Nortriptyline-targeted siRNA-loaded liposomes: Design, affinity, biodistribution, and bioactivity in a murine Herpes Simplex Virus 1 corneal infection model
Bibliographic record
Abstract
Herpes simplex virus type 1 (HSV1) establishes latency in the trigeminal ganglia and reactivates to cause recurrent infections and severe complications, including herpes simplex keratitis (HSK), the leading cause of infectious corneal blindness in developed countries. We developed and characterized a targeted siRNA delivery system designed to suppress HSV1 by silencing the immediate-early gene ICP0, which encodes a protein essential for lytic infection and viral reactivation. To enhance neuronal accumulation, siRNA-loaded liposomes were decorated with nortriptyline (NTP), a ligand with high affinity for neuronal cells, including those in the trigeminal ganglia. The liposomes were optimized to exhibit nanoscale size, low polydispersity, neutral surface charge, high siRNA loading, spherical morphology, and long-term shelf stability. Studies in cell lines confirmed efficient internalization with good tolerability, and targeted liposomes showed enhanced binding to differentiated PC‑12 neuronal cells, competitively inhibited by free NTP, supporting their targeting specificity. Following retro-orbital administration in mice, NTP-targeted siHSV1 liposomes preferentially accumulated in the trigeminal ganglia. Antiviral efficacy was further assessed by topical application of siHSV1‑loaded liposomes within a biocompatible hydrogel in a murine HSV1 corneal perforation model of HSK. In the corneal limbus, targeted liposomes produced a significant reduction in viral load, whereas in the contralateral trigeminal ganglia, they significantly reduced viral load with a pronounced trend toward decreased ICP0 expression. These results demonstrate the feasibility of using NTP-mediated targeting ligand and support the potential of this delivery platform for treating latent HSV1, warranting further optimization for improved therapeutic efficacy.
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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".