Stable Retinoid Analogue Targeted Dual pH-Sensitive Smart Lipid ECO/<i>pDNA</i> Nanoparticles for Specific Gene Delivery in the Retinal Pigment Epithelium
Bibliographic record
Abstract
Dysfunctions caused by gene mutations in the retinal pigment epithelium (RPE) lead to retinal degeneration, visual function loss, and even blindness. RPE-specific gene replacement therapy holds great promise for treating monogenic ocular disorders in the RPE. Although the adeno-associated virus (AAV) has been approved for gene therapy to treat monogenic visual disorders, broad clinical applications of AAV-based gene therapy are limited by its gene loading capacity. In this work, we intended to design and develop a stable retinylamine analogue ACU4429-modified dual pH-sensitive ECO/ pDNA nanoparticles for specific delivery of large therapeutic genes to the RPE. ACU4429 was first conjugated to a PEG 3.4 kD spacer with a pH-sensitive hydrazone linker at the distal end (ACU-PEG-HZ-MAL). The targeted dual pH-sensitive ECO/ pDNA nanoparticles were then prepared by self-assembly of ACU-PEG-HZ-MAL, pH-sensitive lipid carrier ECO, and a plasmid DNA expressing the large ABCA4 gene. The formation of targeted ACU-PEG-HZ-ECO/ pDNA nanoparticles was characterized by dynamic light scattering and gel electrophoresis. The incorporation of a hydrazone linker enhanced the cytosolic gene delivery, which translated to high ABCA4 expression in ARPE-19 cells for ACU-PEG-HZ-ECO/ pABCA4 nanoparticles. The targeted nanoparticles also demonstrated excellent targeting efficiency in the interphotoreceptor matrix of Abca4 – / – mice, resulting in enhanced expression of the ABCA4 gene in the RPE. The ACU4429 PEG hydrazone-modified ECO/ pDNA nanoparticles provide a promising nonviral platform to safely and effectively deliver therapeutic genes with unlimited sizes for the treatment of monogenic visual disorders.
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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) | 0.000 | 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".