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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.

SUBMITTER: Emoto C

PROVIDER: S-EPMC5321811 | biostudies-other | 2017 Feb

REPOSITORIES: biostudies-other

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

Emoto C C Fukuda T T Johnson T N TN Neuhoff S S Sadhasivam S S Vinks A A AA

CPT: pharmacometrics & systems pharmacology 20161209 2

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 rele ...[more]

PMID: 27935268

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Project description:PurposeInter-individual variability in clinical endpoints and occurrence of potentially severe adverse effects represent an enormous challenge in drug development at all phases of (pre-)clinical research. To ensure patient safety it is important to identify adverse events or critical subgroups within the population as early as possible. Hence, a comprehensive understanding of the processes governing pharmacokinetics and pharmacodynamics is of utmost importance. In this paper we combine Bayesian statistics with detailed mechanistic physiologically-based pharmacokinetic (PBPK) models. On the example of pravastatin we demonstrate that this combination provides a powerful tool to investigate inter-individual variability in groups of patients and to identify clinically relevant homogenous subgroups in an unsupervised approach. Since PBPK models allow the identification of physiological, drug-specific and genotype-specific knowledge separately, our approach supports knowledge-based extrapolation to other drugs or populations.MethodsPBPK models are based on generic distribution models and extensive collections of physiological parameters and allow a mechanistic investigation of drug distribution and drug action. To systematically account for parameter variability within patient populations, a Bayesian-PBPK approach is developed rigorously quantifying the probability of a parameter given the amount of information contained in the measured data. Since these parameter distributions are high-dimensional, a Markov chain Monte Carlo algorithm is used, where the physiological and drug-specific parameters are considered in separate blocks.ResultsConsidering pravastatin pharmacokinetics as an application example, Bayesian-PBPK is used to investigate inter-individual variability in a cohort of 10 patients. Correlation analyses infer structural information about the PBPK model. Moreover, homogeneous subpopulations are identified a posteriori by examining the parameter distributions, which can even be assigned to a polymorphism in the hepatic organ anion transporter OATP1B1.ConclusionsThe presented Bayesian-PBPK approach systematically characterizes inter-individual variability within a population by updating prior knowledge about physiological parameters with new experimental data. Moreover, clinically relevant homogeneous subpopulations can be mechanistically identified. The large scale PBPK model separates physiological and drug-specific knowledge which allows, in combination with Bayesian approaches, the iterative assessment of specific populations by integrating information from several drugs.

Project description:OBJECTIVE:The objective of this study was to characterize the population pharmacokinetics of fentanyl and identify factors that contribute to exposure variability in critically ill pediatric patients. METHODS:We conducted a single-center, retrospective cohort study using electronic record data and remnant blood samples in the setting of a mixed medical/surgical intensive care unit (ICU) at a quaternary children's hospital. Children with a predicted ICU length of stay of at least 3 days and presence of an indwelling central venous or arterial line were included. Serum fentanyl measurements were performed for 278 unique remnant samples from 66 patients. Both one- and two-compartment models were evaluated to describe fentanyl disposition. Covariates were introduced into the model in a forward/backward, stepwise approach and included age, sex, race, weight, cytochrome P450 (CYP) 3A5 genotype, and the presence of CYP3A4 or CYP3A5 inducers or inhibitors. Simulations were performed using the successful model to depict the influence of inducers on fentanyl concentrations. RESULTS:A two-compartment base model best described the data. There was good agreement between observed and predicted concentrations in the final model. The typical fentanyl clearance for 70 kg (reference weight) and 20.1 kg (median weight) patients were 34.6 and 13.6 L/h, respectively. The magnitude of the unexplained random inter-individual variability was high for both clearance (60.7%) and apparent volume of the central compartment (V1) (107.2%). Coadministration of the known CYP3A4/5 inducers fosphenytoin and/or phenobarbital was associated with significantly increased fentanyl clearance. Simulations demonstrate that the effect of inducer administration was most pronounced following discontinuation of a fentanyl infusion. CONCLUSIONS:In this study we show the feasibility and utility of using electronic record data and remnant blood samples to successfully construct population pharmacokinetic models for a heterogeneous cohort of critically ill children. A clinically relevant effect of concomitant CYP3A4/5 inducers was identified. Scaling this population pharmacokinetic approach is necessary to craft precision approaches to fentanyl administration for critically ill children.

Dataset Information

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

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

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