Verapamil metabolites: potential P-glycoprotein-mediated multidrug resistance reversal agents
Bibliographic record
Abstract
Multidrug resistance in cancer chemotherapy frequently correlates with overexpression of the P-glycoprotein drug transporter. Attempts to reverse P-glycoprotein-mediated multidrug resistance with racemic verapamil or its less toxic (R)-enantiomer have been complicated by cardiotoxicity. The objective of this study was to investigate the effects of the major verapamil metabolite, norverapamil, as well as the PR-22 and D-620 metabolites, on P-glycoprotein-mediated drug transport. We measured the basolateral-to-apical fluxes of the P-glycoprotein substrates digoxin and vinblastine in the presence and absence of verapamil, (R)-norverapamil, (S)-norverapamil, racemic norverapamil, PR-22, or D-620 across confluent monolayers of Madin-Darby canine kidney (MDCK) cells that express P-glycoprotein on their apical membranes. Verapamil and norverapamil nonstereospecifically inhibited the renal tubular secretion of digoxin and vinblastine similarly in a dose-dependent manner. However, there was no decrease in the cellular accumulation of digoxin and vinblastine, suggesting that neither verapamil nor norverapamil prevent the substrates from entering the MDCK cells. Furthermore, the norverapamil metabolite P-22 also inhibited the secretion of these P-glycoprotein substrates. Our results suggest that the verapamil metabolites norverapamil and PR-22, which are less cardiotoxic than the parent compound, have comparable inhibitory abilities to verapamil (norverapamil greater than PR-22) and may be useful in reversing resistance to P-glycoprotein substrates.
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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 | 0.001 | 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".