Treatment of psoriasis with biologic and non‐biologic targeted therapies in patients with latent tuberculosis infection or at risk for tuberculosis disease progression: Recommendations from a <scp>SPIN</scp>‐<scp>FRT</scp> expert consensus
Bibliographic record
Abstract
Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a significant global health problem. In immunocompetent individuals, the microorganism can remain in a latent, non-contagious form, however, it may become active under conditions of immunosuppression. Tumour necrosis factor (TNF) inhibitors, which are frequently used for the management of immune-mediated disorders like psoriasis, have been associated with a significantly increased risk of reactivating latent TB. Consequently, international guidelines recommend TB screening and preventive treatment before starting anti-TNF therapy. These recommendations have extended to IL-12/23, IL-17, IL-23 and TYK2 inhibitors under a caution principle, despite their different mechanisms of action. However, current evidence suggests that some of these agents are arguably not associated with an increased risk of TB reactivation or development of TB disease after infection, which calls for a critical reassessment of these guidelines. We have conducted a literature search evaluating the risk of TB reactivation associated with these innovative therapies, integrating findings from both randomized clinical trials and real-world evidence. The identified evidence is limited but the low number of identified cases of reactivation with IL-17 and IL-23 inhibitors prompts reconsidering the need for preventive treatment for latent TB in all cases, regardless of biologic class or individual patient's risk of TB reactivation or drug toxicity. This review, along with the clinical insight of a panel of experts on behalf of the SPIN-FRT, led to the development of these consensus recommendations for managing psoriasis treatment in patients with latent TB infection or at risk of TB infection, who are receiving or are intended to receive biologic and non-biologic targeted therapies. These recommendations highlight the need for updates to the existing guidelines, aiming to provide a more differentiated approach that reflects the evolving landscape of psoriasis treatment and its implications for TB management.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".