Production of limit size nanoliposomal systems with potential utility as ultra-small drug delivery agents
Bibliographic record
Abstract
Previous studies from this group have shown that limit size lipid-based systems--defined as the smallest achievable aggregates compatible with the packing properties of their molecular constituents--can be efficiently produced using rapid microfluidic mixing technique. In this work, it is shown that similar procedures can be employed for the production of homogeneously sized unilamellar vesicular systems of 30-40 nm size range. These vesicles can be remotely loaded with the protonable drug doxorubicin and exhibit adequate drug retention properties in vitro and in vivo. In particular, it is demonstrated that whereas sub-40 nm lipid nanoparticle (LNP) systems consisting entirely of long-chain saturated phosphatidylcholines cannot be produced, the presence of such lipids may have a beneficial effect on the retention properties of limit size systems consisting of mixed lipid components. Specifically, a 33-nm diameter doxorubicin-loaded LNP system composed of 1-palmitoyl-2-oleoyl phosphatidylcholine (POPC), 1,2-dipalmitoyl phosphatidylcholine (DPPC), cholesterol, and PEGylated lipid (DSPE-PEG2000) demonstrated adequate, stable drug retention in the circulation, with a half-life for drug release of ∼ 12 h. These results indicate that microfluidic mixing is the technique of choice for the production of bilayer LNP systems with sizes less than 50 nm that could lead to development of a novel class of ultra-small drug delivery vehicles.
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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".