Abstract

IKs (KCNQ1/KCNE1) channels are essential for cardiac repolarization, and their dysfunction contributes to arrhythmogenic disorders such as Long QT Syndrome type 1 (LQT1). Pharmacological activation of IKs has therefore emerged as a promising therapeutic strategy. C28, a recently identified voltage-sensor–targeting activator, has been shown to markedly shift KCNQ1/KCNE1 voltage dependence and enhance current amplitude, validating the voltage-sensing domain (VSD) as a druggable site. However, the structural determinants that underlie C28’s activity remain incompletely understood. In this study, we aimed to identify critical functional groups of C28 by screening a structurally diverse set of C28-like analogs and evaluating their effects on IKs function using two-electrode voltage clamp (TEVC) electrophysiology. Twelve commercially available azo-dye–derived analogs were tested for their ability to modulate IKs activation, and quantitative structure–activity relationships (SARs) were generated using both experimental and computational analyses. A custom graphical user interface was developed using RDKit and Streamlit to automate descriptor generation, activity prediction, and PCA clustering, enabling rapid and reproducible SAR interpretation. Our results reveal three strong activators—C28, Dye-4, and C362—that robustly shift V½ toward depolarized potentials and significantly enhance current amplitude. Moderate and weak analogs exhibited distinguishable structural patterns, allowing preliminary identification of substituents and aromatic scaffolds that correlate with IKs activation strength. These findings advance the mechanistic understanding of VSD-targeting activators and establish a practical SAR-guided screening framework for future IKs drug development. Building on this SAR framework, we further constructed a machine-learning–based model that integrates molecular descriptors with experimentally measured EC₅₀, V½ shifts, and current enhancement, enabling prediction of functional activity directly from SMILES representations and providing an empirical basis for prioritizing idealized candidate compounds. Based on this model, several promising c28 analogue compounds were predicted.

Committee Chair

Jianmin Cui

Committee Members

Nichols Colin Sah Rajan

Degree

Master of Science (MS)

Author's Department

Biomedical Engineering

Author's School

McKelvey School of Engineering

Document Type

Thesis

Date of Award

Spring 5-6-2026

Language

English (en)

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