The role of excipients in lipid nanoparticle metabolism: implications for enhanced therapeutic effect
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) are multicomponent delivery vehicles for nucleic acids that are generally comprised of ionizable lipids, phospholipids, cholesterol and lipid-poly(ethylene glycol) molecules. It is well established that both the composition and relative amounts of each component significantly impact the efficiency of nucleic acid delivery by LNPs, as well as their organ-specific targeting. However, the post-delivery fate of every component is less discussed such as the degradation, clearance, and retention in the body. The longevity and metabolites of each component can greatly influence overall tolerability and safety. For instance, slowly degrading ionizable lipids, which comprise around 50% of the LNP, have been shown to illicit an extended inflammatory response. In this review significant importance is placed on chemistries that improve the tolerability and safety of certain LNP components, such as molecular modifications to ionizable lipids, lipid-poly(ethylene glycol) and nucleic acids. Additionally, we discuss how formulation strategies, such as the amount of cholesterol and phospholipids added to optimize clearance, can enhance biodegradability and reduce inflammation. Furthermore, this review will provide an understanding of the considerations around designing LNP components for better or more predictable metabolism such modified nucleic acids and biodegradable chemical linkers in ionizable lipids.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".