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Modeling interindividual variability in physiologically based pharmacokinetics and its link to mechanistic covariate modeling.

ABSTRACT: Covariate modeling is a key step in the analysis of clinical data and is essential for establishing dosing recommendations for specific populations, e.g., in obese individuals and children. So far, no systematic approach exists to leverage the knowledge inherent in physiologically based pharmacokinetic (PBPK) models in this context. We introduce (i) a novel approach to model interindividual variability in PBPK models based on lean body weight (LBW); and (ii) a systematic approach to translate interindividual variability into the design of mechanistic covariate models. We derive a new covariate relation for the volume of distribution at steady state (Vss) that seamlessly integrates body weight and LBW as covariates, with a weighting factor depending on the physicochemical properties of the drug. We further show that for children, PBPK-based extrapolation and allometric scaling result in very similar predictions for Vss and blood clearance.CPT: Pharmacometrics & Systems Pharmacology (2012) 1, e4; doi:10.1038/psp.2012.3; advance online publication 26 September 2012.

SUBMITTER: Huisinga W

PROVIDER: S-EPMC3603474 | biostudies-other | 2012

REPOSITORIES: biostudies-other

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Modeling interindividual variability in physiologically based pharmacokinetics and its link to mechanistic covariate modeling.

Huisinga W W Solms A A Fronton L L Pilari S S

CPT: pharmacometrics & systems pharmacology 20120926

Covariate modeling is a key step in the analysis of clinical data and is essential for establishing dosing recommendations for specific populations, e.g., in obese individuals and children. So far, no systematic approach exists to leverage the knowledge inherent in physiologically based pharmacokinetic (PBPK) models in this context. We introduce (i) a novel approach to model interindividual variability in PBPK models based on lean body weight (LBW); and (ii) a systematic approach to translate in ...[more]

PMID: 23835884

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Project description:In this investigation, we tested the hypothesis that a physiologically based pharmacokinetic (PBPK) model incorporating measured in vitro metrics of off-target binding can largely explain the inter-antibody variability in monoclonal antibody (mAb) pharmacokinetics (PK). A diverse panel of 83 mAbs was evaluated for PK in wild-type mice and subjected to 10 in vitro assays to measure major physiochemical attributes. After excluding for target-mediated elimination and immunogenicity, 56 of the remaining mAbs with an eight-fold variability in the area under the curve (AUC0-672h: 1.74 × 106 -1.38 × 107 ng∙h/mL) and 10-fold difference in clearance (2.55-26.4 mL/day/kg) formed the training set for this investigation. Using a PBPK framework, mAb-dependent coefficients F1 and F2 modulating pinocytosis rate and convective transport, respectively, were estimated for each mAb with mostly good precision (coefficient of variation (CV%) <30%). F1 was estimated to be the mean and standard deviation of 0.961 ± 0.593, and F2 was estimated to be 2.13 ± 2.62. Using principal component analysis to correlate the regressed values of F1/F2 versus the multidimensional dataset composed of our panel of in vitro assays, we found that heparin chromatography retention time emerged as the predictive covariate to the mAb-specific F1, whereas F2 variability cannot be well explained by these assays. A sigmoidal relationship between F1 and the identified covariate was incorporated within the PBPK framework. A sensitivity analysis suggested plasma concentrations to be most sensitive to F1 when F1 > 1. The predictive utility of the developed PBPK model was evaluated against a separate panel of 14 mAbs biased toward high clearance, among which area under the curve of PK data of 12 mAbs was predicted within 2.5-fold error, and the positive and negative predictive values for clearance prediction were 85% and 100%, respectively. MAb heparin chromatography assay output allowed a priori identification of mAb candidates with unfavorable PK.

Project description:Interindividual variability in anatomical and physiological properties results in significant differences in drug pharmacokinetics. The consideration of such pharmacokinetic variability supports optimal drug efficacy and safety for each single individual, e.g. by identification of individual-specific dosings. One clear objective in clinical drug development is therefore a thorough characterization of the physiological sources of interindividual variability. In this work, we present a Bayesian population physiologically-based pharmacokinetic (PBPK) approach for the mechanistically and physiologically realistic identification of interindividual variability. The consideration of a generic and highly detailed mechanistic PBPK model structure enables the integration of large amounts of prior physiological knowledge, which is then updated with new experimental data in a Bayesian framework. A covariate model integrates known relationships of physiological parameters to age, gender and body height. We further provide a framework for estimation of the a posteriori parameter dependency structure at the population level. The approach is demonstrated considering a cohort of healthy individuals and theophylline as an application example. The variability and co-variability of physiological parameters are specified within the population; respectively. Significant correlations are identified between population parameters and are applied for individual- and population-specific visual predictive checks of the pharmacokinetic behavior, which leads to improved results compared to present population approaches. In the future, the integration of a generic PBPK model into an hierarchical approach allows for extrapolations to other populations or drugs, while the Bayesian paradigm allows for an iterative application of the approach and thereby a continuous updating of physiological knowledge with new data. This will facilitate decision making e.g. from preclinical to clinical development or extrapolation of PK behavior from healthy to clinically significant populations.

Dataset Information

Modeling interindividual variability in physiologically based pharmacokinetics and its link to mechanistic covariate modeling.

Publications

Modeling interindividual variability in physiologically based pharmacokinetics and its link to mechanistic covariate modeling.

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