CD47 peptide-cloaked lipid nanoparticles promote cell-specific mRNA delivery
Bibliographic record
Abstract
mRNA-based therapeutics delivered via lipid nanoparticles (LNP-mRNA) hold great promise for treating diverse diseases. However, further improvements are needed to refine outcomes in non-vaccine, extrahepatic applications, such as minimizing the rapid clearance and off-target uptake in undesired tissues of the mononuclear phagocyte system (MPS). We propose modifying LNP surfaces with the phagocytic cell "don't eat me" signal, CD47, in combination with our previously established antibody-based targeted LNP (tLNP) to create a CD47/tLNP platform with reduced phagocytic clearance and off-target effects and improved efficiency for cell-specific delivery. We showed that CD47 modification decreased macrophage and hepatic uptake both in vitro and in vivo. Combining CD47 modification with antibodies targeting endothelial cells, T cells, or hematopoietic stem cells (HSCs) increased targeting efficiency up to 3-fold compared to tLNP alone. Enhanced targeting of CD47/tLNP to HSCs with reduced off-targeting enabled the delivery of pro-apoptotic mRNA for HSC depletion as a preconditioning strategy prior to bone marrow transplant. Additionally, CD47-modified LNPs showed diminished inflammatory effects on hepatic tissue and an altered protein corona. Our CD47/tLNP-mRNA platform, with its reduced phagocytic clearance, mitigated inflammatory effects, and enhanced targeted delivery, should further facilitate the development of in vivo mRNA therapeutics.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".