Abstract
OBJECTIVE: Central to the development of novel antiseizure medications (ASMs) is testing of antiseizure activity in preclinical models. Although various well-established models exist, their predictive validity across the spectrum of clinical epilepsies has been less clear. We sought to establish the translational concordance of commonly used preclinical models to define models with the highest predictive clinical validity for focal onset seizures (FOS). METHODS: The Praxis Analysis of Concordance (PAC) framework was implemented to assess the translational concordance between preclinical and clinical ASM response for 32 US Food and Drug Administration-approved ASMs. Preclinical ASM responses in historically used seizure models were collected. Protective indices based on reported median tolerability and median efficacy values were calculated for each ASM in each preclinical model. A weighted scale representing relative antiseizure effect was used to grade preclinical ASM response for each seizure model. Data depth was further scored based on the number of evaluated ASMs with publicly available data. Established reports of clinical ASM use in patients with FOS were similarly evaluated, and a weighted scale representing prescribing patterns and perceived efficacy was used to grade clinical ASM response. To assess the predictive validity of preclinical models, a unified translational scoring matrix was developed to assign a concordance score spanning the spectrum from complete discordance (-1) to complete concordance (1) between preclinical and clinical ASM responses. Scores were summed and normalized to generate a global translational concordance score. RESULTS: The preclinical models with the highest translational concordance and greatest data depth for FOS were rodent maximal electroshock seizure (MES), mouse audiogenic seizure, mouse 6 Hz (32 mA), and rat amygdala kindling. SIGNIFICANCE: The PAC-FOS framework highlights mouse MES, mouse audiogenic, and mouse 6 Hz (32 mA) as three acute seizure models consistently demonstrating high predictive validity for FOS. We provide a pragmatic decision tree approach to support efficient resource utilization for novel ASM discovery for FOS.