Disease–Drug–Drug Interaction Assessments for Tocilizumab—A Monoclonal Antibody against Interleukin‐6 Receptor to Treat Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
Abstract Tocilizumab (TCZ, Actemra) is a recombinant humanized monoclonal antibody of the immunoglobulin G (IgG) subclass that selectively binds to human interleukin 6 (IL‐6) receptors. TCZ binds to both membrane‐bound and soluble forms of IL‐6R, blocking signal transduction pathways through competitive inhibition of IL‐6 binding. 1 TCZ 8 mg/kg given intravenously (IV) is currently approved for the treatment of rheumatoid arthritis (RA) in over 106 countries, including the United States, Canada, Australia, European countries, and Japan. IL‐6 receptor blockade was considered a mechanism for pharmacologic treatment of RA due to the central role of IL‐6 in inflammatory processes. IL‐6 is a pleiotropic cytokine with numerous activities, including effects on the immune response and inflammation (e.g., interaction with neutrophils in the synovium), bone metabolism, and hematopoiesis. Elevated IL‐6 levels have been found in the serum and synovial fluid of RA patients, and inflammatory cell infiltration has been correlated withIL‐6 levels in synovial tissues. As discussed in this chapter, IL‐6 levels correlate with disease activity in RA patients and improvement of the disease after treatment with disease‐modifying antirheumatic drugs (DMARDs) is accompanied by a reduction in serum IL‐6.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".