B-Cell Receptor Signaling Inhibitors for Treatment of Autoimmune Inflammatory Diseases and B-Cell Malignancies
Bibliographic record
Abstract
B-cell receptor (BCR) signaling is essential for normal B-cell development, selection, survival, proliferation, and differentiation into antibody-secreting cells. Similarly, this pathway plays a key role in the pathogenesis of multiple B-cell malignancies. Genetic and pharmacological approaches have established an important role for the Spleen tyrosine kinase (Syk), Bruton's tyrosine kinase (Btk), and phosphatidylinositol 3-kinase isoform p110delta (PI3Kδ) in coupling the BCR and other BCRs to B-cell survival, migration, and activation. In the past few years, several small-molecule inhibitory drugs that target PI3Kδ, Btk, and Syk have been developed and shown to have efficacy in clinical trials for the treatment of several types of B-cell malignancies. Emerging preclinical data have also shown a critical role of BCR signaling in the activation and function of self-reactive B cells that contribute to autoimmune diseases. Because BCR signaling plays a major role in both B-cell-mediated autoimmune inflammation and B-cell malignancies, inhibition of this pathway may represent a promising new strategy for treating these diseases. This review summarizes recent achievements in the mechanism of action, pharmacological properties, and clinical activity and toxicity of these BCR signaling inhibitors, with a focus on their emerging role in treating lymphoid malignancies and autoimmune disorders.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".