Evidence map›Paper›PMID 41717827›Full record

ArticleCPT: pharmacometrics & systems pharmacology2026

Comprehensive Pathophysiology Repository for PBPK Modeling in Liver Cirrhosis: Quantifying Continuous Disease Progression and Population Variability.

Annika R P Schneider, Vanessa Baier, Jan-Frederik Schlender, Lars Kuepfer

Abstract read
In one paragraph

Article in CPT: pharmacometrics & systems pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Pharmacokinetics in the Fontan circulation: a multi-organ puzzle.European heart journal. Cardiovascular pharmacotherapy · 2026
    Review
  2. Article
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

4 authors.

Annika R P SchneiderInstitute of Applied Microbiology - iAMB, Aachen Biology and Biotechnology - ABBt, RWTH Aachen University, Aachen, Germany.ORCID https://orcid.org/0000-0003-1377-7102
Vanessa BaierInstitute of Applied Microbiology - iAMB, Aachen Biology and Biotechnology - ABBt, RWTH Aachen University, Aachen, Germany.
Jan-Frederik SchlenderModel-Informed Drug Development, Research and Development, Pharmaceuticals, Bayer AG, Leverkusen, Germany.ORCID https://orcid.org/0009-0003-9079-5205
Lars KuepferInstitute for Systems Medicine With Focus on Organ Interaction, University Hospital RWTH Aachen, Aachen, Germany.ORCID https://orcid.org/0000-0002-8741-7786

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Liver cirrhosis is accompanied by pathophysiological changes. Due to multiple absorption, distribution, metabolism and excretion (ADME)-related pathophysiological alterations, the estimation of the net pharmacokinetics (PK) change in cirrhotic patients is complex. Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic modeling technique that combines knowledge of physiological and drug-related properties and, thereby, allows the prediction of organism-specific drug PK. For the integration of pathophysiological changes into a PBPK model, such changes need to be quantified appropriately. To date, published liver cirrhosis pathophysiology repositories contain only average changes for three distinct disease stages limiting clinical applicability. Therefore, the aim of this study was the development of a repository that (1) describes physiological alterations throughout the body during cirrhosis progression, (2) quantifies both mean changes and population variability, and (3) adds parameters of not yet included changes. For this purpose, data was gathered and processed using a Markov-Chain-Monte-Carlo (MCMC)-based approach that allowed the handling of heterogenous data and information on population variability. The resulting repository, based on 216,609 data points from 68 literature studies and 208,851 patients from the IBM Explorys electronic health records database, encompasses 30 physiological parameters. Integration into a PBPK modeling framework revealed good predictive performance with 96% of all data points for predicted PK parameter ratios lying within a twofold prediction range. In summary, the presented approach provides an advancement in the field of PK modeling in liver cirrhosis patients, possibly facilitating the planning and analysis of clinical studies in these patients and moving towards virtual studies.

Indexed as

Liver CirrhosisModels, BiologicalDatabases, FactualDisease ProgressionHumansMarkov ChainsMonte Carlo MethodPharmacokineticsChild‐Pugh scorehepatic impairmentliver cirrhosisPBPK modelingpharmacokinetics

Identifiers

PMID41717827
PMCPMC12962396

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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.