DEVELOPMENT AND VALIDATION OF A STABILITY INDICATING RP-HPLC METHOD FOR THE DETERMINATION OF VALSARTAN
Bibliographic record
Abstract
Objective: A stability indicating RP-HPLC method was developed and validated for the determination of Valsartan using Telmisartan (10 µg/ml) as the internal standard. Methods: In this procedure Phenomenex ODS C-18(250×4.6 mm, packed with 5 micron) column was used with a new mobile phase consisting of methanol: acetonitrile: water (70:15:15 v/v) and the pH was adjusted to 3 by 0.1% glacial acetic acid with a flow rate of 1 ml/min. The eluents were monitored at 249 nm. Valsartan was subjected to stress conditions including hydrolytic degradation in acidic, basic and neutral conditions, oxidation, photolytic, UV degradation and thermal degradation. Results: Linearity was obtained in the concentration range of 10-90 µg/ml (R 2 =0.999) and with a regression equation y=0.074x+0.005. The LOD and LOQ values were 0.261 and 0.791 µg/ml respectively. The drug had shown promising degradation in the acidic, basic, neutral, thermal and oxidative stress conditions. Conclusion: The method was validated for accuracy, precision, linearity, specificity and robustness and revealed that it is specific, accurate, rapid, precise, reliable and reproducible enough to analyze commercial dosage forms as per ICH guidelines.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".