An AI-Enabled Translational Drug Discovery Framework for NALCN Channelopathy: From High-Throughput Screening to Therapeutic Candidate
Background: More than 90% of rare diseases lack an approved therapy, partly reflecting economic barriers in developing treatments for small patient populations. A common bottleneck is costly and resource-intensive hit-to-lead prioritization. Congenital contractures of the limbs and face, hypotonia, and developmental delay (CLIFAHDD) is an ultra-rare neurodevelopmental disease often caused by de novo gain-of-function variants in the sodium leak channel NALCN, with no approved treatments. Methods: We developed a cost and resource-efficient, AI-enabled translational drug discovery framework integrating phenotypic high-throughput screening (HTS), structural biology foundation models for hit-to-lead prioritization, and orthogonal experimental validation. We applied this framework for drug repurposing to identify NALCN inhibitors as therapeutic candidates for CLIFAHDD. We screened ~1,200 approved drugs using a fluorescence-based membrane potential assay in doxycycline-inducible HEK293 cells stably expressing the NALCN channelosome. Boltz-2 modeling predicted binding affinity and mode to prioritize hits. Lead repurposing candidates were selected by a multidisciplinary expert review panel. Lead candidates were evaluated using patch-clamp electrophysiology in HEK293 cells expressing wild-type or CLIFAHDD-associated NALCN variants, primary mouse hippocampal neurons, and a C. elegans NALCN gain-of-function model. Results: HTS identified 11 candidate NALCN inhibitors. Boltz-2 prioritized candidates based on predicted binding affinity and predicted binding within the NALCN pore, consistent with a direct pore-blocking mechanism. Aprepitant emerged as the lead candidate after multidisciplinary evaluation based on its predicted mechanism, pharmacologic properties, blood-brain barrier penetration, established safety profile, and regulatory approval in children and adults. Patch-clamp electrophysiology confirmed partial inhibition of NALCN-mediated sodium leak currents in HEK293 cells (IC50;=2.22 M), with comparable potency against Cav3.1 (IC50=1.63 M). In primary mouse hippocampal neurons, aprepitant reduced sodium leak currents, hyperpolarized resting membrane potential, and decreased spontaneous firing. Aprepitant rescued hyperlocomotor and dystonic phenotypes in a C. elegans gain-of-function NALCN model. There was notable concordance between AI-predicted hits, phenotypic screening hits, and experimental validation. Conclusion: These findings identify aprepitant as a lead therapeutic candidate for CLIFAHDD, and establish an AI-enabled translational drug discovery framework integrating phenotypic screening, structural modeling, and experimental validation for NALCN channelopathies. This scalable and resource-conscious strategy may accelerate hit-to-lead prioritization and drug discovery for rare and ultra-rare diseases lacking effective therapies.