Targeting IL23p19 using risankizumab for the management of moderate-to-severely active Crohn's disease
Bibliographic record
Abstract
Targeting Th17-mediated inflammatory pathways through inhibition of interleukin (IL)-23 has emerged as an important therapeutic mechanism for patients with inflammatory bowel disease. Ustekinumab, a monoclonal antibody blocking both IL-12 and IL-23, was the first agent approved by Health Canada with this mechanism of action, initially for Crohn’s disease (CD) in 2016 and subsequently for ulcerative colitis (UC) in 2020. Over the past decade, there has been increasing attention focused on selectively blocking IL-23, as the key activator of pathogenic Th17 inflammatory cells. Several monoclonal antibodies that target the unique p19 subunit of IL-23 (IL23p19 antagonists) have been developed for psoriasis and psoriatic arthritis, where IL-23 specific blockade results in substantially greater efficacy compared to targeting IL-12/23. The first IL23p19 antagonist, risankizumab, has recently been approved in Canada for the treatment of moderate-to-severely active CD. Here, we describe the mechanism of action of risankizumab and how it differentiates from ustekinumab; review the pivotal clinical trial data that demonstrates the ability of risankizumab to achieve relevant clinical and endoscopic endpoints in both biologic treatment naïve and exposed patients; and summarize key safety data that helps inform decisions about the benefit-risk profile of this novel therapy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".