Evaluating the abilities of diverse nitroaromatic prodrug metabolites to exit a model Gram negative vector for bacterial-directed enzyme-prodrug therapy
Bibliographic record
Abstract
© 2018 Elsevier Inc. Gene-directed enzyme-prodrug therapy (GDEPT) employs tumour-tropic vectors including viruses and bacteria to deliver a genetically-encoded prodrug-converting enzyme to the tumour environment, thereby sensitising the tumour to the prodrug. Nitroreductases, able to activate a range of promising nitroaromatic prodrugs to genotoxic metabolites, are of great interest for GDEPT. The bystander effect (cell-to-cell transfer of activated prodrug metabolites) has been quantified for some nitroaromatic prodrugs in mixed multilayer human cell cultures, however while these provide a good model for viral DEPT (VDEPT) they do not inform on the ability of these prodrug metabolites to exit bacterial vectors (relevant to bacterial-DEPT (BDEPT)). To investigate this we grew two Escherichia coli strains in co-culture; an activator strain expressing the nitroreductase E. coli NfsA and a recipient strain containing an SOS-GFP DNA damage responsive gene construct. In this system, induction of GFP by reduced prodrug metabolites can only occur following their transfer from the activator to the recipient cells. We used this to investigate five clinically relevant prodrugs: metronidazole, CB1954, nitro-CBI-DEI, and two dinitrobenzamide mustard prodrug analogues, PR-104A and SN27686. Consistent with the bystander efficiencies previously measured in human cell multilayers, reduced metronidazole exhibited little bacterial cell-to-cell transfer, whereas nitro-CBI-DEI was passed very efficiently from activator to recipient cells post-reduction. However, in contrast with observations in human cell multilayers, the nitrogen mustard prodrug metabolites were not effectively passed between the two bacterial strains, whereas reduced CB1954 was transferred efficiently. Using nitroreductase enzymes that exhibit different biases for the 2- versus 4-nitro substituents of CB1954, we further showed that the 2-nitro reduction products exhibit substantially higher levels of bacterial cell-to-cell transfer than the 4-nitro reduction products, consistent with their relative bystander efficiencies in human cell culture. Overall, our data suggest that prodrugs may differ in their suitability for VDEPT versus BDEPT applications and emphasise the importance of evaluating an enzyme-prodrug partnership in an appropriate context for the intended vector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".