Interactions between dupilumab and vitiligo: A scoping review
Notice bibliographique
Résumé
In recent years, dupilumab has gained widespread recognition for its efficacy in patients with moderate to severe atopic dermatitis recalcitrant to other topical and systemic therapies.1 Controlled trials and cohort studies have highlighted dupilumab's favourable safety profiles. However, a range of adverse events, including vitiligo (OR = 2.42), have been observed.2 The existing literature offers limited guidance on managing the varied impacts of dupilumab on vitiligo, encompassing both potential improvement and exacerbation. Therefore, our study seeks to examine the current literature, aiming to formulate comprehensive management strategies for the effects of dupilumab on vitiligo. We searched MEDLINE and Embase on 1 March 2024, using keywords related to AD and vitiligo per PRISMA-ScR guidelines.3 To be eligible for inclusion, results had to be peer-reviewed manuscripts available in the English language. A total of eight studies encompassing 21 patients were ultimately included in the review, comprising five case reports, one case series and two retrospective chart reviews. Study quality was assessed using the Joanna Briggs Institute critical appraisal tool. The patient cohort was 50% female (n = 6, 12/21) with an average age of 47.5 years old (12/21). The average length of atopic dermatitis before commencing dupilumab was 11.8 years (3/21). Treatments prior to dupilumab included systemic therapies (n = 5, 26.3%) such as corticosteroids, antihistamines, biologics and immunosuppressive agents, as well as topical corticosteroids (n = 5, 26.3%) and phototherapy (n = 4, 21.1%). In total, 90.5% of patients (19/21) reported occurrence or exacerbation of vitiligo, and 9.5% (2/21) reported improvement of vitiligo while on dupilumab. Vitiligo lesion resolution was reported in 75% of patients (n = 9, 12/21), with the most common vitiligo treatments utilized in conjunction with dupilumab including corticosteroids and immunosuppressive agents (91.7%, 12/21) or phototherapy (91.7%, 12/21). In total, 33.3% of patients had a history of vitiligo (n = 4, 12/21) prior to starting dupilumab, and dupilumab cessation occurred in 40% of patients (n = 4, 10/21). In patients who developed vitiligo, the average duration until vitiligo presentation after starting dupilumab was 3.11 months (10/21) with a range from 1 to 6 months. Patient characteristics are reported in Tables 1 and 2. The occurrence or resolution of vitiligo while taking dupilumab may occur due to an imbalance in T helper pathways resulting in immunomodulation and subsequent T regulatory responses.4, 5 This imbalance may indirectly affect Th17 cell activity, potentially influencing the immune environment in a way that favours either resolution or exacerbation of vitiligo. While this immunomodulation reduces atopic dermatitis severity, it is difficult to predict the impact on lesions in patients who already have vitiligo. Although the risk is minimal, physicians should make patients aware of the possibility of vitiligo development, particularly counselling patients to closely monitor skin pigmentation changes in the first 6 months on dupilumab, as this is the time frame in which vitiligo has been reported to present. Additionally, lesion re-pigmentation on dupilumab was uncommon in our cohort of patients and may not be expected in patients receiving dupilumab for atopic dermatitis who have vitiligo based on our findings. Future research is warranted to elucidate the mechanism by which dupilumab causes re-pigmentation and assess its potential therapeutic role in the treatment of vitiligo with the goal of optimizing management and patient outcomes. Limitations of this study include a small sample size and unclear risk of bias. None. Dr. Vincent Piguet has received honoraria or fees for consulting and/or speaking for AbbVie, Almirall, Celgene, Janssen, Novartis and Pfizer and has received departmental support for Cardiff University from AbbVie, Almirall, Alliance, Beiersdorf UK Ltd, Biotest, Celgene, Dermal, Eli Lilly, Galderma, Genus Pharma, Globe Micro, Janssen-Celag, La Roche-Posay, L'Oreal, LEO Pharma, Meda, MSD, Novartis, Pfizer, Sinclair Pharma, Spirit, Stiefel, Samumed, Thornton Ross, TyPham and UCB and for University of Toronto from Sanofi. Shanti Mehta and Dea Metko have no conflicts of interest to declare. There is no ethical approval applicable. The data that support the findings of this study are available from the corresponding author upon reasonable request.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,009 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,004 |
| Bibliométrie | 0,020 | 0,021 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».