Impact of Degree of Ionization and PEGylation on the Stability of Nanoparticles of Chitosan Derivatives at Physiological Conditions
Bibliographic record
Abstract
Nowadays, the therapeutic efficiency of small interfering RNAs (siRNA) is still limited by the efficiency of gene therapy vectors capable of carrying them inside the target cells. In this study, siRNA nanocarriers based on low molecular weight chitosan grafted with increasing proportions (5 to 55%) of diisopropylethylamine (DIPEA) groups were developed, which allowed precise control of the degree of ionization of the polycations at pH 7.4. This approach made obtaining siRNA nanocarriers with small sizes (100–200 nm), positive surface charge and enhanced colloidal stability (up to 24 h) at physiological conditions of pH (7.4) and ionic strength (150 mmol L−1) possible. Moreover, the PEGylation improved the stability of the nanoparticles, which maintained their colloidal stability and nanometric sizes even in an albumin-containing medium. The chitosan-derivatives displayed non-cytotoxic effects in both fibroblasts (NIH/3T3) and macrophages (RAW 264.7) at high N/P ratios and polymer concentrations (up to 0.5 g L−1). Confocal microscopy showed a successful uptake of nanocarriers by RAW 264.7 macrophages and a promising ability to silence green fluorescent protein (GFP) in HeLa cells. These results were confirmed by a high level of tumor necrosis factor-α (TNFα) knockdown (higher than 60%) in LPS-stimulated macrophages treated with the siRNA-loaded nanoparticles even in the FBS-containing medium, findings that reveal a good correlation between the degree of ionization of the polycations and the physicochemical properties of nanocarriers. Overall, this study provides an approach to enhance siRNA condensation by chitosan-based carriers and highlights the potential of these nanocarriers for in vivo studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".