Validated HPLC‐UV method for determination of naproxen in human plasma with proven selectivity against ibuprofen and paracetamol
Bibliographic record
Abstract
Estimating the influence of interfering compounds present in the biological matrix on the determination of an analyte is one of the most important tasks during bioanalytical method development and validation. Interferences from endogenous components and, if necessary, from major metabolites as well as possible co-administered medications should be evaluated during a selectivity test. This paper describes a simple, rapid and cost-effective HPLC-UV method for the determination of naproxen in human plasma in the presence of two other analgesics, ibuprofen and paracetamol. Sample preparation is based on a simple liquid-liquid extraction procedure with a short, 5 s mixing time. Fenoprofen, which is characterized by a similar structure and properties to naproxen, was first used as the internal standard. The calibration curve is linear in the concentration range of 0.5-80.0 µg/mL, which is suitable for pharmacokinetic studies following a single 220 mg oral dose of naproxen sodium. The method was fully validated according to international guidelines and was successfully applied in a bioequivalence study in humans. Copyright © 2015 John Wiley & Sons, Ltd.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".