Abstract 2658: Anti-GD3 antibodies are targeting molecules for delivery of RNAi to melanoma
Bibliographic record
Abstract
Abstract Passive non-targeted delivery of RNAi therapy into tumours has been shown to be neither very effective, nor selective in vitro and in vivo. Consequently in this study, siRNA will be linked to a drug delivery system that will express a ligand that specifically targets melanoma cells. It has been previously shown that melanoma over-expresses the cell surface ganglioside GD3, thus, the anti-GD3 R24 monoclonal antibody can function as a targeting molecule. To do so, a targeting antibody was conjugated to a coated cationic liposome (CCL) exterior to effectively deliver RNAi to melanoma. Cell surface expression of GD3 on Malme 3M, LOX IMVI and SK-MEL-5 cells was verified by flow cytometry. Internalized levels of siRNA within CCLs were the quantified via spectrometry. Encapsulation efficiency of siRNA was calculated to be greater than 70%. The quantification of antibody expression on CCLs was measured, illustrating that while non-targeted CCLs showed insignificant values of antibody as expected, antibody-conjugated CCLs presented a decreasing trend of expression that can be explained by the multi-step purification process for the final CCL products. Fluorescent microscopy results, from the combination of internalized siRNA and Tex615 RNA within CCLs, displayed greater fluorescence with antibody-conjugated CCLs than non-targeted CCLs when Malme 3M, LOX IMVI, and SK-MEL-5 cells were treated. Taken together, these results conclude that the use of GD3-targeted liposomes can promote siRNA delivery to melanoma cells. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 2658.
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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) | 0.002 | 0.001 |
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".