Stress Degradation Assessment of Lamotrigine Using a Validated Stability‐Indicating HPTLC Method
Bibliographic record
Abstract
In this work, a sensitive and stability‐indicating HPTLC method for the determination of lamotrigine is presented. According to the International Conference on Harmonization guidelines Q1A, lamotrigine was exposed to a variety of stress conditions; these include heating in acidic, basic and neutral media. Its stability towards oxidative stress, humidity, high temperature and direct sunlight was also examined. Separation of the drug from its forced degradation impurities was achieved using TLC silica gel plates and a mobile phase composed of ethyl acetate: methanol: ammonia. The linear regression analysis of the data obtained for the correlation plots showed good linearity over the concentration range of 10–300 ng/spot. The forced degradation studies showed that lamotrigine is susceptible to degradation under acidic, basic, neutral and oxidative conditions, among which alkaline‐induced hydrolysis had the highest degradative potential. Alternatively, the drug was stable under heat, humidity, and daylight stress factors. In order to assess the purity and stability of the drug in tablet formulations, the developed method was applied to the analysis of commercial tablets in brand and generic products. The obtained results showed that the degradation of the drug has not occurred in the marketed formulations that were analyzed by the described methodology.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".