Abstract 581: Development of RNA interference-based therapeutics for bladder cancer and hepatocellular carcinoma
Bibliographic record
Abstract
Abstract Bladder cancer and hepatocellular carcinoma are two of many cancers refractory to current treatments. Small interfering RNAs (siRNAs) are a new therapeutic modality able to specifically silence expression of targets not accessible via current small molecule and antibody options. MDRNA is developing UsiRNAs, a novel siRNA construct containing unlocked nucleobase analogs, with improved specificity for RNA interference (RNAi). Delivery of UsiRNAs to target tissues is achieved using proprietary Di-alkylated Amino Acid (DiLA2)-based liposomes. Survivin, overexpressed in many cancers, is involved in cell division and inhibition of apoptosis. In orthotopic and xenograft models of liver cancer, systemic administration of survivin UsiRNA-DiLA2 liposomes resulted in approximately 60% and 70% reductions in survivin mRNA, respectively, and > 50% decreases in tumor weight. Local intravesical administration in an orthotopic bladder cancer model resulted in 90% inhibition of mRNA expression and substantial tumor growth inhibition. Polo-like kinase 1 (PLK-1), also elevated in many tumors, regulates cell cycle progression and mitosis. Treatment of bladder cancer and liver cancer cell lines with PLK-1 UsiRNA/DiLA2 liposomes leads to significant caspase activation and corresponding apoptotic cell death. In vivo studies with PLK-1 UsiRNA are in progress. These UsiRNAs and UsiRNAs directed against additional targets are being evaluated in vitro and in orthotopic and xenograft models of cancer as single agents, in combinations of target-specific UsiRNAs, and with existing small molecule and antibody therapeutics. 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 581.
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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) | 0.003 | 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".