Improving the Odds of Developing New Drugs from Botanicals: Botanical Review Team's Perspectives
Bibliographic record
Abstract
There is no doubt that plants and animals have provided humankind with numerous purified small molecule drugs and there is reason to hope that botanical mixtures will have more to give us. Botanical mixtures, are widely used as dietary supplements in the United States or as herbal medicines elsewhere, have, for the most part, not been extensively studied through well-controlled clinical trials to show beneficial effects. We hope this will change and that more botanical derived pure compounds as well as botanical mixtures will be developed as drugs. The publication of FDA's “Guidance for Industry-Botanical Drug Products” (drafted in 2000 and finalized in 2004) paved the regulatory pathway for developing botanical mixtures as new drugs. The first botanical drug (Veregen®, derived from green tea) approval through investigational new drug (IND) and new drug application (NDA) processes in 2006 shows that well defined botanical mixtures can be approved as new drugs with demonstration of safety and efficacy through well-controlled clinical trials. Since the publication of the guidance, there has been a growing interest in botanical drug development judged by the increasing numbers of botanical INDs and pre-IND consultations, with a cumulative total of over 350 and growing. Few of the botanical INDs with phase 1 and/or 2 clinical trials have, to date, advanced into late-phase clinical trials. So far, the Veregen® NDA remains the only one submitted and subsequently approved. Although the reasons for this are no doubt different in different cases, several common issues related to quality control and trial designs, among others, have been observed by the Botanical Review Team. A discussion of these issues could shed light on the seemingly low percentage of botanical INDs entering late-stage drug development. We would love to see more botanicals being further developed as new drugs with more success.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".