A proposed screening strategy for evaluating the genotoxicity potential of botanicals and botanical extracts
Bibliographic record
Abstract
Botanicals have long been used to promote health and treat diseases, but the safety of many currently marketed botanicals has not been adequately evaluated. Given the chemical complexity of botanicals, which often contain numerous unknown constituents, and their widespread use, comprehensive toxicity assessments are needed. The Botanical Safety Consortium was established to address this challenge. This international group of experts in toxicology, chemistry, bioinformatics, and pharmacognosy is developing a toolkit of assays to generate reliable toxicological profiles for botanicals. Genotoxicity assessment is especially critical, because, unlike other toxicities, genotoxicity is not adequately identified by adverse event and history-of-use reports, and genotoxicity is directly linked to health consequences such as cancer and birth defects. The Consortium's Genotoxicity Technical Working Group is exploring a genotoxicity testing strategy based on the use of in silico modeling and the bacterial reverse mutation and in vitro micronucleus assays and including several options for additional tests to further characterize genotoxicity and mode of action when indicated. The effectiveness of this testing strategy is being evaluated using 13 well-characterized botanicals with existing toxicological data as case studies. A brief overview of each of these 13 botanicals is provided. The final strategy for developing comprehensive genotoxicity profiles of botanicals will incorporate published genotoxicity data, chemical composition information, in silico and in vitro test data, and human exposure data, reducing the need for animal testing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".