Evidence map›Paper›PMID 26431198›Full record

ArticlePloS one2015

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

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

Abstract read
In one paragraph

Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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  5. Article
  6. Incorporating new approach methods (NAMs) data in dose-response assessments: The future is now!Journal of toxicology and environmental health. Part B, Critical reviews · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Markus KraussComputational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany; Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Aachen, Germany.
Kai TappeComputational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.
Andreas SchuppertComputational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany; Aachen Institute for Advanced Study in Computational Engineering Sciences, RWTH Aachen, Aachen, Germany.
Lars KuepferComputational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.
Linus GoerlitzComputational Systems Biology, Bayer Technology Services GmbH, Leverkusen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Bayes TheoremComputer SimulationModels, BiologicalNonlinear DynamicsPharmacokineticsAdultBiotransformationBody Mass IndexCytochrome P-450 CYP1A2Datasets as TopicFemaleHumansIntestinal AbsorptionKidneyMaleMarkov ChainsCytochrome P-450 CYP1A2Theophylline

Identifiers

PMID26431198
PMCPMC4592188

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.