Unraveling Lyophilization and Redispersion Effects on Miktoarm Polymer-Based Nanoformulations
Bibliographic record
Abstract
To enhance the scope of therapeutic interventions using star polymeric nanoparticles of desired concentrations, an understanding of the effect of converting aqueous formulations into stable redispersible dry powders by freeze drying on their physicochemical and biological properties is essential. We demonstrate that parameters such as the choice of the cryoprotectant, its molecular weight, and concentration play an important role during lyophilization and reconstitution processes. We hypothesized that utilizing cryoprotectants akin to shell-forming polymers may be ideal in protection against aggregation and keeping the nanostructures intact during lyophilization and reconstitution, as well as retaining the overall biological efficacy of their cargo. Through an evaluation of miktoarm polymer-based nanoparticles, we demonstrate that PEG2k at 1% w/v concentration provides the optimized cryoprotection, and the resulting solid formulations upon redispersion in an aqueous medium preserve the desired nanoparticle and curcumin properties. PEG2k at 1% w/v is more efficient than PEG5k and saccharides including glucose, sucrose, trehalose, and mannitol in enhancing the integrity of micelles during lyophilization and reconstitution. Addition of PEG2k 1% w/v (with or without lyophilization and redispersion) enhances drug release in PBS buffer, while it has no impact in the cell culture media. Nanoformulations protect endothelial cells from cytotoxicity of curcumin, and addition of cryoprotectant or the lyophilization/redispersion processes did not impair anti-inflammatory efficacy of curcumin.
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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) | 0.000 | 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".