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Record W2152357527 · doi:10.1517/17425255.2015.1000859

Pharmacokinetic and pharmacodynamic evaluation of macitentan, a novel endothelin receptor antagonist for the treatment of pulmonary arterial hypertension

2015· review· en· W2152357527 on OpenAlexaboutno aff
Patricia N. Sidharta, Stephan Krähenbühl, Jasper Dingemanse

Bibliographic record

VenueExpert Opinion on Drug Metabolism & Toxicology · 2015
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAmbrisentanBosentanEndothelin receptor antagonistMedicinePulmonary hypertensionPharmacologyPharmacodynamicsEndothelin receptorInternal medicineHeart failurePharmacokineticsCardiologyReceptor

Abstract

fetched live from OpenAlex

INTRODUCTION: Pulmonary arterial hypertension (PAH) is a chronic disorder of the pulmonary vasculature characterized by elevated mean pulmonary arterial pressure eventually leading to right-sided heart failure and premature death. Macitentan is an oral, once-daily, dual endothelin (ET)A and ETB receptor antagonist with high affinity and sustained receptor binding that was approved in the USA, Europe, Canada, and Switzerland for the treatment of PAH. AREAS COVERED: This review discusses the pharmacokinetics (PK) and pharmacodynamics (PD) of macitentan and its drug interaction potential based on preclinical and clinical data. EXPERT OPINION: Up to date, macitentan is the only registered treatment for PAH that significantly reduced morbidity and mortality as a combined endpoint in a long-term event-driven study. The safety profile of macitentan is favorable with respect to hepatic safety and edema/fluid retention and may be better than that of other ET receptor antagonists such as bosentan and ambrisentan. The PK profile supports a once-a-day dosing regimen. Macitentan has limited interactions with other drugs. Based on these characteristics macitentan is an important new addition to the treatment of PAH.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.001
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 head0.158
GPT teacher head0.435
Teacher spread0.277 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations30
Published2015
Admission routes1
Has abstractyes

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