Vemurafenib (PLX4032, Zelboraf®), a BRAF Inhibitor, Modulates ABCB1-, ABCG2-, and ABCC10-Mediated Multidrug Resistance
Bibliographic record
Abstract
In this study, we examined the in vitro effects of vemurafenib, a specific inhibitor of V600E mutated BRAFenzyme, on the response of cells overexpressing the ATP binding cassette (ABC) efflux transporters ABCG2, ABCB1, ABCC1 and ABCC10. Vemurafenib, at 5 µM and 20 µM, produced a significant concentration-dependent increase in the cytotoxicity of paclitaxel in cells overexpressing ABCB1 and ABCC10 and mitoxantrone in cells overexpressing ABCG2. Vemurafenib also significantly enhanced the accumulation of paclitaxel in cell lines overexpressing ABCB1 and ABCC10. Vemurafenib significantly increased the intracellular accumulation of mitoxantrone in cells overexpressing ABCG2. In contrast, vemurafenib did not significantly alter the sensitivity of ABCC1 overexpressing HEK/ABCC1 cells to vincristine. Finally, as determined by Western blotting, vemurafenib (20 µM) did not significantly alter the expression of the proteins for ABCG2, ABCC10 or ABCB1. Thus, vemurafenib most likely reverses multidrug resistance by altering the transport function of these aforementioned ABC transporters, as opposed to affecting the expression of ABC proteins. The docking analysis of vemurafenib with the ABCB1 homology model also suggested that vemurafenib binds to the ABCB1 and ABCG2 drug binding site. These findings suggest that combination of specific inhibitors like vemurafenib with chemotherapeutic drugs may be used to overcome multidrug resistance in cells that overexpress ABCB1, ABCC10 and/or ABCG2 transporters.
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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 | 0.000 |
| 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".