scholarly journals An in silico hiPSC-Derived Cardiomyocyte Model Built With Genetic Algorithm

2021 ◽  
Vol 12 ◽  
Author(s):  
Akwasi D. Akwaboah ◽  
Bright Tsevi ◽  
Pascal Yamlome ◽  
Jacqueline A. Treat ◽  
Maila Brucal-Hallare ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
In Silico ◽  
Optimal Parameter ◽  
Ionic Currents ◽  
Patient Specific ◽  
Cardiac Safety ◽  
Human Cardiomyocytes ◽  
Safety Pharmacology ◽  
Biophysical Models ◽  
Cardiac Disorders

The formulation of in silico biophysical models generally requires optimization strategies for reproducing experimentally observed phenomena. In electrophysiological modeling, robust nonlinear regressive methods are often crucial for guaranteeing high fidelity models. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), though nascent, have proven to be useful in cardiac safety pharmacology, regenerative medicine, and in the implementation of patient-specific test benches for investigating inherited cardiac disorders. This study demonstrates the potency of heuristic techniques at formulating biophysical models, with emphasis on a hiPSC-CM model using a novel genetic algorithm (GA) recipe we proposed. The proposed GA protocol was used to develop a hiPSC-CM biophysical computer model by fitting mathematical formulations to experimental data for five ionic currents recorded in hiPSC-CMs. The maximum conductances of the remaining ionic channels were scaled based on recommendations from literature to accurately reproduce the experimentally observed hiPSC-CM action potential (AP) metrics. Near-optimal parameter fitting was achieved for the GA-fitted ionic currents. The resulting model recapitulated experimental AP parameters such as AP durations (APD50, APD75, and APD90), maximum diastolic potential, and frequency of automaticity. The outcome of this work has implications for validating the biophysics of hiPSC-CMs in their use as viable substitutes for human cardiomyocytes, particularly in cardiac safety pharmacology and in the study of inherited cardiac disorders. This study presents a novel GA protocol useful for formulating robust numerical biophysical models. The proposed protocol is used to develop a hiPSC-CM model with implications for cardiac safety pharmacology.

scholarly journals Intrinsic structure of model-derived metrics for in silico proarrhytmic risk assessment identified by global sensitivity analysis

2019 ◽  
Author(s):  
Jaimit Parikh ◽  
Paolo Di Achille ◽  
James Kozloski ◽  
Viatcheslav Gurev
Keyword(s):  
Sensitivity Analysis ◽  
Feature Selection ◽  
Ion Channels ◽  
In Silico ◽  
Global Sensitivity Analysis ◽  
Ionic Currents ◽  
Model Parameters ◽  
Intrinsic Structure ◽  
Global Sensitivity ◽  
Biophysical Models

AbstractMultiscale computational models of heart are being extensively investigated for improved assessment of drug-induced Torsades de Pointes (TdP) risk, a fatal side effect of many drugs. Model-derived metrics (features) such as action potential duration, net charge carried by ionic currents (qNet) and others have been proposed in the past as potential candidates for classifying TdP risk. However, the criteria for selection of new risk metrics are still poorly justified, and they are often trained/tested only on small datasets. Moreover, classifiers built on derived features have thus far not consistently provided increased prediction accuracies compared to classifiers based on in vitro measurements of drug effects on ion channels (direct features). In this paper, we analyze a large population of virtual drugs to examine systematically the sensitivity of several model-derived features. The influence of different ion channels in regulation of the model-derived features is identified using global sensitivity analysis (GSA). Specifically, the analysis points to key differences in the input parameters that affect several model-derived features and the generation of early afterdepolarizations (EAD), thus opposing the idea that these features and sensitivity to EAD might be strongly correlated. We also demonstrate that previously proposed model-derived features could be well fitted by a linear combination of direct features. This well explains the observed comparable performances of classifiers built on direct features and model-derived features. Combining GSA and simple probability analysis, we also show that the odds of any linear metric constructed from direct features to perform as well as qNet is very low. Nevertheless, despite high predictive power of qNet to separate drugs into correct categories of TdP risk, the GSA results suggest that the actual mechanistic interpretation for qNet’s improved performance deserves further investigation. In conclusion, analyses like ours can provide more robust feature selection/construction. Improved experimental designs with increased focus on the critical model parameters indicated by GSA can potentially reduce the uncertainties of key model components and result in increased confidence of TdP risk predicted by in silico models.Author SummaryBiophysical models often have extremely involved intrinsic structure. In the majority of research, either complex methods of non-linear dynamics and empirical analysis are employed to explore the underlying structure of cell processes such as transmembrane ionic currents. Global sensitivity analysis (GSA) could be considered as a brute force alternative to study the model relationships between physical processes, discovering the mechanisms responsible for phenomena of interest. As we demonstrated here, GSA application could be extended to explore the structure of features derived from outputs of biophysical models and used in statistical models to build regressions or classifiers. In particular, GSA seems to be valuable to formalize the methods of feature selection/construction that are used for classification of drugs with respect to their cardiotoxicity.

Abstract P392: The Effect Of Cell Sex On Magnetic Nanoparticle Uptake Of Human Induced Pluripotent Stem Cell-derived Cardiomyocytes

2021 ◽  
Vol 129 (Suppl_1) ◽  
Author(s):  
Morteza Mahmoudi ◽  
Vahid Serpooshan ◽  
Phillip C Yang ◽  
Mahyar Heydarpour
Keyword(s):  
Heart Failure ◽  
Actin Filaments ◽  
Electromechanical Coupling ◽  
Critical Role ◽  
Induced Pluripotent Stem Cell ◽  
Patient Specific ◽  
P Value ◽  
Male And Female ◽  
Cell Therapies ◽  
Human Cardiomyocytes

Introduction: It is well understood that the occurrence, progress, and treatment of heart failure, which is a leading cause of death worldwide, is sex-specific. Over the past decade, the majority of efforts in myocardial regeneration have been centered on cell-based cardiac repair. A promising cell source for these efforts is patient-specific human cardiomyocytes (CMs) differentiated from human inducible pluripotent stem cells (hiPSCs). However, successful use of hiPSC-CMs faces a major limitation, the poor engraftment and electromechanical coupling of transplanted cells with the host myocardial tissue. Magnetic nanoparticles (NPs) demonstrate great potential to address this challenge for treating heart failure via cell therapies. In particular, superparamagnetic iron oxide NPs (SPIONs) have been used to label hiPSC-CMs and, with the aid of external magnetic field, improve their engraftment and electromechanical coupling in the heart tissue. However, the critical role of cell sex in the uptake and labeling efficacy of NPs has not been evaluated. Hypothesis: Significant differences in the molecular and structural (e.g., actin structures and distribution) characteristics of male and female hiPSC-CMs affect their labeling efficacy with SPIONs. Methods and Results: To test our hypothesis, we first performed RNA-Seq analysis on three male and three female (healthy) hiPSC-CM lines. The normalized outcomes were analyzed by edgeR package. We next calculated gene-expression differential between male and female CMs. The results revealed 58 genes with significant differences between the male and female cells (p-value < 0.01). The highest observed sex-specific variation in genes was related to tophit gene (MEG3: logFC = 7.32, P-value = 5.63e -06 ), which is the maternally expressed imprinted gene with a great role in cardiac angiogenesis. Among the identified genes, a number of those were related to the cellular cytoskeletal structures including actin. We probed possible structural differences between actin filaments organization and distribution of male and female hiPSC-CMs using the stochastic optical reconstruction microscopy (STORM) technique. The results demonstrated substantial differences in organization, distribution, and morphology of actin filaments between male and female CMs. Incubation of SPIONs with male and female hiPSC-CMs revealed higher uptake of NPs (~ 3 folds) in female cells as compared to the male cells. The significant differences in the uptake of SPIONs by male vs. female cells could be attributed to the distinct organization, distribution, and morphology of actin in male vs. female cells. Conclusions: Our results indicate that male and female hiPSCs-CMs respond differently to the labeling SPIONs.

scholarly journals Patient-specific in silico models can quantify primary implant stability in elderly human bone

2017 ◽  
Author(s):  
Juri A. Steiner ◽  
Urs A.T. Hofmann ◽  
Patrik Christen ◽  
Jean M. Favre ◽  
Stephen J. Ferguson ◽  
...  
Keyword(s):  
In Silico ◽  
Human Bone ◽  
Patient Specific ◽  
Implant Stability ◽  
Primary Implant ◽  
In Silico Models

In Silico Analysis of Haemodynamics in Patient-Specific Left Atria with Different Appendage Morphologies

2017 ◽  
pp. 412-420 ◽  
Author(s):  
Andy L. Olivares ◽  
Etelvino Silva ◽  
Marta Nuñez-Garcia ◽  
Constantine Butakoff ◽  
Damián Sánchez-Quintana ◽  
...  
Keyword(s):  
In Silico ◽  
In Silico Analysis ◽  
Patient Specific ◽  
Silico Analysis

scholarly journals How to predict relapse in leukaemia using time series data: A comparative in silico study

2020 ◽  
Author(s):  
Helene Hoffmann ◽  
Christoph Baldow ◽  
Thomas Zerjatke ◽  
Andrea Gottschalk ◽  
Sebastian Wagner ◽  
...  
Keyword(s):  
Data Quality ◽  
Computational Methods ◽  
In Silico ◽  
Prediction Accuracy ◽  
Time Course ◽  
Time Series Data ◽  
Specific Treatment ◽  
Series Data ◽  
Patient Specific ◽  
Clinical Markers

SummaryRisk stratification and treatment decisions for leukaemia patients are regularly based on clinical markers determined at diagnosis, while measurements on system dynamics are often neglected. However, there is increasing evidence that linking quantitative time-course information to disease outcomes can improving the predictions for patient-specific treatment response.We analyzed the potential of different computational methods to accurately predict relapse for chronic and acute myeloid leukaemia, particularly focusing on the influence of data quality and quantity. Technically, we used clinical reference data to generate in-silico patients with varying levels of data quality. Based hereon, we compared the performance of mechanistic models, generalized linear models, and neural networks with respect to their accuracy for relapse prediction. We found that data quality has a higher impact on prediction accuracy than the specific choice of the method. We further show that adapted treatment and measurement schemes can considerably improve prediction accuracy. Our proof-of-principle study highlights how computational methods and optimized data acquisition strategies can improve risk assessment and treatment of leukaemia patients.

scholarly journals 248 Empiric profiling of peripheral T cell recall responses to tumor mutanomes versus in silico predictions in NSCLC patients undergoing pembrolizumab treatment ± chemotherapy

2021 ◽  
Vol 9 (Suppl 3) ◽  
pp. A268-A268
Author(s):  
Madison Milaszewski ◽  
James Loizeaux ◽  
Emily Tjon ◽  
Crystal Cabral ◽  
Tulin Dadali ◽  
...  
Keyword(s):  
T Cells ◽  
T Cell ◽  
Combination Therapy ◽  
In Silico ◽  
Ex Vivo ◽  
T Cell Responses ◽  
Patient Specific ◽  
In Silico Prediction ◽  
Cell Responses ◽  
In Silico Predictions

BackgroundEffective immune checkpoint blockade (ICB) treatment is dependent on T-cell recognition of patient-specific mutations (neoantigens). Empirical identification of neoantigens ex vivo has revealed shortcomings of in silico predictions.1 To better understand the impact of ICB treatment on T cell responses and differences between in silico and in vitro methods, neoantigen-specific T cell responses were evaluated in patients with non-small cell lung cancer undergoing first-line therapy with pembrolizumab ± chemotherapy.MethodsTumor and whole blood samples were collected from 14 patients prior to and after immunotherapy; seven each in monotherapy and combination therapy cohorts. The ex vivo ATLAS™ platform was used to profile neoantigen-specific T-cell responses. Patient-specific tumor mutations identified by next-generation sequencing (NGS) were expressed individually as ATLAS clones, processed patient-specific autologous antigen presenting cells, and presented to their T cells in vitro. ATLAS-verified antigens were compared with epitope predictions made using algorithms.ResultsOn average, 150 (range 37–339) non-synonymous mutations were identified. Pre-treatment, ATLAS identified T cell responses to a median of 15% (9–25%) of mutations, with nearly equal proportions of neoantigens (8%, 5–15%) and Inhibigens™, targets of suppressive T cell responses (8%, 3–13%). The combination therapy cohort had more confirmed neoantigens (46, 20–103) than the monotherapy cohort (7, 6–79). After treatment, the median ratio of CD4:CD8 T cells doubled in the monotherapy but not combination cohort (1.2 to 2.4 v. 1.6 to 1.3). Upon non-specific stimulation, T cells from patients on combination therapy expanded poorly relative to monotherapy (24 v. 65-fold, p = 0.014); no significant differences were observed pre-treatment (22 v. 18-fold, p = 0.1578). Post-treatment, the median number of CD8 neoantigens increased in the combination therapy cohort (11 to 15) but in monotherapy were mostly unchanged (6 to 7). Across timepoints, 36% of ATLAS-identified responses overlapped. In silico analysis resulted in 1,895 predicted epitopes among 961 total mutations; among those, 30% were confirmed with ATLAS, although nearly half were Inhibigens, which could not be predicted. Moreover, 50% of confirmed neoantigens were missed by in silico prediction.ConclusionsMonotherapy and combination therapy had differential effects on CD4:CD8 T cell ratios and their non-specific expansion. A greater proportion of neoantigens was identified than previously reported in studies employing in silico predictions prior to empirical verification.2 Overlap between confirmed antigens and in silico prediction was observed, but in silico prediction continued to have a large false negative rate and could not characterize Inhibigens.AcknowledgementsWe would like to acknowledge and thank the patients and their families for participating in this study.ReferencesLam H, McNeil LK, Starobinets H, DeVault VL, Cohen RB, Twardowski P, Johnson ML, Gillison ML, Stein MN, Vaishampayan UN, DeCillis AP, Foti JJ, Vemulapalli V, Tjon E, Ferber K, DeOliveira DB, Broom W, Agnihotri P, Jaffee EM, Wong KK, Drake CG, Carroll PM, Davis TA, Flechtner JB. An empirical antigen selection method identifies neoantigens that either elicit broad antitumor T-cell responses or drive tumor growth. Cancer Discov 2021;11(3):696–713. doi: 10.1158/2159- 8290.CD-20-0377. Epub 2021 January 27. PMID: 33504579. Rosenberg SA. Immersion in the search for effective cancer immunotherapies. Mol Med 27,63(2021). https://doi.org/10.1186/s10020-021-00321-3

scholarly journals Ion Channel Expression and Electrophysiology of Singular Human (Primary and Induced Pluripotent Stem Cell-Derived) Cardiomyocytes

Cells ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 3370
Author(s):  
Christina Schmid ◽  
Najah Abi-Gerges ◽  
Michael Georg Leitner ◽  
Dietmar Zellner ◽  
Georg Rast
Keyword(s):  
Stem Cell ◽  
Single Cell ◽  
Pluripotent Stem Cell ◽  
Quantitative Polymerase Chain Reaction ◽  
Drug Effects ◽  
Induced Pluripotent Stem Cell ◽  
Ionic Currents ◽  
Human Cardiomyocytes ◽  
Ventricular Cells ◽  
Induced Pluripotent

Subtype-specific human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are promising tools, e.g., to assess the potential of drugs to cause chronotropic effects (nodal hiPSC-CMs), atrial fibrillation (atrial hiPSC-CMs), or ventricular arrhythmias (ventricular hiPSC-CMs). We used single-cell patch-clamp reverse transcriptase-quantitative polymerase chain reaction to clarify the composition of the iCell cardiomyocyte population (Fujifilm Cellular Dynamics, Madison, WI, USA) and to compare it with atrial and ventricular Pluricytes (Ncardia, Charleroi, Belgium) and primary human atrial and ventricular cardiomyocytes. The comparison of beating and non-beating iCell cardiomyocytes did not support the presence of true nodal, atrial, and ventricular cells in this hiPSC-CM population. The comparison of atrial and ventricular Pluricytes with primary human cardiomyocytes showed trends, indicating the potential to derive more subtype-specific hiPSC-CM models using appropriate differentiation protocols. Nevertheless, the single-cell phenotypes of the majority of the hiPSC-CMs showed a combination of attributes which may be interpreted as a mixture of traits of adult cardiomyocyte subtypes: (i) nodal: spontaneous action potentials and high HCN4 expression and (ii) non-nodal: prominent INa-driven fast inward current and high expression of SCN5A. This may hamper the interpretation of the drug effects on parameters depending on a combination of ionic currents, such as beat rate. However, the proven expression of specific ion channels supports the evaluation of the drug effects on ionic currents in a more realistic cardiomyocyte environment than in recombinant non-cardiomyocyte systems.

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