Bibliographic record
Abstract
Previously, we showed that long-circulating polyethylene glycol (PEG)-liposomes are cleared rapidly from the circulation when injected repeatedly in the same animal. In this article, we describe the effects of PEG-coating, the circulation time, the lipid dose, and the presence of encapsulated doxorubicin on the pharmacokinetics upon repeated injection in rats. Furthermore, the role of liver and splenic macrophages was investigated. Liposomes without PEG-coating also showed the so-called “enhanced clearance effect”: blood levels at 4 h post injection decreased from 62.8 ± 13.7% of injected dose (%ID) after the first injection to 0.54 ± 0.21%ID after the second injection. This decrease was independent of the circulation time of the first dose. Decreasing the first lipid dose of PEG-liposomes to 0.05 μmol/kg still led to enhanced clearance of a second dose of 5 μmol/kg. No changes in pharmacokinetics were observed when the second dose was 50 μmol/kg. When hepatosplenic macrophages were depleted, no enhanced clearance of repeated liposome injections was observed. A dose of doxorubicin containing PEG-liposomes (Doxil12), injected 1 week after injection of empty PEG-liposomes, was cleared rapidly from the circulation in rats. Our results indicate that hepatosplenic macrophages play an essential role in the enhanced clearance effect and that the change in pharmacokinetic behavior upon repeated injection is a general characteristic of liposomes, unrelated to the presence of PEG. Therefore, these findings may have a considerable impact on the clinical application of liposomal formulations that are administered repeatedly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.271 | 0.105 |
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".