Frontiers in pharmacology · 2026
Drug-associated psoriasis: a pharmacovigilance study based on the FAERS database.
СтатьяНазвание и аннотация: Перевод в очереди
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Yi Sha, Liu Aimei, Huoshen Wuda, Xia Dengmei, Ma Xunmi
Аннотация
BACKGROUND: Drug-induced psoriasis has been increasingly recognized, but the spectrum of potential triggering medications remains incompletely characterized. We aimed to identify the drugs potentially associated with psoriasis using a large-scale and real-world pharmacovigilance analysis.
METHODS: Firstly, disproportionality analyses were used to detect psoriasis-related drug signals. Furthermore, Multivariable logistic regression and Bayesian shrinkage models were applied to evaluate the consistency of the detected associations after adjustment for available demographic and reporting-related factors. Finally, stratified analyses by sex, age, and reporter type were conducted to assess signal robustness.
RESULTS: A total of 30 drugs showed significant associations with psoriasis. These signals involved several pharmacological categories, including anti-infective agents (e.g., Abacavir, Fluconazole, Moxifloxacin, Sulfamethoxazole), anti-inflammatory and immunomodulatory drugs (e.g., Celecoxib, Tofacitinib), cardiovascular agents (e.g., Acebutolol, Telmisartan, Clonidine), neuropsychiatric drugs (e.g., Nortriptyline, Temazepam, Zopiclone, Wellbutrin), hormonal agents (e.g., Progesterone, Prometrium), and respiratory medications (e.g., Ventolin). Bayesian correction generally attenuated extreme effect estimates but confirmed the robustness of most signals. The stratified analysis also approve the above results.
CONCLUSION: Our findings suggest that multiple drug classes are associated with psoriasis signals in pharmacovigilance data. These results highlight the importance of early recognition of drug-associated psoriasis and may assist clinicians in medication review and risk stratification in routine practice.
METHODS: Firstly, disproportionality analyses were used to detect psoriasis-related drug signals. Furthermore, Multivariable logistic regression and Bayesian shrinkage models were applied to evaluate the consistency of the detected associations after adjustment for available demographic and reporting-related factors. Finally, stratified analyses by sex, age, and reporter type were conducted to assess signal robustness.
RESULTS: A total of 30 drugs showed significant associations with psoriasis. These signals involved several pharmacological categories, including anti-infective agents (e.g., Abacavir, Fluconazole, Moxifloxacin, Sulfamethoxazole), anti-inflammatory and immunomodulatory drugs (e.g., Celecoxib, Tofacitinib), cardiovascular agents (e.g., Acebutolol, Telmisartan, Clonidine), neuropsychiatric drugs (e.g., Nortriptyline, Temazepam, Zopiclone, Wellbutrin), hormonal agents (e.g., Progesterone, Prometrium), and respiratory medications (e.g., Ventolin). Bayesian correction generally attenuated extreme effect estimates but confirmed the robustness of most signals. The stratified analysis also approve the above results.
CONCLUSION: Our findings suggest that multiple drug classes are associated with psoriasis signals in pharmacovigilance data. These results highlight the importance of early recognition of drug-associated psoriasis and may assist clinicians in medication review and risk stratification in routine practice.