Brodalumab to the Rescue: Efficacy and Safety of Brodalumab in Patients with Psoriasis and Prior Exposure or Inadequate Response to Biologics
Bibliographic record
Abstract
While biologic therapies for psoriasis are effective for many patients, some patients may lose response, have inadequate control of disease, or develop intolerance to certain biologic agents. It may therefore be beneficial for patients whose psoriasis fails to respond to one biologic to switch to a different biologic therapy, in particular one with a different mechanism of action. However, it remains unclear how prior biologic exposure or lack of response affects the efficacy and safety of subsequent biologics in patients with moderate-to-severe psoriasis. Brodalumab, a fully human anti-interleukin-17 receptor A monoclonal antibody, has previously been shown to be efficacious in treating moderate-to-severe psoriasis in three large phase 3 trials (AMAGINE-1, AMAGINE-2, and AMAGINE-3). In this review, we summarize the efficacy and safety of brodalumab in patients with moderate-to-severe psoriasis and a history of biologic exposure. Further, we describe improvements in skin clearance and quality of life measures as well as safety in patients who had inadequate response to ustekinumab and who were rescued with brodalumab therapy. Lastly, we discuss improvements in skin clearance following rescue with brodalumab in patients whose disease failed to respond to secukinumab and ixekizumab. The findings of our review suggest that brodalumab is a safe and efficacious treatment regardless of past biologic use or lack of response to prior biologic 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) | 0.001 | 0.001 |
| 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 | 0.000 |
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.
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".