<i>In vitro</i> and <i>in vivo</i> antitumor activity of liposomal fenretinide targeted to human neuroblastoma
Bibliographic record
Abstract
Neuroblastoma (NB) is the most common extracranial solid tumor of childhood. In advanced disease stages, prognosis is poor and treatments have limited efficacy, thus novel strategies are warranted. The synthetic retinoid Fenretinide (HPR) induces apoptosis in NB and melanoma cell lines. We reported an in vitro potentiation of HPR effects on melanoma cells when the drug is incorporated into GD2-targeted immunoliposomes (anti-GD2-SIL-HPR). We investigated the antitumor activity of anti-GD2-SIL-HPR against NB cells, both in vitro and in vivo. Anti-GD2-SIL showed specific, competitive binding to and uptake by, various NB cell lines. In in vitro cytotoxicity studies, NB cells, incubated with 30 microM HPR entrapped in anti-GD2-immunoliposomes, showed a significant reduction in cellular growth compared to free HPR, HPR entrapped in Ab-free liposomes or anti-GD2 empty liposomes. In an in vivo NB metastatic model, we demonstrated that anti-GD2-SIL-HPR completely inhibited the development of macroscopic and microscopic metastases in comparison to controls. Similar, but significantly less potent, antitumor effect was observed also in mice treated with anti-GD2 immunoliposomes without HPR (anti-GD2-SIL-blank) or anti-GD2 MAb alone (p = 0.0297 and p = 0.0294, respectively, vs. anti-GD2-SIL-HPR). Moreover, our results clearly demonstrated that although anti-GD2 MAb had a strong antitumor effect in this in vivo NB model, 100% curability was obtained only after treatment with anti-GD2-SIL-HPR (p < 0.0001). Anti-GD2 liposomal HPR should receive clinical evaluation as adjuvant therapy of neuroblastoma.
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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".