Simultaneous quantification of candesartan and irbesartan in rabbit eye tissues by liquid chromatography–tandem mass spectrometry
Bibliographic record
Abstract
Abstract Diabetic retinopathy is a major cause of vision loss in adults. Novel eye‐drop formulations of candesartan and irbesartan are being developed for its cure or treatment. To support a preclinical trial in rabbits, it was critical to develop and validate a new LC–MS/MS method for simultaneous quantification of candesartan and irbesartan in rabbit eye tissues (cornea, aqueous humor, vitreous body and retina/choroid). Eye tissue samples were first homogenized in H 2 O‐diluted rabbit plasma. The candesartan and irbesartan in the supernatants together with their respective internal standards (candesartan‐d 4 and irbesartan‐d 4 ) were extracted by solid‐phase extraction. The extracted samples were injected onto a C 18 column for gradient separation. The MS detection was in the positive electrospray ionization mode using the multiple reaction monitoring transitions of m/z 441 → 263, 445 → 267, 429 → 207, and 433 → 211 for candesartan, candesartan‐d 4 , irbesartan and irbesartan‐d 4 , respectively. For the validated concentration ranges (2–2000 and 5–5000 ng/g for candesartan and irbesartan, respectively), the within‐run and between‐run accuracies (% bias) were within the range of −8.0–10.0. The percentage CV ranged from 0.6 to 7.3. There was no significant matrix interference nor matrix effect from different eye tissues and different rabbits. The validated method was successfully used in the Good Laboratory Practice (GLP) study of rabbits.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".