobiotic Metabolomics

Abstract: Summary Our research group has aimed to integrate computational modelling with in vitro and in vivo experimentation towards the advancement of chemical mixture toxicology while minimising animal use. In the case of complex chemical mixtures and their interactions, the computer-assisted approach of Biochemical Reaction Network Modelling offers a ray of hope. The possible linkage between this novel computational methodology and physiologically-based pharmacokinetic (PBPK) modelling could result in a multi-scale computer simulation platform capable of predicting complex pathway interactions and metabolite concentrations at the molecular level up to tissue and organ concentrations at the whole organism level.

Kinetic and mechanistic data needs for a human phsiologically based pharmacokinetic (PBPK) model for acrylamide: pharmacokinetic model for acrylamide.

Abstract: A pharmacokinetic (PBPK) model has been developed for acrylamide (AMD) and its oxidative metabolite, glycidamide (GLY), in the rat based on available information. Despite gaps and limitations to the database, model parameters have been estimated to provide a relatively consistent description of the kinetics of acrylamide and glycidamide using a single set of values (with minor adjustments in some cases). Future kinetic and mechanistic studies will need to focus on the collection of key data for refining certain model parameters and for model validation, as well as for conducting studies that elucidate the mechanism of action. Development of a validated human AMD/GLY PBPK model capable of predicting target tissue doses at relevant dietary AMD exposures, in combination with expanding data on modes of action, should allow for a substantive improvement in the risk assessment of acrylamide in food.