Antisense precision polymer micelles require less poly(ethylenimine) for efficient gene knockdown
Bibliographic record
Abstract
Therapeutic nucleic acids are powerful molecules for shutting down protein expression. However, their cellular uptake is poor and requires transport vectors, such as cationic polymers. Of these, poly(ethylenimine) (PEI) has been shown to be an efficient vehicle for nucleic acid transport into cells. However, cytotoxicity has been a major hurdle in the development of PEI-DNA complexes as clinically viable therapeutics. We have synthesized antisense-polymer conjugates, where the polymeric block is completely monodisperse and sequence-controlled. Depending on the polymer sequence, these can self-assemble to produce micelles of very low polydispersity. The introduction of linear poly(ethylenimine) to these micelles leads to aggregation into size-defined PEI-mediated superstructures. Subsequently, both cellular uptake and gene silencing are greatly enhanced over extended periods compared to antisense alone, while at the same time cellular cytotoxicity remains very low. In contrast, gene silencing is not enhanced with antisense polymer conjugates that are not able to self-assemble into micelles. Thus, using antisense precision micelles, we are able to achieve significant transfection and knockdown with minimal cytotoxicity at much lower concentrations of linear PEI then previously reported. Consequently, a conceptual solution to the problem of antisense or siRNA delivery is to self-assemble these molecules into 'gene-like' micelles with high local charge and increased stability, thus reducing the amount of transfection agent needed for effective gene silencing.
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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".