Lipid Nanoparticle Systems for Enabling Gene Therapies
Bibliographic record
Abstract
Genetic drugs such as small interfering RNA (siRNA), mRNA, or plasmid DNA provide potential gene therapies to treat most diseases by silencing pathological genes, expressing therapeutic proteins, or through gene-editing applications. In order for genetic drugs to be used clinically, however, sophisticated delivery systems are required. Lipid nanoparticle (LNP) systems are currently the lead non-viral delivery systems for enabling the clinical potential of genetic drugs. Application will be made to the Food and Drug Administration (FDA) in 2017 for approval of an LNP siRNA drug to treat transthyretin-induced amyloidosis, presently an untreatable disease. Here, we first review research leading to the development of LNP siRNA systems capable of silencing target genes in hepatocytes following systemic administration. Subsequently, progress made to extend LNP technology to mRNA and plasmids for protein replacement, vaccine, and gene-editing applications is summarized. Finally, we address current limitations of LNP technology as applied to genetic drugs and ways in which such limitations may be overcome. It is concluded that LNP technology, by virtue of robust and efficient formulation processes, as well as advantages in potency, payload, and design flexibility, will be a dominant non-viral technology to enable the enormous potential of gene therapy.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".