Development of a RP-HPLC method for analysis of docetaxel in tumor-bearing mice plasma and tissues following injection of docetaxel-loaded pH responsive targeting polymeric micelles
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: A simple, rapid, and sensitive reversed-phase high performance liquid chromatography (RP-HPLC) method based on liquid-liquid extraction was developed and validated for determination of docetaxel (DTX) in plasma and homogenate tissues of tumor-bearing mice. EXPERIMENTAL APPROACH: Samples were spiked with celecoxib as the internal standard and separation was achieved on a μ-Bondapak C18 HPLC column. The mobile phase consisted of a mixture of acetonitrile/water (40/60 v/v) at flow rate of 1.2 mL/min and the effluent was monitored at 230 nm. RESULTS: Calibration curves were linear over the concentration range of 0.1-10 μg/mL of DTX in plasma and 0.25-50 μg/mL in tissue homogenates with acceptable precision and accuracy. The mean recoveries of the drug from plasma extraction was 94.6 ± 1.44% while those of tissue homogenates ranged from 73.5 ± 3.2 to 85.3 ± 2.8% depending on the type of tissues examined. DTX was stable in biological samples with no evidence of degradation during 3 freeze-thaw cycles and two months of storage at -70 ± 15 °C. The developed HPLC method was applied to quantify DTX in the mouse plasma and tissues after intravenous administration of 7.5 mg equivalent DTX/kg dose of DTX-loaded folic acid-polyethylene glycol-heparin-tocopherol (FA-PEG-HEP-CA-TOC) micelle formulation to female Balb/c mice. CONCLUSION: A simple, sensitive, rapid, accurate, and prudent RP-HPLC method was developed, validated, and applied for DTX determination in plasma and tissues.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".