Generation of Virtual Patients for in Silico Cardiomyopathies Drug Development

The revolution in modelling and simulation methodologies accompanied with the recent events in the high-performance computing (HPC) helped the development of in silico clinical platforms which integrate advanced and individualized simulation models to support drug development. These platforms incorporate patient specific models to create and generate virtual patients (VPs). A parametric methodology for resampling and generating VPs is the multivariate normal distribution which in the current work is optimized through an iterative pipeline by the Kolmogorov-Smirnov goodness-of-fit test. The proposed VP generator is integrated in the multi-repository VP model of SILICOFCM which is a multi-modular, innovative in silico clinical trials solution for the design and functional optimization of the whole heart performance and monitoring effectiveness of pharmacological treatment for familial cardiomyopathies, with aim to reduce the animal studies and the human clinical trials.