Machine Learning-Driven Prediction Models for Brodalumab Therapeutic Effect and Response Speed in Plaque Psoriasis.

Purpose: Biologic therapies have transformed plaque psoriasis treatment, but patient responses remain variable, neces+sitating machine prediction model for personalized therapy.

Patients and methods: Transcriptomic and clinical data from moderate-to-severe psoriatic patient biopsies were sourced from GSE117468. Differential gene analysis identified Brodalumab treatment-associated genes. Lasso regression selected response-related genes, and LightGBM was used to build machine learning models. Model robustness was assessed using five-fold cross-validation.

Results: Biopsies (n=491) from 116 patients' lesional (LS) and non-lesional (NL) tissues were analyzed, divided into Brodalumab (140 mg or 210 mg) and placebo groups. Responders were defined as achieving ≥75% improvement in Psoriasis Area and Severity Index at week 12. Lasso identified genes from classical psoriasis pathways (IL-17, PPAR signaling, HLA-D alleles) and novel targets (WIF1, SLC44A5, LOC441528, SAA1). Six LightGBM models were trained to predict 12-week treatment response and 4-week response speed using LS, NL, and combined (LS_&_NL) data. LS_&_NL models showed superior performance, achieving AUC-ROC values of 95.14% (140 mg) and 92.83% (210 mg) for 12-week response prediction and 98.70% (140 mg) and 97.51% (210 mg) for 4-week response speed prediction.

Conclusion: These models provide robust tools for predicting Brodalumab response, supporting precision medicine and optimizing resource allocation in plaque psoriasis management.