ArticleALTEX2023
Development of physiologically-based gut absorption model for probabilistic prediction of environmental chemical bioavailability.
Article in ALTEX, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Human Small Intestinal Tissue Models to Assess Barrier Permeability: Comparative Analysis of Caco-2 Cells, Jejunal and Duodenal Enteroid-Derived Cells, and EpiIntestinalBioengineering (Basel, Switzerland) · 2025Article
- Comparative analysis of Caco-2 cells and human jejunal and duodenal enteroid-derived cells in gel- and membrane-based barrier models of intestinal permeability.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Incorporating new approach methods (NAMs) data in dose-response assessments: The future is now!Journal of toxicology and environmental health. Part B, Critical reviews · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Absorption in the gastrointestinal tract is a key factor determining the bioavailability of chemicals after oral exposure but is frequently assumed to have a conservative value of 100% for environmental chemicals, particularly in the context of high-throughput toxicokinetics for in vitro-to-in vivo extrapolation (IVIVE). For pharmaceutical compounds, the physiologically based advanced compartmental absorption and transit (ACAT) model has been used extensively to predict gut absorption but has not generally been applied to environmental chemicals. Here we develop a probabilistic environmental compartmental absorption and transit (PECAT) model, adapting the ACAT model to environmental chemicals. We calibrated the model parameters to human in vivo, ex vivo, and in vitro datasets of drug permeability and fractional absorption by considering two key factors: (1) differences between permeability in Caco-2 cells and in vivo permeability in the jejunum, and (2) differences in in vivo permeability across different gut segments. Incorporating these factors probabilistically, we found that given Caco-2 permeability measurements, predictions of the PECAT model are consistent with the (limited) available gut absorption data for environmental chemicals. However, the substantial chemical-to-chemical variability observed in the calibration data often led to wide probabilistic confidence bounds in the predicted fraction absorbed and resulting steady state blood concentration. Thus, while the PECAT model provides a statistically rigorous, physiologically based approach for incorporating in vitro data on gut absorption into toxicokinetic modeling and IVIVE, it also highlights the need for more accurate in vitro models and data for measuring gut segment-specific in vivo permeability of environmental chemicals.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.