Syn-COM: A Multi-Level Predictive Synergy Framework for Innovative Drug Combinations

Abstract

Drug prediction and treatment using bioinformatics and large-scale modeling have emerged as pivotal research areas. This study proposes a novel multi-level collaboration framework named Syn-COM for feature extraction and data integration of diseases and drugs. The framework aims to explore optimal drug combinations and interactions by integrating molecular virtuality, similarity clustering, overlap area, and network distance. It uniquely combines the characteristics of Chinese herbal medicine with clinical experience and innovatively assesses drug interaction and correlation through a synergy matrix. Gouty arthritis (GA) was used as a case study to validate the framework’s reliability, leading to the identification of an effective drug combination for GA treatment, comprising Tamaricis Cacumen (Si = 0.73), Cuscutae Semen (Si = 0.68), Artemisiae Annuae Herba (Si = 0.62), Schizonepetae Herba (Si = 0.73), Gleditsiae Spina (Si = 0.89), Prunellae Spica (Si = 0.75), and Achyranthis Bidentatae Radix (Si = 0.62). The efficacy of the identified drug combination was confirmed through animal experiments and traditional Chinese medicine (TCM) component analysis. Results demonstrated significant reductions in the blood inflammatory factors IL1A, IL6, and uric acid, as well as downregulation of TGFB1, PTGS2, and MMP3 expression (p < 0.05), along with improvements in ankle joint swelling in GA mice. This drug combination notably enhances therapeutic outcomes in GA by targeting key genes, underscoring the potential of integrating traditional medicine with modern bioinformatics for effective disease treatment.