Predictive factors for responders to upadacitinib treatment in patients with atopic dermatitis

Abstract

Background

Janus kinase 1 inhibitor upadacitinib is therapeutically effective for atopic dermatitis (AD). However, predictive factors for high responders to upadacitinib have not been established in real-world clinical practice.

Objectives

To identify predictive factors for responders to upadacitinib 15 mg or 30 mg, defined as achievers of investigator’s global assessment (IGA) 0/1 with ≥ 2-point improvement from basal IGA.

Methods

A retrospective study was conducted from August 2021 to July 2023 on 159 AD patients treated with upadacitinib 15 mg and 52 patients with 30 mg. Patients in each group were categorized into responders (achievers of IGA 0/1 at week 12) and non-responders (non-achievers). We compared baseline values of clinical and laboratory parameters between responders and non-responders. Logistic regression analysis was used to detect variables predicting responders. Receiver-operating characteristic curves were used for evaluating prediction capabilities of the variables.

Results

In logistic regression analysis, responders to 15 mg upadacitinib were associated with lower total EASI and higher age whereas responders to 30 mg were associated with lower LDH and lower IgE.

Conclusions

Lower total EASI and higher age may predict responders to upadacitinib 15 mg while lower IgE and lower LDH may predict responders to 30 mg.

Keywords:

• Atopic dermatitis
• upadacitinib
• Janus kinase inhibitor
• predictive factor
• responder

Ethical approval

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Ethics Committee of Nippon Medical School Chiba Hokusoh Hospital (protocol code H-2022-945 and February 10, 2022 of approval).

Authors’ contributions

Teppei Hagino conceptualized the study, and mainly organized the manuscript. Mai Yoshida and Risa Hamada performed the statistical analyses. Naoko Kanda supervised the study. Hidehisa Saeki and Eita Fujimoto revised the manuscript.

Disclosure statement

Hidehisa Saeki received a lecture fee and research cost from AbbVie GK. Teppei Hagino and Naoko Kanda received lecture fees from AbbVie GK.

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research was partially supported by the grant, Initiative for Realizing Diversity in the Research Environment from MEXT, Japan.