Exploring the association of disease-modifying therapies for multiple sclerosis and BTK inhibitors with epilepsy
Bibliographic record
Abstract
Background: Multiple lines of evidence suggest a role of inflammation in epilepsy. Seizure incidence in patients with multiple sclerosis (MS) is twofold to threefold higher than the age-matched general population. Objectives: To explore the association of MS disease-modifying therapies (DMTs) and FDA-approved Bruton tyrosine kinase inhibitors (for lymphocytic malignancies) with the occurrence of epilepsy using the US Food and Drug Administration Adverse Event Reporting System (FAERS) database. Design: Secondary analysis of the FAERS database. Methods: We conducted a disproportionality analysis of FAERS between 2003-Q4 and 2023-Q3. MS DMTs and the Bruton tyrosine kinase inhibitor, ibrutinib, were included in the analysis. An inverse association was defined by a 95% confidence interval (CI) upper limit of reporting odds ratio (ROR) <1. Results: We found an inverse association of ibrutinib, ocrelizumab, ofatumumab, rituximab, and teriflunomide with epilepsy. The strongest inverse association was seen with ibrutinib (ROR: 0.338; 95% CI: 0.218-0.524). Conclusion: Our findings suggest the possibility of considering these medications for repurposing opportunities in epilepsy and support a potential pathogenic role of leukocyte subsets in seizure perpetuation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".