MétaCan
Menu
Back to cohort

PCSK9: Regulation and Target for Drug Development for Dyslipidemia

2016· review· en· W2290120395 on OpenAlexaff
Amy C. Burke, Jacqueline S. Dron, Robert A. Hegele, Murray W. Huff

Bibliographic record

VenueThe Annual Review of Pharmacology and Toxicology · 2016
Typereview
Languageen
FieldMedicine
TopicLipoproteins and Cardiovascular Health
Canadian institutionsWestern University
Fundersnot available
KeywordsPCSK9KexinProprotein convertaseLDL receptorSubtilisinProprotein ConvertasesDrug developmentDrugDyslipidemiaPharmacologyDrug discoveryReceptorBiologyBioinformaticsCholesterolMedicineLipoproteinEndocrinologyInternal medicineBiochemistryDiseaseEnzyme

Abstract

fetched live from OpenAlex

Proprotein convertase subtilisin/kexin type-9 (PCSK9) is a secreted zymogen expressed primarily in the liver. PCSK9 circulates in plasma, binds to cell surface low-density lipoprotein (LDL) receptors, is internalized, and then targets the receptors to lysosomal degradation. Studies of naturally occurring PCSK9 gene variants that caused extreme plasma LDL cholesterol (LDL-C) deviations and altered atherosclerosis risk unleashed a torrent of biological and pharmacological research. Rapid progress in understanding the physiological regulation of PCSK9 was soon translated into commercially available biological inhibitors of PCSK9 that reduced LDL-C levels and likely also cardiovascular outcomes. Here we review the swift evolution of PCSK9 from novel gene to drug target, to animal and human testing, and finally to outcome trials and clinical applications. In addition, we explore how the genetics-guided path to PCSK9 inhibitor development exemplifies a new paradigm in pharmacology. Finally, we consider some potential challenges as PCSK9 inhibition becomes established in the clinic.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.952
Threshold uncertainty score0.680

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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)0.0000.000

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 head0.032
GPT teacher head0.390
Teacher spread0.358 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations79
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueThe Annual Review of Pharmacology and ToxicologySame topicLipoproteins and Cardiovascular HealthFrench-language works237,207