Treating autism with Bumetanide: Identification of responders using Q-Finder machine learning algorithm

Abstract

Bumetanide, a specific NKCC1 co-transporter inhibitor, restores deficient GABAergic inhibition implicated in various brain disorders, including Autism Spectrum Disorders (ASD). In keeping with this mechanism, nine successful phase 2 clinical trials, conducted by seven independent teams using an identical protocol, have shown significant improvements in ASD symptoms among individuals treated with Bumetanide. Despite these promising results, two large phase 3 clinical trials (over 400 children recruited in approximately 50 centers and covering age groups 2–6 and 7–17 years) failed with no significant difference between patients treated by placebo or Bumetanide. This failure may stem from the substantial heterogeneity of ASD symptom profiles across the study population, potentially diluting the overall observed treatment effect. To address this, we reanalyzed the phase 3 data using Q-Finder, a supervised machine learning algorithm, aiming to identify subgroups of patients who responded to the treatment. This analysis was based on clinical parameters collected at the baseline of trial and used the same standard endpoints and success criteria defined in the original phase 3 protocol. It enabled the identification of responder subgroups showing a statistically significant difference between placebo and Bumetanide treatment arms. We report detailed descriptions and statistical evaluations of these subgroups. The discovered responder subgroups, representing up to 40% of participants, were cross validated between the two study populations. These findings suggest that meaningful treatment responses can be uncovered within negative phase 3 trials, highlighting the limitations of a one-size-fits-all approach for heterogeneous conditions such as ASD. Machine learning appears to be a promising tool to support this precision medicine strategy.