Early response to SPN-812 (viloxazine extended-release) can predict efficacy outcome in pediatric subjects with ADHD: a machine learning post-hoc analysis of four randomized clinical trials

Abstract

Machine learning (ML) was used to determine whether early response can predict efficacy outcome in pediatric subjects with ADHD treated with SPN-812. We used data from four Phase 3 placebo-controlled trials of 100- to 600-mg/day SPN-812 (N=1397; 6-17 years of age). The treatment response was defined as having a ≥50% reduction in change from baseline (CFB) in ADHD Rating Scale-5 (ADHD-RS-5) Total score at Week 6. The variables used were: ADHD-RS-5 Total score, age, body weight, and body mass index at baseline; CFB ADHD-RS-5 Total score at Week 1, cumulative change in ADHD-RS-5 Total score at Week 2, and cumulative change in ADHD-RS-5 Total score at Week 3; Clinical Global Impressions-Improvement (CGI-I) score at Week 1, 2, and 3; and target dose. Using the best selected model, lasso regression, to generate importance scores, we found that change in ADHD-RS-5 Total score and CGI-I score were the best predictors of efficacy outcome. Change in ADHD-RS-5 Total score at Week 2 could predict treatment response at Week 6 (75% positive predictive power, 75% sensitivity, 74% specificity). Therefore, early response after two weeks of treatment with once-daily SPN-812 in pediatric patients with ADHD can predict efficacy outcome at Week 6.

Keywords: ADHD-RS-5; Attention-deficit/hyperactivity disorder; CGI-I; predictor; treatment response.