## 4 Discussion
By comparing the models with DCM and HCM, we may observe from Figures 6 and 7 that the application of different models affected visible changes in volume but that there were negligibly small differences in pressure change (max and min pressure values). The analogous effects can be seen by comparing the results in Figures 6a,b and 7a,b for different concentration profiles, and Figures 6c,d and 7c,d regarding the influence of the wall stiffness and inlet/outlet velocities. In the introductory section, we described the mechanism for basic groups of drugs like ACE inhibitors, ARBs, nitric-oxide-induced vasodilators, etc. We have shown that these drugs influence cardiac PV diagrams and the ejection fraction. Additionally, we have investigated the influence of Ca2+ concentration profiles, heart wall stiffness, and the inlet/outlet velocities on heart performance. It was shown that drugs affect one or more heart parameters and properties. As can be seen from Figures 6a and 7a, for triangular, parabolic, and steep time Ca2+ time functions, with calcium concentrations that have the slowest decrease rate (i.e., triangular), muscles are activated for a prolonged period and, as a result, more blood is ejected from the left ventricle. When calcium starts to rise prematurely, which is the case for parabolic functions, the isovolumetric contraction occurs earlier than compared to other Ca2+ concentrations. The results from Figures 6c and 7c show lower pressure during isovolumetric contraction and systole with lower stiffness of the left ventricle wall. In Figure 8c, we show that a similar trend occurs with the two model geometries--the ejection fraction increases with wall stiffness. For the lower outlet velocity, less blood is ejected during systole, and vice versa. Also, the stroke volume during diastole is increased with an increase in the inlet velocity. We also showed the influence of drugs on the cardiac cycle, in Figure 8d,e. The systolic pressure and ejection fraction rise as a result of the rise in intracellular calcium concentration, which also causes an increase in heart wall tension. It can be seen from Figure 8d that the calcium concentration is highest with digoxin, resulting in the largest force generated by the muscles in comparison to with disopyramide, mavacamten, and dATP. In analysing the PV diagrams for cases using these four drugs, we find the stroke volume is also the largest with digoxin. On the other hand, pressure rises slower with digoxin during systole in comparison with the other drugs. With disopyramide, the calcium decrease rate is the slowest during systole compared to the other drugs. Mavacamten produces a similar calcium concentration and similar effects to disopyramide. With dATP, less calcium is produced compared to with disopyramide, which results in a smaller ejection fraction. The decrease rate of the calcium during systole is larger with dATP than with mavacamten and disopyramide; therefore, the pressure is greater with dATP than with mavacamten and disopyramide. Our computational model provides a valuable tool for studying the effects of drugs on the properties of cardiac tissue, specifically in the context of cardio-myopathy-afflicted heart cycles. However, it is essential to acknowledge the inherent complexities and limitationsassociated with any in silico model. While our model attempts to capture the key characteristics of cardiac myopathies, it is vital to recognize that it simplifies the intricate biological and physiological processes occurring in real patients. The representation of specific heart diseases in our model is based on well-established parameters and clinical data, but it does not account for the full spectrum of individual variation seen in real patients. Conducting in silico trials to explore how drugs impact the properties of cardiomyopathy-afflicted heart cycles may provide valuable insights into assessing the dynamic fluctuations within the parameters and conditions accounted for by our model.