Provisional Biopharmaceutical Classification of Some Common Herbs Used in Western Medicine
Bibliographic record
Abstract
The aim of this study was to classify some markers of common herbs used in Western medicine according to the Biopharmaceutical Classification System (BCS). The BCS is a scientific approach to classify drug substances based upon their intestinal permeability and their solubility, at the highest single dose used, within the physiologically relevant pH ranges. Known marker components of twelve herbs were chosen from the USP Dietary Supplement Compendium Monographs. Different BCS parameters such as intestinal permeability (P(eff)) and solubility (C(s)) were predicted using the ADMET Predictor, which is a software program to estimate biopharmaceutical relevant molecular descriptors. The dose number (D₀) was calculated when information from the literature was available to identify an upper dose for individual markers. In these cases the herbs were classified according to the traditional BCS parameters using P(eff) and D₀. When no upper dose could be determined, then the amount of a marker that is just soluble in 250 mL of water was calculated. This value, M(x), defines when a marker is changing from highly soluble to poorly soluble according to BCS criteria. This biopharmaceutically relevant value can be a useful tool for marker selection. The present study showed that a provisional BCS classification of herbs is possible but some special considerations need to be included into the classification strategy. The BCS classification can be used to choose appropriate quality control tests for products containing these markers. A provisional BCS classification of twelve common herbs and their 35 marker compounds is presented.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".