Development of translatable neurophysiological biomarkers to accelerate therapeutic development in Rett syndrome

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Rett syndrome (RTT), is a severe neurodevelopmental disorder caused by loss-of function mutations in the X- linked gene Methyl-CpG-binding Protein 2 (MECP2) and characterized by loss of speech and hand skills, problems walking, and repetitive hand movements. Genetic restoration of MECP2 in symptomatic mice can reverse symptoms providing hope that disease-modifying therapies can be created. Impeding the development of transformative therapies are a lack of biomarkers of treatment-response. Ideally, a biomarker can be applied in mice and humans to enhance effective translation of preclinical treatment studies and improve human trial design and execution. Neurophysiological assessments have potential as biomarkers as they are non-invasive, measure neurological changes, and are translatable between humans and animal models. Recent work in RTT, from our group, has found differences in neurophysiological measures in both affected humans and mouse models that correlate with disease severity, but an urgent need exists to identify well-validated and translatable treatment-response biomarkers in RTT. To address this need, we propose here to develop neurophysiological biomarkers that can fulfil a specific primary Context of Use (COU), an early treatment response biomarker, to facilitate and speed both preclinical and clinical trials of novel therapies in RTT. The primary goal of the R61 phase of the proposal is to identify candidate neurophysiological biomarkers of disease improvement in a mouse model of RTT and establish human multi-site standard operating procedures and normative data. These parallel projects will be foundational to identify a true treatment responsive biomarker in RTT. To do this we will first determine if potential biomarkers, quantitative EEG and evoked potentials will change predictively in a mouse model of RTT that allows for genetic rescue of the RTT phenotype. Simultaneously, we will develop and optimize standard operating procedures to enable multi-site evaluation of candidate human neurophysiological biomarkers. Additionally, we will evaluate test-retest reliability of the biomarkers we are developing. Finally, we will determine if the putative neurophysiological biomarkers change during active clinical change in RTT. For the R33 phase, we will demonstrate that our human proof-of-concept of candidate neurophysiological biomarkers are stable over the time frame relevant to clinical trials in RTT and that these biomarkers correlate with RTT clinical severity. Overall, this proposal takes advantage of the ability to use mouse models to identify and validate robust human neurophysiological features as putative biomarkers. These neurophysiological measures will allow for accelerated therapy development via the replacement of subjective clinical findings with quantitative measures of early treatment-response. Together, this work will facilitate biomarker development to be employed in interventional therapy development.