Isoreserpine Reverses Multidrug Resistance Mediated by ABCB1
Bibliographic record
Abstract
One of the major obstacles to successful cancer chemotherapy is the development of multidrug resistance (MDR) that is associated with the overexpression of ATP-binding cassette (ABC) drug transporter ABCB1 (P-glycoprotein/ MDR1). Currently, the most efficient way to overcome ABCB1-mediated MDR in cancer is by direct inhibition of ABCB1 function. Many drugs with known biological activities have been discovered to inhibit the function of ABCB1 and reverse ABCB1-mediated MDR in cancers. However, clinical trial results suggested that many of these clinically active drugs should not be used as ABCB1 modulators due to direct toxicity or undesirable side effects. In this study, we demonstrated that isoreserpine, an indole alkaloid with relatively low toxicity, can significantly inhibit ABCB1-mediated efflux of calcein-AM, a known substrate of ABCB1, in a dose-dependent manner. Moreover, we showed that at non-toxic concentrations, isoreserpine potently reversed ABCB1-mediated resistance to doxorubicin and colchicine in ABCB1-overexpressing human KB-V-1 epidermal cancer cells. Collectively, our findings revealed thatby inhibiting the transport function of ABCB1, isoreserpine can restore drug sensitivity of ABCB1-overexpressing cells to conventional chemotherapeutic drugs. In conclusion, isoreserpine should be further developed into a promising reversal agent for the treatment of MDR in ABCB1-overexpressing cancers.
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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 | 0.001 |
| 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".