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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".