B-Cell-Directed Therapies: A New Era in Multiple Sclerosis Treatment
Bibliographic record
Abstract
Multiple sclerosis (MS) is a chronic autoimmune demyelinating disease of the central nervous system (CNS) that often progresses to severe disability. Previous studies have highlighted the role of T cells in disease pathophysiology; however, the success of B-cell-targeted therapies has led to an increased interest in how B cells contribute to disease immunopathology. In this review, we summarize evidence of B-cell involvement in MS disease mechanisms, starting with pathology and moving on to review aspects of B cell immunobiology potentially relevant to MS. We describe current theories of critical B cell contributions to the inflammatory CNS milieu in MS, namely (i) production of autoantibodies, (ii) antigen presentation, (iii) production of proinflammatory cytokines (bystander activation), and (iv) EBV involvement. In the second part of the review, we summarize medications that have targeted B cells in patients with MS and their current position in the therapeutic armamentarium based on clinical trials and real-world data. Covered therapeutic strategies include the targeting of surface molecules such as CD20 (rituximab, ocrelizumab, ofatumumab, ublituximab) and CD19 (inebilizumab), and molecules necessary for B-cell activation such as B cell activating factor (BAFF) (belimumab) and Bruton's Tyrosine Kinase (BTK) (evobrutinib). We finally discuss the use of B-cell-targeted therapeutics in pregnancy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".