NBI-921352, a first-in-class, NaV1.6 selective, sodium channel inhibitor that prevents seizures in Scn8a gain-of-function mice, and wild-type mice and rats

NBI-921352 (formerly XEN901) is a novel sodium channel inhibitor designed to specifically target NaV1.6 channels. Such a molecule provides a precision-medicine approach to target SCN8A-related epilepsy syndromes (SCN8A-RES), where gain-of-function (GoF) mutations lead to excess NaV1.6 sodium current, or other indications where NaV1.6 mediated hyper-excitability contributes to disease (Gardella and Møller, 2019; Johannesen et al., 2019; Veeramah et al., 2012). NBI-921352 is a potent inhibitor of NaV1.6 (IC500.051 µM), with exquisite selectivity over other sodium channel isoforms (selectivity ratios of 756 X for NaV1.1, 134 X for NaV1.2, 276 X for NaV1.7, and >583 Xfor NaV1.3, NaV1.4, and NaV1.5). NBI-921352is a state-dependent inhibitor, preferentially inhibiting inactivatedchannels. The state dependence leads to potent stabilization of inactivation, inhibiting NaV1.6 currents, including resurgent and persistent NaV1.6 currents, while sparing the closed/rested channels. The isoform-selective profile of NBI-921352 led to a robust inhibition of action-potential firing in glutamatergic excitatory pyramidal neurons, while sparing fast-spiking inhibitory interneurons, where NaV1.1 predominates. Oral administration of NBI-921352 prevented electrically induced seizures in a Scn8a GoF mouse,as well as in wild-type mouse and ratseizure models. NBI-921352 was effective in preventing seizures at lower brain and plasma concentrations than commonly prescribed sodium channel inhibitor anti-seizure medicines (ASMs) carbamazepine, phenytoin, and lacosamide. NBI-921352 waswell tolerated at higher multiples of the effective plasma and brain concentrations than those ASMs. NBI-921352 is entering phase II proof-of-concept trials for the treatment of SCN8A-developmental epileptic encephalopathy (SCN8A-DEE) and adult focal-onset seizures.


Sample-size estimation
• You should state whether an appropriate sample size was computed when the study was being designed • You should state the statistical method of sample size computation and any required assumptions • If no explicit power analysis was used, you should describe how you decided what sample (replicate) size (number) to use Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission:

Replicates
• You should report how often each experiment was performed • You should include a definition of biological versus technical replication • The data obtained should be provided and sufficient information should be provided to indicate the number of independent biological and/or technical replicates • If you encountered any outliers, you should describe how these were handled • Criteria for exclusion/inclusion of data should be clearly stated • High-throughput sequence data should be uploaded before submission, with a private link for reviewers provided (these are available from both GEO and ArrayExpress) Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: We did not perform formal power calculations for in vitro and in vivo experiments, but used generally accepted sample sizes depending on the type of experiment and our past experience. Sample size is denoted in figure legends, the accompanying Excel data file, and in some cases, in the Results section. For in vivo experiments only data points with n≥ 4 were included.
The number of experiments is denoted by animal sample size, numbers of cells, or number of assay replicates. All replicates are defined as biologic replicates, in that distinct animals/cells/wells were analysed once. No outliers were identified or excluded as part of the in vivo or ex vivo datasets. For automated electrophysiology experiments, the analysis included only those cells that met pre-specified acceptance criteria for seal quality, current size and series resistance. More detailed information can be found in figure legends, the accompanying Excel data file, and in the Results and Materials and Methods sections.

Statistical reporting
• Statistical analysis methods should be described and justified • Raw data should be presented in figures whenever informative to do so (typically when N per group is less than 10) • For each experiment, you should identify the statistical tests used, exact values of N, definitions of center, methods of multiple test correction, and dispersion and precision measures (e.g., mean, median, SD, SEM, confidence intervals; and, for the major substantive results, a measure of effect size (e.g., Pearson's r, Cohen's d) • Report exact p-values wherever possible alongside the summary statistics and 95% confidence intervals. These should be reported for all key questions and not only when the p-value is less than 0.05.
Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: (For large datasets, or papers with a very large number of statistical tests, you may upload a single table file with tests, Ns, etc., with reference to sections in the manuscript.)

Group allocation
• Indicate how samples were allocated into experimental groups (in the case of clinical studies, please specify allocation to treatment method); if randomization was used, please also state if restricted randomization was applied • Indicate if masking was used during group allocation, data collection and/or data analysis Please outline where this information can be found within the submission (e.g., sections or figure legends), or explain why this information doesn't apply to your submission: Additional data files ("source data") • We encourage you to upload relevant additional data files, such as numerical data that are represented as a graph in a figure, or as a summary table All statistical methods are described in detail in the Material and Methods section. Information on data presentation is provided in the figure legends as space allowed and the complete datasets (including N) can be found in the accompanying Excel data file. The statistical test for each experiment is described in the figure legend, along with values for N, mean, error (SEM, STD or confident intervals based on type of experiment and statistical test used) and p-values where p<0.05. No corrections for multiple testing were required.
As described in the Materials and Methods sections regarding in vivo experiments, the technical staff member performing drug administration differed from the person performing the test. On each testing day, individual treatment groups were assigned a random label (e.g., A, B, C, etc.) by the technical staff administering the compound. Therefore, the experimenter conducting testing was blinded to treatment group (e.g., drug or vehicle treatment, dose, and time point). Randomization of animals into various treatment groups occurred on a per-animal (e.g., rather than a per-cage) basis. Therefore, each animal was randomly assigned to a treatment group, and all animals tested in each experiment had an equal chance of assignment to any treatment group. Prior to each study, a randomization sequence was obtained (www.graphpad.com/quickcalcs).