Gene Silencing in the Development of Personalized Cancer Treatment: The Targets, the Agents and the Delivery Systems
Bibliographic record
Abstract
The advent of sophisticated experimental tools that can probe the molecular pathology of cancer has revealed a number of genes and gene families that could prove attractive targets for cancer therapy. Thus, gene silencing strategies have been envisioned to treat cancer by targeting the cancer cell's capacity to: (I) resist conventional treatment methods (chemotherapy and radiotherapy), (II) promote angiogenesis, and (III) metastasize and/or to survive microenvironments that normally would promote cell apoptosis/necrosis. The realization of such strategies is limited by the lack of pharmaceutically-viable technologies that enable the safe and effective delivery of gene-targeting agents to neoplastic cells following systemic administration. There are many reasons for this, including an incomplete understanding of how cancer cells respond when genes are silenced. Further the pharmacokinetic and pharmacodynamic attributes of gene therapy products are not well understood. This review will discuss gene therapy strategies that have been developed based on gene inhibition by the use of antisense oligonucleotides, ribozymes and RNA interference (RNAi). In this context, several particularly promising targets will be described, with a focus on strategies that have progressed to the stage where clinical trials have been initiated. The review highlights product development strategies that emphasize non-viral systemic formulations and the potential for delivery systems to become an enabling technology for development of effective gene therapy products.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".