Evidence mapPaperPMID 41132528Full record

ArticleFrontiers in pharmacology2025

A digital twin of glimepiride for personalized and stratified diabetes treatment.

Michelle Elias, Matthias König

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2025. 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. Review
  2. Review
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

2 authors.

Michelle EliasFaculty of Life Science, Institute for Biology, Systems Medicine of the Liver, Humboldt-Universität zu Berlin, Berlin, Germany.
Matthias KönigFaculty of Life Science, Institute for Biology, Systems Medicine of the Liver, Humboldt-Universität zu Berlin, Berlin, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Optimizing glimepiride therapy for type 2 diabetes (T2DM) is challenged by pronounced inter-individual variability in pharmacokinetics. Methods: We developed a whole-body physiologically based pharmacokinetic (PBPK) model as a digital twin of glimepiride, enabling systematic evaluation of how patient-specific factors influence drug disposition. Using curated data from 20 clinical studies, the digital twin mechanistically simulates glimepiride's absorption, distribution, metabolism, and excretion (ADME). It accounts for key determinants of variability including renal and hepatic function, CYP2C9 genotype, and bodyweight. Results: The model accurately reproduced observed pharmacokinetics and quantified these factors' impact on drug exposure. Increased glimepiride exposure was predicted in individuals with hepatic dysfunction or specific CYP2C9 variants, highlighting substantial genetic and physiological effects. Discussion: This digital twin provides mechanistic insights into pharmacokinetic variability and serves as an

Indexed as

diabetesdigital twinglimepiridePBPKpersonalized medicinepharmacokineticsphysiologically based pharmacokinetic model

Identifiers

PMID41132528
PMCPMC12540372

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.