REAL-WORLD EXPERIENCE OF EFFECTIVENESS OF NONMEDICAL SWITCH FROM ORIGINATOR TO BIOSIMILAR RITUXIMAB AND BETWEEN BIOSIMILARS IN CONNECTIVE TISSUE DISEASES AND VASCULITIS
Bibliographic record
Abstract
PV267 / #708 Poster Topic: AS24 - SLE-Treatment Background/Purpose In rheumatoid arthritis (RA), we previously showed that nonmedical switch from rituximab originator (RTX-O) to rituximab biosimilar (RTX-B) was largely effective with comparable 18-month retention rates between those who switched vs remained on RTX-O, 76% and 82% respectively.[1] However, the uptake of nonmedical switch in SLE and other connective tissue diseases and vasculitis (CTD-VAS) has been slow due to a concern with cross-reactivity of antibodies. Our study objectives were to evaluate the effectiveness of nonmedical switch from RTX-O to RTX-B or between RTX-Bs in CTD-VAS. Methods We conducted a retrospective observational cohort study of rheumatic and musculoskeletal diseases (RMD) patients in a single center between October 2017 (Index date) and November 2024. During this period, all patients were encouraged to switch to RTX-B (Truxima ® ) unless declined by the patient or specified by the treating clinician. Furthermore, between 2021-2023, patients on Truxima ® were switched to Rixathon ® and then reverted to Truxima ® in 2024 due to contractual agreement. Due to differences in disease activity tools, clinical responses were graded into full response; partial; and nonresponse. Other measures of effectiveness include the depth of CD20+ cells depletion by highly sensitive flow cytometry and 5-year rituximab retention rate between those who underwent nonmedical switch (Group 1) vs remained on RTX-O (Group 2). Results At Index date, of 829 RMD patients treated with rituximab, 306 (37%) were given for CTD-VAS, while the remaining for RA. Of these, 84/306 (27%) underwent nonmedical switch, Group 1 [RTX-O to RTX-B=58 (69%); between RTX-Bs=26 (31%)]. They had mean (SD) age 51 (15) years, 61 (72%) were female, 61 (72%) had European ancestry, and diagnoses were SLE (54%), AAV (31%), Sjögren (5%), Myopathies (2%) and other CTD (8%). 16/306 (5%) patients remained on RTX-O (Group 2), while 206/306 (67%) initiated treatment with RTX-B. At the last follow-up, of 84 patients in Group 1, 72 (86%) remained on RTX-B [64/72 (89%) switched from RTX-O to RTX-B; 6/72 (8%) switched between RTX-Bs; and 2/72 (3%) reverted to previous RTX-B brand]. 5/84 (6%) of patients on RTX-B reverted to RTX-O and regained response. Reasons were infusion reaction=1, serum sickness=1; neutropenic sepsis 5 days post-rituximab switch=1; skin lesion=1; incomplete depletion and inferior response=1. Of 82/84 and 74/84 patients in Group 1 with paired clinical response and B cells data respectively, there was no difference in response rate (partial or full) and CD20+ cell complete depletion in the rituximab cycle before and after switch, p=0.289 and p=0.815 respectively (McNemara’s test). Patients in Group 2 had more comorbidities, number of rituximab cycles, and number of previous immunosuppressants than those in Group 1. At 5 years, 8/84 (9.5%) patients discontinued rituximab in Group 1 (inefficacy=7 including 2 who reverted to RTX-O); death due to pneumonia=1), while all 16 patients in Group 2 continued therapy. Unadjusted Kaplan-Meier analysis showed no difference in 5-year rituximab retention between Group 1 and Group 2; p=0.152 (Figure 1). Figure 1: Kaplan-Meier plot of rituximab retention survival from Index date Conclusions Our findings support the nonmedical switch either from RTX-O to RTX-B or between RTX-Bs in CTD-VAS with no difference in clinical response and depth of B cell depletion before and after switch. 5-year rituximab retention rate was very good regardless of nonmedical switch and appeared higher than in RA. Analysis of outcomes of patients who initiated RTX-B is in progress and will help estimate number needed to harm with nonmedical rituximab switch. References: [1.] Melville A. Rheumatology 2021;60(8):3679-88.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".