Back

Effectiveness and Tolerability of Nonmedical Switching from Originator (MabThera) to Biosimilar (Truxima) Rituximab in People with Multiple Sclerosis: A Tertiary Single-Center Observational Study

Althobaiti, A. H.; Alnughaimish, A. A.; Alqahtani, S. S.; Aldosari, F.

2026-08-03 neurology
10.64898/2026.08.01.26359456 medRxiv
Show abstract

Background: Rituximab is used off-label for multiple sclerosis (MS), and biosimilar substitution raises a distinct extrapolation challenge, as MS is not an approved indication for the reference product. Real-world nonmedical switching data inform biosimilar appropriateness decisions by clinicians, societies, and payers. Objective: To report the effectiveness and tolerability of nonmedical switching from originator (MabThera) to biosimilar rituximab (Truxima) in people with MS (pwMS). Methods: A retrospective, single-center observational cohort study of 50 pwMS switched after at least two originator infusions, followed for two years. Results: Annualized relapse rate declined from 0.45 (95% CI 0.28 - 0.68) prerituximab to 0.02 (95% CI 0.00 - 0.13) on originator and 0.00 (95% CI 0.00 - 0.05) on biosimilar (p = 0.367 between products). In paired imaging analysis (n = 29), the proportion with active scans declined progressively (50.0%, 34.5%, 17.2%; Cochrans Q, p = 0.040), with no difference between the originator and biosimilar periods (McNemar, p = 0.227). B-cell depletion deepened progressively. All patients remained on biosimilar through the end of follow-up. Conclusion: Nonmedical switching from originator to biosimilar rituximab was associated with comparable clinical and radiological outcomes, supporting its use in pwMS without concern for inferior efficacy or diminished tolerability.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.