Targeted Delivery of RNAi Therapeutics With Endogenous and Exogenous Ligand-Based Mechanisms
Bibliographic record
Abstract
Lipid nanoparticles (LNPs) have proven to be highly efficient carriers of short-interfering RNAs (siRNAs) to hepatocytes in vivo; however, the precise mechanism by which this efficient delivery occurs has yet to be elucidated. We found that apolipoprotein E (apoE), which plays a major role in the clearance and hepatocellular uptake of physiological lipoproteins, also acts as an endogenous targeting ligand for ionizable LNPs (iLNPs), but not cationic LNPs (cLNPs). The role of apoE was investigated using both in vitro studies employing recombinant apoE and in vivo studies in wild-type and apoE(-/-) mice. Receptor dependence was explored in vitro and in vivo using low-density lipoprotein receptor (LDLR(-/-))-deficient mice. As an alternative to endogenous apoE-based targeting, we developed a targeting approach using an exogenous ligand containing a multivalent N-acetylgalactosamine (GalNAc)-cluster, which binds with high affinity to the asialoglycoprotein receptor (ASGPR) expressed on hepatocytes. Both apoE-based endogenous and GalNAc-based exogenous targeting appear to be highly effective strategies for the delivery of iLNPs to liver.
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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) | 0.000 | 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 | 0.001 | 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 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".