Credibility assessment of a mechanistic model of atherosclerosis to predict cardiovascular outcomes under lipid-lowering therapy

Abstract 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 treatmen...

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Main Authors: Yishu Wang, Eulalie Courcelles, Emmanuel Peyronnet, Solène Porte, Alizée Diatchenko, Evgueni Jacob, Denis Angoulvant, Pierre Amarenco, Franck Boccara, Bertrand Cariou, Guillaume Mahé, Philippe Gabriel Steg, Alexandre Bastien, Lolita Portal, Jean-Pierre Boissel, Solène Granjeon-Noriot, Emmanuelle Bechet
Format: Article
Language:English
Published: Nature Portfolio 2025-03-01
Series:npj Digital Medicine
Online Access:https://doi.org/10.1038/s41746-025-01557-7
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Summary:Abstract 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.
ISSN:2398-6352