B‐cell targeted therapies in human autoimmune diseases: an updated perspective
Bibliographic record
Abstract
The advent of therapies that specifically target the B-lymphocyte lineage in human disease has rejuvenated interest in the mechanistic biology by which B cells mediate autoimmunity. B cells have a multitude of effector functions including production of self-reactive antibodies, ability to present antigen to T lymphocytes in the context of costimulation, involvement in generation and maintenance of neo-organogenesis at sites of disease, and opposing function through production of both immunostimulatory and immunomodulatory cytokines. In this review, we first discuss the role of B cells in driving autoimmune diseases such as rheumatoid arthritis, multiple sclerosis, systemic lupus erythematosus, and Sjögren's syndrome, and discuss how studies in these diseases have revealed differentially important roles for the multiple B-cell effector functions. These data reveal the complex and interrelated roles of B cells working in concert with other components of the innate and adaptive immune system to drive pathogenesis. We then focus on data from mouse and human in which B cells in the setting of disease have been targeted with drugs directed against CD20, CD22, and the BAFF (B-cell activating factor belonging to the tumor necrosis factor family)/APRIL (a proliferation inducing ligand) pathways. Pre-clinical studies in animal models in addition to and clinical trials targeting B cells have added further to the understanding of the differential roles B cells play in disease both through demonstration of clinical efficacy in the context of B-cell depletion or modulation, and also by failure of B-cell targeting in some diseases and disease patient subgroups. Moving forward, it will be imperative to apply these lessons to new interventional trials to ensure better targeting of the B-cell lineage and concomitantly better selection of patients most likely to benefit from these therapies.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".