REAL-WORLD EXPERIENCE OF EFFECTIVENESS OF NONMEDICAL SWITCH FROM ORIGINATOR TO BIOSIMILAR RITUXIMAB AND BETWEEN BIOSIMILARS IN CONNECTIVE TISSUE DISEASES AND VASCULITIS
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».