Simultaneous Determination of Docetaxel and Celecoxib in Porous Microparticles and Rat Plasma by Liquid-Liquid Extraction and HPLC with UV Detection: in vitro and in vivo Validation and Application
Bibliographic record
Abstract
PURPOSE: A simple, rapid, sensitive, and reliable HPLC method with UV detection was developed and validated for simultaneous quantitation of docetaxel and celecoxib and paclitaxel for dissolution characterization and pharmacokinetic studies. METHODS: The HPLC assay was performed isocratically on a reversed-phase C18 μ-Bondapack column using a mobile phase of acetonitrile:water (45:55, v/v) at a flow rate of 1.2 mL/min, and the analytes were detected at 230 nm. Paclitaxel was used as an internal standard for analysis of plasma samples following simple liquid-liquid extraction with n-hexane:isoamyl alcohol (97:3). The method was validated for specificity, linearity, sensitivity, precision, accuracy, robustness, and in vitro-in vivo application. RESULTS: The retention times for docetaxel, paclitaxel, and celecoxib were 10.94, 12.4, and 16.81 min, respectively. The standard curves covering 0.1-1 μg/mL and 0.05-4 μg/mL were linear using dissolution medium and rat plasma, respectively. The limit of quantitation of the method was 50 ng/mL using 100 μL of rat plasma sample and injection of 50 μL of the residue. Within- and between-day precision and accuracy did not exceed 16.86% and 12.10%, respectively. This validated method was successfully used to quantify docetaxel and celecoxib simultaneously in the release study of docetaxel- celecoxib -loaded porous microparticles and pharmacokinetics studies. The methods were found to be simple, specific, precise, accurate, and reproducible. In this study, paclitaxel was used as the internal standard while dexamethasone, flutamide, and budesonide proved suitable alternative as an internal standard. CONCLUSION: Since docetaxel and celecoxib could be co-administered for the treatment of a wide range of cancers such as non-small cell lung carcinoma, the developed method is particularly advantageous for routine therapeutic drug monitoring and pharmacokinetic studies of these drugs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".