title: Bayesian Population Physiologically-Based Pharmacokinetic (PBPK) Approach for a Physiologically Realistic Characterization of Interindividual Variability in Clinically Relevant Populations.
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.
authors.affiliation: Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany; Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Aachen, Germany.
authors.forename: Markus
authors.initials: M
authors.lastname: Krauss
authors.affiliation: Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.
authors.forename: Kai
authors.initials: K
authors.lastname: Tappe
authors.affiliation: Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany; Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Aachen, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Schuppert
authors.affiliation: Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.
authors.forename: Lars
authors.initials: L
authors.lastname: Kuepfer
authors.affiliation: Computational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.
authors.forename: Linus
authors.initials: L
authors.lastname: Goerlitz
mesh_terms: D000328:Adult; D001499:Bayes Theorem; D001711:Biotransformation / Q000502:physiology*; D015992:Body Mass Index; D003198:Computer Simulation; D019388:Cytochrome P-450 CYP1A2 / Q000378:metabolism; D066264:Datasets as Topic; D005260:Female; D006801:Humans; D007408:Intestinal Absorption; D007668:Kidney / Q000378:metabolism; D008297:Male; D008390:Markov Chains; D008657:Metabolic Clearance Rate; D008954:Models, Biological; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D010599:Pharmacokinetics; D057285:Precision Medicine / Q000379:methods*; D013806:Theophylline / Q000493:pharmacokinetics; D014018:Tissue Distribution
chemical_list: D013806:Theophylline; D019388:Cytochrome P-450 CYP1A2