Stability indicating novel analytical method development by RP-UPLC for the estimation of Gitingensine in bulk
Bibliographic record
Abstract
A new method was proposed using RP-UPLC for the determination of Gitingensine in bulk, which exhibits its power of stability output. Gitingensine is a natural product found in Kibatalia laurifolia belonging to Apocynaceae which is a steroid having the activities such as anti-inflammatory, anti-spasmodic and anti-cancer activity. Cevadine is used as an internal standard for chromatographic analysis. The elution was performed on BEH C18 (2.1 × 50 mm, 1.7 µm) column at 30 °C with a mobile phase distribution Acetonitrile: 0.1% orthophosphoric acid (60: 40) respectively. The flow rate was well- kept at 0.3 mL min-1. Retention times for Gitingensine and Cevadine were found to be 2.005 and 1. 395 min, respectively. The regression equation was found to be linear in the range of 12.5 – 75 µg/mL with a high correlation coefficient (0.999). Recovery of Gitingensine was obtained as 100.04%. Validation was done as claimed by ICH guidelines with respect to accuracy, sensitivity, robustness, and precision studies. From the insignificant variations in the analysis by changing the mobile phase, temperature, and flow rate, the robustness was studied. All the validation parameters were found to be within the specifications. Forced degradation studies revealed that when the influence of acid, alkali, peroxide, thermal, photolytic, and hydrolytic conditions were applied on the drug, it was stable. Hence, it can be concluded that the developed RP-UPLC method is economical, precise, and robust and can be adopted in regular Quality control analysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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 | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".