Rh2 or its aglycone aPPD in combination with docetaxel for treatment of prostate cancer
Bibliographic record
Abstract
BACKGROUND: Docetaxel is one of the few chemotherapeutic drugs that are considered highly effective when used to treat prostate cancer patients that have relapsed and/or metastatic disease, it is therefore reasonable to expect further improvements in treatment outcomes when it is combined with other therapeutic agents active in prostate cancer. This study assesses the combination of well tolerated and orally bioavailable formulations of ginsenoside Rh2 or its aglycone aPPD with docetaxel. METHODS: The in vitro activity of Rh2, aPPD, and docetaxel was determined in four prostate cancer cell lines: PC-3, LNCaP, DU145, and C4-2. Combinations of Rh2 or aPPD with docetaxel were assessed using the constant ratio combination design. Combination Indices (CI) and Dose Reduction Indices (DRI) were subsequently estimated using Calcusyn. In vivo efficacy studies and Immunohistochemical analyses (PC-3 model) were also evaluated. RESULTS: In PC-3, DU145 and C4-2 prostate cancer cells combinations of Rh2 or aPPD with docetaxel were predominantly additive or synergistic. Combinations of Rh2 + docetaxel and aPPD + docetaxel caused established PC-3 tumors to regress from their initial size by 15% and 27%, respectively. Tumor cell proliferation rate (measured by Ki-67 positive cells) was significantly lower for combinations of Rh2 + docetaxel and aPPD + docetaxel, compared to animals treated with docetaxel alone. CONCLUSIONS: Rh2 and aPPD can be combined with docetaxel to yield additive or synergistic activity in vitro and in vivo. Pending further assessment of toxicity and pharmacodynamic behavior, this study supports testing of combinations of ginsenoside Rh2 or its aglycone aPPD with docetaxel in a clinical setting.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".