48 Ixazomib, an oral proteasome inhibitor, reduces antibody production by depleting plasma cells in a t cell dependent antigen response model
Bibliographic record
Abstract
Background and aims Pathogenic auto-antibodies produced by plasma cells are key drivers of many auto-immune diseases such as Systemic Lupus Erythematosus (SLE), Rheumatoid Arthritis (RA), and Sjogren’s Syndrome (SS). In addition, solid organ transplant rejection is also mediated by antibodies produced against the donor organ. Plasma cells are highly metabolically active antibody factories and thus sensitive to depletion by proteasome inhibitors. Ixazomib, an oral proteasome inhibitor, was recently approved in the US and Canada for use in combination with lenalidomide and dexamethasone in patients with multiple myeloma who have received at least 1 prior therapy. The Keyhole limpet hemocyanin (KLH) model of T cell-dependent antigen response was used to determine if ixazomib depletes plasma cells resulting in a reduction of antibodies. Methods Briefly, rats were immunised with KLH and TiterMax adjuvant then treated with ixazomib twice weekly until study termination. Results Treatment with ixazomib significantly inhibited anti-KLH antibodies by 34% (p<0.05) versus vehicle. Additionally, KLH plasma cells quantified by ELISpot were decreased 78% (p<0.01) in the spleen and 53% (p<0.01) in the bone marrow compared to control. To gain some understanding of the selectivity of plasma cell depletion total White Blood Cells, Red Blood Cells (RBC) Platelets, Neutrophils, and total Lymphocytes were quantified with small a reduction only seen in RBCs and platelets. Conclusions Ixazomib depleted plasma cells resulting in reduced antibodies suggesting further preclinical studies are warranted in diseases with pathogenic antibodies such as SLE, RA, SS and solid organ transplant rejection.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".