Polymorphism of the MDR1/ABCB1 C3435T drug‐transporter and resistance to anticonvulsant drugs: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Approximately one-third of patients with epilepsy patients have recurrent seizures despite therapy. It has been suggested that therapeutic failure is associated with high expression of the multidrug efflux ABCB1 (MDR1) drug-transporter; specifically, that patients with the 3435CC genotype have higher efflux of anticonvulsants out of brain tissue, with correspondingly lower concentrations in the central nervous system. METHODS: We conducted a meta-analysis to examine the association between MDR1 polymorphisms and the response to anticonvulsants. We included all published studies until September 2007, in which patients with responsive and unresponsive seizure disorders underwent genotyping for ABCB1 C3435T. Individual and summary odds ratios were calculated using a random effects model. A secondary analysis was also performed, stratifying the studies by their ethnic distribution to account for genetic heterogeneity. We also performed a cumulative analysis by date of publication for the included studies using a random effects model. RESULTS: We identified 11 case-control studies involving 3,371 patients (1,646 patients with drug-resistant epilepsy and 1,725 controls). We identified no significant association between anticonvulsant drug resistance and MDR1 polymorphism [odds ratio 1.15; 95% confidence interval (CI) 0.78-1.70; p = 0.48). Subanalysis of studies according to ethnicity yielded similar findings [European cohort: OR = 1.31; 95% CI 0.89-1.94, p = 0.18; Asian cohort: OR = 0.99; 95% CI 0.51-1.89, p = 0.96). CONCLUSIONS: We found no association between ABCB1 genotype and response to anticonvulsant drugs. At the present time, genetic typing for MDR1 polymorphism is not warranted for patients with drug-resistant epilepsy.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.035 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".