Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 177

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

New Technologies and Screening Strategies for Hepatotoxicity: Use of In Vitro Models

Dambach, Donna M., Andrews, Barbara A., Moulin, Frederic·2005·Toxicologic Pathology

Abstract

Hepatotoxicity remains a significant cause for drug failures during clinical trials. This is due, in part, to the idiosyncratic nature of toxicity in humans and inherent physiological differences between humans and preclinical species leading to limited correct prediction of adverse responses in humans. To address this issue, robust screening assays are being developed, which have heightened predictive capacity for human hepatotoxicity, and may be utilized throughout the discovery and development phases in conjunction with traditional in vivo methods, for decision making during drug selection and risk assessment. This manuscript describes an example application of in vitro-based strategies using human hepatocyte cultures in lead optimization screening in conjunction with ADME profiling, for evaluation of compound-associated CYP450 induction potential, and the identification of potentially useful biomarkers as predictors of hepatotoxicity for use in vitro, and in preclinical species and humans.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/01926230590522284

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 2 references

Cited by

See all 177

Metadata

Authors
Dambach, D. M., Andrews, B. A., & Moulin, F.
Venue
Toxicologic Pathology
Published
2005-01
Volume
33
Issue
1
Pages
17-26
ISSN
  • 0192-6233 (ISSN-L)
  • 1533-1601
OpenAlex
W2127284788
Cited by
177
References
2
FWCI
9.98
Type
journal article
Language
en
Open access
closed

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: New Technologies and Screening Strategies for Hepatotoxicity: Use of In Vitro Models. https://app.cochat.ai/papers/10.1080/01926230590522284
The paper
Dambach, D. M., Andrews, B. A., & Moulin, F. (2005). New Technologies and Screening Strategies for Hepatotoxicity: Use of In Vitro Models. Toxicologic Pathology. https://doi.org/10.1080/01926230590522284

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.