Antitumor Activity and Prolonged Survival by Carbon‐Nanotube‐Mediated Therapeutic siRNA Silencing in a Human Lung Xenograft Model
Bibliographic record
Abstract
Carbon nanotubes are novel nanomaterials that are thought to offer potential benefits to a variety of biomedical and clinical applications. In this study, the treatment of a human lung carcinoma model in vivo using siRNA sequences leading to cytotoxicity and cell death is carried out using either cationic liposomes (DOTAP:cholesterol) or amino-functionalized multi-walled carbon nanotubes (MWNT - NH(+)(3)). Validation for the most cytotoxic siRNA sequence using a panel of human carcinoma and murine cells reveals that the proprietary siTOX sequence is human specific and can lead to significant cytotoxic activities delivered both by liposome or MWNT - NH(+)(3) in vitro. A comparative study using both types of vector indicates that only MWNT - NH(+)(3):siRNA complexes administered intratumorally can elicit delayed tumor growth and increased survival of xenograft-bearing animals. siTOX delivery via the cationic MWNT - NH(+)(3) is biologically active in vivo by triggering an apoptotic cascade, leading to extensive necrosis of the human tumor mass. This suggests that carbon-nanotube-mediated delivery of siRNA by intratumoral administration leads to successful and statistically significant suppression of tumor volume, followed by a concomitant prolongation of survival of human lung tumor-bearing animals. The direct comparison between carbon nanotubes and liposomes demonstrates the potential advantages offered by carbon nanotubes for the intracellular delivery of therapeutic agents in vivo. The present work may act as the impetus for further studies to explore the therapeutic capacity of chemically functionalized carbon nanotubes to deliver siRNA directly into the cytoplasm of target cells and achieve effective therapeutic silencing in various disease indications where local delivery is feasible or desirable.
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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".