Alemtuzumab for the Treatment of Relapsing–Remitting Multiple Sclerosis
Bibliographic record
Abstract
Alemtuzumab, a humanized monoclonal antibody that targets CD52, was recently approved in the EU and Canada for the treatment of patients with active relapsing-remitting multiple sclerosis. Alemtuzumab induces rapid depletion of circulating B- and T-lymphocytes followed by repopulation that leads to a distinctive lymphocyte profile, including an increased proportion of regulatory T-lymphocytes and memory B- and T-lymphocytes. In early open-label studies, alemtuzumab treatment reduced the number of clinical relapses and new MRI lesions in participants with secondary progressive MS. However, most participants had continued worsening of disability, which led to the evaluation of alemtuzumab in patients with early stages of disease in the Genzyme (MA, USA)-sponsored clinical development program in MS. In one Phase II and two Phase III trials, alemtuzumab reduced the number of clinical relapses versus the active comparator, subcutaneous IFN-β-1a, in treatment-naive and treatment-experienced participants with relapsing-remitting multiple sclerosis. Two of these trials showed reduction in risk of confirmed worsening of disability, and all showed reduction in cerebral atrophy. Safety issues include infusion reactions that are mitigated by pretreatment with corticosteroids in addition to symptomatic management with antihistamines; mild to moderate infections; and autoimmune adverse events. In this context, post-marketing risk mitigation strategies will be needed so that potential adverse events can be identified and managed early and effectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".