Perspectives on the recent developments with green tea polyphenols in drug discovery.
Bibliographic record
Abstract
INTRODUCTION: Increasing evidence has expanded the role of green tea from a traditional beverage to a source of pharmacologically active molecules with diverse health benefits. However, conclusive clinical results are needed to better elucidate the cancer-preventive and therapeutic effects of green tea polyphenols (GTPs). Areas covered: The authors describe GTPs' chemical compositions and metabolic biotransformations, and their recent developments in drug discovery, focusing on their cancer chemopreventive and therapeutic effects. They then review the recent development of GTP-loaded nanoparticles and GTP prodrugs. Expert opinion: GTPs possess potent anticarcinogenic activities through interfering with the initiation, development and progression phases of cancer. There are several challenges (e.g. poor bioavailability) in developing GTPs as therapeutic agents. Use of nanoparticle-based delivery systems has provided unique advantages over purified GTPs. However, there is still a need to determine the actual magnitude and pharmacological mechanisms of GTPs encapsulated in nanoparticles, in order to address newly emerging safety issues associated with the potential 'local overdose' effect. The use of Pro- epigallocatechin gallate (Pro-EGCG) as a prodrug appears to offer improved in vitro stability as well as better in vivo bioavailability and efficacies in a number of animal studies, suggesting its potential as a therapeutic agent for further study and development.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".