Methods to Improve Efficacy in Suicide Gene Therapy Approaches: Targeting Prodrug-Activating Enzymes Carboxypeptidase G2 and Nitroreductase to Different Subcellular Compartments
Bibliographic record
Abstract
Current cancer chemotherapy strategies are often hampered by the lack of tumor selectivity, resulting in unwanted damage to healthy tissue. Gene-directed enzyme-prodrug therapy (GDEPT) ( 1 ) and virus-directed enzyme-prodrug therapy (VDEPT) ( 2 ) are suicide gene therapy approaches that aim to deliver cytotoxic agents to tumor cells in a specific manner, thus improving the selectivity of chemotherapy and protecting normal cells from adverse side effects. In the first step, a gene encoding a foreign enzyme is delivered to tumor cells ( see Fig. 1 ). The aim is to express the enzyme only in the tumor cells and then administer a prodrug. Prodrugs are small molecules that are nontoxic to normal cells, but are converted to potent cytotoxic agents in tumors. In GDEPT, the foreign enzyme performs this conversion, so if the enzyme is successfully targeted, the toxin will only be produced in the tumor, sparing normal tissues from excessive damage. Schematic diagram of GDEPT. ( A ) Delivery of the gene encoding the prodrug-activating enzyme by recombinant viruses. ( B ) expression of the enzyme by cells that have been transduced. Upon prodrug administration, the enzyme converts the prodrug into the active, cytotoxic drug (magnified square), which spreads to adjacent cells that do not express the enzyme (bystander effect). ( C ) The cytotoxic drug induces cell death in expressing and nonexpressing cells. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".