Body Physiologically-based Pharmacokinetic Model Research Articles

Bridging the gap between in silico and in vivo by modeling opioid disposition in a kidney proximal tubule microphysiological system

Opioid overdose, dependence, and addiction are a major public health crisis. Patients with chronic kidney disease (CKD) are at high risk of opioid overdose, therefore novel methods that provide accurate prediction of renal clearance (CLr) and systemic disposition of opioids in CKD patients can facilitate the optimization of therapeutic regimens. The present study aimed to predict renal clearance and systemic disposition of morphine and its active metabolite morphine-6-glucuronide (M6G) in CKD patients using a vascularized human proximal tubule microphysiological system (VPT-MPS) coupled with a parent-metabolite full body physiologically-based pharmacokinetic (PBPK) model. The VPT-MPS, populated with a human umbilical vein endothelial cell (HUVEC) channel and an adjacent human primary proximal tubular epithelial cells (PTEC) channel, successfully demonstrated secretory transport of morphine and M6G from the HUVEC channel into the PTEC channel. The in vitro data generated by VPT-MPS were incorporated into a mechanistic kidney model and parent-metabolite full body PBPK model to predict CLr and systemic disposition of morphine and M6G, resulting in successful prediction of CLr and the plasma concentration–time profiles in both healthy subjects and CKD patients. A microphysiological system together with mathematical modeling successfully predicted renal clearance and systemic disposition of opioids in CKD patients and healthy subjects.

Optimizing the Clinical Use of Carvedilol in Liver Cirrhosis Using a Physiologically Based Pharmacokinetic Modeling Approach.

Liver cirrhosis is a complex pathophysiological condition that can affect the pharmacokinetics (PK) and hereby dosing of administered drugs. The physiologically based pharmacokinetic (PBPK) models are a valuable tool to explore PK of drugs in cirrhosis patients. The objective of this study was to develop and evaluate a PBPK-carvedilol-cirrhosis model with the available clinical data in liver cirrhosis patients and to recommend model-based drug dosing after exploring the underlying differences in unbound and total (bound and unbound) systemic carvedilol concentrations with the different disease stages. A whole body PBPK model was developed using the population-based PBPK simulator, Simcyp®. After model development and evaluation in healthy adults, system parameters were modified according to the pathophysiological changes that occur in liver cirrhosis, and predictions were compared to available experimental data from liver cirrhosis Child-Pugh [CP]-C patients. A two-fold error range for the observed/predicted ratios (ratioObs/Pred) of the pharmacokinetic parameters was used for model evaluation. Simulations were then extended to cirrhosis CP-A and CP-B populations were no experimental data that are available to explore changes in drug disposition in these patients. Finally, drug unbound and total (bound and unbound) exposure were predicted in cirrhotic patients of different disease severity, and the results were compared to those of healthy adults. The developed model has successfully described carvedilol PK in healthy and cirrhosis CP-C patients. The model predictions showed that, there was an ~13-fold increase in unbound and ~7-fold increase in total (bound and unbound) systemic exposure of carvedilol between healthy and CP-C populations. To have comparable predicted unbound drug exposure in cirrhosis CP-A, CP-B, and CP-C populations as in healthy subjects receiving a dose of 25mg, reductions of administered doses to 9.375mg in CP-A, 4.68mg in CP-B, and 2.34mg in CP-C population were recommended. The presented model-generated data can guide the optimization of carvedilol therapy on the basis of differences in unbound and total drug exposures with respect to disease severity and can help improve the design of some necessary clinical studies in the drug development process.

Abstract 4519: Development of a whole body physiologically-based pharmacokinetic (PBPK) model with individualized tumor compartment for topotecan (TPT) in mice bearing neuroblastoma (NB)

Abstract Intratumoral pharmacokinetic (PK) and pharmacodynamic (PD) heterogeneity contribute to variability in NB tumor response to chemotherapy and can be responsible for tumor relapse. Herein we propose to develop a whole body PBPK model with an individualized tumor compartment to derive individual tumor specific concentration-time profiles for the NB standard of care drug TPT. This model can then relate intratumoral heterogeneity in tumor blood flow to PD response and antitumor effects. PK studies of TPT (0.6, 1.25, 5, and 20 mg/kg, IV bolus) will be performed in CD1 nude mice (n = 3 mice/time point) bearing orthotopic NB (NB5) xenograft. Blood samples will be collected at predetermined time points using cardiac puncture, and plasma separated and stored until analysis. Animals will be perfused using saline solution to remove residual blood, and tissue samples including tumor, muscle, adipose, bone, liver, gallbladder, kidney, spleen, lungs, brain, heart, duodenum, and large intestine collected. TPT concentrations in plasma and tissue homogenate samples will be quantified using a validated HPLC fluorescence spectrophotometry method. Tumor samples will be divided into two sections each, one for TPT quantification and one for immunohistochemistry of PD markers for DNA damage (γ-H2AX) and apoptosis (CASP3). A cohort of mice will be used to quantify tumor blood flow using contrast-enhanced ultrasound (CEUS) using MicroMarker® microbubbles prior to dosing the mice for the PK study. TPT plasma and tissue concentration-time data will be used to develop the whole-body PBPK model with an individualized tumor compartment using NONMEM. Individual tumor perfusion data obtained using CEUS will be combined with the PBPK model to derive tumor specific concentration-time profiles. A preliminary study conducted in non-tumor bearing mice receiving TPT 5 mg/kg showed that TPT plasma and tissue concentration-time data were reasonably described by our PBPK model. As expected from our previous studies, the brain tissue was found to have the lowest exposure to TPT with a brain to plasma partition coefficient (Kp,brain ∼ 8%). We also observed high permeability of TPT (Kp > 1) into the gallbladder, duodenum, large intestine, spleen, liver and kidney. In future we will study the correlations between individual tumor concentrations based on our comprehensive PBPK model and γ-H2AX and CASP3 activity. Citation Format: Yogesh T. Patel, Megan O. Jacus, Abbas Shirinifard, Abigail D. Davis, Suresh Thiagarajan, Stacy L. Throm, Vinay M. Daryani, Andras Sablauer, Clinton F. Stewart. Development of a whole body physiologically-based pharmacokinetic (PBPK) model with individualized tumor compartment for topotecan (TPT) in mice bearing neuroblastoma (NB). [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4519. doi:10.1158/1538-7445.AM2015-4519

Use of in vitro data in developing a physiologically based pharmacokinetic model: Carbaryl as a case study

In vitro-derived information has been increasingly used to support and improve human health risk assessment for exposure to chemicals. Physiologically based pharmacokinetic (PBPK) modeling is a key component in the movement toward in vitro-based risk assessment, providing a tool to integrate diverse experimental data and mechanistic information to relate in vitro effective concentrations to equivalent human exposures. One of the challenges, however, in the use of PBPK models for this purpose has been the need for extensive chemical-specific parameters. With the remarkable advances in in vitro methodologies in recent years, in vitro-derived parameters can now be easily incorporated into PBPK models. In this study we demonstrate an in vitro data based parameterization approach to develop a physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) model, using carbaryl as a case study. In vitro experiments were performed to provide the chemical-specific pharmacokinetic (PK) and pharmacodynamic (PD) parameters for carbaryl in the PBPK model for this compound. Metabolic clearance and cholinesterase (ChE) interaction parameters for carbaryl were measured in rat and human tissues. These in vitro PK and PD data were extrapolated to parameters in the whole body PBPK model using biologically appropriate scaling. The PBPK model was then used to predict the kinetics and ChE inhibition dynamics of carbaryl in vivo. This case study with carbaryl provides a reasonably successful example of utilizing the in vitro to in vivo extrapolation (IVIVE) approach for PBPK model development. This approach can be applied to other carbamates with an anticholinesterase mode of action as well as to environmental chemicals in general with further refinement of the current shortcomings in the approach. It will contribute to minimizing the need for in vivo human data for PBPK model parameterization and evaluation in human risk assessments.

Kinetic modeling of β-chloroprene metabolism: Probabilistic in vitro–in vivo extrapolation of metabolism in the lung, liver and kidneys of mice, rats and humans

β-Chloroprene (chloroprene) is carcinogenic in inhalation bioassays with B6C3F1 mice and Fischer rats, but the potential effects in humans have not been adequately characterized. In order to provide a better basis for evaluating chloroprene exposures and potential effects in humans, we have explored species and tissue differences in chloroprene metabolism. This study implemented an in vitro–in vivo extrapolation (IVIVE) approach to parameterize a physiologically based pharmacokinetic (PBPK) model for chloroprene and evaluate the influence of species and gender differences in metabolism on target tissue dosimetry. Chloroprene metabolism was determined in vitro using liver, lung and kidney microsomes from male or female mice, rats, and humans. A two compartment PK model was used to estimate metabolism parameters for chloroprene in an in vitro closed vial system, which were then extrapolated to the whole body PBPK model. Two different strategies were used to estimate parameters for the oxidative metabolism of chloroprene: a deterministic point-estimation using the Nelder-Mead nonlinear optimization algorithm and probabilistic Bayesian analysis using the Markov Chain Monte Carlo technique. Target tissue dosimetry (average amount of chloroprene metabolized in lung per day) was simulated with the PBPK model using the in vitro-based metabolism parameters. The model-predicted target tissue dosimetry, as a surrogate for a risk estimate, was similar between the two approaches; however, the latter approach provided a measure of uncertainty in the metabolism parameters and the opportunity to evaluate the impact of that uncertainty on predicted risk estimates.

PBPK Modeling of Intestinal and Liver Enzymes and Transporters in Drug Absorption and Sequential Metabolism

Experimental strategies have long been applied for in vitro or in vivo evaluation of the effect of transporters and/or enzymes on the bioavailability. However, the lack of specific inhibitors or inducers of transporters and enzymes and the multiplicity of nuclear receptors in gene regulation and cross talk have led to compromised assessment of these effects in vivo. These and other causes have resulted in confusion and controversy in transporter-enzyme interplay. In this review, physiologically-based pharmacokinetic (PBPK) intestinal and liver models are utilized to predict the contributions of enzymes and transporters on intestinal availability (F(I)) and hepatic availability (F(H)), with the aim to fully understand the impact of these variables on bioavailability (F(sys)) in vivo. We emphasize the often overlooked impact of influx and efflux clearances, and apply the PBPK models and their solutions to examine individual organ clearances of the intestine and the liver. In order to accurately predict oral bioavailability, these organ models are incorporated into the whole body PBPK model, and additional complicated scenarios such as segmental differences and zonal heterogeneity of transporters and enzymes in the intestine and liver and segregated blood flow patterns of the intestine are further discussed. The sequential metabolism of a drug to form primary and secondary metabolites in the first-pass organs is considered in PBPK modeling, revealing that the segregated flow model (SFM) of the intestine is more appropriate than the traditional PBPK intestinal model (TM). Examples are included to highlight the potential application of these PBPK models on the quantitative prediction of bioavailability.

Utility of a single adjusting compartment: a novel methodology for whole body physiologically-based pharmacokinetic modelling

BackgroundThere are various methods for predicting human pharmacokinetics. Among these, a whole body physiologically-based pharmacokinetic (WBPBPK) model is useful because it gives a mechanistic description. However, WBPBPK models cannot predict human pharmacokinetics with enough precision. This study was conducted to elucidate the primary reason for poor predictions by WBPBPK models, and to enable better predictions to be made without reliance on complex concepts.MethodsThe primary reasons for poor predictions of human pharmacokinetics were investigated using a generic WBPBPK model that incorporated a single adjusting compartment (SAC), a virtual organ compartment with physiological parameters that can be adjusted arbitrarily. The blood flow rate, organ volume, and the steady state tissue-plasma partition coefficient of a SAC were calculated to fit simulated to observed pharmacokinetics in the rat. The adjusted SAC parameters were fixed and scaled up to the human using a newly developed equation. Using the scaled-up SAC parameters, human pharmacokinetics were simulated and each pharmacokinetic parameter was calculated. These simulated parameters were compared to the observed data. Simulations were performed to confirm the relationship between the precision of prediction and the number of tissue compartments, including a SAC.ResultsIncreasing the number of tissue compartments led to an improvement of the average-fold error (AFE) of total body clearances (CLtot) and half-lives (T1/2) calculated from the simulated human blood concentrations of 14 drugs. The presence of a SAC also improved the AFE values of a ten-organ model from 6.74 to 1.56 in CLtot, and from 4.74 to 1.48 in T1/2. Moreover, the within-2-fold errors were improved in all models; incorporating a SAC gave results from 0 to 79% in CLtot, and from 14 to 93% in T1/2 of the ten-organ model.ConclusionBy using a SAC in this study, we were able to show that poor prediction resulted mainly from such physiological factors as organ blood flow rate and organ volume, which were not satisfactorily accounted for in previous WBPBPK models. The SAC also improved precision in the prediction of human pharmacokinetics. This finding showed that the methodology of our study may be useful for functionally reinforcing a WBPBPK model.

Physiologically based pharmacokinetic modeling of a homologous series of barbiturates in the rat: a sensitivity analysis.

Sensitivity analysis studies the effects of the inherent variability and uncertainty in model parameters on the model outputs and may be a useful tool at all stages of the pharmacokinetic modeling process. The present study examined the sensitivity of a whole-body physiologically based pharmacokinetic (PBPK) model for the distribution kinetics of nine 5-n-alkyl-5-ethyl barbituric acids in arterial blood and 14 tissues (lung, liver, kidney, stomach, pancreas, spleen, gut, muscle, adipose, skin, bone, heart, brain, testes) after i.v. bolus administration to rats. The aims were to obtain new insights into the model used, to rank the model parameters involved according to their impact on the model outputs and to study the changes in the sensitivity induced by the increase in the lipophilicity of the homologues on ascending the series. Two approaches for sensitivity analysis have been implemented. The first, based on the Matrix Perturbation Theory, uses a sensitivity index defined as the normalized sensitivity of the 2-norm of the model compartmental matrix to perturbations in its entries. The second approach uses the traditional definition of the normalized sensitivity function as the relative change in a model state (a tissue concentration) corresponding to a relative change in a model parameter. Autosensitivity has been defined as sensitivity of a state to any of its parameters; cross-sensitivity as the sensitivity of a state to any other states' parameters. Using the two approaches, the sensitivity of representative tissue concentrations (lung, liver, kidney, stomach, gut, adipose, heart, and brain) to the following model parameters: tissue-to-unbound plasma partition coefficients, tissue blood flows, unbound renal and intrinsic hepatic clearance, permeability surface area product of the brain, have been analyzed. Both the tissues and the parameters were ranked according to their sensitivity and impact. The following general conclusions were drawn: (i) the overall sensitivity of the system to all parameters involved is small due to the weak connectivity of the system structure; (ii) the time course of both the auto- and cross-sensitivity functions for all tissues depends on the dynamics of the tissues themselves, e.g., the higher the perfusion of a tissue, the higher are both its cross-sensitivity to other tissues' parameters and the cross-sensitivities of other tissues to its parameters; and (iii) with a few exceptions, there is not a marked influence of the lipophilicity of the homologues on either the pattern or the values of the sensitivity functions. The estimates of the sensitivity and the subsequent tissue and parameter rankings may be extended to other drugs, sharing the same common structure of the whole body PBPK model, and having similar model parameters. Results show also that the computationally simple Matrix Perturbation Analysis should be used only when an initial idea about the sensitivity of a system is required. If comprehensive information regarding the sensitivity is needed, the numerically expensive Direct Sensitivity Analysis should be used.