Isocratic RP-UHPLC method development and validation of stability-indicating for simultaneous determination of teneligliptin and metformin in fixed-dose combination
Bibliographic record
Abstract
The pharmaceutical combination of Teneligliptin Hydrobromide hydrate (TEN) and Metformin Hydrochloride (MET) drugs is used in the treatment of type 2 diabetes mellitus. A new analytical method: QuEChERS (Quick, Easy, Cheap, Effective, Rugged, Safe) has been developed for the quantification of Teneligliptin (TEN) and Metformin (MET) in bulk and tablet dosage forms. The analysis was performed on Agilent symmetry analytical column Eclipse plus C18 (150 mm × 4.6 mm, 5 μm) ultra- performance liquid chromatography-Diode Array Detectors (UHPLC-DAD), while the detection was performed on 233 nm using Diode Array Detectors. Buffer and acetonitrile (65:35 v/v) were the mobile phase, run at a flow rate of 0.7 mL min−1 for isocratic elution. The buffer used in the mobile phase contained 50 mM potassium di-hydrogen phosphate, pH adjusted to 3.5±0.02 with orthophosphoric acid. The mean values of recovery were found to be 100.50% and 99.81%. The proposed method could be ideal for quantitative evaluation in pharmaceutical preparations of these drugs and also for their quality control in bulk manufacturing. Stress test covers: acid, base, peroxide, thermal and photolytic degradation; were conducted to show the specificity of the method and degradation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".