Evidence map›Paper›PMID 40559912›Full record

ArticleToxics2025

Comparative Analysis of Chemical Distribution Models for Quantitative In Vitro to In Vivo Extrapolation.

Hsing-Chieh Lin, Lucie C Ford, Ivan Rusyn, Weihsueh A Chiu

Abstract read
In one paragraph

Article in Toxics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Extrapolation steps in quantitativeCurrent research in toxicology · 2026
    Review
  5. 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.

Hsing-Chieh LinDepartment of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA.
Lucie C FordDepartment of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA.
Ivan RusynDepartment of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA.
Weihsueh A ChiuDepartment of Veterinary Physiology and Pharmacology, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA.

Funding

Single cell, multi-parametric high throughput platform to classify endocrine disruptor potential of mixturesP42ES027704 · NIEHS · TEXAS A&M UNIVERSITY · PI Efstratios Pistikopoulos · 2017 to 2026
$21.2M
Texas A&M Center for Environmental Health Research (TiCER)P30ES029067 · NIEHS · TEXAS A&M UNIVERSITY · PI Sakhila Banu · 2019 to 2026
$13.0M
Regulatory Science in Environmental Health and ToxicologyT32ES026568 · NIEHS · TEXAS A&M UNIVERSITY · PI Weihsueh A Chiu, Natalie M Johnson · 2016 to 2026
$3.8M
California Environmental Protection Agency Office of Environmental Health Hazard Assessment Agreement Number 2022-E0036NIEHS NIH HHS P30 ES029067NIEHS NIH HHS P42 ES027704NIEHS NIH HHS P42 ES027704, P30 ES029067NIEHS NIH HHS T32 ES026568Unilever, PLC MA-2023-02180NU.S. Environmental Protection Agency RD84003201; RD84045001
6 · The paper itself

Abstract

Quantitative in vitro to in vivo extrapolation (QIVIVE) utilizes in vitro data to predict in vivo toxicity. However, there may be differences between reported nominal concentrations and the biologically effective free concentrations in media or cells. This study evaluated the performance of four in vitro mass balance models for predicting free media or cellular concentrations. Comparing model predictions to experimentally measured values for a wide range of chemicals and test systems, we found that predictions of media concentrations were more accurate than those for cells, and that the Armitage model had slightly better performance overall. Through sensitivity analyses, we found that chemical property-related parameters were most influential for media predictions, while cell-related parameters were also important for cellular predictions. Assessing the impact of these models on QIVIVE accuracy for a small dataset of 15 chemicals with both in vitro and regulatory in vivo points-of-departure, we found that incorporating in vitro and in vivo bioavailability resulted in at best modest improvements to in vitro-in vivo concordance. Based on these results, we conclude that a reasonable first-line approach for incorporating in vitro bioavailability into QIVIVE would be to use the Armitage model to predict media concentrations, while prioritizing accurate chemical property data as input parameters.

Indexed as

bioavailable concentrationfree concentrationin vitro assaysmass balance modelsQIVIVE

Identifiers

PMID40559912
PMCPMC12196727

What OpenQuestion 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 OpenQuestion graph.