Global Comparison of Stability Testing Parameters and Testing Methods for Finished Herbal Products
Bibliographic record
Abstract
Quality consistencies of drug products are essential to guarantee expected therapeutic activities, and achieving consistent qualities for herbal products is challenging because of their physicochemical complexities and inherent variabilities. Regulatory authorities worldwide have issued regulations or guidelines for stability testing parameters and testing procedures for herbal products stored in proposed conditions. These testing parameters and methods for finished herbal products are detailed in the guidelines and regulations issued by 5 global authorities and 15 countries, that is, the Association of Southeast Asian Nations (ASEAN), the Eurasian Economic Commission (EEC), the European Medicines Agency (EMA), the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), the World Health Organization (WHO), Australia, Brazil, Canada, China, Egypt, Hong Kong, India, Japan, Kenya, Republic of Korea, the Philippines, Qatar, Switzerland, USA, and Zambia. Physical, chemical, and biological stability tests were compared between different dosage forms, and the testing conditions (temperature and relative humidity) used for long-term, accelerated, or intermediate testing were included in the guidelines and regulations. Comparisons of global regulations and guidelines addressing stability testing are fundamental for the international harmonization of herbal product quality assessments. This review aids understanding of the global situation regarding the testing of herbal product quality with respect to storages.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".