Ligand-based pharmacophore modeling for the discovery of NaV1.7 inhibitors

Abstract

Voltage-gated sodium channel NaV1.7 is a key mediator of electrical excitability and signal transmission in peripheral nociceptors and has emerged as a highly attractive therapeutic target for the development of novel analgesic agents. However, the development of selective NaV1.7 inhibitors has been characterized by significant challenges, with repeated failures in clinical trials despite encouraging preclinical data. In this study, we developed and validated a series of ligand-based pharmacophore models (LBPMs) that can be useful for the discovery of novel NaV1.7 inhibitors with improved selectivity profiles. Using the VGSC database as our primary data source, we focused on sulfonamide-based inhibitors targeting the voltage-sensing domain IV (VSD-IV). Validation against active compounds and decoys demonstrated that most models achieved good discrimination performance with high areas under the curve (AUC) and strong enrichment factors. External validation using 22 inhibitors extracted from recent literature confirmed the models’ capability to identify novel NaV1.7 inhibitors. Virtual screening of 3 million commercially available compounds retrieved promising hits and known inhibitors, with molecular docking studies revealing binding modes consistent with established sulfonamide-based inhibitors. Experimental validation identified one compound with measurable selectivity for NaV1.7 over NaV1.5, providing preliminary support for the utility of the developed virtual screening workflow. In parallel, we developed NaV1.5 LBPMs to assess selectivity profiles and minimize potential cardiotoxic effects. Overall, our findings provide valuable computational tools and structural insights for the rational design of selective NaV1.7 inhibitors, offering important starting points for developing analgesics with reduced off-target effects.