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Record W1820440715 · doi:10.1517/17425255.2015.1085506

Pharmacokinetic and pharmacodynamic profile of degarelix for prostate cancer

2015· review· en· W1820440715 on OpenAlexaff
Laurence Klotz

Bibliographic record

VenueExpert Opinion on Drug Metabolism & Toxicology · 2015
Typereview
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsProstate cancerPharmacodynamicsMedicinePharmacokineticsCancerOncologyPharmacologyInternal medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Luteinizing hormone-releasing hormone (LHRH) agonists have been the mainstay of androgen deprivation therapy (ADT) for advanced prostate cancer for over two decades. However, their limitations include a transient initial rise in testosterone, failure to reduce testosterone to castrate levels in some patients, incomplete suppression of follicle-stimulating hormone (FSH), and an increased risk of cardiovascular (CV) events in those with pre-existing CV disease. This article considers whether the LHRH antagonist degarelix offers significant advantages over LHRH agonists. AREAS COVERED: This review covers the development and introduction of degarelix, its pharmacodynamic and pharmacokinetic properties, and the efficacy and safety results of Phase II and III clinical studies. EXPERT OPINION: Degarelix has clear pharmacodynamic advantages over the LHRH agonist leuprolide in terms of almost immediate suppression of testosterone to castrate levels and sustained suppression of FSH levels. It reduces the risk of CV events vs agonists in men with pre-existing CV disease. This finding, which may reflect differential effects on FSH and/or endothelial plaques, requires confirmation in a prospective study; however, it is the view of the author that the differential effects on CV events are real and suggest that men with pre-existing CV disease requiring ADT should preferentially be treated with degarelix.

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 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 score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.085
GPT teacher head0.462
Teacher spread0.377 · 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

Citations19
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueExpert Opinion on Drug Metabolism & ToxicologySame topicProstate Cancer Treatment and ResearchFrench-language works237,207