Distribution and Metabolism of Lipocurc™ (Liposomal Curcumin) in Dog and Human Blood Cells: Species Selectivity and Pharmacokinetic Relevance
Bibliographic record
Abstract
Background/Aim: The aim of this study was to investigate the distribution of curcumin (in the form of Lipocurc™) and its major metabolite tetrahydrocurcumin (THC) in Beagle dog and human red blood cells, peripheral blood mononuclear cells (PBMC) and hepatocytes. Materials and Methods: Lipocurc™ was used as the source of curcumin for the cell distribution assays. In vitro findings with red blood cells were also compared to in vivo pharmacokinetic data available from preclinical studies in dogs and phase I clinical studies in humans. Results: High levels of curcumin were measured in PBMCs (625.5 ng/g w.w. cell pellet or 7,297 pg/106 cells in dog and 353.7 ng/g w.w. cell pellet or 6,809 pg/106 cells in human) and in hepatocytes (414.5 ng/g w.w. cell pellet or 14,005 pg/106 cells in dog and 813.5 ng/g w.w. cell pellet or 13,780 pg/106 cells in human). Lower curcumin levels were measured in red blood cells (dog: 78.4 ng/g w.w. cell pellet or 7.2 pg/106 cells, human: 201.5 ng/g w.w. cell pellet or 18.6 pg/106 cells). A decrease in the medium concentration of curcumin was observed in red blood cells and hepatocytes, but not in PBMCs. Red blood cell levels of THC were ~5-fold higher in dog compared to human and similar between dog and human for hepatocytes and PBMCs. The ratio of THC to curcumin found in the red blood cell medium following incubation was 6.3 for dog compared to 0.006 for human, while for PBMCs and hepatocytes the ratio of THC to curcumin in the medium did not display such marked species differences. Conclusion: There was an excellent correlation between the in vitro disposition of curcumin and THC following incubation with red blood cells and in vivo plasma levels of curcumin and THC in dog and human following intravenous infusion. The disposition of curcumin in blood cells is, therefore, species-dependent and of pharmacokinetic relevance.
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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".