Development of a Korean‐specific virtual population for physiologically based pharmacokinetic modelling and simulation

Physiologically based pharmacokinetic (PBPK) modelling and simulation is a useful tool in predicting the PK profiles of a drug, assessing the effects of covariates such as demographics, ethnicity, genetic polymorphisms and disease status on the PK, and evaluating the potential of drug–drug interactions. We developed a Korean‐specific virtual population for the SimCYP® Simulator (version 15 used) and evaluated the population's predictive performance using six substrate drugs (midazolam, S‐warfarin, metoprolol, omeprazole, lorazepam and rosuvastatin) of five major drug metabolizing enzymes (DMEs) and two transporters. Forty‐three parameters including the proportion of phenotypes in DMEs and transporters were incorporated into the Korean‐specific virtual population. The simulated concentration–time profiles in Koreans were overlapped with most of the observed concentrations for the selected substrate drugs with a < 2‐fold difference in clearance. Furthermore, we found some drug models within the SimCYP® library can be improved, e.g., the minor allele frequency of ABCG2 and the fraction metabolized by UGT2B15 should be incorporated for rosuvastatin and lorazepam, respectively. The Korean‐specific population can be used to evaluate the impact of ethnicity on the PKs of a drug, particularly in various stages of drug development.     

Predicting the oral absorption of a poorly soluble,poorly permeable weak base using biorelevant dissolution and transfer model tests coupled with a physiologically based pharmacokinetic model

通过构建瑞舒伐他汀空腹状态下的生理药动学(physiologically based pharmacokinetic model,PBPK)模型,预测其餐后状态下的吸收,并探究其可能的食物效应机制,为服用他汀类药物的高脂血症患者提出合理的饮食建议,提高BCSⅢ类他汀类药物的药物吸收。

根据文献和已有研究获得瑞舒伐他汀建模的理化参数、生物药剂学参数以及药动学参数,利用GastroPlus^TM软件建立瑞舒伐他汀餐后给药的PBPK预测模型,并结合实测的血药浓度数据验证模型,判断是否可以准确预测出瑞舒伐他汀餐后的药物吸收结果,并进行参数敏感性分析。

通过构建瑞舒伐他汀PBPK模型预测其餐后吸收,计算得到模型预测数据与实测数据的平均折叠误差和绝对平均折叠误差<2,结合模型验证的拟合相关系数表明拟合效果较好,同时参数敏感性分析提示高热量饮食、药物的油水分配系数(LogD)和渗透性对瑞舒伐他汀的吸收影响较大。

所建立的模型能够较好地预测瑞舒伐他汀餐后状态下的吸收,基于参数敏感性分析结果,为服用BCSⅢ类他汀类药物的高脂血症患者提出合理的饮食建议,包括适当增加饮食中蛋白质的比重、减少脂肪和水溶性膳食纤维的占比等,可提高BCSⅢ类他汀类药物的肠道吸收。

     

A physiologically based pharmacokinetic model for methyl tert-butyl ether in humans: implementing sensitivity and variability analyses.

Methyl tert-butyl ether (MTBE) is added to gasoline to reduce carbon monoxide and ozone precursors from automobile emissions. The objectives of this study were to verify the ability of a physiologically based pharmacokinetic (PBPK) model to predict MTBE blood levels in humans and to investigate the effect of variability in the metabolism of MTBE and its influence on the predicted MTBE blood levels. The model structure for MTBE was flow-limited and had six essential compartments: lung, liver, rapidly perfused tissues, slowly perfused tissues, fat, and kidney. In this model, two pathways of metabolism are described to occur in the liver by Michaelis-Menten kinetics. Metabolic rate constants were measured in vitro using human liver microsomes and extrapolated to in vivo whole-body metabolism. Model predictions were compared with data on blood levels of MTBE taken from humans during and after a 1-h inhalation exposure to 1.7 ppm MTBE and after 4-h inhalation exposures to 4 or 40 ppm MTBE. The PBPK model accurately predicted MTBE pharmacokinetics at the high and low MTBE exposure concentrations for all time points. At the intermediate MTBE exposure concentration, however, the model underpredicted early time points while adequately predicting later time points. Results of the sensitivity analysis indicated that the influence of metabolic parameters on model output was dependent on MTBE exposure concentration. Subsequent variability analysis indicated that there was more variability in the actual measured MTBE blood levels than in the blood levels predicted by the PBPK model when using the range of metabolic parameters measured in vitro in human liver samples. By incorporating an understanding of the metabolic processes, this PBPK model can be used to predict blood levels of MTBE, which is important in determining target tissue dose estimates for risk assessment.     

Characterizing uncertainty and variability in physiologically based pharmacokinetic models: state of the science and needs for research and implementation.

Physiologically based pharmacokinetic (PBPK) models are used in mode-of-action based risk and safety assessments to estimate internal dosimetry in animals and humans. When used in risk assessment, these models can provide a basis for extrapolating between species, doses, and exposure routes or for justifying nondefault values for uncertainty factors. Characterization of uncertainty and variability is increasingly recognized as important for risk assessment; this represents a continuing challenge for both PBPK modelers and users. Current practices show significant progress in specifying deterministic biological models and nondeterministic (often statistical) models, estimating parameters using diverse data sets from multiple sources, using them to make predictions, and characterizing uncertainty and variability of model parameters and predictions. The International Workshop on Uncertainty and Variability in PBPK Models, held 31 Oct-2 Nov 2006, identified the state-of-the-science, needed changes in practice and implementation, and research priorities. For the short term, these include (1) multidisciplinary teams to integrate deterministic and nondeterministic/statistical models; (2) broader use of sensitivity analyses, including for structural and global (rather than local) parameter changes; and (3) enhanced transparency and reproducibility through improved documentation of model structure(s), parameter values, sensitivity and other analyses, and supporting, discrepant, or excluded data. Longer-term needs include (1) theoretical and practical methodological improvements for nondeterministic/statistical modeling; (2) better methods for evaluating alternative model structures; (3) peer-reviewed databases of parameters and covariates, and their distributions; (4) expanded coverage of PBPK models across chemicals with different properties; and (5) training and reference materials, such as cases studies, bibliographies/glossaries, model repositories, and enhanced software. The multidisciplinary dialogue initiated by this Workshop will foster the collaboration, research, data collection, and training necessary to make characterizing uncertainty and variability a standard practice in PBPK modeling and risk assessment.     

Utility of real time breath analysis and physiologically based pharmacokinetic modeling to determine the percutaneous absorption of methyl chloroform in rats and humans.

Due to the large surface area of the skin, percutaneous absorption has the potential to contribute significantly to the total bioavailability of some compounds. Breath elimination data, acquired in real-time using a novel MS/MS system, was assessed using a PBPK model with a dermal compartment to determine the percutaneous absorption of methyl chloroform (MC) in rats and humans from exposures to MC in non-occluded soil or occluded water matrices. Rats were exposed to MC using a dermal exposure cell attached to a clipper-shaved area on their back. The soil exposure cell was covered with a charcoal patch to capture volatilized MC and prevent contamination of exhaled breath. This technique allowed the determination of MC dermal absorption kinetics under realistic, non-occluded conditions. Human exposures were conducted by immersing one hand in 0.1% MC in water, or 0.75% MC in soil. The dermal PBPK model was used to estimate skin permeability (Kp) based on the fit of the exhaled breath data. Rat skin K(p)s were estimated to be 0.25 and 0.15 cm/h for MC in water and soil matrices, respectively. In comparison, human permeability coefficients for water matrix exposures were 40-fold lower at 0.006 cm/h. Due to evaporation and differences in apparent Kp, nearly twice as much MC was absorbed from the occluded water (61.3%) compared to the non-occluded soil (32.5%) system in the rat. The PBPK model was used to simulate dermal exposures to MC-contaminated water and soil in children and adults using worst-case EPA default assumptions. The simulations indicate that neither children nor adults will absorb significant amounts of MC from non-occluded exposures, independent of the length of exposure. The results from these simulations reiterate the importance of conducting dermal exposures under realistic conditions.     

Prediction of drug‐drug interactions using physiologically‐based pharmacokinetic models of CYP450 modulators included in Simcyp software

In recent years, physiologically based PharmacoKinetic (PBPK) modeling has received growing interest as a useful tool for the assessment of drug pharmacokinetics. It has been demonstrated to be informative and helpful to quantify the modification in drug exposure due to specific physio‐pathological conditions, age, genetic polymorphisms, ethnicity and particularly drug–drug interactions (DDIs). In this paper, the prediction success of DDIs involving various cytochrome P450 isoenzyme (CYP) modulators namely ketoconazole (a competitive inhibitor of CYP3A), itraconazole (a competitive inhibitor of CYP3A), clarithromycin (a mechanism‐based inhibitor of CYP3A), quinidine (a competitive inhibitor of CYP2D6), paroxetine (a mechanism‐based inhibitor of CYP2D6), ciprofloxacin (a competitive inhibitor of CYP1A2), fluconazole (a competitive inhibitor of CYP2C9/2C19) and rifampicin (an inducer of CYP3A) were assessed using Simcyp^® software. The aim of this report was to establish confidence in each CYP‐specific modulator file so they can be used in the future for the prediction of DDIs involving new victim compounds. Our evaluation of these PBPK models suggested that they can be successfully used to evaluate DDIs in untested scenarios. The only noticeable exception concerned a quinidine inhibitor model that requires further improvement. Additionally, other important aspects such as model validation criteria were discussed.     

Application of a physiologically‐based pharmacokinetic model for the prediction of bumetanide plasma and brain concentrations in the neonate

Bumetanide is a loop diuretic that is proposed to possess a beneficial effect on disorders of the central nervous system, including neonatal seizures. Therefore, prediction of unbound bumetanide concentrations in the brain is relevant from a pharmacological prospective. A physiologically‐based pharmacokinetic (PBPK) model was developed for the prediction of bumetanide disposition in plasma and brain in adult and paediatric populations. A compound file was built for bumetanide integrating physicochemical data and in vitro data. Bumetanide concentration profiles were simulated in both plasma and brain using the Simcyp PBPK model. Simulations of plasma bumetanide concentrations were compared against plasma levels published in the literature. The model performance was verified with data from adult studies before predictions in the paediatric population were undertaken. The adult and paediatric intravenous models predicted pharmacokinetic factors, namely area under the concentration–time curve, maximum concentration in plasma and time to maximum plasma concentration, within two‐fold of observed values. However, predictions of plasma concentrations within the neonatal intravenous model did not produce a good fit with the observed values. The PBPK approach used in this study produced reasonable predictions of plasma concentrations of bumetanide, except in the critically ill neonatal population. This PBPK model requires more information regarding metabolic intrinsic clearance and transport parameters prior to further validation of drug disposition predictions in the neonatal population. Given the lack of information surrounding certain parameters in this special population, the model is not appropriately robust to support the recommendation of a suitable dose of bumetanide for use as an adjunct antiepileptic in neonates.     

The application of physiologically based pharmacokinetic modelling to assess the impact of antiretroviral‐mediated drug–drug interactions on piperaquine antimalarial therapy during pregnancy

Antimalarial therapy during pregnancy poses important safety concerns due to potential teratogenicity and maternal physiological and biochemical changes during gestation. Piperaquine (PQ) has gained interest for use in pregnancy in response to increasing resistance towards sulfadoxine–pyrimethamine in sub‐Saharan Africa. Coinfection with HIV is common in many developing countries, however, little is known about the impact of antiretroviral (ARV) mediated drug–drug interaction (DDI) on piperaquine pharmacokinetics during pregnancy. This study applied mechanistic pharmacokinetic modelling to predict pharmacokinetics in non‐pregnant and pregnant patients, which was validated in distinct customised population groups from Thailand, Sudan and Papua New Guinea. In each population group, no significant differences in day 7 concentrations were observed during different gestational weeks (GW) (weeks 10–40), supporting the notion that piperaquine is safe throughout pregnancy with consistent pharmacokinetics, although possible teratogenicity may limit this. Antiretroviral‐mediated DDIs (efavirenz and ritonavir) had moderate effects on piperaquine during different gestational weeks with a predicted AUC_ratio in the range 0.56–0.8 and 1.64–1.79 for efavirenz and ritonavir, respectively, over GW 10–40, with a reduction in circulating human serum albumin significantly reducing the number of subjects attaining the day 7 (post‐dose) therapeutic efficacy concentrations under both efavirenz and ritonavir DDIs. This present model successfully mechanistically predicted the pharmacokinetics of piperaquine in pregnancy to be unchanged with respect to non‐pregnant women, in the light of factors such as malaria/HIV co‐infection. However, antiretroviral‐mediated DDIs could significantly alter piperaquine pharmacokinetics. Further model refinement will include collation of relevant physiological and biochemical alterations common to HIV/malaria patients.     

Probabilistic risk assessment of gold nanoparticles after intravenous administration by integrating in vitro and in vivo toxicity with physiologically based pharmacokinetic modeling

This study aimed to conduct an integrated and probabilistic risk assessment of gold nanoparticles (AuNPs) based on recently published in vitro and in vivo toxicity studies coupled to a physiologically based pharmacokinetic (PBPK) model. Dose–response relationships were characterized based on cell viability assays in various human cell types. A previously well-validated human PBPK model for AuNPs was applied to quantify internal concentrations in liver, kidney, skin, and venous plasma. By applying a Bayesian-based probabilistic risk assessment approach incorporating Monte Carlo simulation, probable human cell death fractions were characterized. Additionally, we implemented in vitro to in vivo and animal-to-human extrapolation approaches to independently estimate external exposure levels of AuNPs that cause minimal toxicity. Our results suggest that under the highest dosing level employed in existing animal studies (worst-case scenario), AuNPs coated with branched polyethylenimine (BPEI) would likely induce ～90–100% cellular death, implying high cytotoxicity compared to <10% cell death induced by low-to-medium animal dosing levels, which are commonly used in animal studies. The estimated human equivalent doses associated with 5% cell death in liver and kidney were around 1 and 3?mg/kg, respectively. Based on points of departure reported in animal studies, the human equivalent dose estimates associated with gene expression changes and tissue cell apoptosis in liver were 0.005 and 0.5?mg/kg, respectively. Our analyzes provide insights into safety evaluation, risk prediction, and point of departure estimation of AuNP exposure for humans and illustrate an approach that could be applied to other NPs when sufficient data are available.     

Application of unbound liver-to-plasma concentration ratio to quantitative projection of cytochrome P450-mediated drug–drug interactions using physiologically based pharmacokinetic modelling approach

Abstract

1. This study evaluated the prediction accuracy of cytochrome P450 (CYP)-mediated drug-drug interaction (DDI) using minimal physiologically-based pharmacokinetic (PBPK) modelling incorporating the hepatic accumulation factor of an inhibitor (i.e. unbound liver/unbound plasma concentration ratio [K_p,uu,liver]) based on 22 clinical DDI studies.

2. K_p,uu,liver values were estimated using three methods: (1) ratio of cell-to-medium ratio in human cryopreserved hepatocytes (C/M_u) at 37?°C to that on ice (K_p,uu,C/M), (2) multiplication of total liver/unbound plasma concentration ratio (K_p,u,liver) estimated from C/M_u at 37?°C with unbound fraction in human liver homogenate (K_p,uu,cell) and (3) observed K_p,uu,liver in rats after intravenous infusion (K_p,uu,rat).

3. PBPK model using each K_p,uu,liver projected the area under the curve (AUC) increase of substrates more accurately than the model assuming a K_p,uu,liver of 1 for the average fold error and root mean square error did. Particularly, the model with a K_p,uu,liver of 1 underestimated the AUC increase of triazolam following co-administration with CYP3A4 inhibitor itraconazole by five-fold, whereas the AUC increase projected using the model incorporating the K_p,uu,C/M, K_p,uu,cell, or K_p,uu,rat of itraconazole and hydroxyitraconazole was within approximately two-fold of the actual value.

4. The results indicated that incorporating K_p,uu,liver into the PBPK model improved the accuracy of DDI projection.     

Simulation of the pharmacokinetics of bisoprolol in healthy adults and patients with impaired renal function using whole-body physiologically based pharmacokinetic modeling

Aim:

To develop and evaluate a whole-body physiologically based pharmacokinetic (WB-PBPK) model of bisoprolol and to simulate its exposure and disposition in healthy adults and patients with renal function impairment.

Methods:

Bisoprolol dispositions in 14 tissue compartments were described by perfusion-limited compartments. Based the tissue composition equations and drug-specific properties such as log P, permeability, and plasma protein binding published in literatures, the absorption and whole-body distribution of bisoprolol was predicted using the ''Advanced Compartmental Absorption Transit'' (ACAT) model and the whole-body disposition model, respectively. Renal and hepatic clearances were simulated using empirical scaling methods followed by incorporation into the WB-PBPK model. Model refinements were conducted after a comparison of the simulated concentration-time profiles and pharmacokinetic parameters with the observed data in healthy adults following intravenous and oral administration. Finally, the WB-PBPK model coupled with a Monte Carlo simulation was employed to predict the mean and variability of bisoprolol pharmacokinetics in virtual healthy subjects and patients.

Results:

The simulated and observed data after both intravenous and oral dosing showed good agreement for all of the dose levels in the reported normal adult population groups. The predicted pharmacokinetic parameters (AUC, C_max, and T_max) were reasonably consistent (<1.3-fold error) with the observed values after single oral administration of doses ranging from of 5 to 20 mg using the refined WB-PBPK model. The simulated plasma profiles after multiple oral administration of bisoprolol in healthy adults and patient with renal impairment matched well with the observed profiles.

Conclusion:

The WB-PBPK model successfully predicts the intravenous and oral pharmacokinetics of bisoprolol across multiple dose levels in diverse normal adult human populations and patients with renal insufficiency.     

A physiologically based pharmacokinetic model to predict the pharmacokinetics of highly protein‐bound drugs and the impact of errors in plasma protein binding

Predicting the pharmacokinetics of highly protein‐bound drugs is difficult. Also, since historical plasma protein binding data were often collected using unbuffered plasma, the resulting inaccurate binding data could contribute to incorrect predictions. This study uses a generic physiologically based pharmacokinetic (PBPK) model to predict human plasma concentration–time profiles for 22 highly protein‐bound drugs. Tissue distribution was estimated from in vitro drug lipophilicity data, plasma protein binding and the blood: plasma ratio. Clearance was predicted with a well‐stirred liver model. Underestimated hepatic clearance for acidic and neutral compounds was corrected by an empirical scaling factor. Predicted values (pharmacokinetic parameters, plasma concentration–time profile) were compared with observed data to evaluate the model accuracy. Of the 22 drugs, less than a 2‐fold error was obtained for the terminal elimination half‐life (t_1/2, 100% of drugs), peak plasma concentration (C_max, 100%), area under the plasma concentration–time curve (AUC_0‐t, 95.4%), clearance (CL_h, 95.4%), mean residence time (MRT, 95.4%) and steady state volume (V_ss, 90.9%). The impact of f_up errors on CL_h and V_ss prediction was evaluated. Errors in f_up resulted in proportional errors in clearance prediction for low‐clearance compounds, and in V_ss prediction for high‐volume neutral drugs. For high‐volume basic drugs, errors in f_up did not propagate to errors in V_ss prediction. This is due to the cancellation of errors in the calculations for tissue partitioning of basic drugs. Overall, plasma profiles were well simulated with the present PBPK model. Copyright © 2016 John Wiley & Sons, Ltd.     

Physiologically based pharmacokinetic modeling to predict complex drug–drug interactions: a case study of AZD2327 and its metabolite,competitive and time‐dependent CYP3A inhibitors

4‐{(R)‐(3‐Aminophenyl)[4‐(4‐fluorobenzyl)‐piperazin‐1‐yl]methyl}‐N,N‐diethylbenzamide (AZD2327) is a highly potent and selective agonist of the δ ‐opioid receptor. AZD2327 and N‐deethylated AZD2327 (M1) are substrates of cytochrome P450 3A (CYP3A4) and comprise a complex multiple inhibitory system that causes competitive and time‐dependent inhibition of CYP3A4. The aim of the current work was to develop a physiologically based pharmacokinetic (PBPK) model to predict quantitatively the magnitude of CYP3A4 mediated drug–drug interaction with midazolam as the substrate. Integrating in silico, in vitro and in vivo PK data, a PBPK model was successfully developed to simulate the clinical accumulation of AZD2327 and its primary metabolite. The inhibition of CYP3A4 by AZD2327, using midazolam as a probe drug, was reasonably predicted. The predicted maximum concentration (C_max) and area under the concentration–time curve (AUC) for midazolam were increased by 1.75 and 2.45‐fold, respectively, after multiple dosing of AZD2327, indicating no or low risk for clinically relevant drug–drug interactions (DDI). These results are in agreement with those obtained in a clinical trial with a 1.4 and 1.5‐fold increase in C_max and AUC of midazolam, respectively. In conclusion, this model simulated DDI with less than a two‐fold error, indicating that complex clinical DDI associated with multiple mechanisms, pathways and inhibitors (parent and metabolite) can be predicted using a well‐developed PBPK model. Copyright © 2015 John Wiley & Sons, Ltd.     

基于胃肠道生理模型的口服药物吸收预测

药物的口服吸收受到许多生理因素的影响,如胃肠液成分、pH、肠道内传输、转运和代谢等。在进行非临床和临床体内试验之前,如果仅仅依靠体外数据能够准确地预测药物的口服吸收,将会大大提高新药研发的效率。在深入理解口服吸收过程的基础上发展起来的胃肠道生理模型为从事新药研发的科研工作者提供了这种机会。这些生理模型可以与经典的药动学模型紧密衔接,用于预测药物的口服吸收速度和程度。本文综述了胃肠道生理模型的最新进展,并对不同的模型进行了比较和讨论。     

Physiologically based pharmacokinetic model of docetaxel and interspecies scaling: comparison of simple injection with folate receptor-targeting amphiphilic copolymer-modified liposomes

1.?To compare the disposition of docetaxel (DTX) in male/female rats after intravenous administration of simple injection and folate-poly(PEG-cyanoacrylate-co-cholesteryl cyanoacrylate)-modified liposomes utilising a physiologically based pharmacokinetic (PBPK) modelling method, and extrapolate this model to mice and humans by taking into account the interspecies differences in physiological- and chemical-specific parameters.

2.?Four structural models for single organs were evaluated, and the whole-body PBPK model included artery, vein, lung, brain, heart, spleen, liver, gastrointestinal tract, kidney, muscle and remainder compartment.

3.?Rats following modified liposomes administration were characterised by significant decrease in the partition coefficients for brain, spleen, liver and remainder compartment. The blood-to-plasma partition coefficient also decreased significantly, while a marked rise of partition coefficients for lung, kidney and muscle was revealed. Partition coefficient for heart was approximately 1.3-fold higher in females than males, while the decrease of intestinal clearance was revealed in females compared to males. The final model successfully characterised the time course of DTX in rats, mice and humans.

4.?This PBPK model is beneficial to the prediction of the effects of DTX in different species. It also represented a platform to encompass both formulation- and sex-related effects on DTX disposition and elimination in the future.     

Physiologically based pharmacokinetic modeling revealed minimal codeine intestinal metabolism in first‐pass removal in rats

The physiologically based model with segregated flow to the intestine (SFM‐PBPK; partial, lower flow to enterocyte region vs. greater flow to serosal region) was found to describe the first‐pass glucuronidation of morphine (M) to morphine‐3β‐glucuronide (MG) in rats after intraduodenal (i.d.) and intravenous (i.v.) administration better than the traditional model (TM), for which a single intestinal flow perfused the whole of the intestinal tissue. The segregated flow model (SFM) described a disproportionately greater extent of intestinal morphine glucuronidation for i.d. vs. i.v. administration. The present study applied the same PBPK modeling approaches to examine the contributions of the intestine and liver on the first‐pass metabolism of the precursor, codeine (C, 3‐methylmorphine) in the rat. Unexpectedly, the profiles of codeine, morphine and morphine‐3β‐glucuronide in whole blood, bile and urine, assayed by LCMS, were equally well described by both the TM‐PBPK and SFM‐PBPK. The fitted parameters for the models were similar, and the net formation intrinsic clearance of morphine (from codeine) for the liver was much higher, being 9‐ to 13‐fold that of the intestine. Simulations, based on the absence of intestinal formation of morphine, correlated well with observations. The lack of discrimination of SFM and TM with the codeine data did not invalidate the SFM‐PBPK model but rather suggests that the liver is the only major organ for codeine metabolism. Because of little or no contribution by the intestine to the metabolism of codeine, both the TM‐ and SFM‐PBPK models are equally consistent with the data. Copyright © 2016 John Wiley & Sons, Ltd.