Machine-Learning-Based Prediction of Long-Term Efficacy of Nemolizumab: Post Hoc Analysis of Pooled Data from Two...

Nemolizumab is a humanized monoclonal antibody that specifically inhibits the receptor for interleukin-31, the major pruritogen in atopic dermatitis (AD). While the patient profile associated with the early therapeutic response to nemolizumab in AD has been increasingly delineated, the characteristics of patients who benefit from long-term nemolizumab therapy remain unclear. This study aimed to identify early clinical features predicting long-term improvement in pruritus Visual Analog Scale (VAS) score among patients receiving nemolizumab.

This study included patients participating in two Japanese clinical trials (JP01 and JP02) who did not achieve a 50% improvement in pruritus VAS score (VAS50) at week 16 after initiation of nemolizumab treatment. Patients who achieved VAS50 at week 52 were classified as responders, and those who did not were nonresponders. A machine-learning decision tree model was developed using 213 candidate variables. Model performance was evaluated using the F₁ score. Shapley additive explanation (SHAP) values were used to interpret variable importance.

Among 118 eligible patients, the final model achieved an F₁ score of 0.737. The two strongest predictors of long-term response were the absence of worsening of AD and/or the occurrence of other cutaneous disorders until week 16, and a weekly mean itch scale score < 2.1 at week 16. Patients meeting both criteria were highly likely to achieve VAS50 by week 52.

Machine-learning identified straightforward and clinically applicable predictors of long-term nemolizumab efficacy in patients who did not have early VAS improvement. These findings may support decision-making for treatment continuation beyond week 16.

JapicCTI-173740 (JP01), and JapicCTI-183894 (JP02).