High‐throughput compound screen reveals mTOR inhibitors as potential therapeutics to reduce (auto)antibody production by human plasma cells
Bibliographic record
Abstract
Antibody production by the B cell compartment is a crucial part of the adaptive immune response. Dysregulated antibody production in the form of autoantibodies can cause autoimmune disease. To date, B-cell depletion with anti-CD20 antibodies is commonly applied in autoimmunity, but pre-existing plasma cells are not eliminated in this way. Alternative ways of more selective inhibition of antibody production would add to the treatment of these autoimmune diseases. To explore novel therapeutic targets in signaling pathways essential for plasmablast formation and/or immunoglobulin production, we performed a compound screen of almost 200 protein kinase inhibitors in a robust B-cell differentiation culture system. This study yielded 35 small cell-permeable compounds with a reproducible inhibitory effect on B-cell activation and plasmablast formation, among which was the clinically applied mammalian target of rapamycin (mTOR) inhibitor rapamycin. Two additional compounds targeting the phosphoinositide 3-kinase-AKT-mTOR pathway (BKM120 and WYE-354) did not affect proliferation and plasmablast formation, but specifically reduced the immunoglobulin production. With this compound screen we successfully applied a method to investigate therapeutic targets for B-cell differentiation and identified compounds in the phosphoinositide 3-kinase-AKT-mTOR pathway that could specifically inhibit immunoglobulin production only. These drugs may well be explored to be of value in current B-cell-depleting treatment regimens in autoimmune disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".