Abstract 4064: Development of ALN-VSP: An RNAi therapeutic for solid tumors
Bibliographic record
Abstract
Abstract Malignancies of the liver, including primary (hepatocellular carcinoma) and secondary (metastatic) tumors, represent a significant unmet medical need. In most patients with liver metastases, extra-hepatic tumors are also present. We are developing a therapeutic for solid tumors that is comprised of lipid particle (SNALP)-formulated small interfering RNAs (siRNAs) targeting VEGF and the mitotic kinesin, KSP (Eg5). For each target, potent siRNA duplexes were selected following extensive screening in tissue culture cells. A SNALP-formulated combination of the KSP and VEGF siRNAs (referred to as ALN-VSP) was tested in orthotopic liver tumor models, as well as models of extra-hepatic tumors. To generate orthotopic liver tumors, human hepatoma (Hep3B) or colorectal carcinoma (HCT116) cells were implanted directly into the livers of immunocompromised mice. These cell lines were also used to establish tumors at extra-hepatic sites including the lymph nodes and peritoneal cavity. Studies in which tumor-bearing mice were treated with ALN-VSP demonstrated that each siRNA makes a distinct contribution to efficacy. ALN-VSP treatment led to accumulation of aberrant mitotic figures (monoasters), a hallmark of KSP inhibition, in both types of orthotopic liver tumors, as well as in extra-hepatic tumors of different origin. Evidence of therapeutic VEGF inhibition was shown by marked reductions in tumor microvessel density and intratumoral hemorrhage in orthotopic tumors. Similar effects on tumor vasculature were obtained with a SNALP formulation of the VEGF siRNA alone. Finally, we demonstrated that multi-dose administration of ALN-VSP significantly prolongs survival of mice harboring advanced orthotopic liver tumors. A Phase 1 clinical trial of ALN-VSP is ongoing. 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 4064.
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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 | 0.001 |
| 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".