A Delicate Balance When Substituting a Small Hydrophobe onto Low Molecular Weight Polyethylenimine to Improve Its Nucleic Acid Delivery Efficiency
Bibliographic record
Abstract
High molecular weight (HMW) polyethylenimine (PEI) is one of the most versatile nonviral gene vectors that was extensively investigated over the past two decades. The cytotoxic profile of HMW PEI, however, encouraged a search for safer alternatives. Because of lack of cytotoxicity of low molecular weight (LMW) PEI, enhancing its performance via hydrophobic modifications has been pursued to this end. Since the performance of modified PEIs depends on the nature and extent of substituents, we systematically investigated the effect of hydrophobic modification of LMW (1.2 kDa) PEI with a short propionic acid (PrA). Moderate enhancements in PEI hydrophobicity resulted in enhanced cellular uptake of polyplexes and siRNA-induced silencing efficacy, whereas further increase in PrA substitution abolished the uptake as well as the silencing. We performed all-atom molecular dynamics simulations to elucidate the mechanistic details behind these observations. A new assembly mechanism was observed by the presence of hydrophobic PrA moieties, where PrA migrated to core of the polyplex. This phenomenon caused higher surface hydrophobicity and surface charge density at low substitutions, and it caused deleterious effects on surface hydrophobicity and cationic charge at higher substitutions. It is evident that an optimal balance of hydrophobicity/hydrophilicity is needed to achieve the desired polyplex properties for an efficient siRNA delivery, and our mechanistic findings should provide valuable insights for the design of improved substituents on nonviral carriers.
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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".