A phase 1 dose-escalation study on the safety, tolerability and activity of liposomal curcumin (Lipocurc™) in patients with locally advanced or metastatic cancer
Bibliographic record
Abstract
This study was conducted to investigate the safety and tolerability of increasing doses of liposomal curcumin in patients with metastatic cancer. Investigations of anti-tumor activity and of the pharmacokinetics of curcumin were secondary objectives. In this phase I, single-center, open-label study in patients with metastatic tumors, liposomal curcumin was administered as a weekly intravenous infusion for 8 weeks. Dose escalation was started at 100 mg/m 2 over 8 h and the dose increased to 300 mg/m 2 over 6 h. 32 patients were treated. No dose-limiting toxicity was observed in 26 patients at doses between 100 and 300 mg/m 2 over 8 h. Of six patients receiving 300 mg/m 2 over 6 h, one patient developed hemolysis, and three other patients experienced hemoglobin decreases > 2 g/dL without signs of hemolysis. Pharmacokinetic analyses revealed stable curcumin plasma concentrations during infusion followed by rapid declines to undetectable levels after the infusion. Anti-tumor activity by RECIST V1.1 was not detected. Significant tumor marker responses and transient clinical benefit were observed in two patients. 300 mg/m 2 liposomal curcumin over 6 h was the maximum tolerated dose in these heavily pretreated patients, and is the recommended starting dose for anti-cancer trials.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".