Pharmacokinetic study of Sudaxine in dog plasma using novel LC–MS/MS method
Bibliographic record
Abstract
Abstract Context Sudaxine is a novel respiratory stimulant that increases ventilatory drive via NO + ‐thiolate signaling and is under development for reversal of opioid‐induced respiratory depression and other critical care indications. Objective This study investigates the pharmacokinetic characteristics after intravenous administration of Sudaxine by using a simple liquid chromatography–tandem mass spectrometry (LC–MS/MS) method. Materials and methods A sensitive LC–MS/MS method was validated to determine the concentration of Sudaxine in beagle dog plasma after intravenous administration of Sudaxine at (3, 10, 30, and 100 mg/kg). Blood samples (1 mL) were collected at designated time points and SDX concentration was measured for pharmacokinetic study. Results The calibration curve was linear within the range of 50–5,000 ng/mL with the lower limit of quantification at 50 ng/mL. The C Tmax for all doses was reached at 10 minutes (T max ). Over the dose range studied, average concentration – time curves and systemic exposure (C Tmax and AUC 0–t ) increased to Sudaxine dose. The terminal half‐life of Sudaxine in dogs ranged from 10 to 30 minutes and about 17.3 ± 1.0% of Sudaxine was protein‐bound in dog plasma. Discussion and conclusions We developed a novel LC–MS/MS method of Sudaxine detection and quantification and determined its pharmacokinetic profiles after intravenous administration in canine subjects. Sudaxine followed first‐order kinetics with rapid dose‐dependent clearance rates and short half‐life making it an ideal candidate for use in a critical care setting with intramuscular or IV administration.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".