IN SILICO SEARCH FOR NEW SELECTIVE NAV1.2 BLOCKERS FOR THE TREATMENT OF DRAVET SYNDROME

FALLICO, MAXIMILIANO; DENIS N. PRADA GORI; LLANOS, MANUEL A.; LUCAS N. ALBERCA; LUCIANA GAVERNET; ALAN TALEVI

Mar del Plata

Encuentro; Reunion Anual de Sociedades de Biociencias; 2022

Sociedad Argentina de Investigación Clínica

Dravet syndrome is severe childhood epilepsy, characterized byloss-of-function mutations in NaV1.1 channels. Here, we pursuedthe computer-aided identification of compounds that selectivelyblock Nav1.2, without blocking Nav1.1, as potential treatments. Forthat purpose, we resorted to a combination of ligand-based andstructure-based models. Two datasets of compounds previously assayedagainst Nav1.1 and Nav1.2 were compiled from the literature.After data curation, 91 and 167 compounds tested against Nav1.1and Nav1.2 were kept. 2000 and 4000 linear classifiers based onrandom subspace exploration of Mordred molecular descriptorswere built for Nav1.1 and Nav1.2, respectively. Model ensembleswere selectively obtained using the area under the ROC curve inretrospective screens as a selection parameter. The top 12 and 11models for Nav1.1 and Nav1.2 were combined, in that order, usingthe MIN operator. Two repurposing databases, DrugBank and DrugRepurposing Hub, were then prospectively screened. Compoundsselected as Nav1.2 blockers were subsequently screened by theensemble of Nav1.1 models, which is used here as an anti-target,to ensure selectivity. 40 selective in silico hits were selected andfurther analyzed using molecular docking. The candidates weredocked into the experimental 3D structure of Nav1.2, and the bindinginteractions were analyzed. 3 of the resulting hits were acquiredand submitted for experimental confirmation in patch clamp. Ourresults confirmed the predictivity of our hybrid approach to detectnew anticonvulsant agents with potential applications as Dravettreatment.