Effect of Leukotriene Modifiers on Transport Activity of Multidrug Resistance Proteins (MRPs)
Bibliographic record
Abstract
Multidrug resistance protein 1 (MRP1), MRP2, MRP3, and MRP4 are membrane transporters that mediate ATP‐dependent efflux of xenobiotics, their metabolites, and many physiological organic anions. MRP1‐4 have distinct substrate profiles; however, they can all transport the glucuronide conjugate of estradiol (E 2 17bG). An established physiological role of MRP1 is to efflux the proinflammatory leukotriene C 4 (LTC 4 ). MK‐571 was originally designed as a leukotriene receptor 1 (CysLT 1 R) antagonist to treat asthma; it is also the most popular MRP1 inhibitor. However, MK‐571 is non‐specific and inhibits most MRP homologs as well as some solute carrier organic anion (SCLO) importers, limiting its usefulness as an experimental tool. Other leukotriene modifiers (LTMs) have been developed to treat inflammatory diseases but little is known of their ability to modulate MRP1 and its homologs. In this study, the effect of a series of LTMs specific for either CysLT 1 R or CysLT 2 R on E 2 17bG uptake into MRP1, MRP2, MRP3, or MRP4‐enriched membrane vesicles was measured. The IC 50 values for the 5 LTMs tested ranged from 0.91 to 25.27 uM; MRP1 and MRP4 IC 50 values were the most alike. In contrast, some LTMs stimulated MRP2 and MRP3. Thus, LY171883 stimulated MRP2 and MRP3‐mediated E 2 17bG uptake by about 2.3 and 4‐fold, respectively, while Montelukast modulated MRP2 in a biphasic manner. CysLT 1 R specific LTMs were generally a less potent class of MRP modulators. These data suggest that like MK‐571, most LTMs are non‐selective modulators of MRP1‐4 transport, and should therefore be used with caution because of their potential to confound data interpretation. Supported by Canadian Institutes of Health Research MOP‐133584
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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) | 0.002 | 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".