Efficacy of MLN9708 (ixazomib) in experimental autoimmune myasthenia gravis and in anti-AChR producing primary thymic cell cultures from myasthenia gravis patients
Bibliographic record
Abstract
Proteasome inhibitors can eliminate malignant, alloreactive, or autoreactive plasma cells. These cells are key players in antibody-mediated autoimmune disorders and thus suitable therapeutic targets for these drugs. However, certain proteasome inhibitors cause toxic peripheral neuropathy in patients. Ixazomib (MLN9708, Ninlaro), an oral proteasome inhibitor, has a more favorable safety profile in multiple myeloma patients. Here we tested its efficacy in preventing and treating experimental autoimmune myasthenia gravis (EAMG). Female Lewis rats were treated with two subcutaneous doses of 0.35 mg/kg of ixazomib per week, starting either 4 weeks before or at disease onset; both substantially lowered final total IgG and rat acetylcholine receptor (AChR) autoantibody levels. Interestingly, two weekly doses of 0.20 mg/kg of ixazomib for the last 4 weeks did not reduce autoantibody levels. A single dose of 0.50 mg/kg was acutely toxic in rats. In cultures of thymic cells from early-onset myasthenia gravis (EOMG) patients, 30 nM ixazomib or higher almost completely eliminated plasma cells and halted their IgG and AChR antibody production. We conclude that proteasome inhibition with ixazomib effectively depletes plasma cells from MG patients in vitro and in a rat model in vivo. These results encourage further investigations into therapeutic plasma cell targeting for MG patients.
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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) | 0.001 | 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 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".