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Reza S, Kovarovic B, Bluestein D. Assessing Post-TAVR Cardiac Conduction Abnormalities Risk Using a Digital Twin of a Beating Heart. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.28.24305028. [PMID: 38585979 PMCID: PMC10996731 DOI: 10.1101/2024.03.28.24305028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Transcatheter aortic valve replacement (TAVR) has rapidly displaced surgical aortic valve replacement (SAVR). However, certain post-TAVR complications persist, with cardiac conduction abnormalities (CCA) being one of the major ones. The elevated pressure exerted by the TAVR stent onto the conduction fibers situated between the aortic annulus and the His bundle, in proximity to the atrioventricular (AV) node, may disrupt the cardiac conduction leading to the emergence of CCA. In his study, an in-silico framework was developed to assess the CCA risk, incorporating the effect of a dynamic beating heart and pre-procedural parameters such as implantation depth and preexisting cardiac asynchrony in the new onset of post-TAVR CCA. A self-expandable TAVR device deployment was simulated inside an electro-mechanically coupled beating heart model in five patient scenarios, including three implantation depths, and two preexisting cardiac asynchronies: (i) a right bundle branch block (RBBB) and (ii) a left bundle branch block (LBBB). Subsequently, several biomechanical parameters were analyzed to assess the post-TAVR CCA risk. The results manifested a lower cumulative contact pressure on the conduction fibers following TAVR for aortic deployment (0.018 MPa) compared to baseline (0.29 MPa) and ventricular deployment (0.52 MPa). Notably, the preexisting RBBB demonstrated a higher cumulative contact pressure (0.34 MPa) compared to the baseline and preexisting LBBB (0.25 MPa). Deeper implantation and preexisting RBBB cause higher stresses and contact pressure on the conduction fibers leading to an increased risk of post-TAVR CCA. Conversely, implantation above the MS landmark and preexisting LBBB reduces the risk.
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2
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Guan D, Tian L, Li W, Gao H. Using LDDMM and a kinematic cardiac growth model to quantify growth and remodelling in rat hearts under PAH. Comput Biol Med 2024; 171:108218. [PMID: 38428098 DOI: 10.1016/j.compbiomed.2024.108218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 01/20/2024] [Accepted: 02/25/2024] [Indexed: 03/03/2024]
Abstract
Pulmonary arterial hypertension (PAH) is a rapidly progressive and fatal disease, with right ventricular failure being the primary cause of death in patients with PAH. This study aims to determine the mechanical stimuli that may initiate heart growth and remodelling (G&R). To achieve this, two bi-ventricular models were constructed: one for a control rat heart and another for a rat heart with PAH. The growth of the diseased heart was estimated by warping it to the control heart using an improved large deformation diffeomorphic metric mapping (LDDMM) framework. Correlation analysis was then performed between mechanical cues (stress and strain) and growth tensors, which revealed that principal strains may serve as a triggering stimulus for myocardial growth and remodelling under PAH. The growth tensors, estimated from in vivo images, could explain 84.3% of the observed geometrical changes in the diseased heart with PAH by using a kinematic cardiac growth model. Our approach has the potential to quantify G&R using sparse in vivo images and to provide insights into the underlying mechanism of triggering right heart failure from a biomechanical perspective.
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Affiliation(s)
- Debao Guan
- School of Control Science and Engineering, Shandong University, China; School of Mathematics and Statistics, University of Glasgow, UK
| | - Lian Tian
- Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, UK
| | - Wei Li
- School of Control Science and Engineering, Shandong University, China
| | - Hao Gao
- School of Mathematics and Statistics, University of Glasgow, UK.
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3
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Guan D, Zhuan X, Luo X, Gao H. An updated Lagrangian constrained mixture model of pathological cardiac growth and remodelling. Acta Biomater 2023; 166:375-399. [PMID: 37201740 DOI: 10.1016/j.actbio.2023.05.022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 05/03/2023] [Accepted: 05/10/2023] [Indexed: 05/20/2023]
Abstract
Progressive left ventricular (LV) growth and remodelling (G&R) is often induced by volume and pressure overload, characterized by structural and functional adaptation through myocyte hypertrophy and extracellular matrix remodelling, which are dynamically regulated by biomechanical factors, inflammation, neurohormonal pathways, etc. When prolonged, it can eventually lead to irreversible heart failure. In this study, we have developed a new framework for modelling pathological cardiac G&R based on constrained mixture theory using an updated reference configuration, which is triggered by altered biomechanical factors to restore biomechanical homeostasis. Eccentric and concentric growth, and their combination have been explored in a patient-specific human LV model under volume and pressure overload. Eccentric growth is triggered by overstretching of myofibres due to volume overload, i.e. mitral regurgitation, whilst concentric growth is driven by excessive contractile stress due to pressure overload, i.e. aortic stenosis. Different biological constituent's adaptations under pathological conditions are integrated together, which are the ground matrix, myofibres and collagen network. We have shown that this constrained mixture-motivated G&R model can capture different phenotypes of maladaptive LV G&R, such as chamber dilation and wall thinning under volume overload, wall thickening under pressure overload, and more complex patterns under both pressure and volume overload. We have further demonstrated how collagen G&R would affect LV structural and functional adaption by providing mechanistic insight on anti-fibrotic interventions. This updated Lagrangian constrained mixture based myocardial G&R model has the potential to understand the turnover processes of myocytes and collagen due to altered local mechanical stimuli in heart diseases, and in providing mechanistic links between biomechanical factors and biological adaption at both the organ and cellular levels. Once calibrated with patient data, it can be used for assessing heart failure risk and designing optimal treatment therapies. STATEMENT OF SIGNIFICANCE: Computational modelling of cardiac G&R has shown high promise to provide insight into heart disease management when mechanistic understandings are quantified between biomechanical factors and underlying cellular adaptation processes. The kinematic growth theory has been dominantly used to phenomenologically describe the biological G&R process but neglecting underlying cellular mechanisms. We have developed a constrained mixture based G&R model with updated reference by taking into account different mechanobiological processes in the ground matrix, myocytes and collagen fibres. This G&R model can serve as a basis for developing more advanced myocardial G&R models further informed by patient data to assess heart failure risk, predict disease progression, select the optimal treatment by hypothesis testing, and eventually towards a truly precision cardiology using in-silico models.
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Affiliation(s)
- Debao Guan
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
| | - Xin Zhuan
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
| | - Xiaoyu Luo
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK
| | - Hao Gao
- School of Mathematics and Statistics, University of Glasgow, Glasgow G12 8QQ, UK.
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4
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Lazarus A, Dalton D, Husmeier D, Gao H. Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics. Biomech Model Mechanobiol 2022; 21:953-982. [PMID: 35377030 PMCID: PMC9132878 DOI: 10.1007/s10237-022-01571-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Accepted: 02/28/2022] [Indexed: 01/08/2023]
Abstract
Personalized computational cardiac models are considered to be a unique and powerful tool in modern cardiology, integrating the knowledge of physiology, pathology and fundamental laws of mechanics in one framework. They have the potential to improve risk prediction in cardiac patients and assist in the development of new treatments. However, in order to use these models for clinical decision support, it is important that both the impact of model parameter perturbations on the predicted quantities of interest as well as the uncertainty of parameter estimation are properly quantified, where the first task is a priori in nature (meaning independent of any specific clinical data), while the second task is carried out a posteriori (meaning after specific clinical data have been obtained). The present study addresses these challenges for a widely used constitutive law of passive myocardium (the Holzapfel-Ogden model), using global sensitivity analysis (SA) to address the first challenge, and inverse-uncertainty quantification (I-UQ) for the second challenge. The SA is carried out on a range of different input parameters to a left ventricle (LV) model, making use of computationally efficient Gaussian process (GP) surrogate models in place of the numerical forward simulator. The results of the SA are then used to inform a low-order reparametrization of the constitutive law for passive myocardium under consideration. The quality of this parameterization in the context of an inverse problem having observed noisy experimental data is then quantified with an I-UQ study, which again makes use of GP surrogate models. The I-UQ is carried out in a Bayesian manner using Markov Chain Monte Carlo, which allows for full uncertainty quantification of the material parameter estimates. Our study reveals insights into the relation between SA and I-UQ, elucidates the dependence of parameter sensitivity and estimation uncertainty on external factors, like LV cavity pressure, and sheds new light on cardio-mechanic model formulation, with particular focus on the Holzapfel-Ogden myocardial model.
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Affiliation(s)
- Alan Lazarus
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - David Dalton
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Dirk Husmeier
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
| | - Hao Gao
- School of Mathematics and Statistics, University of Glasgow, Glasgow, UK
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5
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Stimm J, Nordsletten DA, Jilberto J, Miller R, Berberoğlu E, Kozerke S, Stoeck CT. Personalization of biomechanical simulations of the left ventricle by in-vivo cardiac DTI data: Impact of fiber interpolation methods. Front Physiol 2022; 13:1042537. [PMID: 36518106 PMCID: PMC9742433 DOI: 10.3389/fphys.2022.1042537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 11/14/2022] [Indexed: 11/29/2022] Open
Abstract
Simulations of cardiac electrophysiology and mechanics have been reported to be sensitive to the microstructural anisotropy of the myocardium. Consequently, a personalized representation of cardiac microstructure is a crucial component of accurate, personalized cardiac biomechanical models. In-vivo cardiac Diffusion Tensor Imaging (cDTI) is a non-invasive magnetic resonance imaging technique capable of probing the heart's microstructure. Being a rather novel technique, issues such as low resolution, signal-to noise ratio, and spatial coverage are currently limiting factors. We outline four interpolation techniques with varying degrees of data fidelity, different amounts of smoothing strength, and varying representation error to bridge the gap between the sparse in-vivo data and the model, requiring a 3D representation of microstructure across the myocardium. We provide a workflow to incorporate in-vivo myofiber orientation into a left ventricular model and demonstrate that personalized modelling based on fiber orientations from in-vivo cDTI data is feasible. The interpolation error is correlated with a trend in personalized parameters and simulated physiological parameters, strains, and ventricular twist. This trend in simulation results is consistent across material parameter settings and therefore corresponds to a bias introduced by the interpolation method. This study suggests that using a tensor interpolation approach to personalize microstructure with in-vivo cDTI data, reduces the fiber uncertainty and thereby the bias in the simulation results.
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Affiliation(s)
- Johanna Stimm
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - David A Nordsletten
- Department of Biomedical Engineering and Cardiac Surgery, University of Michigan, Ann Arbor, MI, United States.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Javiera Jilberto
- Department of Biomedical Engineering and Cardiac Surgery, University of Michigan, Ann Arbor, MI, United States
| | - Renee Miller
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Ezgi Berberoğlu
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Christian T Stoeck
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland.,Division of Surgical Research, University Hospital Zurich, University Zurich, Zurich, Switzerland
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6
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Han T, Lee T, Ledwon J, Vaca E, Turin S, Kearney A, Gosain AK, Tepole AB. Bayesian calibration of a computational model of tissue expansion based on a porcine animal model. Acta Biomater 2022; 137:136-146. [PMID: 34634507 PMCID: PMC8678288 DOI: 10.1016/j.actbio.2021.10.007] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/04/2021] [Accepted: 10/05/2021] [Indexed: 01/03/2023]
Abstract
Tissue expansion is a technique used clinically to grow skin in situ to correct large defects. Despite its enormous potential, lack of fundamental knowledge of skin adaptation to mechanical cues, and lack of predictive computational models limit the broader adoption and efficacy of tissue expansion. In our previous work, we introduced a finite element model of tissue expansion that predicted key patterns of strain and growth which were then confirmed by our porcine animal model. Here we use the data from a new set of experiments to calibrate the computational model within a Bayesian framework. Four 10×10cm2 patches were tattooed in the dorsal skin of four 12 weeks-old minipigs and a total of six patches underwent successful tissue expander placement and inflation to 60cc for expansion times ranging from 1 h to 7 days. Six patches that did not have expanders implanted served as controls for the analysis. We find that growth can be explained based on the elastic deformation. The predicted area growth rate is k∈[0.02,0.08] [h-1]. Growth is anisotropic and reflects the anisotropic mechanical behavior of porcine dorsal skin. The rostral-caudal axis shows greater deformation than the transverse axis, and the time scale of growth in the rostral-caudal direction is given by rate parameters k1∈[0.04,0.1] [h-1] compared to k2∈[0.01,0.05] [h-1] in the transverse direction. Moreover, the calibration results underscore the high variability in biological systems, and the need to create probabilistic computational models to predict tissue adaptation in realistic settings. STATEMENT OF SIGNIFICANCE: Tissue expansion is a widely used technique in reconstructive surgery because it triggers growth of skin for the correction of large skin lesions and for breast reconstruction after mastectomy. Despite of its potential, complications and undesired outcomes persist due to our incomplete understanding of skin mechanobiology. Here we quantify the deformation and growth fields induced by an expander over 7 days in a porcine animal model and use these data to calibrate a computational model of skin growth using finite element simulations and a Bayesian framework. The calibrated model is a leap forward in our understanding skin growth, we now have quantitative understanding of this process: area growth is anisotropic and it is proportional to stretch with a characteristic rate constant of k∈[0.02,0.08] [h-1].
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Affiliation(s)
- Tianhong Han
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Taeksang Lee
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Joanna Ledwon
- Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Elbert Vaca
- Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Sergey Turin
- Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Aaron Kearney
- Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Arun K Gosain
- Ann and Robert H. Lurie Children's Hospital, Chicago, IL, USA
| | - Adrian B Tepole
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
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7
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Corporan D, Segura A, Padala M. Ultrastructural Adaptation of the Cardiomyocyte to Chronic Mitral Regurgitation. Front Cardiovasc Med 2021; 8:714774. [PMID: 34733889 PMCID: PMC8559873 DOI: 10.3389/fcvm.2021.714774] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 09/14/2021] [Indexed: 01/18/2023] Open
Abstract
Introduction: Mitral regurgitation (MR) imposes volume overload on the left ventricle (LV) and elevates wall stress, triggering its adverse remodeling. Pronounced LV dilation, minimal wall thinning, and a gradual decline in cardiac ejection fraction (EF) are observed. The structural changes in the myocardium that define these gross, organ level remodeling are not known. Cardiomyocyte elongation and slippage have both been hypothesized, but neither are confirmed, nor are the changes to the cardiomyocyte structure known. Using a rodent model of MR, we used immunohistochemistry and transmission electron microscopy (TEM) to describe the ultrastructural remodeling of the cardiomyocyte. Methods: Twenty-four male Sprague-Dawley rats (350–400 g) were assigned to two groups: group (1) rats induced with severe MR (n = 18) and group (2) control rats that were healthy and age and weight matched (n = 6). MR was induced in the beating heart using a 23-G ultrasound-guided, transapical needle to perforate the anterior mitral leaflet, and the rats were followed to 2, 10, and 20 weeks (n = 6/time-point). Echocardiography was performed to quantify MR severity and to measure LV volume and function at each time-point. Explanted myocardial tissue were examined with TEM and immunohistochemistry to investigate the ultrastructural changes. Results: MR induced rapid and significant increase in end-diastolic volume (EDV), with a 50% increase by 2 weeks, compared with control. Rise in end-systolic volume (ESV) was more gradual; however, by 20 weeks, both EDV and ESV in MR rats were increased by 126% compared with control. A significant decline in EF was measured at 10 weeks of MR. At the ultrastructural level, as early as 2 weeks after MR, cardiomyocyte elongation and increase in cross-sectional area were observed. TEM depicted sarcomere shortening, with loss of Z-line and I-band. Desmin, a cytoskeletal protein that is uniformly distributed along the length of the cardiomyocyte, was disorganized and localized to the intercalated disc, in the rats induced with MR and not in the controls. In the rats with MR, the linear registry of the mitochondrial arrangement along the sarcomeres was lost, with mitochondrial fragmentation, aggregation around the nucleus, and irregularities in the cristae. Discussion: In the setting of chronic mitral regurgitation, LV dilatation occured by cardiomyocyte elongation, which manifests at the subcellular level as distinct ultrastructural alterations of the sarcomere, cytoskeleton, and mitochondria. Since the cytoskeleton not only provides tensegrity but has functional consequences on myocyte function, further investigation into the impact of cytoskeletal remodeling on progressive heart failure or recovery of function upon correcting the valve lesion are needed.
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Affiliation(s)
- Daniella Corporan
- Structural Heart Research and Innovation Laboratory, Carlyle Fraser Heart Center, Emory University Hospital Midtown, Atlanta, GE, United States.,Division of Cardiothoracic Surgery, Department of Surgery, School of Medicine, Emory University, Atlanta, GE, United States
| | - Ana Segura
- Department of Pathology, Texas Heart Institute, Houston, TX, United States
| | - Muralidhar Padala
- Structural Heart Research and Innovation Laboratory, Carlyle Fraser Heart Center, Emory University Hospital Midtown, Atlanta, GE, United States.,Division of Cardiothoracic Surgery, Department of Surgery, School of Medicine, Emory University, Atlanta, GE, United States
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8
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Rolf-Pissarczyk M, Wollner MP, Pacheco DRQ, Holzapfel GA. Efficient computational modelling of smooth muscle orientation and function in the aorta. Proc Math Phys Eng Sci 2021. [DOI: 10.1098/rspa.2021.0592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Understanding the mechanical effects of smooth muscle cell (SMC) contraction on the initiation and the propagation of cardiovascular diseases such as aortic dissection is critical. Framed by elastic lamellar sheets in the lamellar unit, there are SMCs in the media with a distinct radial tilt, which indicates their contribution to the radial strength. However, the mechanical effects of this type of anisotropy have not been fully discussed. Therefore, in this study, we propose a constitutive framework that models the passive and active mechanics of the aorta, taking into account the dispersed nature of the aortic constituents by applying the discrete fibre dispersion method. We suggest an isoparametric approach by evaluating various numerical integration methods and introducing a non-uniform discretization of the unit hemisphere to increase its computational efficiency. Finally, the constitutive parameters are fitted to layer-specific experimental data and initial computational results are briefly presented. The radial tilt of SMCs is also analysed, which has a noticeable influence on the mechanical behaviour of the aorta. In the absence of sufficient experimental data, the results indicate that the active contribution of SMCs has a remarkable impact on the mechanics of the healthy aorta.
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Affiliation(s)
| | - Maximilian P. Wollner
- Institute of Biomechanics, Graz University of Technology, Graz, Austria
- Institute for Solid Mechanics, Dresden University of Technology, Dresden, Germany
| | | | - Gerhard A. Holzapfel
- Institute of Biomechanics, Graz University of Technology, Graz, Austria
- Department of Structural Engineering, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
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9
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Cheng L, Qiu Y, Schmidt BJ, Wei GW. Review of applications and challenges of quantitative systems pharmacology modeling and machine learning for heart failure. J Pharmacokinet Pharmacodyn 2021; 49:39-50. [PMID: 34637069 PMCID: PMC8837528 DOI: 10.1007/s10928-021-09785-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2021] [Accepted: 09/22/2021] [Indexed: 12/24/2022]
Abstract
Quantitative systems pharmacology (QSP) is an important approach in pharmaceutical research and development that facilitates in silico generation of quantitative mechanistic hypotheses and enables in silico trials. As demonstrated by applications from numerous industry groups and interest from regulatory authorities, QSP is becoming an increasingly critical component in clinical drug development. With rapidly evolving computational tools and methods, QSP modeling has achieved important progress in pharmaceutical research and development, including for heart failure (HF). However, various challenges exist in the QSP modeling and clinical characterization of HF. Machine/deep learning (ML/DL) methods have had success in a wide variety of fields and disciplines. They provide data-driven approaches in HF diagnosis and modeling, and offer a novel strategy to inform QSP model development and calibration. The combination of ML/DL and QSP modeling becomes an emergent direction in the understanding of HF and clinical development new therapies. In this work, we review the current status and achievement in QSP and ML/DL for HF, and discuss remaining challenges and future perspectives in the field.
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Affiliation(s)
- Limei Cheng
- Quantitative Systems Pharmacology and Physiologically Based Pharmacokinetics, Bristol Myers Squibb, Princeton, NJ, 08536, USA.
| | - Yuchi Qiu
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA
| | - Brian J Schmidt
- Quantitative Systems Pharmacology and Physiologically Based Pharmacokinetics, Bristol Myers Squibb, Princeton, NJ, 08536, USA
| | - Guo-Wei Wei
- Department of Mathematics, Michigan State University, East Lansing, MI, 48824, USA.,Department of Electrical and Computer Engineering, Michigan State University, East Lansing, MI, 48824, USA.,Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI, 48824, USA
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10
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Fan Y, Coll-Font J, van den Boomen M, Kim JH, Chen S, Eder RA, Roche ET, Nguyen CT. Characterization of Exercise-Induced Myocardium Growth Using Finite Element Modeling and Bayesian Optimization. Front Physiol 2021; 12:694940. [PMID: 34434115 PMCID: PMC8381603 DOI: 10.3389/fphys.2021.694940] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Accepted: 07/19/2021] [Indexed: 02/03/2023] Open
Abstract
Cardiomyocyte growth can occur in both physiological (exercised-induced) and pathological (e.g., volume overload and pressure overload) conditions leading to left ventricular (LV) hypertrophy. Studies using animal models and histology have demonstrated the growth and remodeling process at the organ level and tissue-cellular level, respectively. However, the driving factors of growth and the mechanistic link between organ, tissue, and cellular growth remains poorly understood. Computational models have the potential to bridge this gap by using constitutive models that describe the growth and remodeling process of the myocardium coupled with finite element (FE) analysis to model the biomechanics of the heart at the organ level. Using subject-specific imaging data of the LV geometry at two different time points, an FE model can be created with the inverse method to characterize the growth parameters of each subject. In this study, we developed a framework that takes in vivo cardiac magnetic resonance (CMR) imaging data of exercised porcine model and uses FE and Bayesian optimization to characterize myocardium growth in the transverse and longitudinal directions. The efficacy of this framework was demonstrated by successfully predicting growth parameters of 18 synthetic LV targeted masks which were generated from three LV porcine geometries. The framework was further used to characterize growth parameters in 4 swine subjects that had been exercised. The study suggested that exercise-induced growth in swine is prone to longitudinal cardiomyocyte growth (58.0 ± 19.6% after 6 weeks and 79.3 ± 15.6% after 12 weeks) compared to transverse growth (4.0 ± 8.0% after 6 weeks and 7.8 ± 9.4% after 12 weeks). This framework can be used to characterize myocardial growth in different phenotypes of LV hypertrophy and can be incorporated with other growth constitutive models to study different hypothetical growth mechanisms.
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Affiliation(s)
- Yiling Fan
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Jaume Coll-Font
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States,Harvard Medical School, Boston, MA, United States
| | - Maaike van den Boomen
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States,Harvard Medical School, Boston, MA, United States
| | - Joan H. Kim
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States
| | - Shi Chen
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States
| | - Robert Alan Eder
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States
| | - Ellen T. Roche
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States,Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, United States,Harvard Medical School, Boston, MA, United States,*Correspondence: Ellen T. Roche,
| | - Christopher T. Nguyen
- Cardiovascular Bioengineering and Imaging Laboratory, Cardiology Division, Massachusetts General Hospital, Charlestown, MA, United States,Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States,Harvard Medical School, Boston, MA, United States,Christopher T. Nguyen,
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11
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El-Bouri WK, Gue Y, Lip GYH. 'Rise of the machines': the next frontier in individualized medicine. Cardiovasc Res 2021; 117:e129-e131. [PMID: 34279590 DOI: 10.1093/cvr/cvab220] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Affiliation(s)
- Wahbi K El-Bouri
- Department of Cardiovascular and Metabolic Medicine, Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK
| | - Ying Gue
- Department of Cardiovascular and Metabolic Medicine, Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK
| | - Gregory Y H Lip
- Department of Cardiovascular and Metabolic Medicine, Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool, UK.,Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
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12
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Stowers C, Lee T, Bilionis I, Gosain AK, Tepole AB. Improving reconstructive surgery design using Gaussian process surrogates to capture material behavior uncertainty. J Mech Behav Biomed Mater 2021; 118:104340. [PMID: 33756416 PMCID: PMC8087634 DOI: 10.1016/j.jmbbm.2021.104340] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2020] [Revised: 01/12/2021] [Accepted: 01/15/2021] [Indexed: 10/22/2022]
Abstract
To produce functional, aesthetically natural results, reconstructive surgeries must be planned to minimize stress as excessive loads near wounds have been shown to produce pathological scarring and other complications (Gurtner et al., 2011). Presently, stress cannot easily be measured in the operating room. Consequently, surgeons rely on intuition and experience (Paul et al., 2016; Buchanan et al., 2016). Predictive computational tools are ideal candidates for surgery planning. Finite element (FE) simulations have shown promise in predicting stress fields on large skin patches and in complex cases, helping to identify potential regions of complication. Unfortunately, these simulations are computationally expensive and deterministic (Lee et al., 2018a). However, running a few, well selected FE simulations allows us to create Gaussian process (GP) surrogate models of local cutaneous flaps that are computationally efficient and able to predict stress and strain for arbitrary material parameters. Here, we create GP surrogates for the advancement, rotation, and transposition flaps. We then use the predictive capability of these surrogates to perform a global sensitivity analysis, ultimately showing that fiber direction has the most significant impact on strain field variations. We then perform an optimization to determine the optimal fiber direction for each flap for three different objectives driven by clinical guidelines (Leedy et al., 2005; Rohrer and Bhatia, 2005). While material properties are not controlled by the surgeon and are actually a source of uncertainty, the surgeon can in fact control the orientation of the flap with respect to the skin's relaxed tension lines, which are associated with the underlying fiber orientation (Borges, 1984). Therefore, fiber direction is the only material parameter that can be optimized clinically. The optimization task relies on the efficiency of the GP surrogates to calculate the expected cost of different strategies when the uncertainty of other material parameters is included. We propose optimal flap orientations for the three cost functions and that can help in reducing stress resulting from the surgery and ultimately reduce complications associated with excessive mechanical loading near wounds.
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Affiliation(s)
- Casey Stowers
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Taeksang Lee
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Ilias Bilionis
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Arun K Gosain
- Lurie Children Hospital, Northwestern University, Chicago, IL, USA
| | - Adrian Buganza Tepole
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA; Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA.
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13
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Peng GCY, Alber M, Tepole AB, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E. Multiscale modeling meets machine learning: What can we learn? ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING : STATE OF THE ART REVIEWS 2021; 28:1017-1037. [PMID: 34093005 PMCID: PMC8172124 DOI: 10.1007/s11831-020-09405-5] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Accepted: 02/09/2020] [Indexed: 05/10/2023]
Abstract
Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where classical physics-based simulation seems to remain irreplaceable. In this review, we identify areas in the biomedical sciences where machine learning and multiscale modeling can mutually benefit from one another: Machine learning can integrate physics-based knowledge in the form of governing equations, boundary conditions, or constraints to manage ill-posted problems and robustly handle sparse and noisy data; multiscale modeling can integrate machine learning to create surrogate models, identify system dynamics and parameters, analyze sensitivities, and quantify uncertainty to bridge the scales and understand the emergence of function. With a view towards applications in the life sciences, we discuss the state of the art of combining machine learning and multiscale modeling, identify applications and opportunities, raise open questions, and address potential challenges and limitations. We anticipate that it will stimulate discussion within the community of computational mechanics and reach out to other disciplines including mathematics, statistics, computer science, artificial intelligence, biomedicine, systems biology, and precision medicine to join forces towards creating robust and efficient models for biological systems.
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Affiliation(s)
| | - Mark Alber
- University of California, Riverside, USA
| | | | - William R Cannon
- Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Suvranu De
- Rensselaer Polytechnic Institute, Troy, New York, USA
| | | | | | | | | | | | - Linda Petzold
- University of California, Santa Barbara, California, USA
| | - Ellen Kuhl
- Stanford University, Stanford, California, USA
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14
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Teixeira VP, Miranda K, Scalzo S, Rocha-Resende C, Silva MM, Tezini GCSV, Melo MB, Souza-Neto FP, Silva KSC, Jesus ICG, Santos AK, de Oliveira M, Szawka RE, Salgado HC, Prado MAM, Poletini MO, Guatimosim S. Increased cholinergic activity under conditions of low estrogen leads to adverse cardiac remodeling. Am J Physiol Cell Physiol 2021; 320:C602-C612. [PMID: 33296286 DOI: 10.1152/ajpcell.00142.2020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2020] [Accepted: 12/03/2020] [Indexed: 02/07/2023]
Abstract
Cholinesterase inhibitors are used in postmenopausal women for the treatment of neurodegenerative diseases. Despite their widespread use in the clinical practice, little is known about the impact of augmented cholinergic signaling on cardiac function under reduced estrogen conditions. To address this gap, we subjected a genetically engineered murine model of systemic vesicular acetylcholine transporter overexpression (Chat-ChR2) to ovariectomy and evaluated cardiac parameters. Left-ventricular function was similar between Chat-ChR2 and wild-type (WT) mice. Following ovariectomy, WT mice showed signs of cardiac hypertrophy. Conversely, ovariectomized (OVX) Chat-ChR2 mice evolved to cardiac dilation and failure. Transcript levels for cardiac stress markers atrial natriuretic peptide (ANP) and B-type natriuretic peptide (BNP) were similarly upregulated in WT/OVX and Chat-ChR2/OVX mice. 17β-Estradiol (E2) treatment normalized cardiac parameters in Chat-ChR2/OVX to the Chat-ChR2/SHAM levels, providing a link between E2 status and the aggravated cardiac response in this model. To investigate the cellular basis underlying the cardiac alterations, ventricular myocytes were isolated and their cellular area and contractility were assessed. Myocytes from WT/OVX mice were wider than WT/SHAM, an indicative of concentric hypertrophy, but their fractional shortening was similar. Conversely, Chat-ChR2/OVX myocytes were elongated and presented contractile dysfunction. E2 treatment again prevented the structural and functional changes in Chat-ChR2/OVX myocytes. We conclude that hypercholinergic mice under reduced estrogen conditions do not develop concentric hypertrophy, a critical compensatory adaptation, evolving toward cardiac dilation and failure. This study emphasizes the importance of understanding the consequences of cholinesterase inhibition, used clinically to treat dementia, for cardiac function in postmenopausal women.
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MESH Headings
- Acetylcholine/metabolism
- Animals
- Cholinergic Fibers/metabolism
- Estradiol/pharmacology
- Estrogen Replacement Therapy
- Estrogens/deficiency
- Female
- Heart/innervation
- Heart Rate
- Hypertrophy, Left Ventricular/metabolism
- Hypertrophy, Left Ventricular/pathology
- Hypertrophy, Left Ventricular/physiopathology
- Hypertrophy, Left Ventricular/prevention & control
- Mice, Inbred C57BL
- Mice, Transgenic
- Myocardial Contraction
- Myocytes, Cardiac/drug effects
- Myocytes, Cardiac/metabolism
- Myocytes, Cardiac/pathology
- Ovariectomy
- Signal Transduction
- Ventricular Dysfunction, Left/metabolism
- Ventricular Dysfunction, Left/pathology
- Ventricular Dysfunction, Left/physiopathology
- Ventricular Dysfunction, Left/prevention & control
- Ventricular Function, Left/drug effects
- Ventricular Remodeling/drug effects
- Vesicular Acetylcholine Transport Proteins/genetics
- Vesicular Acetylcholine Transport Proteins/metabolism
- Mice
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Affiliation(s)
- Vanessa P Teixeira
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Kiany Miranda
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Sergio Scalzo
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Cibele Rocha-Resende
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Mário Morais Silva
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Geisa C S V Tezini
- Ribeirão Preto Medical School, Universidade de São Paulo, Riberão Preto, São Paulo, Brazil
| | - Marcos B Melo
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Fernando Pedro Souza-Neto
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Kaoma S C Silva
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Itamar C G Jesus
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Anderson K Santos
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Mauro de Oliveira
- Ribeirão Preto Medical School, Universidade de São Paulo, Riberão Preto, São Paulo, Brazil
| | - Raphael E Szawka
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Helio C Salgado
- Ribeirão Preto Medical School, Universidade de São Paulo, Riberão Preto, São Paulo, Brazil
| | - Marco Antonio Máximo Prado
- Robarts Research Institute, Department of Physiology and Pharmacology and Department of Anatomy and Cell Biology, University of Western Ontario, London, Ontario, Canada
| | - Maristela O Poletini
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Silvia Guatimosim
- Department of Physiology and Biophysics, Institute of Biological Sciences, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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15
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Pepin ME, Ha CM, Potter LA, Bakshi S, Barchue JP, Haj Asaad A, Pogwizd SM, Pamboukian SV, Hidalgo BA, Vickers SM, Wende AR. Racial and socioeconomic disparity associates with differences in cardiac DNA methylation among men with end-stage heart failure. Am J Physiol Heart Circ Physiol 2021; 320:H2066-H2079. [PMID: 33769919 PMCID: PMC8163657 DOI: 10.1152/ajpheart.00036.2021] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Heart failure (HF) is a multifactorial syndrome that remains a leading cause of worldwide morbidity. Despite its high prevalence, only half of patients with HF respond to guideline-directed medical management, prompting therapeutic efforts to confront the molecular underpinnings of its heterogeneity. In the current study, we examined epigenetics as a yet unexplored source of heterogeneity among patients with end-stage HF. Specifically, a multicohort-based study was designed to quantify cardiac genome-wide cytosine-p-guanine (CpG) methylation of cardiac biopsies from male patients undergoing left ventricular assist device (LVAD) implantation. In both pilot (n = 11) and testing (n = 31) cohorts, unsupervised multidimensional scaling of genome-wide myocardial DNA methylation exhibited a bimodal distribution of CpG methylation found largely to occur in the promoter regions of metabolic genes. Among the available patient attributes, only categorical self-identified patient race could delineate this methylation signature, with African American (AA) and Caucasian American (CA) samples clustering separately. Because race is a social construct, and thus a poor proxy of human physiology, extensive review of medical records was conducted, but ultimately failed to identify covariates of race at the time of LVAD surgery. By contrast, retrospective analysis exposed a higher all-cause mortality among AA (56.3%) relative to CA (16.7%) patients at 2 yr following LVAD placement (P = 0.03). Geocoding-based approximation of patient demographics uncovered disparities in income levels among AA relative to CA patients. Although additional studies are needed, the current analysis implicates cardiac DNA methylation as a previously unrecognized indicator of socioeconomic disparity in human heart failure outcomes. NEW & NOTEWORTHY A bimodal signature of cardiac DNA methylation in heart failure corresponds with racial differences in all-cause mortality following mechanical circulatory support. Racial differences in promoter methylation disproportionately affect metabolic signaling pathways. Socioeconomic factors are associated with racial differences in the cardiac methylome among men with end-stage heart failure. Listen to this article’s corresponding podcast at https://ajpheart.podbean.com/e/racial-socioeconomic-determinants-of-the-cardiac-epigenome/.
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Affiliation(s)
- Mark E Pepin
- Division of Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama.,Institute for Experimental Cardiology, Heidelberg University Hospital, Heidelberg, Germany
| | - Chae-Myeong Ha
- Division of Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Luke A Potter
- Division of Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Sayan Bakshi
- Division of Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Joseph P Barchue
- Division of Cardiovascular Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Ayman Haj Asaad
- Division of Cardiovascular Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Steven M Pogwizd
- Division of Cardiovascular Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Salpy V Pamboukian
- Division of Cardiovascular Medicine, Department of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Bertha A Hidalgo
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama
| | - Selwyn M Vickers
- Office of the Dean and Senior Vice President For Medicine, School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama
| | - Adam R Wende
- Division of Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, Alabama
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16
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Precision medicine in human heart modeling : Perspectives, challenges, and opportunities. Biomech Model Mechanobiol 2021; 20:803-831. [PMID: 33580313 PMCID: PMC8154814 DOI: 10.1007/s10237-021-01421-z] [Citation(s) in RCA: 62] [Impact Index Per Article: 20.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/07/2021] [Indexed: 01/05/2023]
Abstract
Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.
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17
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Rykiel G, López CS, Riesterer JL, Fries I, Deosthali S, Courchaine K, Maloyan A, Thornburg K, Rugonyi S. Multiscale cardiac imaging spanning the whole heart and its internal cellular architecture in a small animal model. eLife 2020; 9:e58138. [PMID: 33078706 PMCID: PMC7595733 DOI: 10.7554/elife.58138] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Accepted: 10/09/2020] [Indexed: 12/18/2022] Open
Abstract
Cardiac pumping depends on the morphological structure of the heart, but also on its subcellular (ultrastructural) architecture, which enables cardiac contraction. In cases of congenital heart defects, localized ultrastructural disruptions that increase the risk of heart failure are only starting to be discovered. This is in part due to a lack of technologies that can image the three-dimensional (3D) heart structure, to assess malformations; and its ultrastructure, to assess organelle disruptions. We present here a multiscale, correlative imaging procedure that achieves high-resolution images of the whole heart, using 3D micro-computed tomography (micro-CT); and its ultrastructure, using 3D scanning electron microscopy (SEM). In a small animal model (chicken embryo), we achieved uniform fixation and staining of the whole heart, without losing ultrastructural preservation on the same sample, enabling correlative multiscale imaging. Our approach enables multiscale studies in models of congenital heart disease and beyond.
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Affiliation(s)
- Graham Rykiel
- Biomedical Engineering, Oregon Health & Science UniversityPortlandUnited States
| | - Claudia S López
- Biomedical Engineering, Oregon Health & Science UniversityPortlandUnited States
- Multiscale Microscopy Core, Oregon Health & Science UniversityPortlandUnited States
| | - Jessica L Riesterer
- Biomedical Engineering, Oregon Health & Science UniversityPortlandUnited States
- Multiscale Microscopy Core, Oregon Health & Science UniversityPortlandUnited States
| | - Ian Fries
- Biomedical Engineering, Oregon Health & Science UniversityPortlandUnited States
| | - Sanika Deosthali
- Biomedical Engineering, Oregon Health & Science UniversityPortlandUnited States
| | | | - Alina Maloyan
- Center for Developmental Health, Knight Cardiovascular Institute, Oregon Health & Science UniversityPortlandUnited States
| | - Kent Thornburg
- Center for Developmental Health, Knight Cardiovascular Institute, Oregon Health & Science UniversityPortlandUnited States
| | - Sandra Rugonyi
- Biomedical Engineering, Oregon Health & Science UniversityPortlandUnited States
- Center for Developmental Health, Knight Cardiovascular Institute, Oregon Health & Science UniversityPortlandUnited States
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18
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Robbins ER, Pins GD, Laflamme MA, Gaudette GR. Creation of a contractile biomaterial from a decellularized spinach leaf without ECM protein coating: An in vitro study. J Biomed Mater Res A 2020; 108:2123-2132. [PMID: 32323417 PMCID: PMC7725356 DOI: 10.1002/jbm.a.36971] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 03/24/2020] [Accepted: 03/28/2020] [Indexed: 01/08/2023]
Abstract
Myocardial infarction (MI) results in the death of cardiac tissue, decreases regional contraction, and can lead to heart failure. Tissue engineered cardiac patches containing human induced pluripotent stem cell-derived cardiomyocytes (hiPS-CMs) can restore contractile function. However, cells within thick patches require vasculature for blood flow. Recently, we demonstrated fibronectin coated decellularized leaves provide a suitable scaffold for hiPS-CMs. Yet, the necessity of this additional coating step is unclear. Therefore, we compared hiPS-CM behavior on decellularized leaves coated with collagen IV or fibronectin extracellular matrix (ECM) proteins to noncoated leaves for up to 21 days. Successful coating was verified by immunofluorescence. Similar numbers of hiPS-CMs adhered to coated and noncoated decellularized leaves for 21 days. At Day 14, collagen IV coated leaves contracted more than noncoated leaves (3.25 ± 0.39% vs. 1.54 ± 0.60%; p < .05). However, no differences in contraction were found between coated leaves, coated tissue culture plastic (TCP), noncoated leaves, or noncoated TCP at other time points. No significant differences were observed in hiPS-CM spreading or sarcomere lengths on leaves with or without coating. This study demonstrates that cardiac scaffolds can be created from decellularized leaves without ECM coatings. Noncoated decellularized leaf surfaces facilitate robust cell attachment for an engineered tissue patch.
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Affiliation(s)
- Emily R. Robbins
- Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts
| | - George D. Pins
- Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts
| | - Michael A. Laflamme
- McEwen Stem Cell Institute, University Health Network, Toronto, Ontario, Canada
| | - Glenn R. Gaudette
- Biomedical Engineering, Worcester Polytechnic Institute, Worcester, Massachusetts
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19
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McEvoy E, Wijns W, McGarry P. A thermodynamic transient cross-bridge model for prediction of contractility and remodelling of the ventricle. J Mech Behav Biomed Mater 2020; 113:104074. [PMID: 33189012 DOI: 10.1016/j.jmbbm.2020.104074] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Revised: 01/20/2020] [Accepted: 08/27/2020] [Indexed: 12/16/2022]
Abstract
Cardiac hypertrophy is an adaption of the heart to a change in cardiovascular loading conditions. The current understanding is that progression may be stress or strain driven, but the multi-scale nature of the cellular remodelling processes have yet to be uncovered. In this study, we develop a model of the contractile left ventricle, with the active cell tension described by a thermodynamically motivated cross-bridge cycling model. Simulation of the transient recruitment of myosin results in correct patterns of ventricular pressure predicted over a cardiac cycle. We investigate how changes in tissue loading and associated deviations in transient force generation can drive restructuring of cellular myofibrils in the heart wall. Our thermodynamic framework predicts in-series sarcomere addition (eccentric remodelling) in response to volume overload, and sarcomere addition in parallel (concentric remodelling) in response to valve and signalling disfunction. This framework provides a significant advance in the current understanding of the fundamental sub-sarcomere level biomechanisms underlying cardiac remodelling. Simulations reveal that pathological tissue loading conditions can significantly alter actin-myosin cross-bridge cycling over the course of the cardiac cycle. The resultant variation in sarcomere stress pushes an imbalance between the internal free energy of the myofibril and that of unbound contractile proteins, initiating remodelling. The link between cross-bridge thermodynamics and myofibril remodelling proposed in this study may significantly advance current understanding of cardiac disease onset.
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Affiliation(s)
- Eoin McEvoy
- Biomedical Engineering, National University of Ireland, Galway, Ireland
| | - William Wijns
- The Lambe Institute for Translational Medicine, University Hospital, Galway, Ireland
| | - Patrick McGarry
- Biomedical Engineering, National University of Ireland, Galway, Ireland.
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20
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Lee T, Bilionis I, Tepole AB. Propagation of uncertainty in the mechanical and biological response of growing tissues using multi-fidelity Gaussian process regression. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2020; 359:112724. [PMID: 32863456 PMCID: PMC7453758 DOI: 10.1016/j.cma.2019.112724] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
A key feature of living tissues is their capacity to remodel and grow in response to environmental cues. Within continuum mechanics, this process can be captured with the multiplicative split of the deformation gradient into growth and elastic contributions. The mechanical and biological response during tissue adaptation is characterized by inherent variability. Accounting for this uncertainty is critical to better understand tissue mechanobiology, and, moreover, it is of practical importance if we aim to develop predictive models for clinical use. However, the current gold standard in computational models of growth and remodeling remains the use of deterministic finite element (FE) simulations. Here we focus on tissue expansion, a popular technique in which skin is stretched by a balloon-like device inducing its growth. We construct FE models of tissue expansion with various levels of detail, and show that a sufficiently broad set of FE simulations from these models can be used to train an accurate and efficient multi-fidelity Gaussian process (GP) surrogate. The approach is not limited to simulation data, rather, it can fuse different kinds of data, including from experiments. The main appeal of the framework relies on the common experience that highly detailed models (or experiments) are more accurate but also more costly, while simpler models (or experiments) can be easily evaluated but are bound to have some error. In these situations, doing uncertainty analysis tasks with the high fidelity models alone is not feasible and, conversely, relying solely on low fidelity approximations is also undesirable. We show that a multi-fidelity GP outperforms the high fidelity GP and low fidelity GP when tested against the most detailed FE model. In turn, having trained the multi-fidelity GP model, we showcase the propagation of uncertainty from the mechanical and biological response parameters to the spatio-temporal growth outcomes. We expect that the methods and applications in this paper will enable future research in parameter calibration under uncertainty and uncertainty propagation in real clinical scenarios involving tissue growth and remodeling.
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Affiliation(s)
- Taeksang Lee
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Ilias Bilionis
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
| | - Adrian Buganza Tepole
- School of Mechanical Engineering, Purdue University, West Lafayette, IN, USA
- Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, USA
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21
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Alber M, Buganza Tepole A, Cannon WR, De S, Dura-Bernal S, Garikipati K, Karniadakis G, Lytton WW, Perdikaris P, Petzold L, Kuhl E. Integrating machine learning and multiscale modeling-perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences. NPJ Digit Med 2019; 2:115. [PMID: 31799423 PMCID: PMC6877584 DOI: 10.1038/s41746-019-0193-y] [Citation(s) in RCA: 160] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Accepted: 11/01/2019] [Indexed: 12/12/2022] Open
Abstract
Fueled by breakthrough technology developments, the biological, biomedical, and behavioral sciences are now collecting more data than ever before. There is a critical need for time- and cost-efficient strategies to analyze and interpret these data to advance human health. The recent rise of machine learning as a powerful technique to integrate multimodality, multifidelity data, and reveal correlations between intertwined phenomena presents a special opportunity in this regard. However, machine learning alone ignores the fundamental laws of physics and can result in ill-posed problems or non-physical solutions. Multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function. However, multiscale modeling alone often fails to efficiently combine large datasets from different sources and different levels of resolution. Here we demonstrate that machine learning and multiscale modeling can naturally complement each other to create robust predictive models that integrate the underlying physics to manage ill-posed problems and explore massive design spaces. We review the current literature, highlight applications and opportunities, address open questions, and discuss potential challenges and limitations in four overarching topical areas: ordinary differential equations, partial differential equations, data-driven approaches, and theory-driven approaches. Towards these goals, we leverage expertise in applied mathematics, computer science, computational biology, biophysics, biomechanics, engineering mechanics, experimentation, and medicine. Our multidisciplinary perspective suggests that integrating machine learning and multiscale modeling can provide new insights into disease mechanisms, help identify new targets and treatment strategies, and inform decision making for the benefit of human health.
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Affiliation(s)
- Mark Alber
- Department of Mathematics, University of California, Riverside, CA USA
| | | | - William R. Cannon
- Computational Biology Group, Pacific Northwest National Laboratory, Richland, WA USA
| | - Suvranu De
- Department of Mechanical, Aerospace and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, NY USA
| | | | - Krishna Garikipati
- Departments of Mechanical Engineering and Mathematics, University of Michigan, Ann Arbor, MI USA
| | | | - William W. Lytton
- SUNY Downstate Medical Center and Kings County Hospital, Brooklyn, NY USA
| | - Paris Perdikaris
- Department of Mechanical Engineering, University of Pennsylvania, Philadelphia, PA USA
| | - Linda Petzold
- Department of Computer Science and Mechanical Engineering, University of California, Santa Barbara, CA USA
| | - Ellen Kuhl
- Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA USA
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22
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Aboelkassem Y, Powers JD, McCabe KJ, McCulloch AD. Multiscale Models of Cardiac Muscle Biophysics and Tissue Remodeling in Hypertrophic Cardiomyopathies. CURRENT OPINION IN BIOMEDICAL ENGINEERING 2019; 11:35-44. [PMID: 31886450 DOI: 10.1016/j.cobme.2019.09.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Myocardial hypertrophy is the result of sustained perturbations to the mechanical and/or neurohormonal homeostasis of cardiac cells and is driven by integrated, multiscale biophysical and biochemical processes that are currently not well defined. In this brief review, we highlight recent computational and experimental models of cardiac hypertrophy that span mechanisms from the molecular level to the tissue level. Specifically, we focus on: (i) molecular-level models of the structural dynamics of sarcomere proteins in hypertrophic hearts, (ii) cellular-level models of excitation-contraction coupling and mechanosensitive signaling in disease-state myocytes, and (iii) organ-level models of myocardial growth kinematics and predictors thereof. Finally, we discuss how spanning these scales and combining multiple experimental/computational models will provide new information about the processes governing hypertrophy and potential methods to prevent or reverse them.
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Affiliation(s)
- Yasser Aboelkassem
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Joseph D Powers
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Kimberly J McCabe
- Department of Computational Physiology, Simula Research Laboratory, Lysaker, Norway
| | - Andrew D McCulloch
- Department of Bioengineering, University of California San Diego, La Jolla, CA 92093, USA
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23
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Ambrosi D, Ben Amar M, Cyron CJ, DeSimone A, Goriely A, Humphrey JD, Kuhl E. Growth and remodelling of living tissues: perspectives, challenges and opportunities. J R Soc Interface 2019; 16:20190233. [PMID: 31431183 PMCID: PMC6731508 DOI: 10.1098/rsif.2019.0233] [Citation(s) in RCA: 76] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2019] [Accepted: 07/26/2019] [Indexed: 12/29/2022] Open
Abstract
One of the most remarkable differences between classical engineering materials and living matter is the ability of the latter to grow and remodel in response to diverse stimuli. The mechanical behaviour of living matter is governed not only by an elastic or viscoelastic response to loading on short time scales up to several minutes, but also by often crucial growth and remodelling responses on time scales from hours to months. Phenomena of growth and remodelling play important roles, for example during morphogenesis in early life as well as in homeostasis and pathogenesis in adult tissues, which often adapt to changes in their chemo-mechanical environment as a result of ageing, diseases, injury or surgical intervention. Mechano-regulated growth and remodelling are observed in various soft tissues, ranging from tendons and arteries to the eye and brain, but also in bone, lower organisms and plants. Understanding and predicting growth and remodelling of living systems is one of the most important challenges in biomechanics and mechanobiology. This article reviews the current state of growth and remodelling as it applies primarily to soft tissues, and provides a perspective on critical challenges and future directions.
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Affiliation(s)
- Davide Ambrosi
- Dipartimento di Matematica, Politecnico di Milano, Milan, Italy
| | - Martine Ben Amar
- Laboratoire de Physique Statistique, Ecole Normale Supérieure, Paris, France
| | - Christian J. Cyron
- Institute of Continuum Mechanics and Materials, Hamburg University of Technology, Hamburg, Germany
- Institute of Materials Research, Helmholtz-Zentrum Geesthacht, Geesthacht, Germany
| | - Antonio DeSimone
- Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy
| | - Alain Goriely
- Mathematical Institute, University of Oxford, Oxford, UK
| | - Jay D. Humphrey
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
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24
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Peirlinck M, Sahli Costabal F, Sack KL, Choy JS, Kassab GS, Guccione JM, De Beule M, Segers P, Kuhl E. Using machine learning to characterize heart failure across the scales. Biomech Model Mechanobiol 2019; 18:1987-2001. [PMID: 31240511 DOI: 10.1007/s10237-019-01190-w] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 06/16/2019] [Indexed: 12/31/2022]
Abstract
Heart failure is a progressive chronic condition in which the heart undergoes detrimental changes in structure and function across multiple scales in time and space. Multiscale models of cardiac growth can provide a patient-specific window into the progression of heart failure and guide personalized treatment planning. Yet, the predictive potential of cardiac growth models remains poorly understood. Here, we quantify predictive power of a stretch-driven growth model using a chronic porcine heart failure model, subject-specific multiscale simulation, and machine learning techniques. We combine hierarchical modeling, Bayesian inference, and Gaussian process regression to quantify the uncertainty of our experimental measurements during an 8-week long study of volume overload in six pigs. We then propagate the experimental uncertainties from the organ scale through our computational growth model and quantify the agreement between experimentally measured and computationally predicted alterations on the cellular scale. Our study suggests that stretch is the major stimulus for myocyte lengthening and demonstrates that a stretch-driven growth model alone can explain [Formula: see text] of the observed changes in myocyte morphology. We anticipate that our approach will allow us to design, calibrate, and validate a new generation of multiscale cardiac growth models to explore the interplay of various subcellular-, cellular-, and organ-level contributors to heart failure. Using machine learning in heart failure research has the potential to combine information from different sources, subjects, and scales to provide a more holistic picture of the failing heart and point toward new treatment strategies.
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Affiliation(s)
- M Peirlinck
- Biofluid, Tissue and Solid Mechanics for Medical Applications (IBiTech, bioMMeda), Ghent University, Ghent, Belgium
| | - F Sahli Costabal
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
| | - K L Sack
- Department of Human Biology, University of Cape Town, Cape Town, South Africa
- Department of Surgery, University of California at San Francisco, San Francisco, CA, USA
| | - J S Choy
- California Medical Innovations Institute, Inc., San Diego, CA, USA
| | - G S Kassab
- California Medical Innovations Institute, Inc., San Diego, CA, USA
| | - J M Guccione
- Department of Surgery, University of California at San Francisco, San Francisco, CA, USA
| | - M De Beule
- Biofluid, Tissue and Solid Mechanics for Medical Applications (IBiTech, bioMMeda), Ghent University, Ghent, Belgium
| | - P Segers
- Biofluid, Tissue and Solid Mechanics for Medical Applications (IBiTech, bioMMeda), Ghent University, Ghent, Belgium
| | - E Kuhl
- Departments of Mechanical Engineering and Bioengineering, Stanford University, Stanford, CA, USA.
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25
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Costabal FS, Matsuno K, Yao J, Perdikaris P, Kuhl E. Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2019; 348:313-333. [PMID: 32863454 PMCID: PMC7454226 DOI: 10.1016/j.cma.2019.01.033] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Prolonged QT intervals are a major risk factor for ventricular arrhythmias and a leading cause of sudden cardiac death. Various drugs are known to trigger QT interval prolongation and increase the proarrhythmic potential. Yet, how precisely the action of drugs on the cellular level translates into QT interval prolongation on the whole organ level remains insufficiently understood. Here we use machine learning techniques to systematically characterize the effect of 30 common drugs on the QT interval. We combine information from high fidelity three-dimensional human heart simulations with low fidelity one-dimensional cable simulations to build a surrogate model for the QT interval using multi-fidelity Gaussian process regression. Once trained and cross-validated, we apply our surrogate model to perform sensitivity analysis and uncertainty quantification. Our sensitivity analysis suggests that compounds that block the rapid delayed rectifier potassium current I Kr have the greatest prolonging effect of the QT interval, and that blocking the L-type calcium current I CaL and late sodium current I NaL shortens the QT interval. Our uncertainty quantification allows us to propagate the experimental variability from individual block-concentration measurements into the QT interval and reveals that QT interval uncertainty is mainly driven by the variability in I Kr block. In a final validation study, we demonstrate an excellent agreement between our predicted QT interval changes and the changes observed in a randomized clinical trial for the drugs dofetilide, quinidine, ranolazine, and verapamil. We anticipate that both the machine learning methods and the results of this study will have great potential in the efficient development of safer drugs.
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Affiliation(s)
| | - Kristen Matsuno
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
| | - Jiang Yao
- Dassault Systèmes Simulia Corporation, Johnston, RI 02919, USA
| | - Paris Perdikaris
- Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, CA 94305, USA
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