Evidence map›Paper›PMID 38794269›Full record

ArticlePharmaceutics2024

In Vitro Human Liver Model for Toxicity Assessment with Clinical and Preclinical Instrumentation.

Eneko Madorran, Lidija Kocbek Šaherl, Mateja Rakuša, Miha Munda

Abstract read
In one paragraph

Article in Pharmaceutics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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.

Eneko MadorranFaculty of Medicine, Institute of Anatomy, Histology and Embryology, University of Maribor, Taborska Ulica 8, 2000 Maribor, Slovenia.ORCID 0000-0001-6524-4119
Lidija Kocbek ŠaherlFaculty of Medicine, Institute of Anatomy, Histology and Embryology, University of Maribor, Taborska Ulica 8, 2000 Maribor, Slovenia.ORCID 0000-0001-6720-2342
Mateja RakušaFaculty of Medicine, Institute of Anatomy, Histology and Embryology, University of Maribor, Taborska Ulica 8, 2000 Maribor, Slovenia.
Miha MundaFaculty of Medicine, Institute of Anatomy, Histology and Embryology, University of Maribor, Taborska Ulica 8, 2000 Maribor, Slovenia.

Funding

European Union's Seventh Framework Programme 311820
6 · The paper itself

Abstract

The existing in vitro toxicological models lack translational potential, which makes difficult the application of gathered information to clinical usage. To tackle this issue, we built a model with four different types of primary liver cells: hepatic sinusoidal endothelial cells, hepatic stellate cells, Kupffer cells and hepatocytes. We cultured them in different combinations of composition and volumes of cell medium, hepatocyte proportions of total cells and additions of extracellular matrixes. We added rifampicin (RIF), ibuprofen (IBU) and 5-fluorouracil (5-FU) to this model and observed the microanatomy and physiology changes for a week with preclinical and clinical instruments. Among the different model configurations, we selected the feature combination of the in vitro model that had similar biomarker values to those measured in clinical diagnostics. When we exposed the selected model configuration to RIF, IBU and 5-FU, we observed similar glucose, triglyceride and albumin dynamics as in vivo (from clinical data). Therefore, we have built an in vitro liver model that resembles the liver microenvironment, and we have analysed it with clinical instrumentation to facilitate data translation. Furthermore, during these observations, we found that Kupffer and LSEC cells are suitable candidates for the search for clinical diagnostic markers of liver function.

Indexed as

in vitro toxicityliver in vitro modelnovel biomarkerstranslational research

Identifiers

PMID38794269
PMCPMC11124512

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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.