Population Pharmacokinetics of Vericiguat in Patients With Heart Failure With Reduced Ejection Fraction: An Integrated Analysis
Bibliographic record
Abstract
Vericiguat, a novel stimulator of soluble guanylate cyclase (sGC), is indicated for the treatment of patients following a hospitalization for heart failure or need for outpatient intravenous diuretics, with symptomatic chronic heart failure and ejection fraction less than 45%. Pharmacokinetic (PK) data from the phase II trial SOCRATES-REDUCED (Soluble Guanylate Cyclase Stimulator in Heart Failure Study) and the phase III trial VICTORIA (Vericiguat Global Study in Patients With Heart Failure With Reduced Ejection Fraction) were used to characterize vericiguat PK. A total of 8,092 concentration records from 2,321 participants (362 from SOCRATES-REDUCED and 1,959 from VICTORIA) were utilized for the development of the population PK model. The final PK model was a one-compartment model with first-order absorption and linear elimination. Baseline body weight and time-varying body weight were identified as statistically significant covariates affecting apparent clearance (CL/F) and volume of distribution, respectively. Age, sex, race, bilirubin, estimated glomerular filtration rate, and albumin did not affect vericiguat PK. Baseline disease-related factors, such as left ventricular ejection fraction, New York Heart Association (NYHA) class, and N-terminal pro B-type natriuretic peptide, also did not influence vericiguat PK. Since vericiguat is a titrated drug, the impact of vericiguat PK on the titration to and maintenance of the target dose in VICTORIA was assessed. The distribution of steady-state doses in VICTORIA was similar across CL/F quartiles, suggesting that the ability to reach and maintain dosing at the target 10-mg dose was not related to vericiguat exposure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".