Lyophilization and stability of antibody-conjugated mesoporous silica nanoparticle with cationic polymer and PEG for siRNA delivery
Bibliographic record
Abstract
INTRODUCTION: Long-term stability of therapeutic candidates is necessary toward their clinical applications. For most nanoparticle systems formulated in aqueous solutions, lyophilization or freeze-drying is a common method to ensure long-term stability. While lyophilization of lipid, polymeric, or inorganic nanoparticles have been studied, little has been reported on lyophilization and stability of hybrid nanoparticle systems, consisting of polymers, inorganic particles, and antibody. Lyophilization of complex nanoparticle systems can be challenging with respect to preserving physicochemical properties and the biological activities of the materials. We recently reported an effective small-interfering RNA (siRNA) nanoparticle carrier consisting of 50-nm mesoporous silica nanoparticles decorated with a copolymer of polyethylenimine and polyethyleneglycol, and antibody. MATERIALS AND METHODS: Toward future personalized medicine, the nanoparticle carriers were lyophilized alone and loaded with siRNA upon reconstitution by a few minutes of simple mixing in phosphate-buffered saline. Herein, we optimize the lyophilization of the nanoparticles in terms of buffers, lyoprotectants, reconstitution, and time and temperature of freezing and drying steps, and monitor the physical and chemical properties (reconstitution, hydrodynamic size, charge, and siRNA loading) and biological activities (gene silencing, cancer cell killing) of the materials after storing at various temperatures and times. RESULTS: The material was best formulated in Tris-HCl buffer with 5% w/w trehalose. Freezing step was performed at -55°C for 3 h, followed by a primary drying step at -40°C (100 µBar) for 24 h and a secondary drying step at 20°C (20 µBar) for 12 h. The lyophilized material can be stored stably for 2 months at 4°C and at least 6 months at -20°C. CONCLUSION: We successfully developed the lyophilization process that should be applicable to other similar nanoparticle systems consisting of inorganic nanoparticle cores modified with cationic polymers, PEG, and antibodies.
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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".