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Record W4404639357 · doi:10.1038/s41419-024-07251-y

Seratrodast inhibits ferroptosis by suppressing lipid peroxidation

2024· article· en· W4404639357 on OpenAlexafffund
Juliane Tschuck, Wulf Tonnus, Shubhangi Gavali, Andrea Kolak, Melodie Mallais, Francesca Maremonti, Mami Sato, Ina Rothenaigner, José Pedro Friedmann Angeli, Derek A. Pratt, Andreas Linkermann, Kamyar Hadian

Bibliographic record

VenueCell Death and Disease · 2024
Typearticle
Languageen
FieldMedicine
TopicFerroptosis and cancer prognosis
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLipid peroxidationNecroptosisPharmacologyProgrammed cell deathApoptosisOxidative stressGPX4ChemistryDrugCancer researchMedicineBiochemistrySuperoxide dismutase

Abstract

fetched live from OpenAlex

Ferroptosis is a regulated and non-apoptotic form of cell death mediated by iron-dependent peroxidation of polyunsaturated fatty acyl tails in phospholipids. Research of the past years has shed light on the occurrence of ferroptosis in organ injury and degenerative diseases of the brain, kidney, heart, and other tissues. Hence, ferroptosis inhibition may prove therapeutically beneficial to treat distinct diseases. In this study, we explored the ferroptosis-modulating activity of seratrodast, an inhibitor of thromboxane A2 (TXA2) receptor, which is approved in some countries for the treatment of asthma. Interestingly, seratrodast suppressed ferroptosis, but not apoptosis and necroptosis; thus, demonstrating selective anti-ferroptotic activity. While seratrodast itself does not inhibit lipid peroxidation, it exhibits potent radical-trapping antioxidant activity upon reduction to its corresponding hydroquinone form-analogously to ubiquinone and vitamin K. Importantly, seratrodast ameliorated the severity of renal ischemia-reperfusion injury in mice. Together, this study provides a drug repurposing case, where seratrodast-a marketed drug-can undergo fast-forward preclinical/clinical development for the inhibition of ferroptosis in distinct degenerative diseases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

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

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 head0.013
GPT teacher head0.254
Teacher spread0.242 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations14
Published2024
Admission routes2
Has abstractyes

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