Model-Informed Development of Sotalol Loading and Dose Escalation Employing an Intravenous Infusion
Bibliographic record
Abstract
BACKGROUND: Sotalol is often employed to prevent recurrence of symptomatic atrial flutter/atrial fibrillation. Because sotalol can prolong the QT interval excessively causing ventricular arrhythmias, a 3-day in-hospital loading or dose escalation period is mandated with oral administration in the product label for patient safety. In patients with normal renal function, 3 days (five oral doses) are required to obtain steady state maximum sotalol concentration, which results in maximum QT prolongation. The aim of this study is to develop an intravenous to oral loading regime for sotalol therapy that reduces the 3-day in-hospital initiation or dose escalation with oral administration to 1 day without compromising patient safety. METHODS: Using model-informed drug development techniques, simulations were developed for initiation and dose escalation of sotalol therapy by employing an intravenous loading dose followed by oral sotalol administrations. RESULTS: In patients with normal renal function, an initial 1-h loading dose of intravenous sotalol followed by two oral doses in 24 h has been developed permitting attainment of three maximum serum concentrations reflecting maximum QT prolongation in a 1-day observation period. Dosing regimens for patients with impaired renal function are also developed. CONCLUSIONS: In patients with normal renal function, using an intravenous loading dose followed by oral administrations permits safe initiation or dose escalation of sotalol in 1 day instead of the 3-day dosing regimen with oral administration.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".