Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations.

Markus Krauss,Lars Kuepfer,Andreas Schuppert,Kai Tappe,Linus Goerlitz,Markos Leggas

doi:10.1371/journal.pone.0139423

Abstract

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.

Highlights

Providing a safe and efficacious drug therapy for large and often heterogeneous populations is a challenging objective in clinical drug development
A combined Bayesian population physiologically-based pharmacokinetic (PBPK) approach is presented for the characterization and identification of physiologically-realistic interindividual variability
The approach consists of an hierarchical model, which describes the experimental data at an individual level and at the same time identifies the variability in physiological parameters at the population level

Summary

Introduction

Providing a safe and efficacious drug therapy for large and often heterogeneous populations is a challenging objective in clinical drug development. The PK of healthy adults, which represent the natural study population in early clinical phases may differ greatly from that of the diseased target population, which is e.g. represented by elderly or children. For these populations often only sparse literature information is available about their PK behavior or physiological variability. In-silico approaches are considered for the identification and characterization of sources of interindividual variability Such approaches should be able to improve individualand population-specific simulations of the PK of drugs and to support the knowledge-based extrapolation to other drugs or populations

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Conclusion