Optimization of HPLC method using central composite design for estimation of Torsemide and Eplerenone in tablet dosage form
Bibliographic record
Abstract
Abstract A simple, precise, accurate and robust high performance liquid chromatographic method has been developed for simultaneous estimation of Torsemide and Eplerenone in tablet dosage form. Design of experiment was applied for multivariate optimization of the experimental conditions of RP-HPLC method. A Central composite design was used to study the response surface methodology and to analyse in detail the effects of these independent factors on responses. Total eleven experiments along with 3 center points were performed. Two factors were selected to design the matrix, one factor is variation in ratio of Acetonitrile and the second factor is flow rate (mL/min). Optimization in chromatographic conditions was achieved by applying Central composite design. The optimized and predicted data from contour diagram comprised mobile phase (acetonitrile, water and methanol in the ratio of 50: 30: 20 v/v/v respectively), at a flow rate of 1.0 ml/min and at ambient column temperature. Using these optimum conditions baseline separation of both drugs with good resolution and run time of less than 5 minutes were achieved. The optimized assay conditions were validated as per the ICH guidelines (2005). Hence, the results showed that the Quality by design approach could successfully optimize RP-HPLC method for simultaneous estimation of Torsemide and Eplerenone.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".