This study is supported by the European Union&#39;s Horizon 2020 research and innovation program under grant agreement SILICOFCM 777204 and the Ministry of Education, Science and Technological Development of the Republic of Serbia through Contracts No. 451-03-68/2022-14/200107. This article reflects only the authors&#39; views. The European Commission is not responsible for any use that may be made of the information the article contains.

The protocol in this study was approved by the UK National Health Service Health Research Authority North East-Tyne &#38; Wear South Research Ethics Committee with the reference number 18/NE/0318 on 6 February 2019 and was adopted by the Institutional Review Board of each participating center. The study was conducted within the principles of Good Clinical Practice and following the Declaration of Helsinki. Informed consent was obtained from all subjects involved in the study. The patient information is kept anonymous.
1. Workflow for ultrasound M-mode or Apical view DICOM image analysis and LV parameter extraction
NOTE: To begin this protocol, the user must log in to the SILICOFCM platform8 and choose the appropriate workflow (i.e., ultrasound image analysis using M-mode or Apical view). The workflow for ultrasound M-mode DICOM image analysis and LV parameter extraction, for example, involves several steps. The first step is template matching. Templates to be matched include all the relevant necessary borders. It should be emphasized that, for every new dataset, the template should be extracted manually just once for each dataset that corresponds to a specific ultrasound machine. The workflow diagram is presented in Figure 1. The area matching the template will be extracted from the analyzed image after Felsenszwalb&#39;s efficient graph-based image segmentation algorithm finds &#34;strong&#34; boundaries that correspond to the borders of the septum and the wall of the LV. Based on these borders and the places where the heart diameters are the largest (corresponds to diameter in diastole) and the smallest (corresponds to diameter in systole), various LV dimensions will be calculated. The user should define whether the view is M-mode or Apical view.
<ol>
	<li>Log in to the platform with a username and password. Under Virtual Population Module, choose ultrasound M-mode or Apical view workflow.</li>
	<li>In the list of available workflows, select the ultrasound-merged workflow.</li>
	<li>In the File Upload section, upload images and DICOM files stored locally on the user&#39;s computer (Figure 2). 
	NOTE: Inputs to the module M-mode view DICOM image analysis from a specific patient and LV parameter extraction are the testing image (provided by the user in DICOM format representing M-mode or Apical view) and static files (Figure 1). The static files comprise a) the input.txt file, which is a template file used to overwrite the calculated output measures from the module (new output files are created, in turn, from this file), and b) the template image file, which is used for template matching (i.e., template_dicom_GEMS.jpg).</li>
	<li>Select either Private or Public folders as the Destination Folder for the files. Type the desired comment or note in the Comments section before starting the workflow. Select the Apical view and M-mode LV ultrasound images and DICOM files uploaded previously (Step 1.3.) (Figure 3). 
	NOTE: Ultrasound Heart Segmentation tool consists of two sub-modules: the apical view and the M-mode view. Both modules are accessed via the platform. The analysis includes the calculation of the characteristic parameters visible in corresponding cross-sections. In both cases, the necessary values are written to a file that is taken as input to the parametric heart model from the user side (see the discussion for details).</li>
	<li>Click on the Execute button. The interactive platform notifies the user when the running workflow is finished.</li>
	<li>Visualize the created geometry of LV directly on the platform (a 3D model will appear automatically on the screen, which can be rotated using the mouse). Available options include Shaded and Wireframe models for visualization. 
	&#8203;NOTE: After the LV dimensions are extracted, based on mesh options, the mesh of finite elements is generated, and finite element analysis simulation is run with user-prescribed inlet and outlet velocities (see below).</li>
</ol>
2. PAK finite element solver for fluid-structure (FS) simulations
NOTE: This tool can be used for the finite element analysis of the coupled solid-fluid problems. It supports both strong and loose coupling between solid and fluid. Elements can be hexahedrons or tetrahedrons, with or without an additional node at the center of the element. This solver has built-in material models such as the Holzapfel model, hunter muscle model, etc. The PAK-FS information flow diagram is depicted in Figure 4. It starts from the input file and PAK preprocessor. The PAK preprocessor tool outputs a DAT file, which will serve as an input file for the finite element solver. The final output from the solver is VTK files that include the results of the finite element simulation: velocities, pressures, strains, and stresses in the left ventricle.
<ol>
	<li>Download the template files for the boundary conditions of inlet and outlet velocities using the buttons in the bottom section (Figure 3); if patient-specific flow boundary conditions are available, download and use these files. Download the mesh options by clicking on the corresponding button in the bottom section. Save these files into either Private or Public folders.</li>
	<li>Upload these files in a similar way as uploading images (Figure 3). The prescribed inlet and outlet velocities simulate the drug condition, while the mesh options control the density of the finite element mesh. For simulating patient-specific conditions, modify the default values of pressure, flow, material properties, and calcium function.</li>
	<li>Click on the Execute button. A new running workflow will appear in the list. If any of the sections of the workflow are not clear, click on the Help File button (Figure 3) to view detailed instructions on how to use this workflow, as well as to interpret the results. 
	NOTE: If everything executes without errors, the status of the workflow will change from &#34;running&#34; to &#34;finished ok&#34;.</li>
	<li>View the results with several options. Alternatively, download the results; the results folder contains VTK files, CSV files, and animations.
	<ol>
		<li>Click on the eye button to view ejection fraction and global work efficiency values, as well as diagrams of pressure vs. volume, pressure vs. strain, and myocardial work vs. time.</li>
		<li>Click on the camera button to preview and play animations of the displacement, pressure, shear stresses, and velocity fields.</li>
		<li>Click on the 3D visualization button to visualize the outputs online in ParaView Glance.
		<ol>
			<li>Load multiple VTK files previously downloaded as results. See several parameters of interest and change the field, for instance, to velocity for visualization.</li>
			<li>Rotate the model or change the color scheme. Choose Surface with edges or Wireframe for the representation of the surface. Apply the same methodology to every loaded VTK file.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>
3. Determination of ventricular activation sequence from ECG measurement
NOTE: A modified FitzHugh-Nagumo model of the cardiac cell was implemented. The precordial leads were modeled with the standard six electrodes. The potential of the heart was optimized with inverse ECG. Starting from activation in the sinoatrial node (which is a function of time), with heterogeneous action potential through the heart and torso, the user can get electrical activity on the total torso model. The PAK-TORSO information flow diagram is given in Figure 5. The user provides scaling of the torso model in all directions (x,y,z) and ECG signal function. Then the scaled model is created, and its behavior is simulated using the PAK-FS solver. The user provides these input values in a text file. The output of the simulation is a VTK file with the electrical activity of the heart in the torso-embedded environment.
<ol>
	<li>On the homepage, go to Execute Workflow and then choose torse-cwl in the list of available workflows. Add a comment or note in the Comments section before executing the workflow.</li>
	<li>Click on the Input Template File button and save the content shown on the webpage as input.txt file, which will be used for the torso model. In the Input File field, select the downloaded input.txt file. After the file is imported, click on the Execute button to start the calculation. 
	NOTE: A new workflow will appear in the bottom left, with its status shown as &#34;running&#34;; the calculation lasts about 0.5 h, and then the status will change to &#34;finished ok&#34;.</li>
	<li>Click on the eye or camera buttons in the bottom left corner to visualize the available simulation reports or animations directly on the platform. 
	NOTE: The results include distributions of the electrical field, velocity, pressure, shear stress, and deformation at each time step. Animations for each of these distributions are possible to view for the total heart cycle.</li>
	<li>Alternatively, click on the 3D visualization button to visualize the outputs online in ParaView Glance.
	<ol>
		<li>Select the OPEN A FILE button, go to the GIRDER tab, enter user credentials if prompted, and open the Private folder.</li>
		<li>On the next page, select the workflow-outputs folder, and open the torso-cwl folder. Open the first folder on the list.</li>
		<li>See the list of VTK files that represent the simulation results. Choose one or more files and click on the SELECT button to load the file in ParaView Glance.</li>
		<li>Manipulate the model geometry (i.e., move, rotate, zoom in or out, etc.) using the mouse.</li>
		<li>Select different options for model representation as follows.
		<ol>
			<li>Choose the Wireframe option to see the interior of the torso with a heart incorporated within the torso. Choose the Points option to display a dotted representation of the torso model with full heart mesh.</li>
			<li>Adjust the Point Size value to change the display results. Adjust the Opacity value to see the interior of the torso and display results inside the heart mesh.</li>
			<li>Click on the Color By drop-down menu and choose the desired option, for instance, electric potential. Change the default color scale to any of the listed options.</li>
		</ol>
		</li>
	</ol>
	</li>
</ol>

<ol>
	<li>Gibbons Kroeker, C. A., Adeeb, S., Tyberg, J. V., Shrive, N. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+2D+FE+model+of+the+heart+demonstrates+the+role+of+the+pericardium+in+ventricular+deformation.">A 2D FE model of the heart demonstrates the role of the pericardium in ventricular deformation.</a> American Journal of Physiology. 291 (5), 2229-2236 (2006).</li><li>Pullan, A. J., Buist, M. L., Cheng, L. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Mathematically Modelling the Electrical Activity of the Heart - From Cell To Body Surface and Back Again. , (2005).</li><li>Trudel, M. -. C., Dub&#180;e, B., Potse, M., Gulrajani, R. M., Leon, L. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Simulation+of+QRST+integral+maps+with+a+membrane+based+computer+heart+model+employing+parallel+processing.">Simulation of QRST integral maps with a membrane based computer heart model employing parallel processing.</a> IEEE Transactions on Biomedical Engineering. 51 (8), 1319-1329 (2004).</li><li>Kojic, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smeared+multiscale+finite+element+models+for+mass+transport+and+electrophysiology+coupled+to+muscle+mechanics.">Smeared multiscale finite element models for mass transport and electrophysiology coupled to muscle mechanics.</a> Frontiers in Bioengineering and Biotechnology. 7, 381 (2019).</li><li>Baillargeon, B., Rebelo, N., Fox, D. D., Taylor, R. L., Kuhl, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Living+Heart+Project:+A+robust+and+integrative+simulator+for+human+heart+function.">The Living Heart Project: A robust and integrative simulator for human heart function.</a> European Journal of Mechanics - A/Solids. 48, 38-47 (2014).</li><li>Guccione, J. M., McCulloch, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanics+of+active+contraction+in+cardiac+muscle:+Part+I--Constitutive+relations+for+fiber+stress+that+describe+deactivation.">Mechanics of active contraction in cardiac muscle: Part I--Constitutive relations for fiber stress that describe deactivation.</a> TheJournal of Biomechanical Engineering. 115, 72-81 (1993).</li><li>Guccione, J. M., Waldman, L. K., McCulloch, A. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Mechanics+of+active+contraction+in+cardiac+muscle:+Part+II--Cylindrical+models+of+the+systolic+left+ventricle.">Mechanics of active contraction in cardiac muscle: Part II--Cylindrical models of the systolic left ventricle.</a> The Journal of Biomechanical Engineering. 115, 82-90 (1993).</li><li>Kojic, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Smeared+multiscale+finite+element+model+for+electrophysiology+and+ionic+transport+in+biological+tissue.">Smeared multiscale finite element model for electrophysiology and ionic transport in biological tissue.</a> Computers in Biology and Medicine. 108, 288-304 (2019).</li><li>Wang, Y., Rudy, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Application+of+the+method+of+fundamental+solutions+to+potential-based+inverse+electrocardiography.">Application of the method of fundamental solutions to potential-based inverse electrocardiography.</a> Annals of Biomedical Engineering. 34 (8), 1272-1288 (2006).</li><li>Van Oosterom, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+use+of+the+spatial+covariance+in+computing+pericardial+potentials.">The use of the spatial covariance in computing pericardial potentials.</a> IEEE Transactions on Biomedical Engineering. 46 (7), 778-787 (1999).</li><li>Van Oosterom, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+spatial+covariance+used+in+computing+the+pericardial+potential+distribution.">The spatial covariance used in computing the pericardial potential distribution.</a> Computational Inverse Problems in Electrocardiography. , 1-50 (2001).</li><li>Van Oosterom, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Source+models+in+inverse+electrocardiography.">Source models in inverse electrocardiography.</a> International Journal of Bioelectromagnetism. 5, 211-214 (2003).</li><li>Van Oosterom, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+equivalent+double+layer:+source+models+for+repolarization.">The equivalent double layer: source models for repolarization.</a> Comprehensive Electrocardiology. , 227-246 (2010).</li></ol>

The authors have no conflicts of interest.

The SILICOFCM project is an in silico clinical trials platform to design virtual patient populations for risk prediction, testing the effects of pharmacological treatment, and reducing animal experiments and human clinical trials. Testing the effects of pharmacological treatment was modeled with prescribed inlet/outlet boundary flow conditions, calcium function, and material wall properties. This platform integrates multiscale methods on the sarcomeric level with whole-heart performance and the functional optimization level for the risk prediction of patient-specific conditions during cardiomyopathy disease progression.
Heart modeling for cardiomyopathy and electromechanical coupling of the LV in the SILICOFCM8 project has been presented. The geometry of the heart model included seven different regions: 1) sinoatrial node; 2) atria; 3) atrioventricular node; 4) His bundle; 5) bundle fibers; 6) Purkinje fibers; and 7) ventricular myocardium. Body surface potential maps in a healthy subject during the progression of ventricular activation in nine sequences corresponding to the ECG signal have been presented.
After the dimensions of the left ventricle are extracted, based on mesh options, the mesh of finite elements is generated and finite element analysis simulation is run with user-prescribed inlet and outlet velocities. Users can visualize solutions directly on the platform by looking at the available animations and diagrams. Users can visualize pressure-volume, pressure-strain, and myocardial work-time diagrams by clicking on the eye symbol. If users click on the camera button, the list of available animations of various fields (displacements, pressures, velocity, shear stresses) appears.
There are some limitations to the study. Parametric LV geometry was extracted from ultrasound images. The future version will go into more detailed geometrical reconstruction. A direct connection with specific drugs for cardiomyopathy is not presented in this manuscript. In this study, it is controlled through boundary conditions for flow and pressure. Genetic data are currently incorporated through calcium heart muscle function and the nonlinear material property of the wall. In the future version, more details from image reconstruction and genetic data for patient-specific cardiomyopathy disease (hypertrophic and dilated genetic variant) will be considered.
The SILICOFCM computational platform8 will open a new avenue for in silico clinical trials, specifically for cardiac disease and risk prediction for the patient-specific condition. The gold standard for today's clinical practice for risk prediction is the standard survival calculator for cardiomyopathy patients. The platform used here can give more information in comparison with the current medical standard, as the modeling includes not just the biomarkers but also patient-specific geometry, flow and pressure hemodynamics conditions, wall material properties (from deformation in the images), and drug response with different combinations of boundary conditions, calcium function, and material properties.

The rapid development of information technologies, simulation software packages, and medical devices in recent years provides the opportunity for collecting a large amount of clinical information. Creating comprehensive and detailed computational tools has, therefore, become essential to process specific information from the abundance of available data.
From the physicians&#39; point of view, it is of paramount importance to distinguish &#34;normal&#34; versus &#34;abnormal&#34; phenotypes in a specific patient to estimate disease progression, therapeutic responses, and future risks. Recent computational models have significantly improved the integrative understanding of the behavior of heart muscles in hypertrophic (HCM) and dilated (DCM) cardiomyopathies1. It is crucial to use a high-resolution, detailed, and anatomically accurate model of whole-heart electrical activity, which necessitates massive computation times, dedicated software, and supercomputers1,2,3. A methodology for a real 3D heart model has been recently developed using a linear elastic and orthotropic material model based on Holzapfel experiments, which can accurately predict the electrical signal transport and displacement field within heart4. The development of novel integrative modeling approaches could be an effective tool for distinguishing the type and severity of symptoms in patients with multigenic disorders and assessing the degree of impairment in normal physical activity.
There are, however, many new challenges for patient-specific modeling. The physical and biological properties of the human heart are not possible to fully determine. Non-invasive measurements usually include noisy data from which it is difficult to estimate specific parameters for the individual patient. Large-scale computation requires a lot of time to run, whereas the clinical time frame is limited. Patient personal data should be managed in such a way that generated metadata can be reused without compromising patient confidentiality. Despite these challenges, multi-scale heart models can include a sufficient level of detail to achieve predictions that closely follow observed transient responses, thereby providing promise for prospective clinical applications.
However, regardless of the substantial scientific effort by multiple research labs and the significant amount of grant support, currently, there is only one commercially available software package for multiscale and whole heart simulations, called SIMULIA Living Heart Model5. It includes dynamic electro-mechanical simulation, refined heart geometry, a blood flow model, and complete cardiac tissue characterization, including passive and active characteristics, fibrous nature, and electrical pathways. This model is targeted for use in personalized medicine, but the active material characterization is based on a phenomenological model introduced by Guccione et al.6,7. Therefore, SIMULIA cannot directly and accurately translate the changes in contractile protein functional characteristics observed in numerous cardiac diseases. These changes are caused by mutations and other abnormalities at the molecular and subcellular levels6. The limited use of SIMULIA software for a small number of applications in clinical practice is a great example of today&#39;s struggles in developing higher-level multiscale human heart models. On the other hand, it motivates the development of a new generation of multiscale program packages that can trace the effects of mutations from the molecular to organ scale.
The main aim of electrophysiology of the heart is to determine signal propagation inside the torso and the properties of all compartments4,5,6. The SILICOFCM8 project predicts cardiomyopathy disease development using patient-specific biological, genetic, and clinical imaging data. It is achieved with multiscale modeling of the realistic sarcomeric system, the patient&#39;s genetic profile, muscle fiber direction, fluid-structure interaction, and electrophysiology coupling. The effects of left ventricle deformation, mitral valve motion, and complex hemodynamics give detailed functional behavior of the heart conditions in a specific patient.
This article demonstrates the use of the SILICOFCM platform for a parametric model of the left ventricle (LV) generated automatically from patient-specific ultrasound images using a fluid-structure heart model with electromechanical coupling. Apical view and M-mode view analyses of LV were generated with a deep learning algorithm. Then, using the mesh generator, the finite element model was built automatically to simulate different boundary conditions of the full cycle for LV contraction9. On this platform, users can directly visualize the simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses. Input parameters from specific patients are geometry from ultrasound images, velocity profile in the input and output boundary flow conditions for LV, and specific drug therapy (e.g., entresto, digoxin, mavacamten, etc.).

<table><tbody><tr><td>SILICOFCM project</td><td></td><td>www.silicofcm.eu</td><td>open access for registered users</td></tr></tbody></table>

in silico clinical trials for cardiovascular disease

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique characteristic of the platform is the integration of patient-specific biological, genetic, and clinical imaging data. The platform allows the testing and optimization of medical treatment to maximize positive therapeutic outcomes. Thus, adverse effects and drug interactions can be avoided, sudden cardiac death can be prevented, and the time between the commencement of drug treatment and the desired result can be shortened. This article presents a parametric model of the left ventricle automatically generated from patient-specific ultrasound images by applying an electromechanical model of the heart. Drug effects were prescribed through specific boundary conditions for inlet and outlet flow, ECG measurements, and calcium function for heart muscle properties. Genetic data from patients were incorporated through the material property of the ventricle wall. Apical view analysis involves segmenting the left ventricle using a previously trained U-net framework and calculating the bordering rectangle based on the length of the left ventricle in the diastolic and systolic cycle. M-mode view analysis includes bordering of the characteristic areas of the left ventricle in the M-mode view. After extracting the dimensions of the left ventricle, a finite elements mesh was generated based on mesh options, and a finite element analysis simulation was run with user-provided inlet and outlet velocities. Users can directly visualize on the platform various simulation results such as pressure-volume, pressure-strain, and myocardial work-time diagrams, as well as animations of different fields such as displacements, pressures, velocity, and shear stresses.

As an example, the workflow for ultrasound M-mode DICOM image analysis and LV parameter extraction is presented in Figure 1. M-mode and apical view could be tested separately or one after another, depending on the parameters of interest. If tested one after another, the results are appended to one common file (separately for systole and diastole phases). If only one view is tested, the values for unknown parameters are taken from the default file input.txt (Figure 1). The SILICOFCM tool analyzes the images depending on the view mode, as follows.
Apical view analysis includes segmenting the LV using a previously trained U-net framework and calculating the bordering rectangle, based on which the parameters LV length, diastolic, 2D (LVLd[cm]) and LV length, systolic, 2D (LVLs[cm]) are calculated. The user should define if the view represents the systolic or diastolic phase. The output files are as follows: a) input_parametric_diastole.stl or input_parametric_systole.stl,&#160;which&#160;represents the input file for PAK solver with created geometry, including the calculated value for LVLd[cm] or LVLs[cm] depending on the user input of whether the image represents diastole or systole; b) output_Apical_view.txt, which contains the data for LVLd[cm] or LVLs[cm], depending on the user input of whether the image represents diastole or systole.
M-mode view analysis includes the bordering of the characteristic areas of LV in this view. Based on these areas, various parameters are calculated. The output files are as follows: a) input_parametric_diastole.stl and input_parametric_systole.stl,&#160;which represent the input geometry for PAK solver, including calculated values for interventricular septum thickness, diastolic, M-mode (IVSd[cm]), interventricular septum thickness, systolic, M-mode (IVSs[cm]), LV internal dimension, diastolic, M-mode (LVIDd[cm]), LV internal dimension, systolic, M-mode (LVIDs[cm]), LV posterior wall thickness, diastolic, M-mode (LVPWd[cm]), and LV posterior wall thickness, systolic, M-mode (LVPWs[cm]); b) output_M_mode.txt, which contains the data for IVSd [cm], IVSs [cm], LVIDd [cm], LVIDs [cm], LVPWd [cm], and LVPWs [cm].
To generate an example of LV, as realistic as possible, a parametric type of model with specific parts, namely the base part, valves (aortic and mitral), and connecting part (i.e., the connection between base and valves), was generated via PAK finite element simulations (Step 2) using the calculated parameters of LV geometry. Every part has its length and radius, as well as the number of layers (division). The finite element model consists of a fluid domain surrounded by a solid wall that is connected to the base and part of the connection layer. To simulate the realistic behavior of the LV model, prescribed functions for velocities at the inlet (mitral; Figure 6A) and outlet (aortic; Figure 6B) valves were used. An algorithm for the automatic calculation of fiber direction was applied to this finite element model. The results for one layer and three-layered solid representation are shown in Figure 7.
Figure 8 displays the pressure distribution inside the parametric model during a time cycle of 1 s, divided into 10 time steps. During the first five steps, there are no significant pressure changes until the contraction, when it reaches the highest value on the scale and the volume of the model starts to increase at the same time. Figure 9 shows how the velocity is distributed inside the fluid part of the LV model. As shown, there are notable value peaks inside the branches, caused by fluid flow during the loading/unloading cycle. Figure 10 shows how the displacements are distributed along the model. Similar to the pressure change, during the first two steps, the displacements are negligible until the contraction, when they become maximal at the bottom part of the model. Throughout the remaining time, the model slowly returns to its undeformed state. Figure 11 shows the resulting pressure-volume (PV) diagram. Figure 12 shows&#160;the vectorial representation of velocities inside the fluid part of the ventricle model. Phase velocity vectors are present inside the mitral valve during the fluid inflow and, after the ventricle is filled with fluid from 0.7 s, fluid goes out through the aortic part.
Monodomain equations for the fully coupled heart torso system were applied in order to simulate electrophysiology. Myocardial and torso conductivities are defined in Table 110,11,12,13,14. Zero flux was used for Vm as a boundary condition for all the interior boundaries in contact with the lungs, torso, and cardiac. Therefore, &#8722;n &#183; &#915; = 0, where n is the unit outward normal vector on the boundary and &#915; is the flux vector through that boundary for the intracellular voltage, equal to &#915; = &#8722; &#963;&#183; &#8706;Vm/&#8706;n. Next, classical approaches were implemented for solving the ECG inverse problem using the epicardial potential formulation. The methods used were the family of Tikhonov methods and L regularization-based methods10,11,12,13,14.
Whole-heart electrical activity in the torso-embedded environment is presented in Figure 13. A specialized conduction system with heterogeneous action potential morphologies throughout the heart was incorporated in the initiation of activation in the sinoatrial node. Figure 14 shows the maps of body surface potential in a healthy subject during the progression of ventricular activation in nine sequences that correspond to the measured ECG signal.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/63573/63573fig01.jpg" /> 
Figure 1: Information flow diagram for Ultrasound M-mode DICOM image analysis and LV parameter extraction. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig01large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/63573/63573fig02.jpg" /> 
Figure 2: Uploading new files for analysis. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig02large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/63573/63573fig03.jpg" /> 
Figure 3: User interface for Ultrasound Apical view DICOM image analysis and LV parameter extraction. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig03large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/63573/63573fig04.jpg" /> 
Figure 4: PAK-FS information flow diagram. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/63573/63573fig05.jpg" /> 
Figure 5: PAK-TORSO information flow diagram. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig05large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/63573/63573fig06.jpg" /> 
Figure 6: Velocity profile. (A) Inlet function of velocity at mitral valve cross-section, and (B) outlet velocity function at aortic valve cross-section. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig06large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/63573/63573fig07.jpg" /> 
Figure 7: Parametric LV model with fibers direction. (A) One-layered solid wall, and (B) three-layered solid wall representation. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig07large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/63573/63573fig08.jpg" /> 
Figure 8: Pressure field inside the parametrical LV model. Five different time steps are presented. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig08large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/63573/63573fig09.jpg" /> 
Figure 9: Velocity field inside the parametrical LV model. Five different time steps are presented. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig09large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 10" class="xfigimg" src="/files/ftp_upload/63573/63573fig10.jpg" /> 
Figure 10: Displacements in the parametrical LV model. Five different time steps are presented. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 11" class="xfigimg" src="/files/ftp_upload/63573/63573fig11.jpg" /> 
Figure 11: Pressure vs. volume (PV) diagram for LV fluid-structure interaction model. The prescribed cycle duration of 1 s is divided into 10 time steps. Five representative time steps are shown. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 12" class="xfigimg" src="/files/ftp_upload/63573/63573fig12.jpg" /> 
Figure 12: Vectorial representation of velocities in the parametrical model of the left ventricle. Four representative time steps are shown. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig12large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 13" class="xfigimg" src="/files/ftp_upload/63573/63573fig13.jpg" /> 
Figure 13: Simulation of whole-heart activation at various time points on the lead II ECG signal. 1&#8211;9 activation sequences in (B) correspond to the ECG signal in (A). The transmembrane potential in mV is denoted by the color bar. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig13large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 14" class="xfigimg" src="/files/ftp_upload/63573/63573fig14.jpg" /> 
Figure 14: Maps of body surface potential in a healthy subject. Progression of ventricular activation in nine sequences (lower panel) corresponding to the ECG signal (upper panel). The heart activity range in mV is denoted by the color bar. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/63573/63573fig14large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td colspan="2">Parameter</td>
			<td>SAN</td>
			<td>Atria</td>
			<td>AVN</td>
			<td>His</td>
			<td>BNL</td>
			<td>Purkinje</td>
			<td>Ventricles</td>
		</tr>
		<tr>
			<td colspan="2">A</td>
			<td>-0.6</td>
			<td>0.13</td>
			<td>0.13</td>
			<td>0.13</td>
			<td>0.13</td>
			<td>0.13</td>
			<td>0.13</td>
		</tr>
		<tr>
			<td colspan="2">B</td>
			<td>-0.3</td>
			<td>0</td>
			<td>0</td>
			<td>0</td>
			<td>0</td>
			<td>0</td>
			<td>0</td>
		</tr>
		<tr>
			<td colspan="2">c1(AsV&#8722;1 m&#8722;3)</td>
			<td>1000</td>
			<td>2.6</td>
			<td>2.6</td>
			<td>2.6</td>
			<td>2.6</td>
			<td>2.6</td>
			<td>2.6</td>
		</tr>
		<tr>
			<td colspan="2">c2 (AsV&#8722;1 m&#8722;3)</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
		</tr>
		<tr>
			<td colspan="2">D</td>
			<td>0</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
			<td>1</td>
		</tr>
		<tr>
			<td colspan="2">e</td>
			<td>0.066</td>
			<td>0.0132</td>
			<td>0.0132</td>
			<td>0.005</td>
			<td>0.0022</td>
			<td>0.0047</td>
			<td>0.006</td>
		</tr>
		<tr>
			<td colspan="2">A (mV)</td>
			<td>33</td>
			<td>140</td>
			<td>140</td>
			<td>140</td>
			<td>140</td>
			<td>140</td>
			<td>140</td>
		</tr>
		<tr>
			<td colspan="2">B (mV)</td>
			<td>-22</td>
			<td>-85</td>
			<td>-85</td>
			<td>-85</td>
			<td>-85</td>
			<td>-85</td>
			<td>-85</td>
		</tr>
		<tr>
			<td colspan="2">k</td>
			<td>1000</td>
			<td>1000</td>
			<td>1000</td>
			<td>1000</td>
			<td>1000</td>
			<td>1000</td>
			<td>1000</td>
		</tr>
		<tr>
			<td colspan="2">&#963; (mS&#183;m-1)</td>
			<td>0.5</td>
			<td>8</td>
			<td>0.5</td>
			<td>10</td>
			<td>15</td>
			<td>35</td>
			<td>8</td>
		</tr>
	</tbody>
</table>
Table 1: Parameters for monodomain model with modified FitzHugh-Nahumo equations.

Watch this Scientific Journal Video about In Silico Clinical Trials for Cardiovascular Disease at JoVE.com

In Silico Clinical Trials for Cardiovascular Disease

This protocol demonstrates the workflow of the SILICOFCM platform for automatically generating a parametric model of the left ventricle from patient-specific ultrasound images by applying a multi-scale electromechanical model of the heart. This platform enables in silico clinical trials intended to reduce real clinical trials and maximize positive therapeutic outcomes.

In Silico Clinical Trials for Cardiovascular Disease

The SILICOFCM project mainly aims to develop a computational platform for in silico clinical trials of familial cardiomyopathies (FCMs). The unique ...

in-silico-clinical-trials-for-cardiovascular-disease

Nanyang Technological University

Research

JoVE Journal

Bioengineering

1.6K Views.  Bioengineering Research and Development Center BioIRC. Ons protocol demonstreert de workflow van het Silico FCM-platform van de linker ventrikel van patiëntspecifieke echografiebeelden door een multischaal elektromechanisch model van het hart toe te passen. Silico FCM-platform kan medicijneffecten onderzoeken die worden voorgeschreven door specifieke randvoorwaarden voor inlaat- en uitlaatstroom, ECG-metingen en calciumfunctie voor hartspiereigenschappen. Toepassing van het Silico FCM-platform kan dierproeven verminderen en echte klinische onder...

Video: In Silico Clinical Trials for Cardiovascular Disease

Evaluation of Exercise Tolerance of Pigs Subjected to Exercise

Exercise Test for Evaluation of the Functional Efficacy of the Pig Cardiovascular System

Despite the progress in treatments, cardiovascular diseases are still one of the biggest causes of mortality and morbidity worldwide. Gene therapy-based therapeutic angiogenesis is a promising approach for treating patients with significant symptoms, despite optimal pharmacological therapy and invasive procedures. However, many promising cardiovascular gene therapy techniques have failed to accomplish expectations in clinical trials. One explanation is a mismatch between preclinical and clinical endpoints used to measure efficacy. In animal models, the emphasis has usually been on easily quantifiable endpoints, such as the number and area of the capillary vessels calculated from histological sections. Apart from mortality and morbidity, endpoints in clinical trials are subjective, such as exercise tolerance and quality of life. However, the preclinical and clinical endpoints likely measure different aspects of the applied therapy. Nevertheless, both types of endpoints are required to develop successful therapeutic approaches. In clinics, the main goal is always to alleviate patients' symptoms and improve their prognosis and quality of life. To achieve better predictive data from preclinical studies, endpoint measurements must be better matched to those in clinical studies. Here, we introduce a protocol for a clinically relevant treadmill exercise test in pigs. This study aims to: (1) provide a reliable exercise test in pigs that can be used to evaluate the safety and functional efficacy of gene therapy and other novel therapies, and (2) better match the endpoints between preclinical and clinical studies.

Author Spotlight: Exercise Test for Evaluation of the Functional Efficacy of the Pig Cardiovascular System  

The present protocol describes a large animal exercise test model to assess the functional capacity of the cardiovascular system for evaluating the efficiency of new therapies in the preclinical setting. It is comparable to a clinical exercise test.

Despite the progress in treatments, cardiovascular diseases are still one of the biggest causes of mortality and morbidity worldwide. Gene therapy-based ...

Genetics

In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography

The use of Micro-Computed Tomography (MicroCT) for in vivo studies of small animals as models of human disease has risen tremendously due to the fact that MicroCT provides quantitative high-resolution three-dimensional (3D) anatomical data non-destructively and longitudinally. Most importantly, with the development of a novel preclinical iodinated contrast agent called eXIA160, functional and metabolic assessment of the heart became possible. However, prior to the advent of commercial MicroCT scanners equipped with X-ray flat-panel detector technology and easy-to-use cardio-respiratory gating, preclinical studies of cardiovascular disease (CVD) in small animals required a MicroCT technologist with advanced skills, and thus were impractical for widespread implementation. The goal of this work is to provide a practical guide to the use of the high-speed Quantum FX MicroCT system for comprehensive determination of myocardial global and regional function along with assessment of myocardial perfusion, metabolism and viability in healthy mice and in a cardiac ischemia mouse model induced by permanent occlusion of the left anterior descending coronary artery (LAD).

In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography

This method outlines the use of Quantum Micro-Computed Tomography (MicroCT) to assess cardiac morphology, function, perfusion, metabolism and viability with iodinated contrast agent in mice with experimentally-induced myocardial ischemia. The technique can be applied for non-destructive high-throughput longitudinal in vivo imaging of various animal models of human heart disease.

The use of Micro-Computed Tomography (MicroCT) for in vivo studies of small animals as models of human disease has risen tremendously due to the fact that ...

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

In the present study, we combined an in vitro 3D lung tumor model with an in silico model to optimize predictions of drug response based on a specific mutational background. The model is generated on a decellularized porcine scaffold that reproduces tissue-specific characteristics regarding extracellular matrix composition and architecture including the basement membrane. We standardized a protocol that allows artificial tumor tissue generation within 14 days including three days of drug treatment. Our article provides several detailed descriptions of 3D read-out screening techniques like the determination of the proliferation index Ki67 staining&#39;s, apoptosis from supernatants by M30-ELISA and assessment of epithelial to mesenchymal transition (EMT), which are helpful tools for evaluating the effectiveness of therapeutic compounds. We could show compared to 2D culture a reduction of proliferation in our 3D tumor model that is related to the clinical situation. Despite of this lower proliferation, the model predicted EGFR-targeted drug responses correctly according to the biomarker status as shown by comparison of the lung carcinoma cell lines HCC827 (EGFR -mutated, KRAS wild-type) and A549 (EGFR wild-type, KRAS-mutated) treated with the tyrosine-kinase inhibitor (TKI) gefitinib. To investigate drug responses of more advanced tumor cells, we induced EMT by long-term treatment with TGF-beta-1 as assessed by vimentin/pan-cytokeratin immunofluorescence staining. A flow-bioreactor was employed to adjust culture to physiological conditions, which improved tissue generation. Furthermore, we show the integration of drug responses upon gefitinib treatment or TGF-beta-1 stimulation - apoptosis, proliferation index and EMT - into a Boolean in silico model. Additionally, we explain how drug responses of tumor cells with a specific mutational background and counterstrategies against resistance can be predicted. We are confident that our 3D in vitro approach especially with its in silico expansion provides an additional value for preclinical drug testing in more realistic conditions than in 2D cell culture.

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

We present a three-dimensional (3D) lung cancer model based on a biological collagen scaffold to study sensitivity towards non-small-cell-lung-cancer-(NSCLC)-targeted therapies. We demonstrate different read-out techniques to determine the proliferation index, apoptosis and epithelial-mesenchymal transition (EMT) status. Collected data are integrated into an in silico model for prediction of drug sensitivity.

In the present study, we combined an in vitro 3D lung tumor model with an in silico model to optimize predictions of drug response based on a specific ...

Automated Contraction Analysis of Human Engineered Heart Tissue for Cardiac Drug Safety Screening

Cardiac tissue engineering describes techniques to constitute three dimensional force-generating engineered tissues. For the implementation of these procedures in basic research and preclinical drug development, it is important to develop protocols for automated generation and analysis under standardized conditions. Here, we present a technique to generate engineered heart tissue (EHT) from cardiomyocytes of different species (rat, mouse, human). The technique relies on the assembly of a fibrin-gel containing dissociated cardiomyocytes between elastic polydimethylsiloxane (PDMS) posts in a 24-well format. Three-dimensional, force-generating EHTs constitute within two weeks after casting. This procedure allows for the generation of several hundred EHTs per week and is technically limited only by the availability of cardiomyocytes (0.4-1.0 x 106/EHT). Evaluation of auxotonic muscle contractions is performed in a modified incubation chamber with a mechanical interlock for 24-well plates and a camera placed on top of this chamber. A software controls a camera moved on an XYZ axis system to each EHT. EHT contractions are detected by an automated figure recognition algorithm, and force is calculated based on shortening of the EHT and the elastic propensity and geometry of the PDMS posts. This procedure allows for automated analysis of high numbers of EHT under standardized and sterile conditions. The reliable detection of drug effects on cardiomyocyte contraction is crucial for cardiac drug development and safety pharmacology. We demonstrate, with the example of the hERG channel inhibitor E-4031, that the human EHT system replicates drug responses on contraction kinetics of the human heart, indicating it to be a promising tool for cardiac drug safety screening.

Here, we show the generation of human engineered heart tissue from induced pluripotent stem cells (hiPSC)-derived cardiomyocytes. We present a method to analyze contraction force and exemplary alteration of contraction pattern by the hERG channel inhibitor E-4031. This method shows high level of robustness and suitability for cardiac drug screening.

Cardiac tissue engineering describes techniques to constitute three dimensional force-generating engineered tissues. For the implementation of these ...

Fabrication of 3D Cardiac Microtissue Arrays using Human iPSC-Derived Cardiomyocytes, Cardiac Fibroblasts, and Endothelial Cells

Generation of human cardiomyocytes (CMs), cardiac fibroblasts (CFs), and endothelial cells (ECs) from induced pluripotent stem cells (iPSCs) has provided a unique opportunity to study the complex interplay among different cardiovascular cell types that drives tissue development and disease. In the area of cardiac tissue models, several sophisticated three-dimensional (3D) approaches use induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) to mimic physiological relevance and native tissue environment with a combination of extracellular matrices and crosslinkers. However, these systems are complex to fabricate without microfabrication expertise and require several weeks to self-assemble. Most importantly, many of these systems lack vascular cells and cardiac fibroblasts that make up over 60% of the nonmyocytes in the human heart. Here we describe the derivation of all three cardiac cell types from iPSCs to fabricate cardiac microtissues. This facile replica molding technique allows cardiac microtissue culture in standard multi-well cell culture plates for several weeks. The platform allows user-defined control over microtissue sizes based on initial seeding density and requires less than 3 days for self-assembly to achieve observable cardiac microtissue contractions. Furthermore, the cardiac microtissues can be easily digested while maintaining high cell viability for single-cell interrogation with the use of flow cytometry and single-cell RNA sequencing (scRNA-seq). We envision that this in vitro model of cardiac microtissues will help accelerate validation studies in drug discovery and disease modeling.

Here, we describe an easy-to-use methodology to generate 3D self-assembled cardiac microtissue arrays composed of pre-differentiated human-induced pluripotent stem cell-derived cardiomyocytes, cardiac fibroblasts, and endothelial cells. This user-friendly and low cell requiring technique to generate cardiac microtissues can be implemented for disease modeling and early stages of drug development.

Generation of human cardiomyocytes (CMs), cardiac fibroblasts (CFs), and endothelial cells (ECs) from induced pluripotent stem cells (iPSCs) has provided ...

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present methods for the development of assays to query potentially clinically significant nonsynonymous changes using in vivo complementation in zebrafish. Zebrafish (Danio rerio) are a useful animal system due to their experimental tractability; embryos are transparent to enable facile viewing, undergo rapid development ex vivo, and can be genetically manipulated.1 These aspects have allowed for significant advances in the analysis of embryogenesis, molecular processes, and morphogenetic signaling. Taken together, the advantages of this vertebrate model make zebrafish highly amenable to modeling the developmental defects in pediatric disease, and in some cases, adult-onset disorders. Because the zebrafish genome is highly conserved with that of humans (~70% orthologous), it is possible to recapitulate human disease states in zebrafish. This is accomplished either through the injection of mutant human mRNA to induce dominant negative or gain of function alleles, or utilization of morpholino (MO) antisense oligonucleotides to suppress genes to mimic loss of function variants. Through complementation of MO-induced phenotypes with capped human mRNA, our approach enables the interpretation of the deleterious effect of mutations on human protein sequence based on the ability of mutant mRNA to rescue a measurable, physiologically relevant phenotype. Modeling of the human disease alleles occurs through microinjection of zebrafish embryos with MO and/or human mRNA at the 1-4 cell stage, and phenotyping up to seven days post fertilization (dpf). This general strategy can be extended to a wide range of disease phenotypes, as demonstrated in the following protocol. We present our established models for morphogenetic signaling, craniofacial, cardiac, vascular integrity, renal function, and skeletal muscle disorder phenotypes, as well as others.

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Here, we present a systematic approach for developing physiologically relevant, sensitive and specific in vivo assays for interpreting variation in human pathology. Transient genetic manipulation via microinjection of WT and mutant human mRNA and morpholino (MO) antisense oligonucleotides harness the tractability of the developing zebrafish embryo to rapidly assay pathogenic mutations, especially, but not exclusively, in the context of human developmental disorders.

Here,  we present methods for the development of assays to query potentially clinically  significant nonsynonymous changes using in  vivo complementation ...

Biology

Quantitative Analysis of Cellular Composition in Advanced Atherosclerotic Lesions of Smooth Muscle Cell Lineage-Tracing Mice

Atherosclerosis remains the leading cause of death worldwide and, despite countless preclinical studies describing promising therapeutic targets, novel interventions have remained elusive. This is likely due, in part, to a reliance on preclinical prevention models investigating the effects of genetic manipulations or pharmacological treatments on atherosclerosis development rather than the established disease. Also, results of these studies are often confounding because of the use of superficial lesion analyses and a lack of characterization of lesion cell populations. To help overcome these translational hurdles, we propose an increased reliance on intervention models that employ investigation of changes in cellular composition at a single cell level by immunofluorescent staining and confocal microscopy. To this end, we describe a protocol for testing a putative therapeutic agent in a murine intervention model including a systematic approach for animal dissection, embedding, sectioning, staining, and quantification of brachiocephalic artery lesions. In addition, due to the phenotypic diversity of cells within late-stage atherosclerotic lesions, we describe the importance of using cell-specific, inducible lineage tracing mouse systems and how this can be leveraged for unbiased characterization of atherosclerotic lesion cell populations. Together, these strategies may assist vascular biologists to more accurately model therapeutic interventions and analyze atherosclerotic disease and will hopefully translate into a higher rate of success in clinical trials.

We propose a standardized protocol to characterize the cellular composition of late-stage murine atherosclerotic lesions including systematic methods of animal dissection, tissue embedding, sectioning, staining, and analysis of brachiocephalic arteries from atheroprone smooth muscle cell lineage tracing mice.

Atherosclerosis remains the leading cause of death worldwide and, despite countless preclinical studies describing promising therapeutic targets, novel ...

Medicine

Large Animal Model for Evaluating the Efficacy of the Gene Therapy in Ischemic Heart

Coronary artery disease is one of the significant causes of mortality and morbidity worldwide. Despite the progression of current therapeutics, a considerable proportion of coronary artery disease patients remain symptomatic. Gene therapy-mediated therapeutic angiogenesis offers a novel therapeutic method for improving myocardial perfusion and relieving symptoms. Gene therapy with different angiogenic factors has been studied in few clinical trials. Due to the novelty of the method, the progress of myocardial gene therapy is a continuous path from bench to bedside. Therefore, large animal models are needed for evaluating the safety and efficacy. The more the large animal model identifies the original disease and the endpoints used in clinics, the more predictable outcomes are from clinical trials. Here, we introduce a large animal model for evaluating the efficacy of the gene therapy in the ischemic porcine heart. We use clinically relevant imaging methods such as ultrasound imaging and 15H2O-PET. For targeting the gene transfers into the desired area, electroanatomical mapping is used. The aim of this method is: (1) to mimic chronic coronary artery disease, (2) to induce therapeutic angiogenesis at hypoxic areas of the heart, and (3) to evaluate the safety and efficacy of the gene therapy by using relevant endpoints.

Myocardial gene therapy for ischemic heart disease holds great promise for future therapeutics. Here, we introduce a large animal model for evaluating the efficacy of gene therapy in the ischemic heart.

Coronary artery disease is one of the significant causes of mortality and morbidity worldwide. Despite the progression of current therapeutics, a ...

Preclinical Evaluation of Coronary Artery Stents

Coronary artery disease is a major contributor to morbidity and mortality worldwide. While lifestyle changes and medication are the cornerstones of treatment, coronary artery balloon angioplasty and stenting are routinely performed on patients with acute coronary syndromes and chronic coronary artery disease who remain symptomatic with optical medical treatment. Several generations of coronary stents have been developed over recent decades. Balloon angioplasty and stenting are supported by the use of pharmaceutical agents applied onto balloons and the stent surface, either to advance the healing properties of the artery post-intervention or to prevent the formation of restenosis. New devices need to be rigorously tested for safety and efficacy before acceptance into clinical practice; thus, there is a continuing need for reliable and reproducible preclinical methods of stent evaluation. We describe here a pig coronary artery model as well as an atherosclerotic rabbit model for coronary artery stent research and describe basic steps in intravascular imaging and stent histology.

New coronary artery stent designs and materials must be tested before clinical use in relevant preclinical models. Here we describe an atherosclerotic rabbit aorta model and a pig coronary artery model for stent research with in vivo and histological analyses.

Coronary artery disease is a major contributor to morbidity and mortality worldwide. While lifestyle changes and medication are the cornerstones of ...

Technical Applications of Microelectrode Array and Patch Clamp Recordings on Human Induced Pluripotent Stem Cell-Derived Cardiomyocytes

Drug-induced cardiotoxicity is the leading cause of drug attrition and withdrawal from the market. Therefore, using appropriate preclinical cardiac safety assessment models is a critical step during drug development. Currently, cardiac safety assessment is still highly dependent on animal studies. However, animal models are plagued by poor translational specificity to humans due to species-specific differences, particularly in terms of cardiac electrophysiological characteristics. Thus, there is an urgent need to develop a reliable, efficient, and human-based model for preclinical cardiac safety assessment. Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have emerged as an invaluable in vitro model for drug-induced cardiotoxicity screening and disease modeling. hiPSC-CMs can be obtained from individuals with diverse genetic backgrounds and various diseased conditions, making them an ideal surrogate to assess drug-induced cardiotoxicity individually. Therefore, methodologies to comprehensively investigate the functional characteristics of hiPSC-CMs need to be established. In this protocol, we detail various functional assays that can be assessed on hiPSC-CMs, including the measurement of contractility, field potential, action potential, and calcium handling. Overall, the incorporation of hiPSC-CMs into preclinical cardiac safety assessment has the potential to revolutionize drug development.

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have emerged as a promising in vitro model for drug-induced cardiotoxicity screening and disease modeling. Here, we detail a protocol for measuring the contractility and electrophysiology of hiPSC-CMs.

Drug-induced cardiotoxicity is the leading cause of drug attrition and withdrawal from the market. Therefore, using appropriate preclinical cardiac safety ...

In Silico Clinical Trials for Cardiovascular Disease

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