Evidence map›Paper›PMID 42093887›Full record

ArticleFrontiers in pharmacology2026

Multiscale zonation-resolved modeling of dose-dependent determinants of acetaminophen-induced liver injury.

Debarshi Ghosh, Stelian Camara Dit Pinto, Alon Malka-Markovitz, Mohammed Cherkaoui, Reza Sadeghi, John M Vierling, Nicolas R Gallo

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Debarshi GhoshSchool of Digital Engineering, Computer Science and Artificial Intelligence, Long Island University, Brooklyn, NY, United States.
Stelian Camara Dit PintoSchool of Digital Engineering, Computer Science and Artificial Intelligence, Long Island University, Brooklyn, NY, United States.
Alon Malka-MarkovitzSchool of Digital Engineering, Computer Science and Artificial Intelligence, Long Island University, Brooklyn, NY, United States.
Mohammed CherkaouiSchool of Digital Engineering, Computer Science and Artificial Intelligence, Long Island University, Brooklyn, NY, United States.
Reza SadeghiDassault Systemes, Velizy-Villacoublay, France.
John M VierlingDepartment of Medicine and Surgery, Baylor College of Medicine, Houston, TX, United States.
Nicolas R GalloSchool of Digital Engineering, Computer Science and Artificial Intelligence, Long Island University, Brooklyn, NY, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Drug-induced liver injury (DILI) is a major cause of morbidity and mortality and has an important impact on drug attrition. Recent guidelines from the FDA and previous research encourage the development of virtual twin Methods: In this study, we used our virtual, scalable model of the human liver lobule coupled with an acetaminophen (APAP) metabolic injury model to expand the mechanistic understanding of metabolic zonation parameters involved in APAP hepatotoxicity. Using clinical overdose data, we generated a representative Results: The results showed a significant difference in the sensitivity of the metabolic parameters at different overdose levels. Some, such as drug uptake rate, led to increased damage; others, such as CYP450 enzymatic activity, showed overdose-dependent effects, and others, such as the sulfation rate, showed only limited effects. Discussion: Overall, this study highlights the importance of collecting proper metabolic expression (specifically drug uptake rate, CYP450 enzymatic activity, and glutathione quantity) to ensure an accurate estimation of patient damage.

Indexed as

APAPlivermetabolismmultiscaletoxicityzonation

Identifiers

PMID42093887
PMCPMC13139076

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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.