Characterization of a liposomal copper(II)-quercetin formulation suitable for parenteral use
Bibliographic record
Abstract
Quercetin (3,3′,4′,5,7-pentahydroxyflavone) is a naturally derived flavonoid that is commonly found in fruits and vegetables. There is mounting evidence to suggest that quercetin has potential anticancer effects and appears to interact synergistically when used in combination with approved chemotherapeutic agents such as irinotecan and cisplatin. Unfortunately, quercetin has shown limited clinical utility, partly due to low bioavailability related to its poor aqueous solutions (< 10 μg/mL). In this study, liposomal formulations of quercetin were developed by exploiting quercetin’s ability to bind copper. Quercetin powder was added directly to pre-formed copper-containing liposomes (2-distearoyl-sn-glycero-3-phosphocholine (DSPC) and cholesterol (CHOL) (55:45 M ratio)). As a function of time and temperature, the formation of copper-quercetin was measured. Using this methodology, a final quercetin-to-lipid (mol:mol) ratio of 0.2 was achievable and solutions containing quercetin at concentrations of > 5 mg/mL were attained, representing at least a > 100-fold increase in apparent solubility. The resulting formulation was suitable for intravenous dosing with no overt toxicities when administered at doses of 50 mg/kg in mice. Pharmacokinetic studies demonstrated that the copper-quercetin formulations had an AUC 0-24H of 8382.1 μg h/mL when administered to mice at 50 mg/kg. These studies suggested that quercetin (not copper-quercetin) dissociates from the liposomes after administration. The resulting formulation is suitable for further development and also serves as a proof-of-concept for formulating other flavonoids and flavonoid-like compounds. Given that quercetin exhibits an IC 50 of >10 μM when tested against cancer cell lines, we believe that the utility of this novel quercetin formulation for cancer indications will ultimately be as a component of a combination product.
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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) | 0.001 | 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".