An AI-Enabled Translational Drug Discovery Framework for NALCN Channelopathy: From High-Throughput Screening to Therapeutic Candidate
Year: 2026
Type: preprint
Abstract: 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 μM), with comparable potency against Ca v 3.1 (IC 50 =1.63 µM). 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.
Institutions: Thammasat University, Centre National de la Recherche Scientifique, Inserm, Institut de Génomique Fonctionnelle, The University of Texas at San Antonio Health Science Center, The University of Texas at San Antonio, University of California, San Francisco, Louisiana State University, Louisiana State University Health Sciences Center Shreveport, Siriraj Hospital, Mahidol University
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7221084059