Surface modification of lipid nanoparticles for gene therapy
Bibliographic record
Abstract
Gene therapies have the potential to target and effectively treat a variety of diseases including cancer as well as genetic, neurological, and autoimmune disorders. Although we have made significant advances in identifying non-viral strategies to deliver genetic cargo, certain limitations remain. In general, gene delivery is challenging for several reasons including the instabilities of nucleic acids to enzymatic and chemical degradation and the presence of restrictive biological barriers such as cell, endosomal and nuclear membranes. The emergence of lipid nanoparticles (LNPs) helped overcome many of these challenges. Despite its success, further optimization is required for LNPs to yield efficient gene delivery to extrahepatic tissues, as LNPs favor accumulation in the liver after systemic administration. In this mini-review, we provide an overview of current preclinical approaches in that LNP surface modification was leveraged for cell and tissue targeting by conjugating aptamers, antibodies, and peptides among others. In addition to their cell uptake and efficiency-enhancing effects, we outline the (dis-)advantages of the different targeting moieties and commonly used conjugation strategies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".