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Record W4415279864 · doi:10.1039/d5md00284b

Development of small molecule inhibitors of ECM collagen secretion

2025· article· en· W4415279864 on OpenAlexafffund
Ross S. Mancini, Pierre-Antoine Bissey, Leonardo Massignan, Vaijinath Mane, Donald F. Weaver, Kenneth W. Yip, Fei‐Fei Liu, Mark A. Reed

Bibliographic record

VenueRSC Medicinal Chemistry · 2025
Typearticle
Languageen
FieldMedicine
TopicRadiopharmaceutical Chemistry and Applications
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreOntario Brain InstituteUniversity Health Network
FundersTemerty Faculty of Medicine, University of TorontoCanadian Institutes of Health ResearchPrincess Margaret Cancer FoundationMinistry of Health, Ontario
KeywordsSmall moleculeExtracellular matrixFibrosisFunction (biology)CD36Matrix metalloproteinaseMechanism of actionExtracellular

Abstract

fetched live from OpenAlex

Tissue fibrosis is a common consequence of many different acute and chronic injuries, which severely impairs the function of affected organs. A significant challenge is the lack of effective strategies to treat fibrotic disorders. The metabolic dysregulation underlying fibrosis may be reversed by the small molecule caffeic acid phenethyl ester (CAPE), but there are limitations which prevent its clinical use. Following the identification of caffeic acid derivative 1 from an in-house library screen, we performed structure-activity relationship studies which led to the discovery of novel small molecule inhibitors of extracellular matrix (ECM) collagen secretion. The small molecules increased PPARG and CD36 expression (markers of fatty acid metabolism), suggesting a mechanism of action involving a metabolic shift from fibrotic-to-normal state. The compounds identified in this study provide a foundation for further development towards a novel, first-in-class therapeutic agent for fibrosis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.307
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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