Safety of interleukin-17A inhibitors in 306 patients with psoriasis with or without latent tuberculosis: a dual-centre retrospective study in China
Bibliographic record
Abstract
BACKGROUND: New interleukin (IL)-17A inhibitors seem to demonstrate smaller effects on tuberculosis (TB) reactivation than expected. OBJECTIVES: To evaluate the risk of TB reactivation, to assess serial interferon (IFN)-γ levels, and to weigh up the risks and benefits of TB chemoprophylaxis in patients with psoriasis treated with IL-17A inhibitors. METHODS: This dual-centre study included patients with psoriasis who were treated with IL-17A inhibitors. The incidence of active TB, serial IFN-γ levels tested by IFN-γ release assays (IGRAs), adverse events (AEs) and effectiveness were evaluated through 1 year in patients with psoriasis treated with IL-17A inhibitors. According to the chemoprophylaxis treatment regimen, patients with latent TB infection (LTBI) were classified into three groups: a complete chemoprophylaxis dose regimen (CCP), an incomplete chemoprophylaxis (ICCP) or no chemoprophylaxis (NCP). RESULTS: In 220 IGRA-negative patients, 17 of 220 (7.3%) became IGRA positive after receiving a mean of 69.1 weeks of IL-17A inhibitor treatment. Only one case of new-onset TB was observed after 52 weeks of ixekizumab therapy. Significant changes in IFN-γ levels were observed in IGRA-negative patients. Similarly, IFN-γ levels [listed as the mean (SD)] significantly increased at 1 year compared with baseline among IGRA-positive patients in the NCP [105 (68.7) vs. 236 (80.8) pg mL-1, P < 0.01] and ICCP [75.3 (48.3) vs. 608 (249) pg mL-1, P < 0.001] groups, whereas the changes were not significant [125 (26.6) vs. 131 (21.7) pg mL-1, P = 0.70] in the CCP group. CONCLUSIONS: During IL-17A inhibitor therapy, there is a need for increased awareness of annual screening and assessment of individual risk for early detection of TB infection. LTBI treatment is generally well tolerated and is effective in preventing increased IFN-γ responses in patients with psoriasis treated with IL-17A inhibitors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".