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Safety and efficacy of the JAK inhibitor tofacitinib citrate in patients with alopecia areata
Milène Kennedy Crispin, … , Anthony E. Oro, Brett A. King
Milène Kennedy Crispin, … , Anthony E. Oro, Brett A. King
Published September 22, 2016
Citation Information: JCI Insight. 2016;1(15):e89776. https://doi.org/10.1172/jci.insight.89776.
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Clinical Medicine Dermatology Inflammation

Safety and efficacy of the JAK inhibitor tofacitinib citrate in patients with alopecia areata

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Abstract

BACKGROUND. Alopecia areata (AA) is an autoimmune disease characterized by hair loss mediated by CD8+ T cells. There are no reliably effective therapies for AA. Based on recent developments in the understanding of the pathomechanism of AA, JAK inhibitors appear to be a therapeutic option; however, their efficacy for the treatment of AA has not been systematically examined.

METHODS. This was a 2-center, open-label, single-arm trial using the pan-JAK inhibitor, tofacitinib citrate, for AA with >50% scalp hair loss, alopecia totalis (AT), and alopecia universalis (AU). Tofacitinib (5 mg) was given twice daily for 3 months. Endpoints included regrowth of scalp hair, as assessed by the severity of alopecia tool (SALT), duration of hair growth after completion of therapy, and disease transcriptome.

RESULTS. Of 66 subjects treated, 32% experienced 50% or greater improvement in SALT score. AA and ophiasis subtypes were more responsive than AT and AU subtypes. Shorter duration of disease and histological peribulbar inflammation on pretreatment scalp biopsies were associated with improvement in SALT score. Drug cessation resulted in disease relapse in 8.5 weeks. Adverse events were limited to grade I and II infections. An AA responsiveness to JAK/STAT inhibitors score was developed to segregate responders and nonresponders, and the previously developed AA disease activity index score tracked response to treatment.

CONCLUSIONS. At the dose and duration studied, tofacitinib is a safe and effective treatment for severe AA, though it does not result in a durable response. Transcriptome changes reveal unexpected molecular complexity within the disease.

TRIAL REGISTRATION. ClinicalTrials.gov NCT02197455 and NCT02312882.

FUNDING. This work was supported by the US Department of Veterans Affairs Office of Research and Development, National Institute of Arthritis and Musculoskeletal and Skin Diseases National Institutes of Health grant R01 AR47223 and U01 AR67173, the National Psoriasis Foundation, the Swedish Society of Medicine, the Fernström Foundation, the Locks of Love Foundation, the National Alopecia Areata Foundation, and the Ranjini and Ajay Poddar Resource Fund for Dermatologic Diseases Research.

Authors

Milène Kennedy Crispin, Justin M. Ko, Brittany G. Craiglow, Shufeng Li, Gautam Shankar, Jennifer R. Urban, James C. Chen, Jane E. Cerise, Ali Jabbari, Mårten C.G. Winge, M. Peter Marinkovich, Angela M. Christiano, Anthony E. Oro, Brett A. King

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Figure 5

Unsupervised biomarker signature predicting nonresponse to tofacitinib.

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Unsupervised biomarker signature predicting nonresponse to tofacitinib.
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(A) Differences between patient-paired samples revealed the existence of 2 subclusters of nonresponders. One of these subclusters had a molecular behavior similar to that in the SR group, and the other had a molecular behavior discordant with response. This was used to partition the nonresponders into presumptive slow responders, who may eventually respond to treatment and are simply delayed, and likely true nonresponders, who exhibit no indication of improvement at a molecular level. Unsupervised hierarchical clustering of the tofacitinib patients using the “slow responder versus nonresponder” gene signature is shown. This gene signature was derived from the top 50 most differentially expressed genes when comparing true nonresponders and slow responders (P < 0.05). The clustering demonstrates that the signature is capable of robustly separating molecular true nonresponders (red patients, n = 8) from slow responders (orange patients, n = 11), which in turn segregated with the molecular true responders (blue patients, n = 9). White lines are superimposed as a visual aid to demarcate the primary superclusters in both gene and sample clustering. (B) A 3-dimensional representation of the first 3 principal components underlying the clustering results provides a spatial representation of the molecular divergence between patients based on predicted molecular response. The true nonresponders (red spheres, n = 8) segregate from a second cluster consisting of a mix of true responders (blue spheres, n = 9) and slow responders (orange spheres, n = 11).

Copyright © 2023 American Society for Clinical Investigation
ISSN 2379-3708

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