Wang Yishu, Courcelles Eulalie, Peyronnet Emmanuel, Porte Solène, Diatchenko Alizée, Jacob Evgueni, Angoulvant Denis, Amarenco Pierre, Boccara Franck, Cariou Bertrand, Mahé Guillaume, Steg Philippe Gabriel, Bastien Alexandre, Portal Lolita, Boissel Jean-Pierre, Granjeon-Noriot Solène, Bechet Emmanuelle
Nova in silico, Lyon, France.
Cardiology department, Hôpital Trousseau, CHRU de Tours & UMR Inserm 1327 ISCHEMIA "Membrane Signaling and Inflammation in Reperfusion Injuries" Université de Tours, F37000, Tours, France.
NPJ Digit Med. 2025 Mar 19;8(1):171. doi: 10.1038/s41746-025-01557-7.
Demonstrating cardiovascular (CV) benefits with lipid-lowering therapy (LLT) requires long-term randomized clinical trials (RCTs) with thousands of patients. Innovative approaches such as in silico trials applying a disease computational model to virtual patients receiving multiple treatments offer a complementary approach to rapidly generate comparative effectiveness data. A mechanistic computational model of atherosclerotic cardiovascular disease (ASCVD) was built from knowledge, describing lipoprotein homeostasis, LLT effects, and the progression of atherosclerotic plaques leading to myocardial infarction, ischemic stroke, major acute limb event and CV death. The ASCVD model was successfully calibrated and validated, and reproduced LLT effects observed in selected RCTs (ORION-10 and FOURIER for calibration; ORION-11, ODYSSEY-OUTCOMES and FOURIER-OLE for validation) on lipoproteins and ASCVD event incidence at both population and subgroup levels. This enables the future use of the model to conduct the SIRIUS programme, which intends to predict CV event reduction with inclisiran, an siRNA targeting hepatic PCSK9 mRNA.
通过降脂治疗(LLT)证明心血管(CV)益处需要对数千名患者进行长期随机临床试验(RCT)。创新方法,如在计算机模拟试验中,将疾病计算模型应用于接受多种治疗的虚拟患者,为快速生成比较有效性数据提供了一种补充方法。基于描述脂蛋白稳态、LLT效应以及导致心肌梗死、缺血性中风、严重急性肢体事件和CV死亡的动脉粥样硬化斑块进展的知识,构建了动脉粥样硬化性心血管疾病(ASCVD)的机制计算模型。ASCVD模型已成功校准和验证,并在人群和亚组水平上重现了在选定RCT(用于校准的ORION - 10和FOURIER;用于验证的ORION - 11、ODYSSEY - OUTCOMES和FOURIER - OLE)中观察到的LLT对脂蛋白和ASCVD事件发生率的影响。这使得该模型未来可用于开展天狼星计划,该计划旨在预测靶向肝脏PCSK9 mRNA的小干扰RNA(siRNA)inclisiran降低CV事件的效果。