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Record W4383370009 · doi:10.3390/ddc2030028

Drugs of the Kallikrein–Kinin System: An Overview

2023· article· en· W4383370009 on OpenAlexafffund
François Marceau

Bibliographic record

VenueDrugs and Drug Candidates · 2023
Typearticle
Languageen
FieldMedicine
TopicCoagulation, Bradykinin, Polyphosphates, and Angioedema
Canadian institutionsUniversité Laval
FundersMedical Research CouncilCanadian Institutes of Health ResearchMedical Research Council Canada
KeywordsKininKallikreinKininogenBradykininProteasesMedicinePrekallikreinPharmacologyAngioedemaReceptorImmunologyBiologyInternal medicineBiochemistry

Abstract

fetched live from OpenAlex

The kallikrein–kinin system consists of the two kininogen substrates present in the blood plasma, and two serine proteases: the plasma and tissue kallikreins. The action of the latter on kininogens produces small peptides, the kinins, short-lived, but endowed by powerful pharmacologic actions on blood vessels and other tissues. Many recent and exciting therapeutic developments in the field are briefly summarized. Notably, various novel strategies are being clinically developed to inhibit the formation of bradykinin or block its receptors in the management of hereditary angioedema. The interventions include orally bioavailable drugs, biotechnological proteins, and gene therapy. These approaches are currently explored in a variety of other inflammatory and thrombotic disorders. Harnessing controlled kinin formation is also of potential therapeutic interest, as shown by the clinical development of recombinant tissue kallikrein for ischemic stroke and renal disease. The biomarkers of kinin-mediated disorders, frequently implicating edemas, include the consumption of kininogen(s), plasma kallikrein activity, and the detection of circulating kinin metabolites such as fragments BK1–5 and BK2–9. Novel opportunities to clinically apply the underexploited drugs of the kallikrein–kinin system are briefly reviewed. This personal perspective is offered by an observer of and a participant in drug characterization throughout the last four decades.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.090
Threshold uncertainty score0.513

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.021
GPT teacher head0.283
Teacher spread0.262 · 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 designObservational
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

Citations8
Published2023
Admission routes2
Has abstractyes

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