Observational studyCNS neuroscience & therapeutics2026
Breath Signatures of Antiseizure Medication Response and Tolerability in Adults With Epilepsy.
Observational study in CNS neuroscience & therapeutics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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12 authors.
Funding
Abstract
purposeAchieving seizure control without intolerable adverse effects remains a major challenge in adult epilepsy, and serum concentrations of antiseizure medications correlate poorly with clinical outcomes. Metabolic processes influencing treatment response are not captured by blood monitoring. This study evaluates whether exhaled breath metabolomic profiles reflect treatment response and adverse effects in adults receiving antiseizure therapy, using epilepsy as a model for pharmacodynamic phenotyping. MATERIALS AND
methodsA single-center observational cohort of 117 adults provided 127 breath samples during stable antiseizure treatment. Exhaled breath samples were analyzed using secondary electrospray ionization high-resolution mass spectrometry. Machine-learning models with nested cross-validation were used to classify treatment response and adverse effects, and pathway-level analyses were performed to provide biological context. Findings were compared with an independent, predominantly pediatric cohort analyzed using the same workflow.
resultsBreath metabolomic profiles distinguished responders from nonresponders and individuals with and without adverse effects, whereas serum drug concentrations showed no meaningful association. Classification models achieved areas under the curve of 0.84 for treatment response and 0.88 for adverse effects, with good calibration. Pathway-level alterations were directionally consistent across cohorts.
conclusionBreath metabolomics supports a non-invasive approach to pharmacodynamic phenotyping that captures treatment-related metabolic states not reflected by serum drug concentrations.
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