Differential interactions between statins and P‐glycoprotein: Implications for exploiting statins as anticancer agents
Bibliographic record
Abstract
Statins, prescribed for decades to control cholesterol, have more recently been shown to have promising anticancer activity. Statins induce tumor-selective apoptosis by inhibiting the mevalonate (MVA) pathway. In addition, we have recently demonstrated that lovastatin modulates drug accumulation in a MVA-independent manner in multidrug-resistant (MDR) tumor cells overexpressing the P-glycoprotein (P-gp) multidrug transporter. P-gp-mediated drug efflux can contribute to chemotherapy failure. However, direct statin-mediated inhibition of P-gp in human MDR tumor cells at clinically achievable concentrations remains unexplored. An understanding of these interactions is crucial, both to appreciate differences in the anticancer potential of different statins and to safely and effectively integrate statins into traditional chemotherapy regimens that include P-gp substrates. Here we evaluate interactions between 4 statins (lovastatin, atorvastatin, fluvastatin and rosuvastatin) and P-gp, at both the molecular level using purified P-gp and at the cellular level using human MDR tumor cells. Lovastatin bound directly to purified P-gp with high affinity and increased doxorubicin accumulation in MDR tumor cells, potentiating DNA damage, growth arrest and apoptosis. By contrast, while atorvastatin inhibited substrate transport by purified P-gp in proteoliposomes, it had no effect on doxorubicin transport in MDR tumor cells. Finally, fluvastatin and rosuvastatin only interacted with P-gp in vitro at high concentrations and did not inhibit doxorubicin transport in MDR cells. These differential interactions should be considered when combining statins with traditional chemotherapeutic drugs.
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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 | 0.000 |
| 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 | 0.000 |
| 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".