Advancement to the clinic of an RNAi therapeutic for solid tumors
Bibliographic record
Abstract
3585 Background: Malignancies of the liver, including primary (hepatocellular carcinoma) and secondary (metastatic) tumors, represent a significant unmet medical need. We are developing a therapeutic for solid tumors involving the liver that is comprised of lipid particle-formulated short 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. Efficacy was demonstrated in a mouse liver tumor model. Methods: To assess efficacy in vivo, a stable nucleic acid lipid particle (SNALP) formulation was developed based on similar formulations previously shown to silence liver-expressed genes via systemic administration in multiple species. A SNALP-formulated combination of the KSP and VEGF siRNAs (referred to as ALN-VSP01) was tested in an orthotopic liver tumor model in which human hepatoma cells (Hep3B) are implanted directly into the livers of immunocompromised mice. Results: Intravenous administration of ALN-VSP01 leads to dose-dependent inhibition of both KSP and VEGF expression in established liver tumors. This was accompanied by the formation of numerous aberrant mitotic figures (“monoasters”) in tumor cells indicative of the pharmacologic inhibition of KSP. In addition, tumor growth was significantly inhibited by a course of ALN-VSP01 treatment, and ALN-VSP01 treatment provided a clear survival benefit even when treatment was initiated in animals with a significant tumor burden. As a control, a SNALP-formulated siRNA targeting Luciferase was administered and shown to have no effect in these studies. Conclusions: Systemic administration of ALN-VSP01 exhibited clear efficacy in a mouse orthotopic liver tumor model. Clinical testing of ALN-VSP01 is expected to initiate in early 2009. [Table: see text]
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".