Treating to Target(s) With Interleukin-17 Inhibitors
Bibliographic record
Abstract
BACKGROUND:: The treat-to-target (T2T) strategy has become established in several medical specialties as a key guidance to optimal therapeutic decision making. T2T may be effective in the assessment of the biologic class of agents called interleukin (IL)-17 inhibitors, which are emerging as a safe and effective treatment option for autoimmune inflammatory conditions such as plaque psoriasis, psoriatic arthritis (PsA), and ankylosing spondylitis (AS). OBJECTIVE:: The objective of this article is to use a T2T approach for the evaluation of the effectiveness and safety of IL-17 inhibitors in the management of patients with plaque psoriasis, PsA, and AS. METHODS:: Following a comprehensive literature search, a full-day meeting was convened to discuss and identify the T2T targets for psoriasis, PsA, and AS. Clinical trial evidence was presented for the approved IL-17 inhibitors-secukinumab, ixekizumab, and brodalumab-to assess whether these data meet T2T safety and efficacy targets. RESULTS:: All 3 approved agents were significantly superior to placebo and active controls in the achievement of T2T targets for psoriasis. Secukinumab and ixekizumab were likewise associated with significantly better outcomes than controls in the PsA targets, and secukinumab resulted in significant AS target improvements vs placebo. The IL-17 inhibitors were also associated with low rates of serious adverse events and exacerbations of common comorbid conditions. CONCLUSION:: Phase III trial results support the T2T benefit and safety of IL-17 inhibitors according to their specific indications for the management of patients with plaque psoriasis, PsA, and AS.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".