Copolymer Micelles and Nanospheres with Different In Vitro Stability Demonstrate Similar Paclitaxel Pharmacokinetics
Bibliographic record
Abstract
Paclitaxel loaded amphiphilic block copolymer nanoparticles have been demonstrated to enhance the aqueous solubility and improve the toxicity profile as compared to the commercially available product Taxol; however, in many cases long circulation of the drug is not achieved due to rapid partitioning of the drug from the carrier and/or carrier instability upon injection. In this work we investigated the effect of increasing the hydrophobic block length of methoxy poly(ethylene glycol)-block-poly(ε-caprolactone) (MePEG-b-PCL) copolymers on the physicochemical properties and in vitro stability of the formed nanoparticles as well as the pharmacokinetics and biodistribution of both the copolymer and solubilized drug. We hypothesized that copolymers composed of high molecular weight hydrophobic blocks (MePEG₁₁₄-b-PCL₁₀₄) that form nanoparticles with a kinetically "frozen core" (which we term nanospheres) would better retain their PTX payload as compared to micelles composed of shorter hydrophobic blocks (MePEG₁₁₄-b-PCL₁₉), thus leading to prolonged drug circulation. Nanospheres solubilized PTX more efficiently, released the drug in a more sustained fashion and were characterized by enhanced stability and drug retention in the presence of plasma proteins as compared to micelles. Using radiolabeled copolymers and PTX, it was found that, upon injection, MePEG₁₁₄-b-PCL₁₀₄ circulated for longer than MePEG₁₁₄-b-PCL₁₉; however, the drug was rapidly eliminated from the blood regardless of the formulation. These results suggest that, despite formulation in more stable nanospheres, PTX was still rapidly extracted from these nanoparticles.
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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".