Poly(Sarcosine) Surface Modification Imparts Stealth‐Like Properties to Liposomes
Bibliographic record
Abstract
Circulation lifetime is a crucial parameter for a successful therapy with nanoparticles. Reduction and alteration of opsonization profiles by surface modification of nanoparticles is the main strategy to achieve this objective. In clinical settings, PEGylation is the most relevant strategy to enhance blood circulation, yet it has drawbacks, including hypersensitivity reactions in some patients treated with PEGylated nanoparticles, which fuel the search for alternative strategies. In this work, lipopolysarcosine derivatives (BA-pSar, bisalkyl polysarcosine) with precise chain lengths and low polydispersity indices are synthesized, characterized, and incorporated into the bilayer of preformed liposomes via a post insertion technique. Successful incorporation of BA-pSar can be realized in a clinically relevant liposomal formulation. Furthermore, BA-pSar provides excellent surface charge shielding potential for charged liposomes and renders their surface neutral. Pharmacokinetic investigations in a zebrafish model show enhanced circulation properties and reduction in macrophage recognition, matching the behavior of PEGylated liposomes. Moreover, complement activation, which is a key factor in hypersensitivity reactions caused by PEGylated liposomes, can be reduced by modifying the surface of liposomes with an acetylated BA-pSar derivative. Hence, this study presents an alternative surface modification strategy with similar benefits as the established PEGylation of nanoparticles, but with the potential of reducing its drawbacks.
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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".