MétaCan
Menu
Back to cohort
Record W2619122711 · doi:10.1093/ndt/gfx113.mo002

MO002APABETALONE (RVX-208) IMPACTS KEY BIOMARKERS AND PATHWAYS ASSOCIATED WITH CHRONIC KIDNEY DISEASE IN PATIENTS WITH SEVERE RENAL IMPAIRMENT

2017· article· en· W2619122711 on OpenAlexaff
Ewelina Kulikowski, Sylwia Wasiak, Laura Tsujikawa, Dean Gilham, Christopher Halliday, Brooke D. Rakai, Ravi Jahagirdar, Kamyar Kalantar‐Zadeh, Mike Sweeney, Jan Johansson, Norman C.W. Wong, Richard A. Robson

Bibliographic record

VenueNephrology Dialysis Transplantation · 2017
Typearticle
Languageen
FieldMedicine
TopicBlood Pressure and Hypertension Studies
Canadian institutionsResverlogix (Canada)
Fundersnot available
KeywordsMedicineKidney diseaseDiseaseInternal medicineKidneyIntensive care medicine

Abstract

fetched live from OpenAlex

INTRODUCTION AND AIMS: Chronic kidney disease (CKD) is associated with a progressive loss of renal function and a high risk of cardiovascular complications that lead to unfavorable outcomes in nearly half of patients. Apabetalone is a first-in-class orally active bromodomain and extraterminal domain inhibitor (BETi) associated with a reduction in major adverse cardiac events in phase 2 clinical trials in patients with cardiovascular disease (CVD). Here, apabetalone was studied in a phase 1, open-label, parallel group study to examine single dose pharmacokinetics (PK) and levels of CKD markers in patients with impaired renal function. METHODS: Eight subjects with stage 4 CKD not on dialysis (mean eGFR=20 ml/min/1.73m2) and eight age-, gender-, and BMI-matched subjects (mean eGFR=78.5 ml/min/1.73m2) received a single 100 mg oral dose of apabetalone. Plasma samples from participants were collected at multiple time points over a period of 48 hours for PK analysis and at 12 hours post dose for proteomic analysis using the SOMAscan® 1.3K platform. Proteomics data were analysed with Ingenuity® Pathway Analysis (IPA) software to identify the top pathways dysregulated in CKD patients compared to healthy subjects, and the effect of apabetalone treatment on those pathways. RESULTS: PK parameters, including mean Cmax, AUClast, and t½, were similar in healthy subjects and CKD patients, with median tmax at 4h in both groups. At baseline, plasma proteomics showed enrichment of markers known to correlate with disease progression in CKD patients, as compared to matched controls, including cystatin C and β2 microglobulin (3-fold and 5-fold enrichment, respectively, p<0.001). Accordingly, pathway analysis of CKD plasma proteome at baseline confirmed an upregulation of pathways known to be activated in CKD such as the inflammatory response, immune response, thrombosis, calcification and oxidative stress, compared to controls. These pathways were robustly and highly significantly downregulated in CKD patients by apabetalone at 12h post dose. Apabetalone treatment also downregulated the abundance of circulating CKD biomarkers involved in vascular inflammation, endothelial dysfunction, acute phase response, coagulation and vascular calcification, including IL-6, TNFα, IL-1, ICAM-1, VCAM-1, CRP, plasminogen activator inhibitor-1 and osteopontin (p<0.05). CONCLUSIONS: In stage 4 CKD patients, a single oral dose of apabetalone rapidly reduces circulating markers and molecular pathways linked to progression of renal disease and accompanying CVD complications. The potential long term impact of apabetalone on biomarkers, renal function and CVD outcomes in patients with impaired kidney function is currently being studied in a subpopulation of the phase 3 BETonMACE CVD outcomes trial.

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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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 designNon-randomized trial
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueNephrology Dialysis TransplantationSame topicBlood Pressure and Hypertension StudiesFrench-language works237,207