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.

Pharmaceutics
|March 29, 2023
PubMed

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.

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