Cardiovascular diseases (CVDs) are the leading cause of death globally, taking an estimated 18 million lives each year. Cardiomyopathies are defined as structural and functional abnormalities of the ventricular myocardium that are unexplained by flow-limiting coronary artery disease or abnormal loading conditions. One in five hundred adults may have this condition. There are four major classifications of cardiomyopathy: hypertrophic (HCM), dilated (DCM), restrictive (RCM), and arrhythmogenic RV (ARVC). Treating the symptoms of cardiomyopathy using established therapies are only partly able to improve the outcome; novel therapies need to be developed to affect the CVDs’ process and time course more fundamentally. Generating molecular insights into pathogenic mechanisms in CVDs are essential for the design of novel specific drugs suitable for clinical use and for defining the proper timing of drug treatment to prevent the initiation and progression of CVDs in mutation carrier individuals.

The existing clinical trials involve animal testing, human health risk, and are very demanding in terms of both time and funds that need to be spent. In silico clinical trials are free of these constraints and deficiencies, and offer alternatives to reduce the costs and number of patients and experimental animals involved in real clinical trials. Currently, in silico clinical trials do not exist in clinical and preclinical practice. In silico clinical trials are the future of medicine and virtual testing and simulation are the future of medical engineering. Usage of a computational platform will provide lower costs and less required time for developing new models of medical devices and drugs.

This Special Issue is devoted to a multimodular, innovative in silico clinical trial solution for the design and functional optimization of whole heart performance and monitoring the effectiveness of pharmacological treatment, with the aim to reduce animal studies and human clinical trials. Integrated multidisciplinary and multiscale methods for the analysis of patient-specific data and the development of patient-specific models for monitoring and assessment of patient conditions through the course of CVDs will be explored. We aim to devote some papers to in silico mechanical stent testing within ISO 25539 standards and in silico stent deployment for metallic and biodegradable materials. This approach will connect basic experimental research with clinical studies and bioinformatics, data mining and image processing tools using very advanced computer models for drug, stent and patient databases to reduce animal and clinical studies. This Special Issue welcomes investigators to share their original research and review articles related to in silico clinical trials with different approaches such as machine learning, computational-based modeling, multiscale modeling, and more.

Potential topics include but are not limited to the following:

• Methods for drug testing for in silico clinical trials in cardiovascular diseases
• Methods for device testing for in silico clinical trials in cardiovascular diseases
• Machine learning or deep learning methods for image processing of cardiovascular diseases (ultrasound, CT, MRI)
• Methods for atherosclerosis development in silico clinical trials
• Review of in silico clinical trials for cardiovascular diseases
• Experimental validation of molecular insights into pathogenic mechanisms in cardiovascular diseases: hypertrophic (HCM), dilated (DCM), restrictive (RCM), and arrhythmogenic RV (ARVC)
• In silico mechanical stent testing and stent deployment in the coronary arteries
• Clinical application or validation of findings from in silico trial analysis