Key result
Mechanistic computational model accurately simulates lipid-lowering therapy effects on lipoproteins and ASCVD trial events.
Why the study?
Can a mechanistic computational model accurately predict cardiovascular event reduction with lipid-lowering therapy?
Can a mechanistic computational model accurately predict cardiovascular event reduction with lipid-lowering therapy?
A mechanistic computational model of ASCVD was successfully validated to predict cardiovascular outcomes under lipid-lowering therapy, enabling future in silico trials.
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.
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Wang et al. (2025) studied atherosclerotic cardiovascular disease (ASCVD). Lipid-lowering therapy (LLT) was evaluated on lipoproteins and ASCVD event incidence. A mechanistic computational model of ASCVD successfully reproduced lipid-lowering therapy effects on lipoproteins and cardiovascular event incidence observed in selected randomized clinical trials.
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