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Introduction <p>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&#xa0;the&#xa0;early therapeutic response&#xa0;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&#xa0;among patients receiving nemolizumab.</p> Methods <p>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 F<sub>1</sub> score. Shapley additive explanation (SHAP) values were used to interpret variable importance.</p> Results <p>Among 118 eligible patients, the final model achieved an F<sub>1</sub> 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 &lt; 2.1 at week 16. Patients meeting both criteria were highly likely to achieve VAS50 by week 52.</p> Conclusions <p>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.</p> Trial Registration Number <p>JapicCTI-173740 (JP01), and JapicCTI-183894 (JP02).</p>

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Machine-Learning-Based Prediction of Long-Term Efficacy of Nemolizumab: Post Hoc Analysis of Pooled Data from Two Phase III Clinical Trials

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月15日

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  • Makoto Kawashima,
  • Tomoya Maeda,
  • Kotaro Iwasaki,
  • Masashi Takizawa,
  • Noriaki Kaneda,
  • Kenji Kabashima

摘要

Introduction

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.

Methods

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.

Results

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.

Conclusions

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.

Trial Registration Number

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