In silico model of atherosclerosis with individual patient calibration to enable precision medicine for cardiovascular disease. (January 2023)

• Record Type: Journal Article
• Authors: Buckler, Andrew J.; Marlevi, David; Skenteris, Nikolaos T.; Lengquist, Mariette; Kronqvist, Malin; Matic, Ljubica; Hedin, Ulf
• Is Part Of: Computers in biology and medicine. Volume 152(2023)
• Publication Date: 2023-01
• Subjects: Atherosclerosis -- Precision medicine -- Systems biology -- Proteomics -- Polypharmacy; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106364
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Objective: Guidance for preventing myocardial infarction and ischemic stroke by tailoring treatment for individual patients with atherosclerosis is an unmet need. Such development may be possible with computational modeling. Given the multifactorial biology of atherosclerosis, modeling must be based on complete biological networks that capture protein-protein interactions estimated to drive disease progression. Here, we aimed to develop a clinically relevant scale model of atherosclerosis, calibrate it with individual patient data, and use it to simulate optimized pharmacotherapy for individual patients. Approach and results: The study used a uniquely constituted plaque proteomic dataset to create a comprehensive systems biology disease model for simulating individualized responses to pharmacotherapy. Plaque tissue was collected from 18 patients with 6735 proteins at two locations per patient. 113 pathways were identified and included in the systems biology model of endothelial cells, vascular smooth muscle cells, macrophages, lymphocytes, and the integrated intima, altogether spanning 4411 proteins, demonstrating a range of 39–96% plaque instability. After calibrating the systems biology models for individual patients, we simulated intensive lipid-lowering, anti-inflammatory, and anti-diabetic drugs. We also simulated a combination therapy. Drug response was evaluated as the degree of change in plaque stability, where an improvement was defined as a reduction of plaque instability. In patients with initially unstable lesions, simulated responses varied from high (20%, on combination therapy) to marginal improvement, whereas patients with initially stable plaques showed generally less improvement. Conclusion: In this pilot study, proteomics-based system biology modeling was shown to simulate drug response based on atherosclerotic plaque instability with a power of 90%, providing a potential strategy for improved personalized management of patients with cardiovascular disease. Highlights: Distinct disease phenotypes exist in CVD that would be treated differently but means to steer patients are needed. Here we illustrated a systems biology-based approach to simulate specific patient response to multiple pharmacotherapies. This method was shown to simulate drug response based on atherosclerotic plaque instability with a power of 90%.