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Record W2981428809 · doi:10.1093/eurheartj/ehz746.0459

P5509Apabetalone (RVX-208) inhibits key drivers of vascular inflammation, calcification, and plaque vulnerability through a BET-dependent epigenetic mechanism

2019· article· en· W2981428809 on OpenAlexaff
Ewelina Kulikowski, Laura Tsujikawa, Dean Gilham, Brooke D. Rakai, C. Halliday, Stephanie C. Stotz, Chris Sarsons, Li Fu, Emily Daze, Sylwia Wasiak, Deborah Studer, Kristina D. Rinker, M.T. Sweeney, Jan O. Johansson, Norman C.W. Wong

Bibliographic record

VenueEuropean Heart Journal · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtein Degradation and Inhibitors
Canadian institutionsUniversity of CalgaryResverlogix (Canada)
Fundersnot available
KeywordsBromodomainInflammationMedicineCell adhesion moleculeBET inhibitorEndotheliumCalcificationEndothelial activationCell adhesionInternal medicineImmunologyEndocrinologyEpigeneticsCellBiochemistryBiologyGene

Abstract

fetched live from OpenAlex

Abstract Apabetalone (RVX-208) is an orally available small molecule bromodomain & extraterminal (BET) protein inhibitor that targets the second bromodomain (BD2) of BET proteins. Apabetalone returns dysregulated BET-dependent transcription toward normal physiological levels. In phase 2 trials, apabetalone treatment reduced the incidence of major adverse cardiac events by 44% in CVD patients and by 57% in diabetic CVD patients. Previous studies have highlighted apabetalone's positive impact on vascular calcification (VC) and inflammation (VI) marker expression in vitro, as well as its ability to lower serum alkaline phosphatase (ALP) levels, and improve atherosclerotic plaque stability parameters in treated patients. In CVD, elevated inflammatory mediators and cell surface adhesion molecules drive VI, resulting in leukocyte adhesion, infiltration, uptake of oxLDL, and ultimately plaque formation. Here we show in vitro that THP-1 monocyte adhesion to human aortic endothelial cells (HAECs) increases with TNFα stimulation and is attenuated by apabetalone treatment, with fewer monocytes attaching to HAECs under flow conditions. This functional outcome is attributed to apabetalone's reduction of key endothelial adhesion genes, VCAM-1 (50%, p=0.0001) and SELE (37%, p=9x10–5). Apabetalone also prevents TNFα induction of endothelial recruitment genes (MCP-1; 75%, p=0.0002) and genes involved in plaque rupture (IL8; 24%, p=2x10–5). Basal HAEC ALP expression, a potential contributor to endothelial dysfunction and VC, also decreases with apabetalone treatment (70%, p=0.005). Induction of VI genes by TNFα is BET-dependent as degradation of BET proteins by MZ-1 prevents an increase in transcripts in response to TNFα treatment. Ingenuity® Pathway Analysis (IPA®), GSEA, and GO analysis of HAEC gene expression data predicts apabetalone inhibition of pro-atherogenic pathways, gene sets, and upstream regulators induced by TNFα. These include cytokine and chemokine, Toll-Like Receptor (TLR), NFkβ, Interferon and TNFα signaling. In addition, IPA® disease and biological function analysis predicts inhibition of immune cell activation and recruitment by apabetalone. Plasma proteomics (SOMAscan®) and IPA® analysis from apabetalone-treated CVD patients in ASSERT and ASSURE phase 2 trials indicate that apabetalone inhibits pro-atherogenic upstream regulators (IL-6 and IFNy), canonical pathways, and diseases and functions. Serum ALP also decreases dose dependently with apabetalone treatment (ASSERT). Epigenetic inhibition of VI and VC driven atherogenesis likely contributes to the reduction in MACE observed in phase 2 apabetalone treated patients. The ongoing phase 3 post-acute coronary syndrome (ACS) clinical trial in T2DM patients, BETonMACE, is currently testing this hypothesis.

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 score0.003
Threshold uncertainty score0.011

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)0.0030.001

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 head0.015
GPT teacher head0.248
Teacher spread0.233 · 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".

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Citations1
Published2019
Admission routes1
Has abstractyes

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