Of Mice and Men: Defining the Role of Interleukin 17 in Rheumatoid Arthritis
Bibliographic record
Abstract
In a recent issue of The Journal , Pavelka and colleagues reported on a negative study of brodalumab, the interleukin 17 (IL-17) inhibitor, in rheumatoid arthritis (RA)1. In addition to the authors’ conclusion that there is no reason to pursue further evaluation of the molecule, an antibody against the IL-17 receptor A (IL-17RA), in this disease, there are a number of other important lessons to be learned from this publication. Combined with previously published data for secukinumab2 and ixekizumab3, 2 anti-IL-17A antibodies, the results of this study strongly suggest that the IL-17 pathway is not an appropriate target in RA. This conclusion comes despite both animal data suggesting potential benefit and clinical evidence that IL-17 inhibition is effective in psoriatic arthritis (PsA). The latter observation is instructive, and may provide a clue to important pathogenic differences between 2 outwardly similar forms of inflammatory arthritis. The study itself compared 3 different dose regimens of brodalumab (70 mg, 140 mg, and 210 mg) and placebo; doses were given every 2 weeks, with an additional loading dose at 1 week. The population studied was a typical methotrexate (MTX) inadequate responder population (∼80% female, 7–8 years of disease, Disease Activity Score28 ∼6.4, Health Assessment Questionnaire-Disability Index 1.4, and mean dose of MTX 17 mg/week). The primary endpoint selected was the ACR50 (American College of Rheumatology 50% improvement) response rate at Week 12. There was no benefit seen for any of the brodalumab doses over placebo, and no dose response seen for the 3 tested doses. None of the secondary endpoints were achieved either. This was in contrast to the brodalumab data in PsA, … Address correspondence to Dr. Ruderman. E-mail: e-ruderman{at}northwestern.edu
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 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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".