Optimizing Lipid Nanoparticles for Delivery in Primates
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) are clinically proven to successfully deliver both small interfering RNA (siRNA) therapeutics and larger mRNA payloads for prophylactic vaccine applications. Non-human primates (NHPs) are generally considered to be the most predictive of human responses. However, for ethical and economic reasons, LNP compositions have historically been optimized in rodents. It has been difficult to translate LNP potency data from rodents to NHPs for intravenously (IV) administered products in particular. This presents a major challenge for preclinical drug development. An attempt to investigate LNP parameters, which have historically been optimized in rodents, is carried out, and seemingly innocuous changes are found to result in large potency differences between species. For example, the ideal particle size for NHPs (50-60 nm) is found to be smaller than for rodents (70-80 nm). Surface chemistry requirements are also different, with almost double the amount of poly(ethylene glycol) (PEG)-conjugated lipid needed for maximal potency in NHPs. By optimizing these two parameters, approximately eight-fold increase in protein expression from intravenously administered messenger RNA (mRNA)-LNP in NHP is gained. The optimized formulations are well tolerated when administered repeatedly with no loss of potency. This advancement enables the design of optimal LNP products for clinical development.
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 | 0.000 | 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) | 0.000 | 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".