Doe Approach: A Validated Rp -Hplc Method For The Determination Of Dapagliflozin
Bibliographic record
Abstract
Background: Dapagliflozin is a competitive inhibitor of sodium / glucose co transporter 2 (SGLT2). It will Inhibiting the reabsorption of filtered glucose in the kidney leads to elevated urinary glucose excretion, thereby lowering blood Method: A new, simple, accurate, rapid, precise, reproducible and cost-effective RP-HPLC method for the quantitative estimation of Dapagliflozin in bulk and pharmaceutical dosage form. The developed RP-HPLC method for the quantitative estimation of Dapagliflozin is based on measurement of absorption at maximum wavelength 254 nm using 0.1% urea: methanol (35:65% v/v) as a solvent. The stock solution for Dapagliflozin was prepared, and subsequent suitable dilution was prepared in mobile phase to obtained standard curve. The standard solution of Dapagliflozin shows absorption maxima at 254 nm. Results: Dapagliflozin will obeys Beers -Lamberts law in the concentration range of 20 - 100μg/ml with regression 0.999 at 254nm.The overall % recovery was found to be 101.59% for Dapagliflozin which reflects that the method was free from the interference of impurities and other impurities, used in bulk and marketed dosage forms. The low value of % RSD was indicative of accuracy and reproducibility of the method. The % RSD for inter-day and intra-day precision was found to be 0.1 for Dapagliflozin respectively which is & it <2% hence proved that method is precise. Conclusion: The results of analysis have been validated as per International Conference on Harmonization (ICH) guidelines. The developed method can be adopted in routine analysis of Dapagliflozin in bulk and tablet dosage form. The Proposed method was found to be rapid, accurate, precise, specific, robust, rugged and economical.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".