Acid‐Responsive Polymer Additives Increase RNA Transfection from Lipid Nanoparticles
Bibliographic record
Abstract
Abstract Lipid nanoparticles (LNPs) are the most clinically advanced RNA delivery technology, but their efficiency is limited by low RNA release after endosome disruption. To improve RNA release, an acid‐responsive polymer is synthesized with which to formulate LNPs for RNA encapsulation and release. Specifically, three acid‐responsive poly(lactic acid)‐ block‐ poly(carboxybetaine) zwitterionic derivatives are designed and synthesized that are cationic and complexed with RNA at pH 7.4, but are neutral following cleavage at endosomal pH, thereby having lower affinity to RNA. The polymers are formulated into each of the clinically approved Onpattro, Moderna, or Pfizer LNP formulations to produce hybrid polymer‐lipid nanoparticles (PLNPs). With the PLNPs, the IC 50 values of multiple small interfering RNAs (siRNAs) decreased up to 5.4‐fold compared to parent LNPs in several cell lines. Moreover, messenger RNA (mRNA) transfection increased up to two fold. The acid‐responsive polymers in PLNPs accounted for the enhanced RNA transfection as this phenomenon is lost with acid‐inert polymers. Confocal microscopy confirmed that cytosolic RNA concentration increased using the acid‐responsive polymers; conversely, uptake and endosomal escape are identical to existing LNPs. This confirmed that enhanced RNA transfection is due to increased RNA dissociation from its carrier. The novel polymer represents a versatile strategy to increase RNA transfection from LNPs.
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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) | 0.002 | 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".