Enterohepatic recirculation of bioactive ginger phytochemicals is associated with enhanced tumor growth-inhibitory activity of ginger extract
Bibliographic record
Abstract
Phytochemical complexity of plant foods confers health-promoting benefits including chemopreventive and anticancer effects. Isolating single constituents from complex foods may render them inactive, emphasizing the importance of preserving the natural composition of whole extracts. Recently, we demonstrated in vitro synergy among the most abundant bioactive constituents of ginger extract (GE), viz., 6-gingerol (6G), 8-gingerol (8G), 10-gingerol (10G) and 6-shogaol (6S). However, no study has yet examined the in vivo collaboration among ginger phytochemicals or evaluated the importance, if any, of the natural 'milieu' preserved in whole extract. Here, we comparatively evaluated in vivo efficacy of GE with an artificial quasi-mixture (Mix) formulated by combining four most active ginger constituents at concentrations equivalent to those present in whole extract. Orally fed GE showed 2.4-fold higher tumor growth-inhibitory efficacy than Mix in human prostate tumor xenografts. Pharmacokinetic evaluations and bioavailability measurements addressed the efficacy differences between GE and Mix. Plasma concentration-time profiles revealed multiple peaking phenomenon for ginger constituents when they were fed as GE as opposed to Mix, indicating enterohepatic recirculation. Bioavailability of 6G, 8G, 10G and 6S was 1.6-, 1.1-, 2.5- and 3.4-fold higher, respectively, when dosed with GE compared with Mix. In addition, gingerol glucuronides were detected in feces upon intravenous administration confirming hepatobiliary elimination. These data ascribe the superior in vivo efficacy of GE to higher area under the concentration time curves, greater residence time and enhanced bioavailability, of ginger phytochemicals, when fed as a natural extract compared with artificial Mix, emphasizing the usefulness of consuming whole foods over single agents.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".