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 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".