Showing papers on "Physiologically based pharmacokinetic modelling published in 2017"

Prediction of Drug-Drug Interactions with Bupropion and Its Metabolites as CYP2D6 Inhibitors Using a Physiologically-Based Pharmacokinetic Model.

Abstract: The potential of inhibitory metabolites of perpetrator drugs to contribute to drug-drug interactions (DDIs) is uncommon and underestimated. However, the occurrence of unexpected DDI suggests the potential contribution of metabolites to the observed DDI. The aim of this study was to develop a physiologically-based pharmacokinetic (PBPK) model for bupropion and its three primary metabolites-hydroxybupropion, threohydrobupropion and erythrohydrobupropion-based on a mixed "bottom-up" and "top-down" approach and to contribute to the understanding of the involvement and impact of inhibitory metabolites for DDIs observed in the clinic. PK profiles from clinical researches of different dosages were used to verify the bupropion model. Reasonable PK profiles of bupropion and its metabolites were captured in the PBPK model. Confidence in the DDI prediction involving bupropion and co-administered CYP2D6 substrates could be maximized. The predicted maximum concentration (Cmax) area under the concentration-time curve (AUC) values and Cmax and AUC ratios were consistent with clinically observed data. The addition of the inhibitory metabolites into the PBPK model resulted in a more accurate prediction of DDIs (AUC and Cmax ratio) than that which only considered parent drug (bupropion) P450 inhibition. The simulation suggests that bupropion and its metabolites contribute to the DDI between bupropion and CYP2D6 substrates. The inhibitory potency from strong to weak is hydroxybupropion, threohydrobupropion, erythrohydrobupropion, and bupropion, respectively. The present bupropion PBPK model can be useful for predicting inhibition from bupropion in other clinical studies. This study highlights the need for caution and dosage adjustment when combining bupropion with medications metabolized by CYP2D6. It also demonstrates the feasibility of applying the PBPK approach to predict the DDI potential of drugs undergoing complex metabolism, especially in the DDI involving inhibitory metabolites.

Physiologically Based Pharmacokinetic (PBPK) Modeling of Pharmaceutical Nanoparticles.

Abstract: With the great interests in the discovery and development of drug products containing nanoparticles, there is a great demand of quantitative tools for assessing quality, safety, and efficacy of these products. Physiologically based pharmacokinetic (PBPK) modeling and simulation approaches provide excellent tools for describing and predicting in vivo absorption, distribution, metabolism, and excretion (ADME) of nanoparticles administered through various routes. PBPK modeling of nanoparticles is an emerging field, and more than 20 PBPK models of nanoparticles used in pharmaceutical products have been published in the past decade. This review provides an overview of the ADME characteristics of nanoparticles and how these ADME processes are described in PBPK models. Recent advances in PBPK modeling of pharmaceutical nanoparticles are summarized. The major challenges in model development and validation and possible solutions are also discussed.

Predicting Oral Drug Absorption: Mini Review on Physiologically-Based Pharmacokinetic Models

Abstract: Most marketed drugs are administered orally, despite the complex process of oral absorption that is difficult to predict Oral bioavailability is dependent on the interplay between many processes that are dependent on both compound and physiological properties Because of this complexity, computational oral physiologically-based pharmacokinetic (PBPK) models have emerged as a tool to integrate these factors in an attempt to mechanistically capture the process of oral absorption These models use inputs from in vitro assays to predict the pharmacokinetic behavior of drugs in the human body The most common oral PBPK models are compartmental approaches, in which the gastrointestinal tract is characterized as a series of compartments through which the drug transits The focus of this review is on the development of oral absorption PBPK models, followed by a brief discussion of the major applications of oral PBPK models in the pharmaceutical industry

Physiologically based pharmacokinetic modeling in regulatory decision-making at the European Medicines Agency.

Abstract: Physiologically based pharmacokinetic (PBPK) modeling is a valuable tool in drug development and regulatory assessment, as it offers the opportunity to simulate the pharmacokinetics of a compound, with a mechanistic understanding, in a variety of populations and situations. This work reviews the use and impact of such modeling in selected regulatory procedures submitted to the European Medicines Agency (EMA) before the end of 2015, together with its subsequent reflection in public documents relating to the assessment of these procedures. It is apparent that the reference to PBPK modeling in regulatory public documents underrepresents its use. A positive trend over time of the number of PBPK models submitted is shown, and in a number of cases the results of these may impact the decision-making process or lead to recommendations in the product labeling. These results confirm the need for regulatory guidance in this field, which is currently under development by the EMA.

Gestation-Specific Changes in the Anatomy and Physiology of Healthy Pregnant Women: An Extended Repository of Model Parameters for Physiologically Based Pharmacokinetic Modeling in Pregnancy

Abstract: In the past years, several repositories for anatomical and physiological parameters required for physiologically based pharmacokinetic modeling in pregnant women have been published. While providing a good basis, some important aspects can be further detailed. For example, they did not account for the variability associated with parameters or were lacking key parameters necessary for developing more detailed mechanistic pregnancy physiologically based pharmacokinetic models, such as the composition of pregnancy-specific tissues. The aim of this meta-analysis was to provide an updated and extended database of anatomical and physiological parameters in healthy pregnant women that also accounts for changes in the variability of a parameter throughout gestation and for the composition of pregnancy-specific tissues. A systematic literature search was carried out to collect study data on pregnancy-related changes of anatomical and physiological parameters. For each parameter, a set of mathematical functions was fitted to the data and to the standard deviation observed among the data. The best performing functions were selected based on numerical and visual diagnostics as well as based on physiological plausibility. The literature search yielded 473 studies, 302 of which met the criteria to be further analyzed and compiled in a database. In total, the database encompassed 7729 data. Although the availability of quantitative data for some parameters remained limited, mathematical functions could be generated for many important parameters. Gaps were filled based on qualitative knowledge and based on physiologically plausible assumptions. The presented results facilitate the integration of pregnancy-dependent changes in anatomy and physiology into mechanistic population physiologically based pharmacokinetic models. Such models can ultimately provide a valuable tool to investigate the pharmacokinetics during pregnancy in silico and support informed decision making regarding optimal dosing regimens in this vulnerable special population.

Development of a Novel Maternal-Fetal Physiologically Based Pharmacokinetic Model II: Verification of the Model for Passive Placental Permeability Drugs

Abstract: Fetal exposure to drugs cannot be readily estimated from single time point cord blood sampling at the time of delivery. Therefore, we developed a physiologically based pharmacokinetic (PBPK) model to estimate fetal drug exposure throughout pregnancy. In this study, we report verification of this novel maternal-fetal PBPK (m-f-PBPK) model for drugs that passively diffuse across the placenta and are not metabolized/transported there. Our recently built m-f-PBPK model was populated with gestational age-dependent changes in maternal drug disposition and maternal-fetal physiology. Using midazolam as an in vivo calibrator, the transplacental passive diffusion clearance of theophylline and zidovudine was first estimated. Then, for verification, the predicted maternal plasma (MP) and umbilical venous (UV) plasma drug concentrations by our m-f-PBPK were compared against those observed at term. Overall, our m-f-PBPK model well predicted the maternal and fetal exposure to the two verification drugs, theophylline and zidovudine, at term, across a range of dosing regimens, with nearly all observed MP and UV plasma drug concentrations falling within the 90% prediction interval [i.e., 5th–95th percentile range of a virtual pregnant population (n = 100)]. Prediction precision and bias of theophylline MP and UV were 14.5% and 12.4%, and 9.4% and 7.5%, respectively. Furthermore, for zidovudine, after the exclusion of one unexpectedly low MP concentration, prediction precision and bias for MP and UV were 50.3% and 30.2, and 28.3% and 15.0%, respectively. This m-f-PBPK should be useful to predict fetal exposure to drugs, throughout pregnancy, for drugs that passively diffuse across the placenta.

Model-Based Analysis of Biopharmaceutic Experiments To Improve Mechanistic Oral Absorption Modeling: An Integrated in Vitro in Vivo Extrapolation Perspective Using Ketoconazole as a Model Drug.

Abstract: Mechanistic modeling of in vitro data generated from metabolic enzyme systems (viz., liver microsomes, hepatocytes, rCYP enzymes, etc.) facilitates in vitro–in vivo extrapolation (IVIV_E) of metabolic clearance which plays a key role in the successful prediction of clearance in vivo within physiologically-based pharmacokinetic (PBPK) modeling. A similar concept can be applied to solubility and dissolution experiments whereby mechanistic modeling can be used to estimate intrinsic parameters required for mechanistic oral absorption simulation in vivo. However, this approach has not widely been applied within an integrated workflow. We present a stepwise modeling approach where relevant biopharmaceutics parameters for ketoconazole (KTZ) are determined and/or confirmed from the modeling of in vitro experiments before being directly used within a PBPK model. Modeling was applied to various in vitro experiments, namely: (a) aqueous solubility profiles to determine intrinsic solubility, salt limiting solubility ...

Predicting Drug Concentration-Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically-Based Pharmacokinetic Model

Abstract: Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System-specific and drug-specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration-time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration-time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development.

Physiologically Based Pharmacokinetic Modeling of Renally Cleared Drugs in Pregnant Women

Abstract: Since pregnant women are considerably underrepresented in clinical trials, information on optimal dosing in pregnancy is widely lacking. Physiologically based pharmacokinetic (PBPK) modeling may provide a method for predicting pharmacokinetic changes in pregnancy to guide subsequent in vivo pharmacokinetic trials in pregnant women, minimizing associated risks. The goal of this study was to build and verify a population PBPK model that predicts the maternal pharmacokinetics of three predominantly renally cleared drugs (namely cefazolin, cefuroxime, and cefradine) at different stages of pregnancy. It was further evaluated whether the fraction unbound (f u) could be estimated in pregnant women using a proposed scaling approach. Based on a recent literature review on anatomical and physiological changes during pregnancy, a pregnancy population PBPK model was built using the software PK-Sim®/MoBi®. This model comprised 27 compartments, including nine pregnancy-specific compartments. The PBPK model was verified by comparing the predicted maternal pharmacokinetics of cefazolin, cefuroxime, and cefradine with observed in vivo data taken from the literature. The proposed scaling approach for estimating the f u in pregnancy was evaluated by comparing the predicted f u with experimentally observed f u values of 32 drugs taken from the literature. The pregnancy population PBPK model successfully predicted the pharmacokinetics of cefazolin, cefuroxime, and cefradine at all tested stages of pregnancy. All predicted plasma concentrations fell within a 2-fold error range and 85% of the predicted concentrations within a 1.25-fold error range. The f u in pregnancy could be adequately predicted using the proposed scaling approach, although a slight underestimation was evident in case of drugs bound to α1-acidic glycoprotein. Pregnancy population PBPK models can provide a valuable tool to predict a priori the pharmacokinetics of predominantly renally cleared drugs in pregnant women. These models can ultimately support informed decision making regarding optimal dosing regimens in this vulnerable special population

A Physiologically-Based Pharmacokinetic Model to Predict Human Fetal Exposure for a Drug Metabolized by Several CYP450 Pathways

Abstract: Pregnant women and their fetuses are exposed to numerous drugs; however, they are orphan populations with respect to the safety and efficacy of drugs. Therefore, the prediction of maternal and fetal drug exposure prior to administration would be highly useful. A physiologically-based pharmacokinetic (PBPK) model for nevirapine, which is metabolized by the cytochrome P450 (CYP) 3A4, 2B6 and 2D6 pathways, was developed to predict maternal and fetal pharmacokinetics (PK). The model was developed in both non-pregnant and pregnant women, and all physiological and enzymatic changes that could impact nevirapine PK were taken into account. Transplacental parameters estimated from ex vivo human placenta perfusion experiments were included in this PBPK model. To validate the model, observed maternal and cord blood concentrations were compared with predicted concentrations, and the impact of fetal clearance on fetal PK was investigated. By implementing physiological changes, including CYP3A4, 2D6 and 2B6 inductions, we predicted a clearance increase of 21 % in late pregnancy. The PBPK model successfully predicted the disposition for both non-pregnant and pregnant populations. Parameters obtained from the ex vivo experiments allowed the prediction of nevirapine concentrations that matched observed cord blood concentrations. The fetal-to-maternal area under the curve ratio (0–24 h interval) was 0.77, and fetal metabolism had no significant effect on fetal PK. The PBPK approach is a useful tool for quantifying a priori the drug exposure of metabolized drugs during pregnancy, and can be applied to evaluate alternative dosing regimens to optimize drug therapy. This approach, including ex vivo human placental perfusion parameters, is a promising approach for predicting human fetal exposure.

Prediction of drug–drug interaction potential using physiologically based pharmacokinetic modeling

Abstract: The occurrence of drug-drug interactions (DDIs) can significantly affect the safety of a patient, and thus assessing DDI risk is important. Recently, physiologically based pharmacokinetic (PBPK) modeling has been increasingly used to predict DDI potential. Here, we present a PBPK modeling concept and strategy. We also surveyed PBPK-related articles about the prediction of DDI potential in humans published up to October 10, 2017. We identified 107 articles, including 105 drugs that fit our criteria, with a gradual increase in the number of articles per year. Studies on antineoplastic and immunomodulatory drugs (26.7%) contributed the most to published PBPK models, followed by cardiovascular (20.0%) and anti-infective (17.1%) drugs. Models for specific products such as herbal products, therapeutic protein drugs, and antibody-drug conjugates were also described. Most PBPK models were used to simulate cytochrome P450 (CYP)-mediated DDIs (74 drugs, of which 85.1% were CYP3A4-mediated), whereas some focused on transporter-mediated DDIs (15 drugs) or a combination of CYP and transporter-mediated DDIs (16 drugs). Full PBPK, first-order absorption modules and Simcyp® software were predominantly used for modeling. Recently, DDI predictions associated with genetic polymorphisms, special populations, or both have increased. The 107 published articles reasonably predicted the DDI potentials, but further studies of physiological properties and harmonization of in vitro experimental designs are required to extend the application scope, and improvement of DDI predictions using PBPK modeling will be possible in the future.

Delineating the Role of Various Factors in Renal Disposition of Digoxin through Application of Physiologically Based Kidney Model to Renal Impairment Populations.

Abstract: Development of submodels of organs within physiologically-based pharmacokinetic (PBPK) principles and beyond simple perfusion limitations may be challenging because of underdeveloped in vitro-in vivo extrapolation approaches or lack of suitable clinical data for model refinement. However, advantage of such models in predicting clinical observations in divergent patient groups is now commonly acknowledged. Mechanistic understanding of altered renal secretion in renal impairment is one area that may benefit from such models, despite knowledge gaps in renal pathophysiology. In the current study, a PBPK kidney model was developed for digoxin, accounting for the roles of organic anion transporting peptide 4C1 (OATP4C1) and P-glycoprotein (P-gp) in its tubular secretion, with the aim to investigate the impact of age and renal impairment (moderate to severe) on renal drug disposition. Initial PBPK simulations based on changes in glomerular filtration rate (GFR) underestimated the observed reduction in digoxin renal excretion clearance (CLR) in subjects with moderately impaired renal function relative to healthy. Reduction in either proximal tubule cell number or the OATP4C1 abundance in the mechanistic kidney model successfully predicted 59% decrease in digoxin CLR, in particular when these changes were proportional to reduction in GFR. In contrast, predicted proximal tubule concentration of digoxin was only sensitive to changes in the transporter expression/ million proximal tubule cells. Based on the mechanistic modeling, reduced proximal tubule cellularity and OATP4C1 abundance, and inhibition of OATP4C1-mediated transport, are proposed as possible causes of reduced digoxin renal secretion in renally impaired patients.

Physiologically Based Pharmacokinetic (PBPK) Modeling of Pitavastatin and Atorvastatin to Predict Drug-Drug Interactions (DDIs)

Abstract: The disposition of statins varies and involves both metabolizing enzymes and transporters, making predictions of statin drug-drug interactions (DDIs) challenging. Physiologically based pharmacokinetic (PBPK) models have, however, demonstrated ability to predict complex DDIs. In this study, PBPK models of two statins (pitavastatin and atorvastatin) were developed and applied to predict pitavastatin and atorvastatin associated DDIs. Pitavastatin and atorvastatin PBPK models were developed using in vitro and human pharmacokinetic data in a population-based PBPK software (SimCYP®) by considering the contribution of both metabolizing enzymes and transporters to their overall pharmacokinetics. The statin PBPK models and software’s built-in or published models of inhibitors were used to predict DDIs under different scenarios. The statin models reasonably predicted the observed exposure change due to Organic Anion Transporting Polypeptide (OATP) 1B1 polymorphism or clinical DDIs with itraconazole, erythromycin, and gemfibrozil, while under-predicted the observed DDIs caused by rifampin and cyclosporine. Further analysis demonstrated that OATP1B1 inhibition by rifampin or cyclosporine in the existing inhibitor models needs to be approximately tenfold stronger to recapitulate the observed DDI with these two inhibitors. Through quantitative assessment of the effect of OATP1B1 genetic polymorphism and inhibitors of transporters and metabolizing enyzmes via PBPK modeling, we confirmed the importance of OATP1B1 in the disposition of these two statins, and explored potential causes for under-prediction of the inhibitory effect of rifampin and cyclosporine.

Quantitative Prediction of the Effect of CYP3A Inhibitors and Inducers on Venetoclax Pharmacokinetics Using a Physiologically Based Pharmacokinetic Model.

Abstract: The objectives of the analysis were to develop and verify a venetoclax physiologically based pharmacokinetic (PBPK) model to predict the effects of cytochrome P450 3A (CYP3A) inhibitors and inducers on the PK of venetoclax and inform dosing recommendations. A minimal PBPK model was developed based on prior in vitro and in vivo clinical data using a "middle-out" approach. The PBPK model was independently verified against clinical studies of the strong CYP3A inhibitor ketoconazole, the strong CYP3A inducer, multiple-dose rifampin, and the steady-state venetoclax PK in chronic lymphocytic leukemia (CLL) subjects by comparing predicted to observed ratios of the venetoclax maximum concentration (Cmax R) and area under the curve from time 0 to infinity (AUC∞ R) from these studies. The verified PBPK model was then used to simulate the effects of different CYP3A inhibitors and inducers on the venetoclax PK. Comparison of the PBPK model predicted to the observed PK parameters indicated good agreement. Verification of the PBPK model demonstrated that the ratios of the predicted:observed Cmax R and AUC∞ R of venetoclax were within 0.8- to 1.25-fold range for strong CYP3A inhibitors and inducers. Model simulations indicated no effect of weak CYP3A inhibitors or inducers on Cmax or AUC∞ , while both moderate and strong CYP3A inducers were estimated to decrease venetoclax exposure. Moderate and strong CYP3A inhibitors were estimated to increase venetoclax AUC∞ , by 100% to 390% and 480% to 680%, respectively. The recommended venetoclax dose reductions of at least 50% and 75% when coadministered with moderate and strong CYP3A inhibitors, respectively, maintain venetoclax exposures between therapeutic and maximally administered safe doses.

A Permeability-Limited Physiologically Based Pharmacokinetic (PBPK) Model for Perfluorooctanoic acid (PFOA) in Male Rats.

Abstract: Physiologically based pharmacokinetic (PBPK) modeling is a powerful in silico tool that can be used to simulate the toxicokinetics and tissue distribution of xenobiotic substances, such as perfluorooctanoic acid (PFOA), in organisms. However, most existing PBPK models have been based on the flow-limited assumption and largely rely on in vivo data for parametrization. In this study, we propose a permeability-limited PBPK model to estimate the toxicokinetics and tissue distribution of PFOA in male rats. Our model considers the cellular uptake and efflux of PFOA via both passive diffusion and transport facilitated by various membrane transporters, association with serum albumin in circulatory and extracellular spaces, and association with intracellular proteins in liver and kidney. Model performance is assessed using seven experimental data sets extracted from three different studies. Comparing model predictions with these experimental data, our model successfully predicts the toxicokinetics and tissue distr...

In Vivo Predictive Dissolution (IPD) and Biopharmaceutical Modeling and Simulation: Future Use of Modern Approaches and Methodologies in a Regulatory Context.

Abstract: The overall objective of OrBiTo, a project within Innovative Medicines Initiative (IMI), is to streamline and optimize the development of orally administered drug products through the creation and efficient application of biopharmaceutics tools. This toolkit will include both experimental and computational models developed on improved understanding of the highly dynamic gastrointestinal (GI) physiology relevant to the GI absorption of drug products in both fasted and fed states. A part of the annual OrBiTo meeting in 2015 was dedicated to the presentation of the most recent progress in the development of the regulatory use of PBPK in silico modeling, in vivo predictive dissolution (IPD) tests, and their application to biowaivers. There are still several areas for improvement of in vitro dissolution testing by means of generating results relevant for the intraluminal conditions in the GI tract. The major opportunity is probably in combining IPD testing and physiologically based in silico models where the i...

Quantitative and Mechanistic Understanding of AZD1775 Penetration across Human Blood-Brain Barrier in Glioblastoma Patients Using an IVIVE-PBPK Modeling Approach.

Abstract: Purpose: AZD1775, a first-in-class, small-molecule inhibitor of the Wee1 tyrosine kinase, is under evaluation as a potential chemo- and radiosensitizer for treating glioblastoma. This study was to prospectively, quantitatively, and mechanistically investigate the penetration of AZD1775 across the human blood–brain barrier (BBB). Experimental Design: AZD1775 plasma and tumor pharmacokinetics were evaluated in 20 patients with glioblastoma. The drug metabolism, transcellular passive permeability, and interactions with efflux and uptake transporters were determined using human derived in vitro systems. A whole-body physiologically based pharmacokinetic (PBPK) model integrated with a four-compartment permeability-limited brain model was developed for predicting the kinetics of AZD1775 BBB penetration and assessing the factors modulating this process. Results: AZD1775 exhibited good tumor penetration in patients with glioblastoma, with the unbound tumor-to-plasma concentration ratio ranging from 1.3 to 24.4 (median, 3.2). It was a substrate for ABCB1, ABCG2, and OATP1A2, but not for OATP2B1 or OAT3. AZD1775 transcellular passive permeability and active efflux clearance across MDCKII–ABCB1 or MDCKII–ABCG2 cell monolayers were dependent on the basolateral pH. The PBPK model well predicted observed drug plasma and tumor concentrations in patients. The extent and rate of drug BBB penetration were influenced by BBB integrity, efflux and uptake active transporter activity, and drug binding to brain tissue. Conclusions: In the relatively acidic tumor microenvironment where ABCB1/ABCG2 transporter-mediated efflux clearance is reduced, OATP1A2-mediated active uptake becomes dominant, driving AZD1775 penetration into brain tumor. Variations in the brain tumor regional pH, transporter expression/activity, and BBB integrity collectively contribute to the heterogeneity of AZD1775 penetration into brain tumors. Clin Cancer Res; 23(24); 7454–66. ©2017 AACR. See related commentary by Peer et al., p. 7437

Impact of physiologically based pharmacokinetic models on regulatory reviews and product labels: Frequent utilization in the field of oncology.

Abstract: Physiologically based pharmacokinetic (PBPK) modeling can be used to predict drug pharmacokinetics in virtual populations using models that integrate understanding of physiological systems. PBPK models have been widely utilized for predicting pharmacokinetics in clinically untested scenarios during drug applications and regulatory reviews in recent years. Here, we provide a comprehensive review of the application of PBPK in new drug application (NDA) review documents from the US Food and Drug Administration (FDA) in the past 4 years.

Characterization of Contributing Factors to Variability in Morphine Clearance Through PBPK Modeling Implemented With OCT1 Transporter.

Abstract: Morphine shows large interindividual variability in its pharmacokinetics; however, the cause of this has not been fully addressed. The variability in morphine disposition is considered to be due to a combination of pharmacogenetic and physiological determinants related to morphine disposition. We previously reported the effect of organic cation transporter (OCT1) genotype on morphine disposition in pediatric patients. To further explore the underlying mechanisms for variability arising from relevant determinants, including OCT1, a physiologically based pharmacokinetic (PBPK) model of morphine was developed. The PBPK model predicted morphine concentration-time profiles well, in both adults and children. Almost all of the observed morphine clearances in pediatric patients fell within a twofold range of median predicted values for each OCT1 genotype in each age group. This PBPK modeling approach quantitatively demonstrates that OCT1 genotype, age-related growth, and changes in blood flow as important contributors to morphine pharmacokinetic (PK) variability.