Challenges in the Authorization of Clinical Trials Involving Botanicals
Bibliographic record
Abstract
Clinical trials involving natural health products (NHPs) in Canada fall under the jurisdiction of the Natural Health Products Regulations [1]. Plant identification is one of the most problematic issues observed during our assessment of >260 clinical trial authorization applications involving NHPs (e.g., Black Cohosh misidentification [2]). Botanicals and their extracts have more than one active constituent, and clinical trials may involve multiple botanicals, e.g., for Traditional Chinese Medicine, making assessment of identity, purity, potency, safety and likelihood of efficacy even more complex. Although guidance is provided [3], other regulatory challenges encountered include insufficient data for chemical contaminants (specifically heavy metals, solvent residues, pesticides, and mycotoxins), data to support the stability of the medicinal ingredients or finished products throughout the trial period, proprietary manufacturing information, potential for interactions and adverse reactions. Acknowledgements: Thanks go to our colleagues in the Clinical Trials Division, Natural Health Products Directorate, Health Canada. References: [1] Government of Canada (2009) Natural Health Products Regulations http://laws.justice.gc.ca/en/showtdm/cr/SOR-2003–196//? showtoc=&instrumentnumber=SOR-2003–196. [2] Marles R, et al. (2008) NHPRS Meeting, Toronto. [3] Health Canada (2007) Natural Health Products Guidance Documents http://www.hc-sc.gc.ca/dhp-mps/prodnatur/legislation/docs/index-eng.php.
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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 | 0.323 | 0.289 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.020 | 0.024 |
| Insufficient payload (model declined to judge) | 0.022 | 0.014 |
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".