Intravesical pH: a potentially important variable affecting efficacy and the further development of anthracycline chemotherapy for superficial bladder cancer
Bibliographic record
Abstract
OBJECTIVE: To assess, using epirubicin-sensitive and multidrug resistant (MDR) derivatives of human bladder cancer cell lines in vitro, the probable effect of intravesical pH changes, with and without the MDR antagonist verapamil, on the uptake, intracellular distribution and cytotoxicity of epirubicin during intravesical chemotherapy. MATERIALS AND METHODS: Incubations for cytotoxicity testing were carried out in buffered medium containing epirubicin, at pH values of 6.0-8.5, with verapamil where appropriate. The cytotoxicity of epirubicin, with and without verapamil, was determined using the tetrazolium cytotoxicity assay. Intracellular epirubicin fluorescence was assessed using flow cytometry and confocal microscopy. Flow cytometric total intracellular epirubicin fluorescence was measured at pH 6.0, 6.4, 6.8, 7.2, and 7.6, and confocal microscopy was carried out at pH 6.0 and 8.0. The MDR-reversing agent verapamil was added at 100 micro g/mL to some incubations. RESULTS: Epirubicin cytotoxicity in resistant cell lines appears considerably enhanced by adding verapamil and further improved, especially in MDR cells, by alkalinization of the drug solution to pH 8.0. Flow cytometry results showed striking and consistent differences in epirubicin handling with pH. Sensitive cells can be induced to absorb considerably more drug at alkaline pH, whilst resistant cells show no such behaviour. Nuclear drug fluorescence was greater in sensitive cells at alkaline pH, but cytoplasmic drug fluorescence in the resistant cells was little changed by pH. Adding verapamil to resistant cells restored the sensitive phenotype of drug handling. CONCLUSION: Buffering epirubicin to an alkaline pH before intravesical application should increase its intrinsic cytotoxicity. The potential for synergy at certain drug combinations will be enhanced by applying these findings. MDR reversal and fatty acid augmentation of drug uptake are discussed as examples.
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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 | 0.001 |
| 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 | 0.000 |
| 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".