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Record W2113754168 · doi:10.1001/archneurol.2009.50

Effect of Anti-CD25 Antibody Daclizumab in the Inhibition of Inflammation and Stabilization of Disease Progression in Multiple Sclerosis

2009· article· en· W2113754168 on OpenAlexafffund
Bibiana Bielekova, Nancy Richert, Gregg Blevins, Joan Ohayon, Thomas A. Waldmann, Henry F. McFarland, Roland Martinꝉ

Bibliographic record

VenueArchives of Neurology · 2009
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsUniversity of Alberta
FundersNational Institute of Neurological Disorders and StrokeNational Cancer InstituteNational Institutes of HealthUniversity of Alberta
KeywordsDaclizumabMedicineMultiple sclerosisInternal medicineInterferon beta-1aGastroenterologyOncologyImmunologyInterferon betaMonoclonal antibodyAntibody

Abstract

fetched live from OpenAlex

BACKGROUND: Several questions arise concerning the use of the anti-CD25 antibody daclizumab to treat multiple sclerosis (MS). OBJECTIVES: To answer the following 3 questions related to the efficacy of daclizumab therapy in patients with MS: Is the therapeutic effect of daclizumab dependent on combination with interferon beta? Is a higher dosage of daclizumab more efficacious in patients with persistent disease activity? Can biomarkers predict full vs partial therapeutic response to daclizumab? DESIGN: An open-label baseline vs treatment phase II clinical trial of daclizumab in patients having MS with inadequate response to interferon beta. Three months of interferon beta treatment at baseline were followed by 5.5 months of interferon beta-daclizumab combination therapy. If patients experienced more than 75% reduction of contrast-enhancing lesions (CELs) on brain magnetic resonance imaging at month 5.5 compared with baseline, daclizumab was continued as monotherapy for 10 months. Otherwise, the dosage of daclizumab was doubled. SETTING: Neuroimmunology Branch, National Institute of Neurological Disorders and Stroke, Bethesda, Maryland. PATIENTS: Fifteen patients with MS receiving standard preparations of interferon beta who experienced more than 1 MS exacerbation or whose clinical disability increased in the preceding 12 months and who had at least 2 CELs on baseline brain magnetic resonance images. INTERVENTION: Daclizumab (1 mg/kg) as an intravenous infusion every 4 weeks in combination with interferon beta (months 0-5.5) and as monotherapy (months 6.5-15.5). MAIN OUTCOME MEASURES: The primary outcome was the reduction of CELs among interferon beta monotherapy, interferon beta-daclizumab combination therapy, and daclizumab monotherapy. The secondary outcomes included immunologic biomarkers and changes in clinical disability. RESULTS: Overall, 5 of 15 patients (33%) experienced adverse effects of therapy. Two patients developed systemic adverse effects, and daclizumab therapy was discontinued. Although daclizumab monotherapy was efficacious in 9 of 13 patients with MS, interferon beta-daclizumab combination therapy was necessary to stabilize disease activity in the other 4 patients. Daclizumab therapy led to 72% inhibition of new CELs and significant improvement in clinical disability. Pilot biomarkers (increase in CD56bright natural killer cells and decrease in CD8+ T cells) were identified that can differentiate between full and partial daclizumab responders. CONCLUSIONS: Daclizumab monotherapy is effective in most patients who experienced persistent MS disease activity with interferon beta therapy. Interferon beta-daclizumab combination therapy or higher dosages of daclizumab may be necessary to achieve optimal therapeutic response in all patients. Biomarkers may identify patients with suboptimal response to daclizumab monotherapy. Administration among a large patient sample during a longer period is needed to fully define the safety and long-term efficacy of daclizumab as treatment for high-inflammatory MS. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00001934.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.322
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations171
Published2009
Admission routes2
Has abstractyes

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