Delayed Progression of Lung Metastases Following Delivery of a Prodrug-activating Enzyme
Bibliographic record
Abstract
BACKGROUND: Chemotherapy is an effective option to treat recurrent or metastatic cancer but its debilitating side-effects limit the dose and time of exposure. Prodrugs that can be activated locally by an activating enzyme can minimize collateral damage from chemotherapy. We previously demonstrated the efficacy of a poly-L-lysine-based theranostic nanoplex containing bacterial cytosine deaminase (bCD) that locally converted 5-fluorocytosine (5-FC) to the chemotherapeutic agent 5-fluorouracil in MDA-MB-231 primary tumor xenografts. MATERIALS AND METHODS: Here we used a more effective variant of bCD to target metastatic red fluorescence protein expressing MDA-MB-435 cells in the lungs. We used an intravenous injection of tumor cells and monitored tumor growth in the lungs for 5 weeks by which time metastatic nodules were detected with optical imaging. The animals were then treated with the bCD-nanoplex and 5-FC. RESULTS: We observed a significant decrease in metastatic burden with a single dose of the enzyme-nanoplex and two consecutive prodrug injections. CONCLUSION: These results are a first step towards the longitudinal evaluation of such a strategy with multiple doses. Additionally, the enzyme can be directly coupled to imaging reporters to time prodrug administration for the detection and treatment of aggressive metastatic cancer.
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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) | 0.001 | 0.000 |
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".