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Record W1966585118 · doi:10.1371/journal.pone.0083190

RVX-208, an Inducer of ApoA-I in Humans, Is a BET Bromodomain Antagonist

2013· article· en· W1966585118 on OpenAlexafffund
Kevin G. McLure, Emily M. Gesner, Laura Tsujikawa, Olesya A. Kharenko, Sarah Attwell, Eric Campeau, Sylwia Wasiak, Adam J. Stein, André White, Eric Fontano, R.K. Suto, Norman C.W. Wong, Gregory S. Wagner, Henrik C. Hansen, Peter R. Young

Bibliographic record

VenuePLoS ONE · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsResverlogix (Canada)
FundersAlberta Innovates
KeywordsBromodomainBRD4EpigeneticsApolipoprotein BInducerBET inhibitorBinding siteChemistryAcetylationIn vivoBiologyBiochemistryCholesterolGeneticsGene

Abstract

fetched live from OpenAlex

Increased synthesis of Apolipoprotein A-I (ApoA-I) and HDL is believed to provide a new approach to treating atherosclerosis through the stimulation of reverse cholesterol transport. RVX-208 increases the production of ApoA-I in hepatocytes in vitro, and in vivo in monkeys and humans, which results in increased HDL-C, but the molecular target was not previously reported. Using binding assays and X-ray crystallography, we now show that RVX-208 selectively binds to bromodomains of the BET (Bromodomain and Extra Terminal) family, competing for a site bound by the endogenous ligand, acetylated lysine, and that this accounts for its pharmacological activity. siRNA experiments further suggest that induction of ApoA-I mRNA is mediated by BET family member BRD4. These data indicate that RVX-208 increases ApoA-I production through an epigenetic mechanism and suggests that BET inhibition may be a promising new approach to the treatment of atherosclerosis.

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)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.237
Teacher spread0.207 · 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

Citations199
Published2013
Admission routes2
Has abstractyes

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Same venuePLoS ONESame topicProtein Degradation and InhibitorsFrench-language works237,207