Evidence map›Paper›PMID 39091793›Full record

ArticlebioRxiv : the preprint server for biology2024

Deep Learning-based Modeling for Preclinical Drug Safety Assessment.

Guillaume Jaume, Simone de Brot, Andrew H Song, Drew F K Williamson, Lukas Oldenburg, Andrew Zhang, Richard J Chen, Javier Asin, Sohvi Blatter, Martina Dettwiler and 9 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

19 authors.

Guillaume JaumeDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Simone de BrotInstitute of Animal Pathology, Vetsuisse, University of Bern, Switzerland.
Andrew H SongDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Drew F K WilliamsonDepartment of Pathology & Laboratory Medicine, Emory University School of Medicine, Atlanta, GA.
Lukas OldenburgDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Andrew ZhangDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Richard J ChenDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Javier AsinCalifornia Animal Health and Food Safety Laboratory, University of California-Davis, San Bernardino, CA.
Sohvi BlatterInstitute of Animal Pathology, Vetsuisse, University of Bern, Switzerland.
Martina DettwilerVetscope Pathologie Dettwiler, Riehen, Switzerland.
Christine GoepfertInstitute of Animal Pathology, Vetsuisse, University of Bern, Switzerland.
Llorenç Grau-RomaInstitute of Animal Pathology, Vetsuisse, University of Bern, Switzerland.
Sara SotoInstitute of Animal Pathology, Vetsuisse, University of Bern, Switzerland.
Stefan M KellerVetscope Pathologie Dettwiler, Riehen, Switzerland.
Sven RottenbergInstitute of Animal Pathology, Vetsuisse, University of Bern, Switzerland.
Jorge Del-PozoRoyal (Dick) School of Veterinary Studies, Roslin, United-Kingdom.
Rowland PettitDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Long Phi LeDepartment of Pathology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.
Faisal MahmoodDepartment of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.

Funding

Multimodal and Generative AI for PathologyR35GM138216 · NIGMS · BROAD INSTITUTE, INC. · PI Faisal Mahmood · 2020 to 2026
$3.4M
NIGMS NIH HHS R35 GM138216
6 · The paper itself

Abstract

In drug development, assessing the toxicity of candidate compounds is crucial for successfully transitioning from preclinical research to early-stage clinical trials. Drug safety is typically assessed using animal models with a manual histopathological examination of tissue sections to characterize the dose-response relationship of the compound - a time-intensive process prone to inter-observer variability and predominantly involving tedious review of cases without abnormalities. Artificial intelligence (AI) methods in pathology hold promise to accelerate this assessment and enhance reproducibility and objectivity. Here, we introduce TRACE, a model designed for toxicologic liver histopathology assessment capable of tackling a range of diagnostic tasks across multiple scales, including situations where labeled data is limited. TRACE was trained on 15 million histopathology images extracted from 46,734 digitized tissue sections from 157 preclinical studies conducted on

Identifiers

PMID39091793
PMCPMC11291027

What OpenQuestion holds

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LicenceCC BY-NC
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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.