Longitudinal EEG model detects antisense oligonucleotide treatment effect and increased UBE3A in Angelman syndrome.

Abstract

Angelman syndrome is a neurodevelopmental disorder caused by deficiency of the maternally inherited UBE3A gene in neurons. Antisense oligonucleotide therapies are under development to reinstate UBE3A protein production. Non-invasive biomarkers to detect target engagement and treatment response are needed to support clinical trials. Delta power measured in the scalp EEG is a reliable biomarker for Angelman syndrome but varies widely across individuals and throughout development, making detection of a treatment effect using single measurements challenging. We utilized a longitudinal dataset of 204 EEG recordings from 56 subjects with Angelman syndrome to develop a natural history model of delta (2-4 Hz) power, with predictors of age, elapsed time, and relative delta power at an initial recording. Using this model, we computed the sample and effect sizes needed to detect a treatment effect in a human clinical trial with 80% power. We applied the same model structure to a mouse model of Angelman syndrome (n = 41) to detect antisense oligonucleotide-mediated treatment effects on absolute delta activity and Ube3a expression. In humans, delta power at a second time point can be reliably predicted using the natural history model. In mice, a treatment effect can be detected after antisense oligonucleotide treatment targeting the Ube3a-antisense transcript through at least 8 weeks post-treatment (P < 1e-15). Deviations in delta power from the expected natural history correlated with Ube3a expression in the mouse model (P < 0.001). Deviations in delta power from a human natural history model in Angelman syndrome can detect antisense oligonucleotide-mediated improvement in Ube3a expression in Angelman syndrome mice and may be relevant for human clinical trials.

• Authors: Elizabeth R Spencer, Wen Shi, Robert W Komorowski, James P Gilbert, Lauren M Ostrowski, Lynne M Bird, Ronald Thibert, Channa Bao, Fiona Molloy, Michael Calhoun, Samir Koirala, Paymaan Jafar-Nejad, Frank Rigo, Mark A Kramer, Catherine J Chu
• Journal: Brain communications (Brain Commun) Vol. 4 Issue 3 Pg. fcac106 ( 2022) ISSN: 2632-1297 [Electronic] England
• PMID: 35611307 (Publication Type: Journal Article)
• Copyright: © The Author(s) 2022. Published by Oxford University Press on behalf of the Guarantors of Brain.