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Record W3045095839 · doi:10.1111/cts.12842

Population Pharmacokinetics of Candesartan in Patients with Chronic Heart Failure

2020· article· en· W3045095839 on OpenAlexaff
Imad Kassem, Steven Sanche, Jun Li, Guillaume Bonnefois, Marie‐Pierre Dubé, Jean‐Lucien Rouleau, Jean‐Claude Tardif, Michel White, Jacques Turgeon, Fahima Nekka, Simon de Denus

Bibliographic record

VenueClinical and Translational Science · 2020
Typearticle
Languageen
FieldMedicine
TopicHeart Failure Treatment and Management
Canadian institutionsUniversité de MontréalMontreal Heart Institute
Fundersnot available
KeywordsCandesartanNONMEMMedicinePharmacokineticsHeart failureRenal functionPopulationInternal medicinePopulation pharmacokineticsDosingPharmacologyCardiologyUrologyBlood pressureAngiotensin II

Abstract

fetched live from OpenAlex

Heart failure (HF) causes pathological changes in multiple organs, thus affecting the pharmacokinetics (PK) of drugs. The aim of this study was to investigate the PK of candesartan in patients with HF while examining significant covariates and their related impact on estimated clearance using a population PK (Pop-PK) modeling approach. Data from a prospective, multicenter study were used. Modeling and simulations were conducted using Nonlinear Mixed-Effects Modeling (NONMEM) and R software. A total of 281 white patients were included to develop the Pop-PK model. The final model developed for apparent oral clearance (CL/F) included weight, estimated glomerular filtration rate (eGFR), and diabetes, which partly explained its interindividual variability. The mean CL/F value estimated was 7.6 L/h (1.7-22.6 L/h). Simulations revealed that an important decrease in CL/F (> 25%) is obtained with the combination of the factors retained in the final model. Considering these factors, a more individualized approach of candesartan dosing should be investigated in patients with HF.

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 score0.015
Threshold uncertainty score0.135

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 head0.030
GPT teacher head0.333
Teacher spread0.303 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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