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Computational Modeling on Drugs Effects for Left Ventricle in Cardiomyopathy Disease
Smiljana Tomasevic1,2, Miljan Milosevic2,3, Bogdan Milicevic1,2
1Faculty of Engineering, University of Kragujevac, 34000 Kragujevac, Serbia.
Insights
Computational modeling accelerates cardiomyopathy drug discovery by simulating drug effects on cardiac function. This approach enhances risk prediction and patient-specific treatment strategies for hypertrophic (HCM) and dilated (DCM) cardiomyopathy.
Area of Science:
- Cardiovascular Research
- Computational Biology
- Pharmacology
Background:
- Cardiomyopathy involves ventricular myocardial abnormalities, classified as hypertrophic (HCM) or dilated (DCM).
- Computational modeling and drug design offer potential to accelerate drug discovery and reduce costs for cardiomyopathy treatment.
Purpose of the Study:
- To develop a multiscale computational platform for simulating drug effects on cardiac electro-mechanics.
- To evaluate the impact of specific drugs on left ventricular (LV) function in HCM and DCM models.
Main Methods:
- Utilized finite element (FE) modeling and fluid-structure interactions (FSI) for LV simulation with nonlinear material properties.
- Coupled macro- and microsimulations to model molecular drug interactions with cardiac cells.
- Simulated two drug scenarios: Ca2+ transient modulators (Disopyramide, Digoxin) and kinetic parameter modifiers (Mavacamten, dATP).
Main Results:
- Presented changes in LV pressures, displacements, velocity distributions, and pressure-volume loops for HCM and DCM models.
- Demonstrated SILICOFCM Risk Stratification Tool and PAK software results aligned with clinical observations for high-risk HCM patients.
- Quantified the impact of tested drugs on cardiac electro-mechanics.
Conclusions:
- The multiscale computational platform provides enhanced patient-specific risk prediction for cardiac diseases.
- Offers valuable insights into estimated drug therapy effects, improving patient monitoring and treatment strategies.
- Supports accelerated and cost-effective drug discovery for cardiomyopathies.
Abstract:
Cardiomyopathy is associated with structural and functional abnormalities of the ventricular myocardium and can be classified in two major groups: hypertrophic (HCM) and dilated (DCM) cardiomyopathy. Computational modeling and drug design approaches can speed up the drug discovery and significantly reduce expenses aiming to improve the treatment of cardiomyopathy. In the SILICOFCM project, a multiscale platform is developed using coupled macro- and microsimulation through finite element (FE) modeling of fluid-structure interactions (FSI) and molecular drug interactions with the cardiac cells. FSI was used for modeling the left ventricle (LV) with a nonlinear material model of the heart wall. Simulations of the drugs' influence on the electro-mechanics LV coupling were separated in two scenarios, defined by the principal action of specific drugs. We examined the effects of Disopyramide and Dygoxin which modulate Ca2+ transients (first scenario), and Mavacamten and 2-deoxy adenosine triphosphate (dATP) which affect changes of kinetic parameters (second scenario). Changes of pressures, displacements, and velocity distributions, as well as pressure-volume (P-V) loops in the LV models of HCM and DCM patients were presented. Additionally, the results obtained from the SILICOFCM Risk Stratification Tool and PAK software for high-risk HCM patients closely followed the clinical observations. This approach can give much more information on risk prediction of cardiac disease to specific patients and better insight into estimated effects of drug therapy, leading to improved patient monitoring and treatment.
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