Abstract
Background/Objectives: Obstructive sleep apnea (OSA) affects approximately 1 billion adults worldwide with extensive comorbidities, including cardiovascular disease, metabolic disorders, and cognitive decline, yet pharmacological therapies remain limited. Conventional bottom-up omics approaches identify numerous genes overlapping with other diseases, hindering therapeutic translation. This study introduces a top-down, comorbidity-driven approach to identify actionable molecular targets and develop rational dual kinase inhibitors for OSA management. Methods: We implemented a five-tier modeling workflow: (1) comorbidity network analysis, (2) disease module identification through NetworkAnalyst, (3) mechanistic pathway reconstruction of the CK1δ-(HIF1A)-PINK1 signaling cascade, (4) molecular docking analysis of Nigella sativa alkaloids and reference inhibitors (IC261, PF-670462) against CK1δ (PDB: 3UYS) and PINK1 (PDB: 5OAT) using AutoDock Vina, and (5) rational design and computational validation of novel dual inhibitors (ICL, PFL) integrating pharmacophoric features from natural alkaloids and established kinase inhibitors. Results: Extensive network analysis revealed a discrete OSA disease module centered on two interconnected protein kinases-CK1δ and PINK1-that mechanistically bridge circadian disruption and neurodegeneration. Among natural alkaloids, Nigellidine showed strongest CK1δ binding (-8.0 kcal/mol) and Nigellicine strongest PINK1 binding (-8.6 kcal/mol). Rationally designed dual inhibitors demonstrated superior binding: ICL (-7.2 kcal/mol PINK1, -8.9 kcal/mol CK1δ) and PFL (-10.8 kcal/mol CK1δ, -11.2 kcal/mol PINK1), representing -2.6-2.8 kcal/mol improvements over reference compounds. Conclusions: This study establishes a comorbidity-driven translational framework identifying the CK1δ-PINK1 axis as a therapeutic target in OSA. The rationally designed dual inhibitors represent third-generation precision therapeutics addressing OSA's multi-dimensional pathophysiology, while the five-tier workflow provides a generalizable template for drug discovery in complex multimorbid diseases.