Physiologically Based Pharmacokinetic Model Parameters Research Articles

Model-Informed Radiopharmaceutical Therapy Optimization: A Study on the Impact of PBPK Model Parameters on Physical, Biological, and Statistical Measures in 177Lu-PSMA Therapy.

Purpose: To investigate the impact of physiologically based pharmacokinetic (PBPK) parameters on physical, biological, and statistical measures in lutetium-177-labeled radiopharmaceutical therapies (RPTs) targeting the prostate-specific membrane antigen (PSMA). Methods: Using a clinically validated PBPK model, realistic time-activity curves (TACs) for tumors, salivary glands, and kidneys were generated based on various model parameters. These TACs were used to calculate the area-under-the-TAC (AUC), dose, biologically effective dose (BED), and figure-of-merit BED (fBED). The effects of these parameters on radiobiological, pharmacokinetic, time, and statistical features were assessed. Results: Manipulating PBPK parameters significantly influenced AUC, dose, BED, and fBED outcomes across four different BED models. Higher association rates increased AUC, dose, and BED values for tumors, with minimal impact on non-target organs. Increased internalization rates reduced AUC and dose for tumors and kidneys. Higher serum protein-binding rates decreased AUC and dose for all tissues. Elevated tumor receptor density and ligand amounts enhanced uptake and effectiveness in tumors. Larger tumor volumes required dosimetry adjustments to maintain efficacy. Setting the tumor release rate to zero intensified the impact of association and internalization rates, enhancing tumor targeting while minimizing the effects on salivary glands and kidneys. Conclusions: Optimizing PBPK parameters can enhance the efficacy of lutetium-177-labeled RPTs targeting PSMA, providing insights for personalized and effective treatment regimens to minimize toxicity and improve therapeutic outcomes.

Evaluating the impact of anatomical and physiological variability on human equivalent doses using PBPK models.

Addressing human anatomical and physiological variability is a crucial component of human health risk assessment of chemicals. Experts have recommended probabilistic chemical risk assessment paradigms in which distributional adjustment factors are used to account for various sources of uncertainty and variability, including variability in the pharmacokinetic behavior of a given substance in different humans. In practice, convenient assumptions about the distribution forms of adjustment factors and human equivalent doses (HEDs) are often used. Parameters such as tissue volumes and blood flows are likewise often assumed to be lognormally or normally distributed without evaluating empirical data for consistency with these forms. In this work, we performed dosimetric extrapolations using physiologically based pharmacokinetic (PBPK) models for dichloromethane (DCM) and chloroform that incorporate uncertainty and variability to determine if the HEDs associated with such extrapolations are approximately lognormal and how they depend on the underlying distribution shapes chosen to represent model parameters. We accounted for uncertainty and variability in PBPK model parameters by randomly drawing their values from a variety of distribution types. We then performed reverse dosimetry to calculate HEDs based on animal points of departure for each set of sampled parameters. Corresponding samples of HEDs were tested to determine the impact of input parameter distributions on their central tendencies, extreme percentiles, and degree of conformance to lognormality. This work demonstrates that the measurable attributes of human variability should be considered more carefully and that generalized assumptions about parameter distribution shapes may lead to inaccurate estimates of extreme percentiles of HEDs.

Predicting in vivo Target Occupancy (TO) Profiles via PBPK-TO Modeling of Warfarin Pharmacokinetics in Blood: Importance of Low Dose Data and Prediction of Stereoselective Target Interactions.

Warfarin is well-recognized for its high-affinity and capacity-limited binding to the pharmacological target and undergoes target-mediated drug disposition (TMDD). Here, we developed a physiologically-based pharmacokinetic (PBPK) model that incorporated saturable target binding and other reported hepatic disposition components of warfarin. The PBPK model parameters were optimized by fitting to the reported blood PK profiles of warfarin with no stereoisomeric separation following oral dosing of racemic warfarin (0.1, 2, 5, or 10 mg) using the Cluster Gauss-Newton Method (CGNM). The CGNM-based analysis yielded multiple "accepted" sets for six optimized parameters, which were then used to simulate the warfarin blood PK and in vivo target occupancy (TO) profiles. When further analyses examined the impact of dose selection on uncertainty in parameter estimation by the PBPK modeling, the PK data from 0.1 mg dose (well below target saturation) was important in practically identifying the target binding-related parameters in vivo When stereoselective differences were incorporated for both hepatic disposition and target interactions, our PBPK modeling predicted that R-warfarin (of slower clearance and lower target affinity than S-warfarin) contributes to TO prolongation following oral dosing of racemic warfarin. Our results extend the validity of the approach by which the PBPK-TO modeling of blood PK profiles can yield TO prediction in vivo (applicable to the drugs with targets of high affinity and abundance and limited distribution volume via non-target interactions). Our findings support that model-informed dose selection and PBPK-TO modeling may aid in TO and efficacy assessment in preclinical and clinical phase-1 studies. Significance Statement The current PBPK modeling incorporated the reported hepatic disposition components and target binding of warfarin and analyzed the blood PK profiles from varying warfarin doses, practically identifying target binding-related parameters in vivo By implementing the stereoselective differences between R- and S-warfarin, our analysis predicted the role of R-warfarin in prolonging overall target occupancy. Our results extend the validity of analyzing blood PK profiles to predict target occupancy in vivo, which may guide efficacy assessment in preclinical and clinical phase-1 studies.

Bayesian PBPK modeling using R/Stan/Torsten and Julia/SciML/Turing.Jl.

Physiologically-based pharmacokinetic (PBPK) models are mechanistic models that are built based on an investigator's prior knowledge of the in vivo system of interest. Bayesian inference incorporates an investigator's prior knowledge of parameters while using the data to update this knowledge. As such, Bayesian tools are well-suited to infer PBPK model parameters using the strong prior knowledge available while quantifying the uncertainty on these parameters. This tutorial demonstrates a full population Bayesian PBPK analysis framework using R/Stan/Torsten and Julia/SciML/Turing.jl.

Deconvolution of Systemic Pharmacokinetics Predicts Inhaled Aerosol Dosimetry of Nicotine

Absorption of inhaled compounds can occur from multiple sites based on upper and lower respiratory tract deposition, and clearance mechanisms leading to differential local and systemic pharmacokinetics. Deriving inhaled aerosol dosimetry and local tissue concentrations for nose-only exposure in rodents and inhaled products in humans is challenging. In this study we use inhaled nicotine as an example to identify regional respiratory tract deposition, absorption fractions, and their contribution toward systemic pharmacokinetics in rodents and humans. A physiologically based pharmacokinetic (PBPK) model was constructed to describe the disposition of nicotine and its major metabolite, cotinine. The model description for the lungs was simplified to include an upper respiratory tract region with active mucociliary clearance and a lower respiratory tract region. The PBPK model parameters such as rate of oral absorption, metabolism and clearance were fitted to the published nicotine and cotinine plasma concentrations post systemic administration and oral dosing. The fractional deposition of inhaled aerosol in the upper and lower respiratory tract regions was estimated by fitting the plasma concentrations. The model predicted upper respiratory tract deposition was 63.9% for nose-only exposure to nicotine containing nebulized aqueous aerosol in rats and 60.2% for orally inhaled electronic vapor product in humans. A marked absorption of nicotine from the upper respiratory tract and the gastrointestinal tract for inhaled aqueous aerosol contributed to the differential systemic pharmacokinetics in rats and humans. The PBPK model derived dosimetry shows that the current aerosol dosimetry models with their posteriori application using independent aerosol physicochemical characterization to predict aerosol deposition are insufficient and will need to consider complex interplay of inhaled aerosol evolutionary process. While the study highlights the needs for future research, it provides a preliminary framework for interpreting pharmacokinetics of inhaled aerosols to facilitate the analysis of in vivo exposure-responses for pharmacological and toxicological assessments.

Cluster Gauss-Newton method analyses of PBPK model parameter combinations of coproporphyrin-I based on OATP1B-mediated rifampicin interaction studies.

Coproporphyrin I (CP‐I) is an endogenous biomarker supporting the prediction of drug–drug interactions (DDIs) involving hepatic organic anion transporting polypeptide 1B (OATP1B). We previously constructed a physiologically‐based pharmacokinetic (PBPK) model for CP‐I using clinical DDI data with an OATP1B inhibitor, rifampicin (RIF). In this study, PBPK model parameters for CP‐I were estimated using the cluster Gauss–Newton method (CGNM), an algorithm used to find multiple approximate solutions for nonlinear least‐squares problems. Eight unknown parameters including the hepatic overall intrinsic clearance (CLint,all), the rate of biosynthesis (v syn), and the OATP1B inhibition constant of RIF(K i,u,OATP) were estimated by fitting to the observed CP‐I blood concentrations in two different clinical studies involving changing the RIF dose. Multiple parameter combinations were obtained by CGNM that could well capture the clinical data. Among those, CLint,all, K i,u,OATP, and v syn were sensitive parameters. The obtained K i,u,OATP for CP‐I was 5.0‐ and 2.8‐fold lower than that obtained for statins, confirming our previous findings describing substrate‐dependent K i,u,OATP values. In conclusion, CGNM analyses of PBPK model parameter combinations enables estimation of the three essential parameters for CP‐I to capture the DDI profiles, even if the other parameters remain unidentified. The CGNM also clarified the importance of appropriate combinations of other unidentified parameters to enable capture of the CP‐I concentration time course under the influence of RIF. The described CGNM approach may also support the construction of robust PBPK models for additional transporter biomarkers beyond CP‐I.

Physiocognitive Modeling: Explaining the Effects of Caffeine on Fatigue.

Most computational theories of cognition lack a representation of physiology. Understanding the cognitive effects of compounds present in the environment is important for explaining and predicting changes in cognition and behavior given exposure to toxins, pharmaceuticals, or the deprivation of critical compounds like oxygen. This research integrates physiologically based pharmacokinetic (PBPK) model predictions of caffeine concentrations in blood and tissues with ACT-R's fatigue module to predict the effects of caffeine on fatigue. Mapping between the PBPK model parameters and ACT-R model parameters is informed by the neurophysiological literature and established associations between ACT-R modules and brain regions. The results from three such parameter mappings are explored to explain observed data from sleep-deprived participants performing the psychomotor vigilance test with and without caffeine. Predicted caffeine concentrations in the brain are used to modulate procedural parameters in the fatigue module to explain caffeine's effects on multiple performance metrics.

Estimation of The Main Effect and Total Effect of a PBPK Model Based on The Uncertainty of Individual Parameter for Treatment Planning in PSMA Therapy

The purpose of this study was to identify the most important physiologically-based pharmacokinetic (PBPK) model parameters determining the absorbed dose (AD) in prostate-specific membrane antigen (PSMA) therapy. The extended-Sobol’ global sensitivity analysis method was used to analyze the sensitivity of the PBPK model parameters obtained from 3 patients. The investigated PBPK model parameters were the blood flow to the organs, PSMA binding rate, biological release rates, and density of organs receptor. The outputs of extended Sobol method were the main effect Si and the total effect STi of the parameter of interests for each ADs. The sampling strategy of extended Sobol has been implemented based on the mean and covariance matrix of the parameters. From the simulations, the most important parameters which determine the ADs to the kidney was the kidney receptor density (Si=0,4, STi=0,8). For tumors, it was shown that tumor receptor density was the most essential parameter (Si=0,7, STi=0,8). In conclusion, measurement of the blood flow and organ receptor densities might be of interest to improve individualized treatment of PSMA therapy.

Treatment planning in PRRT based on simulated PET data and a PBPK model. Determination of accuracy using a PET noise model.

To investigate the accuracy of treatment planning in peptide-receptor radionuclide therapy (PRRT) based on simulated PET data (using a PET noise model) and a physiologicallybasedpharmacokinetic (PBPK) model. The parameters of a PBPK model were fitted to the biokinetic data of 15 patients. True mathematical phantoms of patients (MPPs) were the PBPK model with the fitted parameters. PET measurements after bolus injection of 150 MBq 68Ga-DOTATATE were simulated for the true MPPs. PET noise with typical noise levels was added to the data (i.e. c = 0.3 [low], 3, 30 and 300 [high]). Organ activity data in the kidneys, tumour, liver and spleen were simulated at 0.5, 1 and 4 h p.i. PBPK model parameters were fitted to the simulated noisy PET data to derive the PET-predicted MPPs. Therapy was simulated assuming an infusion of 3.3 GBq of 90Y-DOTATATE over 30 min. Time-integrated activity coefficients (TIACs) of simulated therapy in tumour, kidneys, liver, spleen and remainder were calculated from both, true MPPs (true TIACs) and predicted MPPs (predicted TIACs). Variability v between true TIACs and predicted TIACs were calculated and analysed. Variability ≤ 10 % was considered to be an accurate prediction. For all noise level, variabilities for thekidneys, liver, and spleen showed an accurateprediction for TIACs, e.g.c = 300: vkidney = (5 ± 2)%, vliver = (5 ± 2)%, vspleen = (4 ± 2)%. However, tumour TIAC predictions were not accurate for all noise levels, e.g.c = 0.3: vtumour = (8 ± 5)%. PET-based treatment planning with kidneys as the dose limiting organ seems possible for all reported noise levels using an adequate PBPK model and previous knowledge about the individual patient.

Time-integrated activity coefficient estimation for radionuclide therapy using PET and a pharmacokinetic model: A simulation study on the effect of sampling schedule and noise.

The aim of this study was to investigate the accuracy of PET-based treatment planning for predicting the time-integrated activity coefficients (TIACs). The parameters of a physiologically based pharmacokinetic (PBPK) model were fitted to the biokinetic data of 15 patients to derive assumed true parameters and were used to construct true mathematical patient phantoms (MPPs). Biokinetics of 150 MBq (68)Ga-DOTATATE-PET was simulated with different noise levels [fractional standard deviation (FSD) 10%, 1%, 0.1%, and 0.01%], and seven combinations of measurements at 30 min, 1 h, and 4 h p.i. PBPK model parameters were fitted to the simulated noisy PET data using population-based Bayesian parameters to construct predicted MPPs. Therapy simulations were performed as 30 min infusion of (90)Y-DOTATATE of 3.3 GBq in both true and predicted MPPs. Prediction accuracy was then calculated as relative variability vorgan between TIACs from both MPPs. Large variability values of one time-point protocols [e.g., FSD = 1%, 240 min p.i., vkidneys = (9 ± 6)%, and vtumor = (27 ± 26)%] show inaccurate prediction. Accurate TIAC prediction of the kidneys was obtained for the case of two measurements (1 and 4 h p.i.), e.g., FSD = 1%, vkidneys = (7 ± 3)%, and vtumor = (22 ± 10)%, or three measurements, e.g., FSD = 1%, vkidneys = (7 ± 3)%, and vtumor = (22 ± 9)%. (68)Ga-DOTATATE-PET measurements could possibly be used to predict the TIACs of (90)Y-DOTATATE when using a PBPK model and population-based Bayesian parameters. The two time-point measurement at 1 and 4 h p.i. with a noise up to FSD = 1% allows an accurate prediction of the TIACs in kidneys.

Development of a decision tree to classify the most accurate tissue-specific tissue to plasma partition coefficient algorithm for a given compound

Physiologically based pharmacokinetic (PBPK) modeling is a tool used in drug discovery and human health risk assessment. PBPK models are mathematical representations of the anatomy, physiology and biochemistry of an organism and are used to predict a drug's pharmacokinetics in various situations. Tissue to plasma partition coefficients (Kp), key PBPK model parameters, define the steady-state concentration differential between tissue and plasma and are used to predict the volume of distribution. The experimental determination of these parameters once limited the development of PBPK models; however, in silico prediction methods were introduced to overcome this issue. The developed algorithms vary in input parameters and prediction accuracy, and none are considered standard, warranting further research. In this study, a novel decision-tree-based Kp prediction method was developed using six previously published algorithms. The aim of the developed classifier was to identify the most accurate tissue-specific Kp prediction algorithm for a new drug. A dataset consisting of 122 drugs was used to train the classifier and identify the most accurate Kp prediction algorithm for a certain physicochemical space. Three versions of tissue-specific classifiers were developed and were dependent on the necessary inputs. The use of the classifier resulted in a better prediction accuracy than that of any single Kp prediction algorithm for all tissues, the current mode of use in PBPK model building. Because built-in estimation equations for those input parameters are not necessarily available, this Kp prediction tool will provide Kp prediction when only limited input parameters are available. The presented innovative method will improve tissue distribution prediction accuracy, thus enhancing the confidence in PBPK modeling outputs.

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 (CH3, CH2, 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.

A Bayesian population PBPK model for multiroute chloroform exposure

A Bayesian hierarchical model was developed to estimate the parameters in a physiologically based pharmacokinetic (PBPK) model for chloroform using prior information and biomarker data from different exposure pathways. In particular, the model provides a quantitative description of the changes in physiological parameters associated with hot-water bath and showering scenarios. Through Bayesian inference, uncertainties in the PBPK parameters were reduced from the prior distributions. Prediction of biomarker data with the calibrated PBPK model was improved by the calibration. The posterior results indicate that blood flow rates varied under two different exposure scenarios, with a two-fold increase of the skin's blood flow rate predicted in the hot-bath scenario. This result highlights the importance of considering scenario-specific parameters in PBPK modeling. To demonstrate the application of a probability approach in toxicological assessment, results from the posterior distributions from this calibrated model were used to predict target tissue dose based on the rate of chloroform metabolized in liver. This study demonstrates the use of the Bayesian approach to optimize PBPK model parameters for typical household exposure scenarios.

Quantifying heterogeneity in exposure–risk relationships using exhaled breath biomarkers for 1,3-butadiene exposures

The health effects of human exposure to 1,3-butadiene (BD) have been extensively studied using both epidemiological and animal toxicology approaches. However, various data and knowledge gaps remain, one of which is an understanding of the human heterogeneity in BD dosimetry. The objective of our study was to better understand the role of individual variability in delivered tissue dose. We designed a study of laboratory exposures of a relatively large group of healthy human subjects. Subjects were then exposed to 2.0 ppm BD through a face mask for 20 min, followed by 40 min of breathing clean air. Exhaled breath concentrations of BD were measured at ten time points during and after exposure, and a three-compartment physiologically based pharmacokinetic (PBPK) model was used to quantify the kinetic behavior of BD. We implemented a Markov chain Monte Carlo procedure to fit the model to the experimental data, and used global sensitivity analysis techniques to examine the sensitivity of exhaled breath concentrations to PBPK model parameters. Uptake during exposure was strongly influenced by rebreathing of exhaled BD during exposure; inclusion of rebreathing in the model simulations resulted in a 21% increase in the amount of BD retained in the body. We found that uptake ranged from 38% to 77% across individuals. We measured considerable intra-individual variability from 11 subjects who underwent the testing twice. Most of this variation stemmed from phase I metabolism of BD, which varied by as much as a factor of 2.6 within individuals. Overall, we have sought to quantify the sources of inter- and intra-individual variabilities in the pharmacokinetic behavior of BD. The results of our research may impact the current framework for biomarker and pharmacokinetic studies by improving our understanding of the sources of heterogeneity in response to chemical exposures.

Use of Markov Chain Monte Carlo Analysis with a Physiologically‐Based Pharmacokinetic Model of Methylmercury to Estimate Exposures in U.S. Women of Childbearing Age

A Bayesian approach, implemented using Markov Chain Monte Carlo (MCMC) analysis, was applied with a physiologically-based pharmacokinetic (PBPK) model of methylmercury (MeHg) to evaluate the variability of MeHg exposure in women of childbearing age in the U.S. population. The analysis made use of the newly available National Health and Nutrition Survey (NHANES) blood and hair mercury concentration data for women of age 16-49 years (sample size, 1,582). Bayesian analysis was performed to estimate the population variability in MeHg exposure (daily ingestion rate) implied by the variation in blood and hair concentrations of mercury in the NHANES database. The measured variability in the NHANES blood and hair data represents the result of a process that includes interindividual variation in exposure to MeHg and interindividual variation in the pharmacokinetics (distribution, clearance) of MeHg. The PBPK model includes a number of pharmacokinetic parameters (e.g., tissue volumes, partition coefficients, rate constants for metabolism and elimination) that can vary from individual to individual within the subpopulation of interest. Using MCMC analysis, it was possible to combine prior distributions of the PBPK model parameters with the NHANES blood and hair data, as well as with kinetic data from controlled human exposures to MeHg, to derive posterior distributions that refine the estimates of both the population exposure distribution and the pharmacokinetic parameters. In general, based on the populations surveyed by NHANES, the results of the MCMC analysis indicate that a small fraction, less than 1%, of the U.S. population of women of childbearing age may have mercury exposures greater than the EPA RfD for MeHg of 0.1 microg/kg/day, and that there are few, if any, exposures greater than the ATSDR MRL of 0.3 microg/kg/day. The analysis also indicates that typical exposures may be greater than previously estimated from food consumption surveys, but that the variability in exposure within the population of U.S. women of childbearing age may be less than previously assumed.

Bayesian population analysis of a harmonized physiologically based pharmacokinetic model of trichloroethylene and its metabolites

Bayesian population analysis of a harmonized physiologically based pharmacokinetic (PBPK) model for trichloroethylene (TCE) and its metabolites was performed. In the Bayesian framework, prior information about the PBPK model parameters is updated using experimental kinetic data to obtain posterior parameter estimates. Experimental kinetic data measured in mice, rats, and humans were available for this analysis, and the resulting posterior model predictions were in better agreement with the kinetic data than prior model predictions. Uncertainty in the prediction of the kinetics of TCE, trichloroacetic acid (TCA), and trichloroethanol (TCOH) was reduced, while the kinetics of other key metabolites dichloroacetic acid (DCA), chloral hydrate (CHL), and dichlorovinyl mercaptan (DCVSH) remain relatively uncertain due to sparse kinetic data for use in this analysis. To help focus future research to further reduce uncertainty in model predictions, a sensitivity analysis was conducted to help identify the parameters that have the greatest impact on various internal dose metric predictions. For application to a risk assessment for TCE, the model provides accurate estimates of TCE, TCA, and TCOH kinetics. This analysis provides an important step toward estimating uncertainty of dose–response relationships in noncancer and cancer risk assessment, improving the extrapolation of toxic TCE doses from experimental animals to humans.

Physiologically‐Based Pharmacokinetic Modeling of Benzene in Humans: A Bayesian Approach

Benzene is myelotoxic and leukemogenic in humans exposed at high doses (>1 ppm, more definitely above 10 ppm) for extended periods. However, leukemia risks at lower exposures are uncertain. Benzene occurs widely in the work environment and also indoor air, but mostly below 1 ppm, so assessing the leukemia risks at these low concentrations is important. Here, we describe a human physiologically-based pharmacokinetic (PBPK) model that quantifies tissue doses of benzene and its key metabolites, benzene oxide, phenol, and hydroquinone after inhalation and oral exposures. The model was integrated into a statistical framework that acknowledges sources of variation due to inherent intra- and interindividual variation, measurement error, and other data collection issues. A primary contribution of this work is the estimation of population distributions of key PBPK model parameters. We hypothesized that observed interindividual variability in the dosimetry of benzene and its metabolites resulted primarily from known or estimated variability in key metabolic parameters and that a statistical PBPK model that explicitly included variability in only those metabolic parameters would sufficiently describe the observed variability. We then identified parameter distributions for the PBPK model to characterize observed variability through the use of Markov chain Monte Carlo analysis applied to two data sets. The identified parameter distributions described most of the observed variability, but variability in physiological parameters such as organ weights may also be helpful to faithfully predict the observed human-population variability in benzene dosimetry.

Sensitivity analysis of a physiological model for 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD): assessing the impact of specific model parameters on sequestration in liver and fat in the rat.

2,3,7,8-Tetrachlorodibenzo-p-dioxin (TCDD) has dose-dependent tissue distribution because of induction of CYP1A2, a TCDD-binding protein, in the liver. Induction requires transcriptional activation of the CYP1A2 gene product by TCDD and the Ah receptor. An empirical model for dose-dependent distribution (Carrier et al., 1995, Toxicol. Appl. Pharmacol. 131, 253-266) included two simple descriptors: one for the maximum liver sequestration (Fmax) and the other for body burden leading to half maximum sequestration (Kd). Physiologically based pharmacokinetic (PBPK) models include specific parameters for protein receptors, protein binding, tissue solubility, and protein induction. We have applied a PBPK model to define two macroscopic constants related to these dose-response curves, i.e., the inflection point, and the maximum values of these curves. The dose-response curves generated from the PBPK model were for the proportion sequestered in liver and the liver to fat concentration ratio. Our analysis assessed the specific biological factors in the PBPK model that governed the values of these two macroscopic constants. For the fraction in liver, the Hill coefficient (a shape exponent describing the relationship between the Ah receptor-TCDD complex with the DNA receptor) resulted in the largest shift in inflection when using PBPK model parameters specific for TCDD. For the liver to fat ratio, the inflection point was most affected by the number of available Ah receptors. Conventional normalized sensitivity coefficients for the liver-to-fat ratio at the maximum were highest for the fat-to-blood partition coefficient, CYP1A2 binding affinity, and maximum extent of induction of CYP1A2. A similar pattern was observed for the liver fraction, except that the sensitivity coefficients were much smaller. The behavior of different TCDD congeners was evaluated by altering the value of key parameters. Our results demonstrate that the inflection point is more related to characteristics of DNA binding/induction steps of the Ah receptor-DNA complex than by the CYP1A2 affinity of TCDD or concentrations of CYP1A2. Surprisingly, the maximum is more sensitive to changes in CYP1A2 concentrations and affinity for TCDD. In addition, the analysis showed that the liver-to-fat ratio is a more useful experimental measure than is proportion in liver because the ratio responds with similar sensitivity over a much wider range of input parameters.

In Vitro-based and In Vivo-hased Simulations of Benzene Uptake and Metabolism in Rats

The metabolism of benzene was modelled in the rat by application of a physiologically based pharmacokinetic (PBPK) model. The model parameters were set by using reference physiological parameter values and reported partition coefficients from in vitro studies. Three sets of Vmax and Km values for benzene, derived from published in vitro studies, were substituted into the model while keeping all other model parameters constant. These model simulations were compared with two sets of empirical data on the metabolism or uptake of benzene after inhalation exposure. It was observed that the biotransformation parameter sets derived in vitro predicted all empirical data within a factor of two. In addition, it was observed that simulations across the two sets of empirical data which used biotransformation parameters obtained by fitting to one set of data to simulate the other set, led to results comparable to those in the in vitro-based simulations. It is concluded that the results of in vitro studies can be directly applied in a PBPK model in order to estimate the in vivo uptake and metabolism of benzene on the basis of previously determined model parameter assumptions. These results support earlier studies on the application of in vitro techniques for deriving PBPK model parameters. On the basis of other studies on the simulation of benzene kinetics, it is also concluded that additional studies are required to extend the validity of this approach for other compounds.