Poly(β-aminoester) Physicochemical Properties Govern the Delivery of siRNA from Electrostatically Assembled Coatings
Bibliographic record
Abstract
Localized short interfering RNA (siRNA) therapy has the potential to drive high-specificity molecular-level treatment of a variety of disease states. Unfortunately, effective siRNA therapy suffers from several barriers to its intracellular delivery. Thus, drug delivery systems that package and control the release of therapeutic siRNAs are necessary to overcome these obstacles to clinical translation. Layer-by-layer (LbL) electrostatic assembly of thin film coatings containing siRNA and protonatable, hydrolyzable poly(β-aminoester) (PBAE) polymers is one such drug delivery strategy. However, the impact of PBAE physicochemical properties on the transfection efficacy of siRNA released from LbL thin film coatings has not been systematically characterized. In this study, we investigate the siRNA transfection efficacy of four structurally similar PBAEs in vitro . We demonstrate that small changes in structure yield large changes in physicochemical properties, such as hydrophobicity, p K a, and amine chemical structure, driving differences in the interactions between PBAEs and siRNA in polyplexes and in LbL thin film coatings for wound dressings. In our polymer set, Poly3 forms the most stable interactions with siRNA ( K eff,w/w = 0.298) to slow release kinetics and enhance transfection of reporter cells in both colloidal and thin film coating approaches. This is due to its unique physiochemical properties: high hydrophobicity (clog P = 7.86), effective p K a closest to endosomal pH (p K a = 6.21), and high cooperativity in buffering ( n hill = 7.2). These properties bestow Poly3 with enhanced endosomal buffering and escape properties. Taken together, this work elucidates the connections between small changes in polymer structure, emergent properties, and polyelectrolyte theory to better understand PBAE transfection efficacy.
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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".