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Record W4415434667 · doi:10.1093/ndt/gfaf116.1358

#558 Population pharmacokinetic modelling of canagliflozin in advanced chronic kidney disease

2025· article· en· W4415434667 on OpenAlexaff
Elias Elenjickal, Thomas A. Mavrakanas, Ari Gritsas, Rita S. Suri, Amélie Marsot

Bibliographic record

VenueNephrology Dialysis Transplantation · 2025
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineMcGill UniversityUniversité de MontréalMcGill University Health Centre
Fundersnot available
KeywordsCanagliflozinKidney diseaseCovariatePharmacokineticsPopulationCohortDialysisRenal function

Abstract

fetched live from OpenAlex

Abstract Background and Aims Canagliflozin is an orally active, selective, reversible sodium-glucose cotransport-2 (SGLT-2) inhibitor used in patients with chronic kidney disease (CKD) to prevent cardiovascular (CV) and renal outcomes [1–3]. Its initiation is currently not recommended in advanced CKD (eGFR <30 ml/min/1.73 m²), end-stage kidney disease, or in those on kidney replacement therapies due to insufficient clinical and safety data [4]. This study aimed to develop a population pharmacokinetic (popPK) model using data from patients with advanced CKD, including those on maintenance hemodialysis (HD), to characterize the steady-state pharmacokinetics (PK) of canagliflozin at 100 mg and 300 mg doses, and to assess the impact of significant covariates on its PK. Method PK data were obtained from a single-center, prospective, single-arm, open-label interventional study conducted in two cohorts. The first cohort involved detailed PK sampling in 10 patients receiving intermittent HD, while the second included sparse PK sampling in 13 patients with CKD stage 4 or 5 not yet on dialysis [5]. A total of 332 PK observations from 23 patients were analyzed. Canagliflozin PK parameters were modeled using nonlinear mixed-effects modeling (NONMEM® version 7.5) with the first-order conditional estimation method with interaction. Model performance was evaluated using goodness of fit plot, bootstrap (n = 1000) and normalized prediction distribution error (NPDE). Results A two-compartment popPK model of canagliflozin with lag-time, sequential zero- and first-order absorption, and first-order elimination was developed. Age was a significant covariate of the absorption rate constant (Ka), which increased with age. Sex was a significant covariate for the apparent volume of distribution (V/F), which was lower in females (80.7 L in males and 49.1 L in females). The steady-state area under the curve (AUC) was higher in older patients and females, especially at the 300 mg dose. No difference in steady-state exposures was observed between patients with advanced CKD and those undergoing hemodialysis. Conclusion The developed model effectively characterizes the pharmacokinetics of canagliflozin in patients with advanced CKD, including those undergoing hemodialysis. The study demonstrates that steady state canagliflozin exposure is higher in older patients and females, particularly at the 300 mg dose, while renal function was not a significant determinant of exposure in advanced CKD patients.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.264
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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