A Novel Solid Lipid Nanoparticle Formulation for Active Targeting to Tumor α<sub>v</sub>β<sub>3</sub> Integrin Receptors Reveals Cyclic RGD as A Double‐Edged Sword
Bibliographic record
Abstract
The overexpression of α(v) β(3) integrin receptors on tumor cells and tumor vascular endothelium makes it a useful target for imaging, chemotherapy and anti-angiogenic therapy. However integrin-targeted delivery of therapeutics by nanoparticles have provided only marginal, if any, enhancement of therapeutic effect. This work was thus focused on the development of novel α(v) β(3) -targeted near infrared light-emitting solid lipid nanoparticles (SLN) through conjugation to the α(v) β(3) integrin-specific ligand cyclic Arg-Gly-Asp (cRGD), and the assessment of the effects of α(v) β(3) targeting on nanoparticle biodistribution. Since our previously developed non-targeted "stealth" SLN showed little hepatic accumulation, unlike most reported liposomes and micelles, they served as a reference for quantifying the effects of cRGD-conjugation on tumor uptake and whole animal biodistribution of SLN. Non-targeted SLN, actively targeted (RGD-SLN) and blocked RGD-SLN were prepared to contain near infrared quantum dots for live animal imaging. They were injected intravenously to nude mice bearing xenograft orthotopic human breast tumors or dorsal window chamber breast tumors. Tumor micropharmacokinetics of various SLN formulations were determined using intravital microscopy, and whole animal biodistribution was followed over time by optical imaging. The active tumor targeting with cRGD was found to be a "double-edged sword": while the specificity of RGD-SLN accumulation in tumor blood vessels and their tumor residence time increased, their distribution in the liver, spleen, and kidneys was significantly greater than the non-targeted SLN, leaving a smaller amount of nanoparticles in the tumor tissue. Nevertheless the enhanced specificity and retention of RGD-SLN in tumor neovasculature could make this novel formulation useful for tumor neovascular-specific therapies and imaging applications.
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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".