PREEXISTING ANTIBODIES AGAINST VACCINE ANTIGENS ARE PRESERVED IN PATIENTS WITH SYSTEMIC LUPUS ERYTHEMATOSUS AND SJÖGREN’S DISEASE UPON IANALUMAB TREATMENT WHILE AUTOANTIBODIES DECLINE
Notice bibliographique
Résumé
O062 / #591 Topic:AS24 - SLE-Treatment ABSTRACT CONCURRENT SESSION 10: INTEGRATING PROTEOMIC & TRANSCRIPTOMICS IN SLE 24-05-2025 10:40 AM - 11:40 AM Background/Purpose Ianalumab, an afucosylated monoclonal antibody, depletes B cells through enhanced antibody-dependent cellular cytotoxicity with concurrent blockade of B-cell-activating factor (BAFF):BAFF-receptor (BAFF-R) mediated signals.[1] It is currently being investigated for the treatment of immune-mediated diseases. Given its novel mechanism of action, it is crucial to assess the effects of ianalumab on preexisting antibodies against vaccine antigens. Herein, we evaluated the impact of ianalumab treatment vs placebo on preexisting antibody levels against 7 pathogens in patients with systemic lupus erythematosus (SLE) and Sjögren’s disease (SjD). Methods A retrospective analysis was conducted on serum samples from 2 randomized, double-blind, placebo-controlled phase 2 studies in patients with SjD ( NCT02962895 ) or SLE ( NCT03656562 ). Patients received either placebo or ianalumab 300 mg subcutaneous monthly for 24 weeks (79 patients with SjD) or for 28 weeks (40 patients with SLE). In the SjD study, patients on 300 mg ianalumab at Week 24 (W24) were re-randomized to receive double-blinded monthly ianalumab 300 mg or placebo until W52. Patients on placebo at W24 were switched to a lower ianalumab dose and were not subject to further testing in this analysis. In the SLE study, all patients switched from double-blind to open-label ianalumab up to W52. Antibodies (IgG isotypes) to vaccine antigens and autoantibodies were measured at baseline, W24 (SjD) or W28 (SLE) and W52. Changes in antibody levels from baseline and proportions of patients maintaining protective levels at W52 were assessed. A total of 3 patients (2 SjD and 1 SLE) received booster doses against diphtheria and tetanus toxoid (TTd) under ianalumab treatment. Results The proportion of patients with SjD and SLE maintaining protective levels of antibodies against TTd, measles, mumps, varicella, rubella, diphtheria and influenza remained stable after ianalumab treatment up to 52 weeks. In patients with SjD, the changes from baseline to W24 were <6% for all antigens in both ianalumab- and placebo-treated patients. In patients with SLE, the changes from baseline to W28 were <10% in both ianalumab- and placebo-treated patients for all antigens besides diphtheria (Figure 1). For diphtheria, up to 18% changes were observed under ianalumab treatment, likely due to the low level of preexisting protection (<50% of patients had protective levels at baseline). In contrast, several autoantibodies showed a significant reduction in ianalumab-treated patients (eg, up to 60% reduction of anti-ribosomal P antibodies at W28) compared to placebo (Figure 1). These results are in line with the ability of ianalumab to deplete memory and antibody-producing cells,[2] while likely not affecting the long-lived bone marrow plasma cells that do not express BAFF-R.[3] Among the 3 patients who received booster dose(s) against diphtheria and TTd during ianalumab treatment, 2 showed a subsequent increase in corresponding titers, whereas the other patient received the booster only 10 days before W52 sampling, likely explaining the lack of increased titers. Figure 1. Titers from vaccine antigens and auto-antibody titers following treatment with ianalumab or placebo over time in patients with SLE. Ratio to baseline of antibodies to vaccine antigens and autoantibodies were measured over time in patients with SLE treated with placebo vs ianalumab. All antibodies measured were IgG. Conclusions Treatment with ianalumab up to 52 weeks did not result in a reduction of the antibody titers to previous immunizations against tetanus, varicella, measles, mumps, rubella, diphtheria, and influenza while having a clear impact on autoantibody levels.References:[1.] McWilliams EM. Blood Adv 2019;3(3):447-60. [2.] Dörner T. Ann Rheum Dis 2024;83:956-7. [3.] Darce JR. J Immunol 2007;179(11):7276-86.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».