Abstract
Combination therapy is increasingly favored by pharmaceutical companies and researchers as an effective way to quickly discover new drugs with excellent efficacy, especially in the treatment of complex diseases. Previously, we successfully developed a computational screening method to identify such combinations, although it fell short in elucidating their synergistic mechanisms. In this work, we have transitioned to a highest single agent (HSA) synergy model for network screening, which streamlines the discovery of promising combinations and facilitates the investigation of their synergistic effects. Through this refined approach, the trimebutine-methoxsalen combination emerged as a promising candidate for heart failure (HF) treatment, exhibiting significant in vitro cardioprotective effects and effectively mitigating isoproterenol (ISO)-induced structural remodeling in the mouse heart. Further mechanistic studies have demonstrated that trimebutine and methoxsalen could synergistically inhibit intracellular calcium overload in myocardial cells and reduce the production of ROS, thus exerting cardioprotective effects. Overall, this study introduces an advanced computational strategy that not only identifies a novel combination therapy against HF but also sheds light on its underlying synergistic mechanisms.
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