Determination of Pharmacokinetics of Flurbiprofen in Pakistani Population Using Modified HPLC Method
Bibliographic record
Abstract
Pharmacokinetics of flurbiprofen has been studied in different populations, especially in Caucasian. However, there are very few studies reported from Eastern part of world. Previous studies suggested that genetic and environmental factors may cause inter-individual differences in flurbiprofen disposition, so we investigated the pharmacokinetics of flurbiprofen in Pakistani subjects. A single oral dose of 100 mg of flurbiprofen was administered to 22 healthy male Pakistani adults after overnight fasting for 10 h. Periodical blood sampling was done at 0.25, 0.5, 0.75, 1, 1.5, 2, 2.5, 3, 4, 5, 6, 7, 8, 10, 12, and 24 h after dosing. Plasma concentration of flurbiprofen was determined by a modified high-performance liquid chromatography method, which was simple, sensitive, less time consuming and economical with ordinary internal standard. The method was validated according to ICH guidelines and was found to be sensitive, accurate and precise. The pharmacokinetic parameters observed in Pakistani subjects when compared with other populations (USA, UK, Canadian, French, and Indian) did not show considerable ethnic differences. However, one subject's data was suggestive of being poor metabolizer of flurbiprofen which supports the presence of CYP2C9 polymorphism contributing to inter-individual differences in flurbiprofen disposition. Pharmacogenomic studies are needed to verify this hypothesis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".