88 Ixazomib, an oral proteasome inhibitor, depletes plasma cells reducing autoantibodies and pdcs in pre-clinical model of systemic lupus erythematosus
Bibliographic record
Abstract
Background and aims Auto-antibodies to nuclear constituents and type I Interferons (IFN) such as IFN-a play key roles in pathogenesis of Systemic Lupus Erythematosus (SLE). Ixazomib, an oral proteasome inhibitor, 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. Proteasome inhibitors like ixazomib that may deplete plasma cells and cellular sources of IFN-a are also attractive for autoimmune diseases like SLE. To investigate the potential of ixazomib the MRL/lpr model was used as it has extensively been shown to replicate many features of SLE. Methods MRL/lpr animals received oral ixazomib twice a week for 4 weeks. Results Ixazomib suppressed the time-dependent increase in anti-dsDNA IgG antibodies, resulting in 73% (p<0.01) inhibition of autoantibodies at the end of treatment versus vehicle. In ELISpot assays, ixazomib decreased the number of anti-dsDNA IgG antibody-secreting cells in spleen by 25% (p<0.01). In addition, FACS analysis revealed that ixazomib decreased both splenic plasma cells by 39% (p<0.001) and plasmacytoid dendritic cells (pDCs) by 38% (p<0.01), with treatment. Conclusions These findings suggest that ixazomib may be an effective agent for treating antibody-mediated diseases such as SLE by depleting both plasma cells the source of pathogenic antibodies and pDCs the main source of type I IFN production. An ongoing randomised, double-blind phase Ib study is investigating multiple rising doses of Ixazomib (MLN9708) for the treatment of patients with ISN/RPS class III, IV or V lupus nephritis who have not responded adequately to current therapy.
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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".