MRP1 Polymorphisms Associated With Citalopram Response in Patients With Major Depression
Bibliographic record
Abstract
Multidrug resistance protein 1 (MRP1, ABCC1) transports antidepressive agents in the endothelial cells of the blood-brain barrier. Therefore, polymorphisms in the MRP1 gene may affect the treatment response of antidepressants. This study was aimed to identify the association between genetic variations in MRP1/ABCC1 and the therapeutic response to the antidepressant citalopram. One hundred and twenty-three patients who had been treated with citalopram monotherapy to control their major depressive disorder were recruited, and genotype data from 64 patients who had completed their 8-week follow-up were evaluated together with those from 100 controls. Nine MRP1 single nucleotide polymorphisms (SNPs) showing more than 5% allele frequency in the Korean population were analyzed. The c.4002G>A, a synonymous SNP in exon 28, showed a strong association with the remission state at 8 weeks (P = 0.005, odds ratio [OR], 4.7, 95% confidence interval [CI], 1.5 approximately 14.7). The c.4002G>A forms a linkage disequilibrium block with 3 other SNPs including c.5462T>A in the 3' untranslated region. Accordingly, the haplotype showed a significant association with the remission state (P = 0.014). Subsequent molecular studies also supported the association between these MRP1 polymorphisms and the citalopram response. Thus, kinetic studies using MRP1-enriched membrane vesicles revealed that citalopram is a substrate of MRP1 (Km = 1.99 microM, Vmax = 137 pmol/min per milligram protein). In addition, individuals with c.4002G>A or c.5462T>A polymorphisms showed higher MRP1 mRNA levels in peripheral blood cells. These results suggest that MRP1 polymorphisms may be a predictive marker of citalopram treatment in major depression.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".