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Record W2043889318 · doi:10.2147/dddt.s49778

Profile of epratuzumab and its potential in the treatment of systemic lupus erythematosus

2014· review· en· W2043889318 on OpenAlexafffund
Zahi Touma, Hanan Al Rayes

Bibliographic record

VenueDrug Design Development and Therapy · 2014
Typereview
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsToronto Western HospitalUniversity of Toronto
FundersUniversity of TorontoUniversity Health Network
KeywordsMedicineLupus erythematosusSystemic diseaseImmunologyDermatologyImmunopathologyAntibody

Abstract

fetched live from OpenAlex

Management of systemic lupus erythematosus (SLE) represents a fascinating, emerging field. Research has recently provided us with a better understanding of the immunologic alterations of SLE, leading to the creation of immunomodulatory agents designed to disrupt specific cell targets and pro-inflammatory pathways. Despite the improvement in the prognosis of SLE in the last 50 years with the use of immunosuppressive therapy such as cyclophosphamide and mycophenolate mofetil, cytotoxicity remains a major complication of these medications and the need for more specific targeted immunotherapy is increasing. Early recognition and treatment of SLE with targeted immunotherapy has the potential to improve quality of life and reduce the risk of disease flare-ups and complications. In this review, we will explore the role of B-cells in the pathogenesis of SLE highlighting current insights into SLE development and management. In addition, we will discuss epratuzumab's role in the treatment of SLE. Epratuzumab is a humanized anti-CD22 monoclonal antibody that targets CD22 on B-cell and its role in B-cell modulation, migration, function, and inhibition of B-cell receptor signaling. Epratuzumab is currently in a Phase III study evaluating its efficacy in the management of moderate to severe SLE. All published trials on epratuzumab have shown great promise with safe profiles.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.918
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.062
GPT teacher head0.325
Teacher spread0.263 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations19
Published2014
Admission routes2
Has abstractyes

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