20<i>S</i>-Protopanaxadiol Inhibits P-Glycoprotein in Multidrug Resistant Cancer Cells
Bibliographic record
Abstract
One of the major causes for cancer cells to resist current chemotherapy is attributed to the over-expression of P-glycoprotein (P-gp), resulting in insufficient drug delivery to the tumor sites. Protopanaxadiol ginsenosides Rg3 and Rh2 are known to induce apoptosis and significantly enhance the tumor inhibitory effects of chemotherapeutics in a synergistic fashion. One of the possible mechanisms is by blocking P-gp activity. The final deglycosylation metabolite of protopanaxadiols (PPDs) IN VIVO is 20S-protopapanaxadiol (aglycone PPD, aPPD), which has also shown anticancer activity and synergy with chemotherapy drugs. In the present study, P-gp over-expressing cancer cells were utilized to test whether aPPD also inhibits P-gp activity. We found that aPPD caused similar cytotoxicity in P388adr cells as their parental non-MDR cells, suggesting that aPPD may not be a substrate of P-gp. On the other hand, the calcein AM efflux assay showed that aPPD was able to inhibit P-gp activity as potently as verapamil on MDR cells. The blockage of P-gp activity was highly reversible as wash-out of aPPD resulted in an immediate recovery of P-gp activity. Unlike verapamil, aPPD did not affect ATPase activity of P-gp suggesting a different mechanism of action. The above results indicate that aPPD, unlike its precursor ginsenosides Rg3 and Rh2, is not a substrate of P-gp. It is also the first time that aPPD has showed a reversible nature of its P-gp inhibition. In addition to its pro-apoptotic nature, aPPD may be a potential new P-gp inhibitor for cancer treatment.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".