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Tsai HHD, Ford LC, Burnett SD, Dickey AN, Wright FA, Chiu WA, Rusyn I. Informing Hazard Identification and Risk Characterization of Environmental Chemicals by Combining Transcriptomic and Functional Data from Human-Induced Pluripotent Stem-Cell-Derived Cardiomyocytes. Chem Res Toxicol 2024. [PMID: 39046974 DOI: 10.1021/acs.chemrestox.4c00193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
Environmental chemicals may contribute to the global burden of cardiovascular disease, but experimental data are lacking to determine which substances pose the greatest risk. Human-induced pluripotent stem cell (iPSC)-derived cardiomyocytes are a high-throughput cardiotoxicity model that is widely used to test drugs and chemicals; however, most studies focus on exploring electro-physiological readouts. Gene expression data may provide additional molecular insights to be used for both mechanistic interpretation and dose-response analyses. Therefore, we hypothesized that both transcriptomic and functional data in human iPSC-derived cardiomyocytes may be used as a comprehensive screening tool to identify potential cardiotoxicity hazards and risks of the chemicals. To test this hypothesis, we performed concentration-response analysis of 464 chemicals from 12 classes, including both pharmaceuticals and nonpharmaceutical substances. Functional effects (beat frequency, QT prolongation, and asystole), cytotoxicity, and whole transcriptome response were evaluated. Points of departure were derived from phenotypic and transcriptomic data, and risk characterization was performed. Overall, 244 (53%) substances were active in at least one phenotype; as expected, pharmaceuticals with known cardiac liabilities were the most active. Positive chronotropy was the functional phenotype activated by the largest number of tested chemicals. No chemical class was particularly prone to pose a potential hazard to cardiomyocytes; a varying proportion (10-44%) of substances in each class had effects on cardiomyocytes. Transcriptomic data showed that 69 (15%) substances elicited significant gene expression changes; most perturbed pathways were highly relevant to known key characteristics of human cardiotoxicants. The bioactivity-to-exposure ratios showed that phenotypic- and transcriptomic-based POD led to similar results for risk characterization. Overall, our findings demonstrate how the integrative use of in vitro transcriptomic and phenotypic data from iPSC-derived cardiomyocytes not only offers a complementary approach for hazard and risk prioritization, but also enables mechanistic interpretation of the in vitro test results to increase confidence in decision-making.
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Affiliation(s)
- Han-Hsuan D Tsai
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Lucie C Ford
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Sarah D Burnett
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Allison N Dickey
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, United States
| | - Fred A Wright
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, United States
- Department of Statistics and Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27603, United States
| | - Weihsueh A Chiu
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
| | - Ivan Rusyn
- Interdisciplinary Faculty of Toxicology, Texas A&M University, College Station, Texas 77843, United States
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, Texas 77843, United States
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2
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Casson CL, John SA, Ferrall-Fairbanks MC. Mathematical modeling of cardio-oncology: Modeling the systemic effects of cancer therapeutics on the cardiovascular system. Semin Cancer Biol 2023; 97:30-41. [PMID: 37979714 DOI: 10.1016/j.semcancer.2023.11.004] [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: 11/15/2022] [Revised: 08/25/2023] [Accepted: 11/12/2023] [Indexed: 11/20/2023]
Abstract
Cardiotoxicity is a common side-effect of many cancer therapeutics; however, to-date there has been very little push to understand the mechanisms underlying this group of pathologies. This has led to the emergence of cardio-oncology, a field of medicine focused on understanding the effects of cancer and its treatment on the human heart. Here, we describe how mechanistic modeling approaches have been applied to study open questions in the cardiovascular system and how these approaches are being increasingly applied to advance knowledge of the underlying effects of cancer treatments on the human heart. A variety of mechanistic, mathematical modeling techniques have been applied to explore the link between common cancer treatments, such as chemotherapy, radiation, targeted therapy, and immunotherapy, and cardiotoxicity, nevertheless there is limited coverage in the different types of cardiac dysfunction that may be associated with these treatments. Moreover, cardiac modeling has a rich heritage of mathematical modeling and is well suited for the further development of novel approaches for understanding the cardiotoxicities associated with cancer therapeutics. There are many opportunities to combine mechanistic, bottom-up approaches with data-driven, top-down approaches to improve personalized, precision oncology to better understand, and ultimately mitigate, cardiac dysfunction in cancer patients.
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Affiliation(s)
- Camara L Casson
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA
| | - Sofia A John
- Department of Statistics, University of Florida, Gainesville, FL 32611, USA
| | - Meghan C Ferrall-Fairbanks
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL 32611, USA; University of Florida Health Cancer Center, University of Florida, Gainesville, FL 32611, USA.
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3
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Karabelas E, Longobardi S, Fuchsberger J, Razeghi O, Rodero C, Strocchi M, Rajani R, Haase G, Plank G, Niederer S. Global Sensitivity Analysis of Four Chamber Heart Hemodynamics Using Surrogate Models. IEEE Trans Biomed Eng 2022; 69:3216-3223. [PMID: 35353691 PMCID: PMC9491017 DOI: 10.1109/tbme.2022.3163428] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/19/2022] [Indexed: 11/15/2022]
Abstract
Computational Fluid Dynamics (CFD) is used to assist in designing artificial valves and planning procedures, focusing on local flow features. However, assessing the impact on overall cardiovascular function or predicting longer-term outcomes may requires more comprehensive whole heart CFD models. Fitting such models to patient data requires numerous computationally expensive simulations, and depends on specific clinical measurements to constrain model parameters, hampering clinical adoption. Surrogate models can help to accelerate the fitting process while accounting for the added uncertainty. We create a validated patient-specific four-chamber heart CFD model based on the Navier-Stokes-Brinkman (NSB) equations and test Gaussian Process Emulators (GPEs) as a surrogate model for performing a variance-based global sensitivity analysis (GSA). GSA identified preload as the dominant driver of flow in both the right and left side of the heart, respectively. Left-right differences were seen in terms of vascular outflow resistances, with pulmonary artery resistance having a much larger impact on flow than aortic resistance. Our results suggest that GPEs can be used to identify parameters in personalized whole heart CFD models, and highlight the importance of accurate preload measurements.
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Affiliation(s)
- Elias Karabelas
- Institute of Mathematics and Scientific ComputingUniversity of GrazAustria
| | - Stefano Longobardi
- Cardiac Electromechanics Research Group, School of Biomedical Engineering and Imaging SciencesKing’s College LondonU.K.
| | - Jana Fuchsberger
- Institute of Mathematics and Scientific ComputingUniversity of GrazAustria
| | - Orod Razeghi
- Research IT Services DepartmentUniversity College LondonU.K.
| | - Cristobal Rodero
- Cardiac Electromechanics Research Group, School of Biomedical Engineering and Imaging SciencesKing’s College LondonU.K.
| | - Marina Strocchi
- Cardiac Electromechanics Research Group, School of Biomedical Engineering and Imaging SciencesKing’s College LondonU.K.
| | - Ronak Rajani
- Department of Adult EchocardiographyGuy’s and St Thomas’ Hospitals NHS Foundation TrustU.K.
| | - Gundolf Haase
- Institute of Mathematics and Scientific ComputingUniversity of GrazAustria
| | - Gernot Plank
- Gottfried Schatz Research Center (for Cell Signaling, Metabolism and Aging), Division BiophysicsMedical University of GrazAustria
| | - Steven Niederer
- Cardiac Electromechanics Research Group, School of Biomedical Engineering and Imaging SciencesKing’s College LondonSE1 7EHLondonU.K.
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4
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Coorey G, Figtree GA, Fletcher DF, Snelson VJ, Vernon ST, Winlaw D, Grieve SM, McEwan A, Yang JYH, Qian P, O'Brien K, Orchard J, Kim J, Patel S, Redfern J. The health digital twin to tackle cardiovascular disease-a review of an emerging interdisciplinary field. NPJ Digit Med 2022; 5:126. [PMID: 36028526 PMCID: PMC9418270 DOI: 10.1038/s41746-022-00640-7] [Citation(s) in RCA: 43] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 06/24/2022] [Indexed: 11/16/2022] Open
Abstract
Potential benefits of precision medicine in cardiovascular disease (CVD) include more accurate phenotyping of individual patients with the same condition or presentation, using multiple clinical, imaging, molecular and other variables to guide diagnosis and treatment. An approach to realising this potential is the digital twin concept, whereby a virtual representation of a patient is constructed and receives real-time updates of a range of data variables in order to predict disease and optimise treatment selection for the real-life patient. We explored the term digital twin, its defining concepts, the challenges as an emerging field, and potentially important applications in CVD. A mapping review was undertaken using a systematic search of peer-reviewed literature. Industry-based participants and patent applications were identified through web-based sources. Searches of Compendex, EMBASE, Medline, ProQuest and Scopus databases yielded 88 papers related to cardiovascular conditions (28%, n = 25), non-cardiovascular conditions (41%, n = 36), and general aspects of the health digital twin (31%, n = 27). Fifteen companies with a commercial interest in health digital twin or simulation modelling had products focused on CVD. The patent search identified 18 applications from 11 applicants, of which 73% were companies and 27% were universities. Three applicants had cardiac-related inventions. For CVD, digital twin research within industry and academia is recent, interdisciplinary, and established globally. Overall, the applications were numerical simulation models, although precursor models exist for the real-time cyber-physical system characteristic of a true digital twin. Implementation challenges include ethical constraints and clinical barriers to the adoption of decision tools derived from artificial intelligence systems.
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Affiliation(s)
- Genevieve Coorey
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia. .,The George Institute for Global Health, Sydney, NSW, Australia.
| | - Gemma A Figtree
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia
| | - David F Fletcher
- University of Sydney, School of Chemical and Biomolecular Engineering, Sydney, NSW, Australia
| | - Victoria J Snelson
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Stephen Thomas Vernon
- Kolling Institute of Medical Research, Royal North Shore Hospital, Sydney, NSW, Australia.,Department of Cardiology, Royal North Shore Hospital, Sydney, NSW, Australia
| | - David Winlaw
- Cincinnati Children's Hospital Medical Cente, Cincinnati, OH, USA
| | - Stuart M Grieve
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Alistair McEwan
- The University of Sydney, School of Biomedical Engineering, Sydney, NSW, Australia
| | - Jean Yee Hwa Yang
- University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Pierre Qian
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Westmead Applied Research Centre, Westmead Hospital, Sydney, NSW, Australia
| | - Kieran O'Brien
- Siemens Healthcare Pty Ltd; and Centre for Advanced Imaging, University of Queensland, Brisbane, QLD, Australia
| | - Jessica Orchard
- University of Sydney, Charles Perkins Centre, Sydney, NSW, Australia
| | - Jinman Kim
- University of Sydney, School of Computer Science, Sydney, NSW, Australia
| | - Sanjay Patel
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia.,Royal Prince Alfred Hospital, Sydney, NSW, Australia.,Heart Research Institute, Sydney, NSW, Australia
| | - Julie Redfern
- University of Sydney, Faculty of Medicine and Health, Sydney, NSW, Australia
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5
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Accurate in silico simulation of the rabbit Purkinje fiber electrophysiological assay to facilitate early pharmaceutical cardiosafety assessment: Dream or reality? J Pharmacol Toxicol Methods 2022; 115:107172. [DOI: 10.1016/j.vascn.2022.107172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 03/31/2022] [Accepted: 04/08/2022] [Indexed: 11/24/2022]
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6
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Sher A, Niederer SA, Mirams GR, Kirpichnikova A, Allen R, Pathmanathan P, Gavaghan DJ, van der Graaf PH, Noble D. A Quantitative Systems Pharmacology Perspective on the Importance of Parameter Identifiability. Bull Math Biol 2022; 84:39. [PMID: 35132487 PMCID: PMC8821410 DOI: 10.1007/s11538-021-00982-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 11/30/2021] [Indexed: 12/31/2022]
Abstract
There is an inherent tension in Quantitative Systems Pharmacology (QSP) between the need to incorporate mathematical descriptions of complex physiology and drug targets with the necessity of developing robust, predictive and well-constrained models. In addition to this, there is no “gold standard” for model development and assessment in QSP. Moreover, there can be confusion over terminology such as model and parameter identifiability; complex and simple models; virtual populations; and other concepts, which leads to potential miscommunication and misapplication of methodologies within modeling communities, both the QSP community and related disciplines. This perspective article highlights the pros and cons of using simple (often identifiable) vs. complex (more physiologically detailed but often non-identifiable) models, as well as aspects of parameter identifiability, sensitivity and inference methodologies for model development and analysis. The paper distills the central themes of the issue of identifiability and optimal model size and discusses open challenges.
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Affiliation(s)
- Anna Sher
- Pfizer Worldwide Research, Development and Medical, Massachusetts, USA.
| | | | - Gary R Mirams
- Centre for Mathematical Medicine and Biology, Mathematical Sciences, University of Nottingham, Nottingham, UK
| | | | - Richard Allen
- Pfizer Worldwide Research, Development and Medical, Massachusetts, USA
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Maryland, USA
| | - David J Gavaghan
- Department of Computer Science, University of Oxford, Oxford, UK
| | | | - Denis Noble
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
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7
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Sarma H, Upadhyaya M, Gogoi B, Phukan M, Kashyap P, Das B, Devi R, Sharma HK. Cardiovascular Drugs: an Insight of In Silico Drug Design Tools. J Pharm Innov 2021. [DOI: 10.1007/s12247-021-09587-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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8
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Galli V, Loncaric F, Rocatello G, Astudillo P, Sanchis L, Regueiro A, De Backer O, Swaans M, Bosmans J, Ribeiro JM, Lamata P, Sitges M, de Jaegere P, Mortier P. Towards patient-specific prediction of conduction abnormalities induced by transcatheter aortic valve implantation: a combined mechanistic modelling and machine learning approach. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:606-615. [PMID: 36713106 PMCID: PMC9708019 DOI: 10.1093/ehjdh/ztab063] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/12/2021] [Indexed: 02/01/2023]
Abstract
Aims Post-procedure conduction abnormalities (CA) remain a common complication of transcatheter aortic valve implantation (TAVI), highlighting the need for personalized prediction models. We used machine learning (ML), integrating statistical and mechanistic modelling to provide a patient-specific estimation of the probability of developing CA after TAVI. Methods and results The cohort consisted of 151 patients with normal conduction and no pacemaker at baseline who underwent TAVI in nine European centres. Devices included CoreValve, Evolut R, Evolut PRO, and Lotus. Preoperative multi-slice computed tomography was performed. Virtual valve implantation with patient-specific computer modelling and simulation (CM&S) allowed calculation of valve-induced contact pressure on the anatomy. The primary composite outcome was new onset left or right bundle branch block or permanent pacemaker implantation (PPI) before discharge. A supervised ML approach was applied with eight models predicting CA based on anatomical, procedural and mechanistic data. CA occurred in 59% of patients (n = 89), more often after mechanical than first or second generation self-expanding valves (68% vs. 60% vs. 41%). CM&S revealed significantly higher contact pressure and contact pressure index in patients with CA. The best model achieved 83% accuracy (area under the curve 0.84) and sensitivity, specificity, positive predictive value, negative predictive value, and F1-score of 100%, 62%, 76%, 100%, and 82%. Conclusion ML, integrating statistical and mechanistic modelling, achieved an accurate prediction of CA after TAVI. This study demonstrates the potential of a synergetic approach for personalizing procedure planning, allowing selection of the optimal device and implantation strategy, avoiding new CA and/or PPI.
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Affiliation(s)
- Valeria Galli
- FEops NV, Technologiepark 122, 9052 Ghent, Belgium,Corresponding authors. Tel: +32 3480113684, (V.G.); Tel: +32 474274543, (P.M.)
| | - Filip Loncaric
- Institute of Biomedical Research August Pi Sunyer (IDIBAPS), Carrer del Rosselló, 149, 08036, Barcelona, Spain
| | | | | | - Laura Sanchis
- Cardiovascular Institute, Hospital Clínic and Universitat de Barcelona, C. de Villarroel, 170, 08036 Barcelona, Spain
| | - Ander Regueiro
- Cardiovascular Institute, Hospital Clínic and Universitat de Barcelona, C. de Villarroel, 170, 08036 Barcelona, Spain
| | - Ole De Backer
- Department of Cardiology, Rigshospitalet University Hospital, Blegdamsvej 9, 2100 København, Denmark
| | - Martin Swaans
- Department of Cardiology, St. Antonius Ziekenhuis, Koekoekslaan 1, 3435 CM Nieuwegein, The Netherlands
| | - Johan Bosmans
- Department of Cardiology, University Hospital Antwerp, Drie Eikenstraat 655, 2650 Edegem, Antwerp, Belgium
| | - Joana Maria Ribeiro
- Department of Cardiology, Erasmus MC, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Pablo Lamata
- Department of Biomedical Engineering, King’s College London, Strand, London WC2R 2LS, UK
| | - Marta Sitges
- Institute of Biomedical Research August Pi Sunyer (IDIBAPS), Carrer del Rosselló, 149, 08036, Barcelona, Spain,Cardiovascular Institute, Hospital Clínic and Universitat de Barcelona, C. de Villarroel, 170, 08036 Barcelona, Spain,Centro de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), Instituto de Salud Carlos III, Av. Monforte de Lemos, 3-5, Pabellón 11, Planta 0 28029 Madrid, Spain
| | - Peter de Jaegere
- Department of Cardiology, Erasmus MC, Doctor Molewaterplein 40, 3015 GD Rotterdam, The Netherlands
| | - Peter Mortier
- FEops NV, Technologiepark 122, 9052 Ghent, Belgium,Corresponding authors. Tel: +32 3480113684, (V.G.); Tel: +32 474274543, (P.M.)
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9
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Amuzescu B, Airini R, Epureanu FB, Mann SA, Knott T, Radu BM. Evolution of mathematical models of cardiomyocyte electrophysiology. Math Biosci 2021; 334:108567. [PMID: 33607174 DOI: 10.1016/j.mbs.2021.108567] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 01/10/2021] [Accepted: 02/04/2021] [Indexed: 12/16/2022]
Abstract
Advanced computational techniques and mathematical modeling have become more and more important to the study of cardiac electrophysiology. In this review, we provide a brief history of the evolution of cardiomyocyte electrophysiology models and highlight some of the most important ones that had a major impact on our understanding of the electrical activity of the myocardium and associated transmembrane ion fluxes in normal and pathological states. We also present the use of these models in the study of various arrhythmogenesis mechanisms, particularly the integration of experimental pharmacology data into advanced humanized models for in silico proarrhythmogenic risk prediction as an essential component of the Comprehensive in vitro Proarrhythmia Assay (CiPA) drug safety paradigm.
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Affiliation(s)
- Bogdan Amuzescu
- Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, Bucharest 050095, Romania; Life, Environmental and Earth Sciences Division, Research Institute of the University of Bucharest (ICUB), 91-95 Splaiul Independentei, Bucharest 050095, Romania.
| | - Razvan Airini
- Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, Bucharest 050095, Romania; Life, Environmental and Earth Sciences Division, Research Institute of the University of Bucharest (ICUB), 91-95 Splaiul Independentei, Bucharest 050095, Romania
| | - Florin Bogdan Epureanu
- Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, Bucharest 050095, Romania; Life, Environmental and Earth Sciences Division, Research Institute of the University of Bucharest (ICUB), 91-95 Splaiul Independentei, Bucharest 050095, Romania
| | - Stefan A Mann
- Cytocentrics Bioscience GmbH, Nattermannallee 1, 50829 Cologne, Germany
| | - Thomas Knott
- CytoBioScience Inc., 3463 Magic Drive, San Antonio, TX 78229, USA
| | - Beatrice Mihaela Radu
- Department of Anatomy, Animal Physiology and Biophysics, Faculty of Biology, University of Bucharest, 91-95 Splaiul Independentei, Bucharest 050095, Romania; Life, Environmental and Earth Sciences Division, Research Institute of the University of Bucharest (ICUB), 91-95 Splaiul Independentei, Bucharest 050095, Romania
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10
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Davies MR. Cardiac Safety Pharmacology Modeling. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11545-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
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11
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Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, Gilbert A, Fernandes JF, Bukhari HA, Wajdan A, Martinez MV, Santos MS, Shamohammdi M, Luo H, Westphal P, Leeson P, DiAchille P, Gurev V, Mayr M, Geris L, Pathmanathan P, Morrison T, Cornelussen R, Prinzen F, Delhaas T, Doltra A, Sitges M, Vigmond EJ, Zacur E, Grau V, Rodriguez B, Remme EW, Niederer S, Mortier P, McLeod K, Potse M, Pueyo E, Bueno-Orovio A, Lamata P. The 'Digital Twin' to enable the vision of precision cardiology. Eur Heart J 2020; 41:4556-4564. [PMID: 32128588 PMCID: PMC7774470 DOI: 10.1093/eurheartj/ehaa159] [Citation(s) in RCA: 206] [Impact Index Per Article: 51.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2019] [Revised: 11/29/2019] [Accepted: 02/24/2020] [Indexed: 12/26/2022] Open
Abstract
Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.
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Affiliation(s)
| | - Francesca Margara
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Maciej Marciniak
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Cristobal Rodero
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Filip Loncaric
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Yingjing Feng
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | | | - Joao F Fernandes
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | - Hassaan A Bukhari
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
| | - Ali Wajdan
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | | | | | - Mehrdad Shamohammdi
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Hongxing Luo
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Philip Westphal
- Medtronic PLC, Bakken Research Center, Maastricht, the Netherlands
| | - Paul Leeson
- Radcliffe Department of Medicine, Division of Cardiovascular Medicine, Oxford Cardiovascular Clinical Research Facility, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Paolo DiAchille
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Viatcheslav Gurev
- Healthcare and Life Sciences Research, IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Manuel Mayr
- King’s British Heart Foundation Centre, King’s College London, London, UK
| | - Liesbet Geris
- Virtual Physiological Human Institute, Leuven, Belgium
| | - Pras Pathmanathan
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | - Tina Morrison
- Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, USA
| | | | - Frits Prinzen
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Tammo Delhaas
- CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
| | - Ada Doltra
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marta Sitges
- Institut Clínic Cardiovascular, Hospital Clínic, Universitat de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- CIBERCV, Instituto de Salud Carlos III, (CB16/11/00354), CERCA Programme/Generalitat de, Catalunya, Spain
| | - Edward J Vigmond
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
| | - Ernesto Zacur
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vicente Grau
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Espen W Remme
- The Intervention Centre, Oslo University Hospital, Rikshospitalet, Oslo, Norway
| | - Steven Niederer
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
| | | | | | - Mark Potse
- IHU Liryc, Electrophysiology and Heart Modeling Institute, fondation Bordeaux Université, Pessac-Bordeaux F-33600, France
- IMB, UMR 5251, University of Bordeaux, Talence F-33400, France
- Inria Bordeaux Sud-Ouest, CARMEN team, Talence F-33400, France
| | - Esther Pueyo
- Aragón Institute of Engineering Research, Universidad de Zaragoza, IIS Aragón, Zaragoza, Spain
- CIBER in Bioengineering, Biomaterials and Nanomedicine (CIBER‐BBN), Madrid, Spain
| | - Alfonso Bueno-Orovio
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Pablo Lamata
- Department of Biomedical Engineering, Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK
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12
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Pouranbarani E, Berg LA, Oliveira RS, Dos Santos RW, Nygren A. Improved Accuracy of Cardiac Tissue-Level Simulations by Considering Membrane Resistance as a Cellular-Level Optimization Objective. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:2487-2490. [PMID: 33018511 DOI: 10.1109/embc44109.2020.9176128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Cardiac cellular models are utilized as the building blocks for tissue simulation. One of the imprecisions of conventional cellular modeling, especially when the models are used in tissue-level modeling, stems from the mere consideration of cellular properties (e.g., action potential shape) in parameter tuning of the model. In our previous work, we put forward an accurate framework in which membrane resistance (Rm) reflecting inter-cellular characteristics, i.e., electrotonic effects, was considered alongside cellular features in cellular model fitting. This paper, for the first time, examines the hypothesis that considering Rm as an additional optimization objective improves the accuracy of tissue-level modeling. To study this hypothesis, after cellular-level optimization of a well-known model, source-sink mismatch configurations in a 2-dimensional model are investigated. The results demonstrate that including Rm in the optimization protocol yields a substantial improvement in the relative error of the critical transition border which is defined as the minimum window size between source and sink that wave propagates. Model developers can utilize the proposed concept during parameter tuning to increase the accuracy of models.
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13
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Davies MR, Martinec M, Walls R, Schwarz R, Mirams GR, Wang K, Steiner G, Surinach A, Flores C, Lavé T, Singer T, Polonchuk L. Use of Patient Health Records to Quantify Drug-Related Pro-arrhythmic Risk. CELL REPORTS MEDICINE 2020; 1:100076. [PMID: 33205069 PMCID: PMC7659582 DOI: 10.1016/j.xcrm.2020.100076] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2020] [Revised: 06/09/2020] [Accepted: 07/29/2020] [Indexed: 12/30/2022]
Abstract
There is an increasing expectation that computational approaches may supplement existing human decision-making. Frontloading of models for cardiac safety prediction is no exception to this trend, and ongoing regulatory initiatives propose use of high-throughput in vitro data combined with computational models for calculating proarrhythmic risk. Evaluation of these models requires robust assessment of the outcomes. Using FDA Adverse Event Reporting System reports and electronic healthcare claims data from the Truven-MarketScan US claims database, we quantify the incidence rate of arrhythmia in patients and how this changes depending on patient characteristics. First, we propose that such datasets are a complementary resource for determining relative drug risk and assessing the performance of cardiac safety models for regulatory use. Second, the results suggest important determinants for appropriate stratification of patients and evaluation of additional drug risk in prescribing and clinical support algorithms and for precision health. In vitro data and computational models can assist with calculating pro-arrhythmic risk We use patient health records and FDA Adverse Event Reporting System reports Use of such datasets helps assess relative drug risk and cardiac safety models We quantify how patient characteristics can affect arrhythmia incidence
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Affiliation(s)
| | - Michael Martinec
- PHC Data Science, Personalized Healthcare, Product Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Robert Walls
- PHC Data Science, Personalized Healthcare, Product Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Roman Schwarz
- Safety Analytics and Reporting, Drug Safety, Pharmaceutical Development, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Gary R Mirams
- Centre for Mathematical Medicine & Biology, School of Mathematical Sciences, University of Nottingham, Nottingham NG7 2RD, UK
| | - Ken Wang
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Guido Steiner
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland
| | | | | | - Thierry Lavé
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Thomas Singer
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland
| | - Liudmila Polonchuk
- Roche Pharmaceutical Research and Early Development, Pharmaceutical Sciences, Roche Innovation Center Basel, F. Hoffmann-La Roche AG, Basel, Switzerland
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14
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Hwang M, Lim CH, Leem CH, Shim EB. In silico models for evaluating proarrhythmic risk of drugs. APL Bioeng 2020; 4:021502. [PMID: 32548538 PMCID: PMC7274812 DOI: 10.1063/1.5132618] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2019] [Accepted: 04/27/2020] [Indexed: 02/07/2023] Open
Abstract
Safety evaluation of drugs requires examination of the risk of generating Torsade de Pointes (TdP) because it can lead to sudden cardiac death. Until recently, the QT interval in the electrocardiogram (ECG) has been used in the evaluation of TdP risk because the QT interval is known to be associated with the development of TdP. Although TdP risk evaluation based on QT interval has been successful in removing drugs with TdP risk from the market, some safe drugs may have also been affected due to the low specificity of QT interval-based evaluation. For more accurate evaluation of drug safety, the comprehensive in vitro proarrhythmia assay (CiPA) has been proposed by regulatory agencies, industry, and academia. Although the CiPA initiative includes in silico evaluation of cellular action potential as a component, attempts to utilize in silico simulation in drug safety evaluation are expanding, even to simulating human ECG using biophysical three-dimensional models of the heart and torso under the effects of drugs. Here, we review recent developments in the use of in silico models for the evaluation of the proarrhythmic risk of drugs. We review the single cell, one-dimensional, two-dimensional, and three-dimensional models and their applications reported in the literature and discuss the possibility of utilizing ECG simulation in drug safety evaluation.
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Affiliation(s)
- Minki Hwang
- SiliconSapiens Inc., Seoul 06097, South Korea
| | - Chul-Hyun Lim
- Department of Mechanical and Biomedical Engineering, Kangwon National University, Chuncheon 24341, South Korea
| | - Chae Hun Leem
- Department of Physiology, College of Medicine, University of Ulsan, Asan Medical Center, Seoul 05505, South Korea
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15
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Mirams GR, Niederer SA, Clayton RH. The fickle heart: uncertainty quantification in cardiac and cardiovascular modelling and simulation. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2020; 378:20200119. [PMID: 32448073 PMCID: PMC7287327 DOI: 10.1098/rsta.2020.0119] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Affiliation(s)
- Gary R. Mirams
- School of Mathematical Sciences, University of Nottingham, Mathematical Sciences Building, University Park, Nottingham, Nottinghamshire NG7 2RD, UK
- e-mail:
| | - Steven A. Niederer
- Division of Imaging Sciences and Biomedical Engineering, Kings College London, The Rayne Institute, 4th Floor, Lambeth Wing, St Thomas’ Hospital, London SE1 7EH, UK
| | - Richard H. Clayton
- Computer Science, University of Sheffield, Regent Court, 211 Portobello, Sheffield S1 4DP, UK
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16
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Sahli-Costabal F, Seo K, Ashley E, Kuhl E. Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning. Biophys J 2020; 118:1165-1176. [PMID: 32023435 DOI: 10.1101/545863] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 11/27/2019] [Accepted: 01/13/2020] [Indexed: 05/25/2023] Open
Abstract
All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.
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Affiliation(s)
| | - Kinya Seo
- Department of Medicine, Stanford University, Stanford, California
| | - Euan Ashley
- Department of Medicine, Stanford University, Stanford, California; Department of Pathology, Stanford University, Stanford, California
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, California; Department of Bioengineering, Stanford University, Stanford, California.
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17
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Sahli-Costabal F, Seo K, Ashley E, Kuhl E. Classifying Drugs by their Arrhythmogenic Risk Using Machine Learning. Biophys J 2020; 118:1165-1176. [PMID: 32023435 PMCID: PMC7063479 DOI: 10.1016/j.bpj.2020.01.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2019] [Revised: 11/27/2019] [Accepted: 01/13/2020] [Indexed: 12/17/2022] Open
Abstract
All medications have adverse effects. Among the most serious of these are cardiac arrhythmias. Current paradigms for drug safety evaluation are costly, lengthy, conservative, and impede efficient drug development. Here, we combine multiscale experiment and simulation, high-performance computing, and machine learning to create a risk estimator to stratify new and existing drugs according to their proarrhythmic potential. We capitalize on recent developments in machine learning and integrate information across 10 orders of magnitude in space and time to provide a holistic picture of the effects of drugs, either individually or in combination with other drugs. We show, both experimentally and computationally, that drug-induced arrhythmias are dominated by the interplay between two currents with opposing effects: the rapid delayed rectifier potassium current and the L-type calcium current. Using Gaussian process classification, we create a classifier that stratifies drugs into safe and arrhythmic domains for any combinations of these two currents. We demonstrate that our classifier correctly identifies the risk categories of 22 common drugs exclusively on the basis of their concentrations at 50% current block. Our new risk assessment tool explains under which conditions blocking the L-type calcium current can delay or even entirely suppress arrhythmogenic events. Using machine learning in drug safety evaluation can provide a more accurate and comprehensive mechanistic assessment of the proarrhythmic potential of new drugs. Our study paves the way toward establishing science-based criteria to accelerate drug development, design safer drugs, and reduce heart rhythm disorders.
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Affiliation(s)
| | - Kinya Seo
- Department of Medicine, Stanford University, Stanford, California
| | - Euan Ashley
- Department of Medicine, Stanford University, Stanford, California; Department of Pathology, Stanford University, Stanford, California
| | - Ellen Kuhl
- Department of Mechanical Engineering, Stanford University, Stanford, California; Department of Bioengineering, Stanford University, Stanford, California.
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18
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Zhang JD, Sach-Peltason L, Kramer C, Wang K, Ebeling M. Multiscale modelling of drug mechanism and safety. Drug Discov Today 2020; 25:519-534. [DOI: 10.1016/j.drudis.2019.12.009] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 12/06/2019] [Accepted: 12/23/2019] [Indexed: 12/19/2022]
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19
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Li Z, Mirams GR, Yoshinaga T, Ridder BJ, Han X, Chen JE, Stockbridge NL, Wisialowski TA, Damiano B, Severi S, Morissette P, Kowey PR, Holbrook M, Smith G, Rasmusson RL, Liu M, Song Z, Qu Z, Leishman DJ, Steidl‐Nichols J, Rodriguez B, Bueno‐Orovio A, Zhou X, Passini E, Edwards AG, Morotti S, Ni H, Grandi E, Clancy CE, Vandenberg J, Hill A, Nakamura M, Singer T, Polonchuk L, Greiter‐Wilke A, Wang K, Nave S, Fullerton A, Sobie EA, Paci M, Musuamba Tshinanu F, Strauss DG. General Principles for the Validation of Proarrhythmia Risk Prediction Models: An Extension of the CiPA In Silico Strategy. Clin Pharmacol Ther 2020; 107:102-111. [PMID: 31709525 PMCID: PMC6977398 DOI: 10.1002/cpt.1647] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 09/06/2019] [Indexed: 12/27/2022]
Abstract
This white paper presents principles for validating proarrhythmia risk prediction models for regulatory use as discussed at the In Silico Breakout Session of a Cardiac Safety Research Consortium/Health and Environmental Sciences Institute/US Food and Drug Administration-sponsored Think Tank Meeting on May 22, 2018. The meeting was convened to evaluate the progress in the development of a new cardiac safety paradigm, the Comprehensive in Vitro Proarrhythmia Assay (CiPA). The opinions regarding these principles reflect the collective views of those who participated in the discussion of this topic both at and after the breakout session. Although primarily discussed in the context of in silico models, these principles describe the interface between experimental input and model-based interpretation and are intended to be general enough to be applied to other types of nonclinical models for proarrhythmia assessment. This document was developed with the intention of providing a foundation for more consistency and harmonization in developing and validating different models for proarrhythmia risk prediction using the example of the CiPA paradigm.
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20
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Pouranbarani E, Weber dos Santos R, Nygren A. A robust multi-objective optimization framework to capture both cellular and intercellular properties in cardiac cellular model tuning: Analyzing different regions of membrane resistance profile in parameter fitting. PLoS One 2019; 14:e0225245. [PMID: 31730631 PMCID: PMC6857942 DOI: 10.1371/journal.pone.0225245] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Accepted: 10/31/2019] [Indexed: 02/07/2023] Open
Abstract
Mathematical models of cardiac cells have been established to broaden understanding of cardiac function. In the process of developing electrophysiological models for cardiac myocytes, precise parameter tuning is a crucial step. The membrane resistance (Rm) is an essential feature obtained from cardiac myocytes. This feature reflects intercellular coupling and affects important phenomena, such as conduction velocity, and early after-depolarizations, but it is often overlooked during the phase of parameter fitting. Thus, the traditional parameter fitting that only includes action potential (AP) waveform may yield incorrect values for Rm. In this paper, a novel multi-objective parameter fitting formulation is proposed and tested that includes different regions of the Rm profile as additional objective functions for optimization. As Rm depends on the transmembrane voltage (Vm) and exhibits singularities for some specific values of Vm, analyses are conducted to carefully select the regions of interest for the proper characterization of Rm. Non-dominated sorting genetic algorithm II is utilized to solve the proposed multi-objective optimization problem. To verify the efficacy of the proposed problem formulation, case studies and comparisons are carried out using multiple models of human cardiac ventricular cells. Results demonstrate Rm is correctly reproduced by the tuned cell models after considering the curve of Rm obtained from the late phase of repolarization and Rm value calculated in the rest phase as additional objectives. However, relative deterioration of the AP fit is observed, demonstrating trade-off among the objectives. This framework can be useful for a wide range of applications, including the parameters fitting phase of the cardiac cell model development and investigation of normal and pathological scenarios in which reproducing both cellular and intercellular properties are of great importance.
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Affiliation(s)
- Elnaz Pouranbarani
- Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
- * E-mail:
| | - Rodrigo Weber dos Santos
- Department of Computer Science and the Graduate Program of Computational Modeling, Federal University of Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil
| | - Anders Nygren
- Department of Electrical and Computer Engineering, University of Calgary, Calgary, Alberta, Canada
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21
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Pouranbarani E, Nygren A. A Novel Bi-Level Framework for Fitting the Parameters in Cardiac Cellular Models. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:2370-2373. [PMID: 30440883 DOI: 10.1109/embc.2018.8512883] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Cardiac models constructed from sets of differential equations provide invaluable information about heart mechanism and disorder of both human and animals. As tuning the parameters is a profoundly important step of modeling, this paper presents a novel parametrization technique based on a bilevel framework that benefits from two solution approaches, namely mixed integer genetic algorithm (MIGA) and linear least squares (LLS). In the upper-level optimization step, the action potential (AP) of the model is fitted to the reference AP using MIGA. In the lower-level optimization step, the mismatch between the total current of the model and reference is minimized via a clamp concept-based linearization and LLS solution approach. Notably, the clamp concept can diminish the nonlinearity of the parameter fitting problem. The issue of dependency on initial parameters in the lower-level problem, as well as the sensitivity of model parameters to linearization, are circumvented by MIGA in the upper-level optimization. For evaluation of MIGA-LLS performance, two complex human ventricular models are employed. The results demonstrate that in comparison to the genetic algorithm (GA)-based approach, the proposed framework significantly reduces the average and variation of normalized root-mean-squared error (NRMSE) in terms of the AP and total current in different trials. Variability in the resulting parameter values is considerably decreased as well.
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22
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Mann SA, Heide J, Knott T, Airini R, Epureanu FB, Deftu AF, Deftu AT, Radu BM, Amuzescu B. Recording of multiple ion current components and action potentials in human induced pluripotent stem cell-derived cardiomyocytes via automated patch-clamp. J Pharmacol Toxicol Methods 2019; 100:106599. [PMID: 31228558 DOI: 10.1016/j.vascn.2019.106599] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Revised: 02/06/2019] [Accepted: 06/14/2019] [Indexed: 12/11/2022]
Abstract
INTRODUCTION The Comprehensive in vitro Proarrhythmia Assay (CiPA) initiative proposes a three-step approach to evaluate proarrhythmogenic liability of drug candidates: effects on individual ion channels in heterologous expression systems, integrating these data into in-silico models of the electrical activity of human cardiomyocytes, and comparison with experiments on human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CM). Here we introduce patch-clamp electrophysiology techniques on hiPSC-CM to combine two of the CiPA steps in one assay. METHODS We performed automated patch-clamp experiments on hiPSC-CM (Cor.4U®, Ncardia) using the CytoPatch™2 platform in ruptured whole-cell and β-escin-perforated-patch configurations. A combination of three voltage-clamp protocols allowed recording of five distinct ion current components (voltage-gated Na+ current, L-type Ca2+ current, transient outward K+ current, delayed rectifier K+ current, and "funny" hyperpolarization-activated current) from the same cell. We proved their molecular identity by either Na+ replacement with choline or by applying specific blockers: nifedipine, cisapride, chromanol 293B, phrixotoxin-1, ZD7288. We developed a C++ script for automated analysis of voltage-clamp recordings and computation of ion current/conductance surface density for these five cardiac ion currents. RESULTS The distributions from n = 54 hiPSC-CM in "ruptured" patch-clamp vs. n = 35 hiPSC-CM in β-escin-perforated patch-clamp were similar for membrane capacitance, access resistance, and ion current/conductance surface densities. The β-escin-perforated configuration resulted in improved stability of action potential (AP) shape and duration over a 10-min interval, with APD90 decay rate 0.7 ± 1.6%/min (mean ± SD, n = 4) vs. 4.6 ± 1.1%/min. (n = 3) for "ruptured" approach (p = 0.0286, one-tailed Mann-Whitney test). DISCUSSION The improved stability obtained here will allow development of CiPA-compliant automated patch-clamp assays on hiPSC-CM. Future applications include the study of multi ion-channel blocking properties of drugs using dynamic-clamp protocols, adding a valuable new tool to the arsenal of safety-pharmacology.
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Affiliation(s)
- Stefan A Mann
- Cytocentrics Bioscience GmbH, Nattermannallee 1, 50829 Cologne, Germany
| | - Juliane Heide
- Cytocentrics Bioscience GmbH, Nattermannallee 1, 50829 Cologne, Germany
| | - Thomas Knott
- CytoBioScience Inc., 3463 Magic Drive, San Antonio, TX 78229, USA
| | - Razvan Airini
- Dept. Biophysics & Physiology, Faculty of Biology, University of Bucharest, Splaiul Independentei 91-95, 050095 Bucharest, Romania
| | - Florin Bogdan Epureanu
- Dept. Biophysics & Physiology, Faculty of Biology, University of Bucharest, Splaiul Independentei 91-95, 050095 Bucharest, Romania
| | - Alexandru-Florian Deftu
- Dept. Biophysics & Physiology, Faculty of Biology, University of Bucharest, Splaiul Independentei 91-95, 050095 Bucharest, Romania
| | - Antonia-Teona Deftu
- Dept. Biophysics & Physiology, Faculty of Biology, University of Bucharest, Splaiul Independentei 91-95, 050095 Bucharest, Romania
| | - Beatrice Mihaela Radu
- Dept. Biophysics & Physiology, Faculty of Biology, University of Bucharest, Splaiul Independentei 91-95, 050095 Bucharest, Romania
| | - Bogdan Amuzescu
- Dept. Biophysics & Physiology, Faculty of Biology, University of Bucharest, Splaiul Independentei 91-95, 050095 Bucharest, Romania.
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Lluch È, De Craene M, Bijnens B, Sermesant M, Noailly J, Camara O, Morales HG. Breaking the state of the heart: meshless model for cardiac mechanics. Biomech Model Mechanobiol 2019; 18:1549-1561. [DOI: 10.1007/s10237-019-01175-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 05/27/2019] [Indexed: 01/30/2023]
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24
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Li Z, Garnett C, Strauss DG. Quantitative Systems Pharmacology Models for a New International Cardiac Safety Regulatory Paradigm: An Overview of the Comprehensive In Vitro Proarrhythmia Assay In Silico Modeling Approach. CPT Pharmacometrics Syst Pharmacol 2019; 8:371-379. [PMID: 31044559 PMCID: PMC6617836 DOI: 10.1002/psp4.12423] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 04/15/2019] [Indexed: 12/17/2022] Open
Abstract
As a relatively new discipline, quantitative systems pharmacology has seen a significant increase in the application and utility of drug development. One area that could greatly benefit from such an approach is in the proarrhythmia assessment of new drugs. The Comprehensive In Vitro Proarrhythmia Assay (CiPA) Initiative is a global public-private partnership project that has developed an integrated approach using mechanistic in silico models for proarrhythmia risk prediction. Progress to date has led to the formation of the International Council on Harmonisation Implementation Working Group to revise regulatory guidelines via the Questions-and-Answers process to address the best practices for proarrhythmia models and how they can impact clinical drug development. This article reviews the CiPA in silico model-development process, focusing on its unique development and validation strategy, and summarizes the lessons learned as consideration points for the ongoing implementation of CiPA-like in silico models in drug development.
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Affiliation(s)
- Zhihua Li
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - Christine Garnett
- Division of Cardiovascular and Renal ProductsOffice of Drug Evaluation IOffice of New DrugsCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
| | - David G. Strauss
- Division of Applied Regulatory ScienceOffice of Clinical PharmacologyOffice of Translational SciencesCenter for Drug Evaluation and ResearchUS Food and Drug AdministrationSilver SpringMarylandUSA
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Savoji H, Mohammadi MH, Rafatian N, Toroghi MK, Wang EY, Zhao Y, Korolj A, Ahadian S, Radisic M. Cardiovascular disease models: A game changing paradigm in drug discovery and screening. Biomaterials 2019; 198:3-26. [PMID: 30343824 PMCID: PMC6397087 DOI: 10.1016/j.biomaterials.2018.09.036] [Citation(s) in RCA: 118] [Impact Index Per Article: 23.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Revised: 09/11/2018] [Accepted: 09/22/2018] [Indexed: 02/06/2023]
Abstract
Cardiovascular disease is the leading cause of death worldwide. Although investment in drug discovery and development has been sky-rocketing, the number of approved drugs has been declining. Cardiovascular toxicity due to therapeutic drug use claims the highest incidence and severity of adverse drug reactions in late-stage clinical development. Therefore, to address this issue, new, additional, replacement and combinatorial approaches are needed to fill the gap in effective drug discovery and screening. The motivation for developing accurate, predictive models is twofold: first, to study and discover new treatments for cardiac pathologies which are leading in worldwide morbidity and mortality rates; and second, to screen for adverse drug reactions on the heart, a primary risk in drug development. In addition to in vivo animal models, in vitro and in silico models have been recently proposed to mimic the physiological conditions of heart and vasculature. Here, we describe current in vitro, in vivo, and in silico platforms for modelling healthy and pathological cardiac tissues and their advantages and disadvantages for drug screening and discovery applications. We review the pathophysiology and the underlying pathways of different cardiac diseases, as well as the new tools being developed to facilitate their study. We finally suggest a roadmap for employing these non-animal platforms in assessing drug cardiotoxicity and safety.
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Affiliation(s)
- Houman Savoji
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 170 College St, Toronto, Ontario, M5S 3G9, Canada; Toronto General Research Institute, University Health Network, University of Toronto, 200 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada
| | - Mohammad Hossein Mohammadi
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 170 College St, Toronto, Ontario, M5S 3G9, Canada; Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College St, Toronto, Ontario, M5S 3E5, Canada; Toronto General Research Institute, University Health Network, University of Toronto, 200 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada
| | - Naimeh Rafatian
- Toronto General Research Institute, University Health Network, University of Toronto, 200 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada
| | - Masood Khaksar Toroghi
- Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College St, Toronto, Ontario, M5S 3E5, Canada
| | - Erika Yan Wang
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 170 College St, Toronto, Ontario, M5S 3G9, Canada
| | - Yimu Zhao
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 170 College St, Toronto, Ontario, M5S 3G9, Canada; Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College St, Toronto, Ontario, M5S 3E5, Canada
| | - Anastasia Korolj
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 170 College St, Toronto, Ontario, M5S 3G9, Canada; Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College St, Toronto, Ontario, M5S 3E5, Canada
| | - Samad Ahadian
- Toronto General Research Institute, University Health Network, University of Toronto, 200 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada
| | - Milica Radisic
- Institute of Biomaterials and Biomedical Engineering, University of Toronto, 170 College St, Toronto, Ontario, M5S 3G9, Canada; Department of Chemical Engineering and Applied Chemistry, University of Toronto, 200 College St, Toronto, Ontario, M5S 3E5, Canada; Toronto General Research Institute, University Health Network, University of Toronto, 200 Elizabeth St, Toronto, Ontario, M5G 2C4, Canada.
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26
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Callaghan NI, Hadipour-Lakmehsari S, Lee SH, Gramolini AO, Simmons CA. Modeling cardiac complexity: Advancements in myocardial models and analytical techniques for physiological investigation and therapeutic development in vitro. APL Bioeng 2019; 3:011501. [PMID: 31069331 PMCID: PMC6481739 DOI: 10.1063/1.5055873] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 12/31/2018] [Indexed: 02/06/2023] Open
Abstract
Cardiomyopathies, heart failure, and arrhythmias or conduction blockages impact millions of patients worldwide and are associated with marked increases in sudden cardiac death, decline in the quality of life, and the induction of secondary pathologies. These pathologies stem from dysfunction in the contractile or conductive properties of the cardiomyocyte, which as a result is a focus of fundamental investigation, drug discovery and therapeutic development, and tissue engineering. All of these foci require in vitro myocardial models and experimental techniques to probe the physiological functions of the cardiomyocyte. In this review, we provide a detailed exploration of different cell models, disease modeling strategies, and tissue constructs used from basic to translational research. Furthermore, we highlight recent advancements in imaging, electrophysiology, metabolic measurements, and mechanical and contractile characterization modalities that are advancing our understanding of cardiomyocyte physiology. With this review, we aim to both provide a biological framework for engineers contributing to the field and demonstrate the technical basis and limitations underlying physiological measurement modalities for biologists attempting to take advantage of these state-of-the-art techniques.
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Affiliation(s)
| | | | | | | | - Craig A. Simmons
- Author to whom correspondence should be addressed: . Present address: Ted Rogers Centre for Heart
Research, 661 University Avenue, 14th Floor Toronto, Ontario M5G 1M1, Canada. Tel.:
416-946-0548. Fax: 416-978-7753
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27
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Carusi A, Davies MR, De Grandis G, Escher BI, Hodges G, Leung KMY, Whelan M, Willett C, Ankley GT. Harvesting the promise of AOPs: An assessment and recommendations. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 628-629:1542-1556. [PMID: 30045572 PMCID: PMC5888775 DOI: 10.1016/j.scitotenv.2018.02.015] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 02/02/2018] [Accepted: 02/02/2018] [Indexed: 05/22/2023]
Abstract
The Adverse Outcome Pathway (AOP) concept is a knowledge assembly and communication tool to facilitate the transparent translation of mechanistic information into outcomes meaningful to the regulatory assessment of chemicals. The AOP framework and associated knowledgebases (KBs) have received significant attention and use in the regulatory toxicology community. However, it is increasingly apparent that the potential stakeholder community for the AOP concept and AOP KBs is broader than scientists and regulators directly involved in chemical safety assessment. In this paper we identify and describe those stakeholders who currently-or in the future-could benefit from the application of the AOP framework and knowledge to specific problems. We also summarize the challenges faced in implementing pathway-based approaches such as the AOP framework in biological sciences, and provide a series of recommendations to meet critical needs to ensure further progression of the framework as a useful, sustainable and dependable tool supporting assessments of both human health and the environment. Although the AOP concept has the potential to significantly impact the organization and interpretation of biological information in a variety of disciplines/applications, this promise can only be fully realized through the active engagement of, and input from multiple stakeholders, requiring multi-pronged substantive long-term planning and strategies.
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Affiliation(s)
- Annamaria Carusi
- Medical Humanities Sheffield, University of Sheffield, Medical School, Beech Hill Road, Sheffield S10 2RX, UK.
| | | | - Giovanni De Grandis
- Science, Technology, Engineering and Public Policy (STEaPP), Boston House, 36-37 Fitzroy Square, London W1T 6EY, UK.
| | - Beate I Escher
- UFZ - Helmholtz Centre for Environmental Research, 04318 Leipzig, Germany; Eberhard Karls University Tübingen, Environmental Toxicology, Centre for Applied Geosciences, 72074 Tübingen, Germany.
| | - Geoff Hodges
- Safety and Environmental Assurance Centre, Unilever, Colworth Science Park, Sharnbrook, Bedfordshire MK44 1LQ, UK.
| | - Kenneth M Y Leung
- The Swire Institute of Marine Science and School of Biological Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.
| | - Maurice Whelan
- European Commission, Joint Research Centre (JRC), Ispra, Italy.
| | - Catherine Willett
- The Humane Society of the United States, 700 Professional Drive, Gaithersburg, MD, 20879, USA.
| | - Gerald T Ankley
- US Environmental Protection Agency, 6201 Congdon Blvd, Duluth, MN 55804, USA.
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28
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Tixier E, Lombardi D, Rodriguez B, Gerbeau JF. Modelling variability in cardiac electrophysiology: a moment-matching approach. J R Soc Interface 2018; 14:rsif.2017.0238. [PMID: 28835541 PMCID: PMC5582121 DOI: 10.1098/rsif.2017.0238] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 08/02/2017] [Indexed: 11/16/2022] Open
Abstract
The variability observed in action potential (AP) cardiomyocyte measurements is the consequence of many different sources of randomness. Often ignored, this variability may be studied to gain insight into the cell ionic properties. In this paper, we focus on the study of ionic channel conductances and describe a methodology to estimate their probability density function (PDF) from AP recordings. The method relies on the matching of observable statistical moments and on the maximum entropy principle. We present four case studies using synthetic and sets of experimental AP measurements from human and canine cardiomyocytes. In each case, the proposed methodology is applied to infer the PDF of key conductances from the exhibited variability. The estimated PDFs are discussed and, when possible, compared to the true distributions. We conclude that it is possible to extract relevant information from the variability in AP measurements and discuss the limitations and possible implications of the proposed approach.
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Affiliation(s)
- Eliott Tixier
- Sorbonne Universités, UPMC Université Paris 6, UMR 7598 LJLL, 75005 Paris, France.,Inria Paris, 75012 Paris, France
| | - Damiano Lombardi
- Sorbonne Universités, UPMC Université Paris 6, UMR 7598 LJLL, 75005 Paris, France.,Inria Paris, 75012 Paris, France
| | - Blanca Rodriguez
- Department of Computer Science, BHF Centre of Research Excellence, University of Oxford, Oxford, UK
| | - Jean-Frédéric Gerbeau
- Sorbonne Universités, UPMC Université Paris 6, UMR 7598 LJLL, 75005 Paris, France .,Inria Paris, 75012 Paris, France
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29
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Pathmanathan P, Gray RA. Validation and Trustworthiness of Multiscale Models of Cardiac Electrophysiology. Front Physiol 2018; 9:106. [PMID: 29497385 PMCID: PMC5818422 DOI: 10.3389/fphys.2018.00106] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Accepted: 01/31/2018] [Indexed: 02/06/2023] Open
Abstract
Computational models of cardiac electrophysiology have a long history in basic science applications and device design and evaluation, but have significant potential for clinical applications in all areas of cardiovascular medicine, including functional imaging and mapping, drug safety evaluation, disease diagnosis, patient selection, and therapy optimisation or personalisation. For all stakeholders to be confident in model-based clinical decisions, cardiac electrophysiological (CEP) models must be demonstrated to be trustworthy and reliable. Credibility, that is, the belief in the predictive capability, of a computational model is primarily established by performing validation, in which model predictions are compared to experimental or clinical data. However, there are numerous challenges to performing validation for highly complex multi-scale physiological models such as CEP models. As a result, credibility of CEP model predictions is usually founded upon a wide range of distinct factors, including various types of validation results, underlying theory, evidence supporting model assumptions, evidence from model calibration, all at a variety of scales from ion channel to cell to organ. Consequently, it is often unclear, or a matter for debate, the extent to which a CEP model can be trusted for a given application. The aim of this article is to clarify potential rationale for the trustworthiness of CEP models by reviewing evidence that has been (or could be) presented to support their credibility. We specifically address the complexity and multi-scale nature of CEP models which makes traditional model evaluation difficult. In addition, we make explicit some of the credibility justification that we believe is implicitly embedded in the CEP modeling literature. Overall, we provide a fresh perspective to CEP model credibility, and build a depiction and categorisation of the wide-ranging body of credibility evidence for CEP models. This paper also represents a step toward the extension of model evaluation methodologies that are currently being developed by the medical device community, to physiological models.
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Affiliation(s)
- Pras Pathmanathan
- Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, MD, United States
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30
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Tixier E, Raphel F, Lombardi D, Gerbeau JF. Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block. Front Physiol 2018; 8:1096. [PMID: 29354067 PMCID: PMC5762138 DOI: 10.3389/fphys.2017.01096] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 12/13/2017] [Indexed: 12/19/2022] Open
Abstract
The Micro-Electrode Array (MEA) device enables high-throughput electrophysiology measurements that are less labor-intensive than patch-clamp based techniques. Combined with human-induced pluripotent stem cells cardiomyocytes (hiPSC-CM), it represents a new and promising paradigm for automated and accurate in vitro drug safety evaluation. In this article, the following question is addressed: which features of the MEA signals should be measured to better classify the effects of drugs? A framework for the classification of drugs using MEA measurements is proposed. The classification is based on the ion channels blockades induced by the drugs. It relies on an in silico electrophysiology model of the MEA, a feature selection algorithm and automatic classification tools. An in silico model of the MEA is developed and is used to generate synthetic measurements. An algorithm that extracts MEA measurements features designed to perform well in a classification context is described. These features are called composite biomarkers. A state-of-the-art machine learning program is used to carry out the classification of drugs using experimental MEA measurements. The experiments are carried out using five different drugs: mexiletine, flecainide, diltiazem, moxifloxacin, and dofetilide. We show that the composite biomarkers outperform the classical ones in different classification scenarios. We show that using both synthetic and experimental MEA measurements improves the robustness of the composite biomarkers and that the classification scores are increased.
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Affiliation(s)
- Eliott Tixier
- Inria Paris, Paris, France.,Sorbonne Universités, Université Pierre et Marie Curie-Paris 6, UMR 7598 LJLL, Paris, France
| | - Fabien Raphel
- Inria Paris, Paris, France.,Sorbonne Universités, Université Pierre et Marie Curie-Paris 6, UMR 7598 LJLL, Paris, France
| | - Damiano Lombardi
- Inria Paris, Paris, France.,Sorbonne Universités, Université Pierre et Marie Curie-Paris 6, UMR 7598 LJLL, Paris, France
| | - Jean-Frédéric Gerbeau
- Inria Paris, Paris, France.,Sorbonne Universités, Université Pierre et Marie Curie-Paris 6, UMR 7598 LJLL, Paris, France
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31
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Dunster JL, Panteleev MA, Gibbins JM, Sveshnikova AN. Mathematical Techniques for Understanding Platelet Regulation and the Development of New Pharmacological Approaches. Methods Mol Biol 2018; 1812:255-279. [PMID: 30171583 DOI: 10.1007/978-1-4939-8585-2_15] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Mathematical and computational modeling is currently in the process of becoming an accepted tool in the arsenal of methods utilized for the investigation of complex biological systems. For some problems in the field, like cellular metabolic regulation, neural impulse propagation, or cell cycle, progress is already unthinkable without use of such methods. Mathematical models of platelet signaling, function, and metabolism during the last years have not only been steadily increasing in their number, but have also been providing more in-depth insights, generating hypotheses, and allowing predictions to be made leading to new experimental designs and data. Here we describe the basic approaches to platelet mathematical model development and validation, highlighting the challenges involved. We then review the current theoretical models in the literature and how these are being utilized to increase our understanding of these complex cells.
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Affiliation(s)
- Joanna L Dunster
- Institute for Cardiovascular and Metabolic Research, School of Biological Sciences, University of Reading, Reading, UK.
| | - Mikhail A Panteleev
- Faculty of Physics, Lomonosov Moscow State University, Moscow, Russia
- Center for Theoretical Problems of Physicochemical Pharmacology, Russian Academy of Sciences, Moscow, Russia
- National Scientific and Practical Centre of Pediatric Hematology, Oncology and Immunology Named After Dmitry Rogachev, Moscow, Russia
- Faculty of Biological and Medical Physics, Moscow Institute of Physics and Technology, Dolgoprudnyi, Russia
| | - Jonathan M Gibbins
- Institute for Cardiovascular and Metabolic Research, School of Biological Sciences, University of Reading, Reading, UK
| | - Anastacia N Sveshnikova
- Faculty of Physics, Lomonosov Moscow State University, Moscow, Russia
- Center for Theoretical Problems of Physicochemical Pharmacology, Russian Academy of Sciences, Moscow, Russia
- National Scientific and Practical Centre of Pediatric Hematology, Oncology and Immunology Named After Dmitry Rogachev, Moscow, Russia
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32
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Tylutki Z, Mendyk A, Polak S. Mechanistic Physiologically Based Pharmacokinetic (PBPK) Model of the Heart Accounting for Inter-Individual Variability: Development and Performance Verification. J Pharm Sci 2017; 107:1167-1177. [PMID: 29175411 DOI: 10.1016/j.xphs.2017.11.012] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 11/15/2017] [Accepted: 11/16/2017] [Indexed: 12/14/2022]
Abstract
Modern model-based approaches to cardiac safety and efficacy assessment require accurate drug concentration-effect relationship establishment. Thus, knowledge of the active concentration of drugs in heart tissue is desirable along with inter-subject variability influence estimation. To that end, we developed a mechanistic physiologically based pharmacokinetic model of the heart. The models were described with literature-derived parameters and written in R, v.3.4.0. Five parameters were estimated. The model was fitted to amitriptyline and nortriptyline concentrations after an intravenous infusion of amitriptyline. The cardiac model consisted of 5 compartments representing the pericardial fluid, heart extracellular water, and epicardial intracellular, midmyocardial intracellular, and endocardial intracellular fluids. Drug cardiac metabolism, passive diffusion, active efflux, and uptake were included in the model as mechanisms involved in the drug disposition within the heart. The model accounted for inter-individual variability. The estimates of optimized parameters were within physiological ranges. The model performance was verified by simulating 5 clinical studies of amitriptyline intravenous infusion, and the simulated pharmacokinetic profiles agreed with clinical data. The results support the model feasibility. The proposed structure can be tested with the goal of improving the patient-specific model-based cardiac safety assessment and offers a framework for predicting cardiac concentrations of various xenobiotics.
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Affiliation(s)
- Zofia Tylutki
- Unit of Pharmacoepidemiology and Pharmacoeconomics, Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Str., 30-688 Krakow, Poland.
| | - Aleksander Mendyk
- Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, Medyczna 9 St., 30-688 Krakow, Poland
| | - Sebastian Polak
- Unit of Pharmacoepidemiology and Pharmacoeconomics, Department of Social Pharmacy, Faculty of Pharmacy, Jagiellonian University Medical College, Medyczna 9 Str., 30-688 Krakow, Poland; Simcyp (a Certara Company) Limited, Blades Enterprise Centre, John Street, Sheffield S2 4SU, UK
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33
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Wiśniowska B, Tylutki Z, Polak S. Humans Vary, So Cardiac Models Should Account for That Too! Front Physiol 2017; 8:700. [PMID: 28983251 PMCID: PMC5613127 DOI: 10.3389/fphys.2017.00700] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2017] [Accepted: 08/30/2017] [Indexed: 12/25/2022] Open
Abstract
The utilization of mathematical modeling and simulation in drug development encompasses multiple mathematical techniques and the location of a drug candidate in the development pipeline. Historically speaking they have been used to analyze experimental data (i.e., Hill equation) and clarify the involved physical and chemical processes (i.e., Fick laws and drug molecule diffusion). In recent years the advanced utilization of mathematical modeling has been an important part of the regulatory review process. Physiologically based pharmacokinetic (PBPK) models identify the need to conduct specific clinical studies, suggest specific study designs and propose appropriate labeling language. Their application allows the evaluation of the influence of intrinsic (e.g., age, gender, genetics, disease) and extrinsic [e.g., dosing schedule, drug-drug interactions (DDIs)] factors, alone or in combinations, on drug exposure and therefore provides accurate population assessment. A similar pathway has been taken for the assessment of drug safety with cardiac safety being one the most advanced examples. Mechanistic mathematical model-informed safety evaluation, with a focus on drug potential for causing arrhythmias, is now discussed as an element of the Comprehensive in vitro Proarrhythmia Assay. One of the pillars of this paradigm is the use of an in silico model of the adult human ventricular cardiomyocyte to integrate in vitro measured data. Existing examples (in vitro—in vivo extrapolation with the use of PBPK models) suggest that deterministic, epidemiological and clinical data based variability models can be merged with the mechanistic models describing human physiology. There are other methods available, based on the stochastic approach and on population of models generated by randomly assigning specific parameter values (ionic current conductance and kinetic) and further pruning. Both approaches are briefly characterized in this manuscript, in parallel with the drug-specific variability.
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Affiliation(s)
- Barbara Wiśniowska
- Pharmacoepidemiology and Pharmacoeconomics Unit, Faculty of Pharmacy, Jagiellonian University Medical CollegeKrakow, Poland
| | - Zofia Tylutki
- Pharmacoepidemiology and Pharmacoeconomics Unit, Faculty of Pharmacy, Jagiellonian University Medical CollegeKrakow, Poland
| | - Sebastian Polak
- Pharmacoepidemiology and Pharmacoeconomics Unit, Faculty of Pharmacy, Jagiellonian University Medical CollegeKrakow, Poland.,SimcypCertara, Sheffield, United Kingdom
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34
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Lievano F, Scarazzini L, Shen F, Duhig J, Jokinen J. The future of safety science is happening now: The modernization of the benefit-risk paradigm. Pharmacoepidemiol Drug Saf 2017; 26:869-874. [DOI: 10.1002/pds.4241] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 04/28/2017] [Accepted: 05/07/2017] [Indexed: 12/12/2022]
Affiliation(s)
- Fabio Lievano
- Pharmacovigilance and Patient Safety; AbbVie Inc.; North Chicago IL USA
| | - Linda Scarazzini
- Pharmacovigilance and Patient Safety; AbbVie Inc.; North Chicago IL USA
| | - Frank Shen
- Pharmacovigilance and Patient Safety; AbbVie Inc.; North Chicago IL USA
| | - James Duhig
- Pharmacovigilance and Patient Safety; AbbVie Inc.; North Chicago IL USA
| | - Jeremy Jokinen
- Pharmacovigilance and Patient Safety; AbbVie Inc.; North Chicago IL USA
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35
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Wiśniowska B, Polak S. Am I or am I not proarrhythmic? Comparison of various classifications of drug TdP propensity. Drug Discov Today 2017; 22:10-16. [DOI: 10.1016/j.drudis.2016.09.027] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2016] [Revised: 08/22/2016] [Accepted: 09/28/2016] [Indexed: 12/12/2022]
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36
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Inter-individual variability and modeling of electrical activity: a possible new approach to explore cardiac safety? Sci Rep 2016; 6:37948. [PMID: 27901061 PMCID: PMC5128803 DOI: 10.1038/srep37948] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2016] [Accepted: 11/02/2016] [Indexed: 11/08/2022] Open
Abstract
Safety pharmacology aims to predict rare side effects of new drugs. We explored whether rare pro-arrhythmic effects could be linked to the variability of the effects of these drugs on ion currents and whether taking into consideration this variability in computational models could help to better detect and predict cardiac side effects. For this purpose, we evaluated how intra- and inter-individual variability influences the effect of hERG inhibition on both the action potential duration and the occurrence of arrhythmias. Using two computer simulation models of human action potentials (endocardial and Purkinje cells), we analyzed the contribution of two biological parameters on the pro-arrhythmic effects of several hERG channel blockers: (i) spermine concentration, which varies with metabolic status, and (ii) L-type calcium conductance, which varies due to single nucleotide polymorphisms or mutations. By varying these parameters, we were able to induce arrhythmias in 1 out of 16 simulations although conventional modeling methods to detect pro-arrhythmic molecules failed. On the basis of our results, taking into consideration only 2 parameters subjected to intra- and inter-individual variability, we propose that in silico computer modeling may help to better define the risks of new drug candidates at early stages of pre-clinical development.
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37
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Onal B, Hund TJ. Integrative approaches for prediction of cardiotoxic drug effects and mitigation strategies. J Mol Cell Cardiol 2016; 102:1-2. [PMID: 27894864 DOI: 10.1016/j.yjmcc.2016.10.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Accepted: 10/10/2016] [Indexed: 10/20/2022]
Affiliation(s)
- Birce Onal
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center and The Ohio State University College of Engineering, USA; Department of Biomedical Engineering, The Ohio State University Wexner Medical Center and The Ohio State University College of Engineering, USA
| | - Thomas J Hund
- Dorothy M. Davis Heart and Lung Research Institute, The Ohio State University Wexner Medical Center and The Ohio State University College of Engineering, USA; Department of Biomedical Engineering, The Ohio State University Wexner Medical Center and The Ohio State University College of Engineering, USA; Department of Internal Medicine, The Ohio State University Wexner Medical Center and The Ohio State University College of Engineering, USA.
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38
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Gotta V, Yu Z, Cools F, van Ammel K, Gallacher DJ, Visser SAG, Sannajust F, Morissette P, Danhof M, van der Graaf PH. Application of a systems pharmacology model for translational prediction of hERG-mediated QTc prolongation. Pharmacol Res Perspect 2016; 4:e00270. [PMID: 28097003 PMCID: PMC5226282 DOI: 10.1002/prp2.270] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Accepted: 09/14/2016] [Indexed: 02/06/2023] Open
Abstract
Drug‐induced QTc interval prolongation (ΔQTc) is a main surrogate for proarrhythmic risk assessment. A higher in vivo than in vitro potency for hERG‐mediated QTc prolongation has been suggested. Also, in vivo between‐species and patient populations’ sensitivity to drug‐induced QTc prolongation seems to differ. Here, a systems pharmacology model integrating preclinical in vitro (hERG binding) and in vivo (conscious dog ΔQTc) data of three hERG blockers (dofetilide, sotalol, moxifloxacin) was applied (1) to compare the operational efficacy of the three drugs in vivo and (2) to quantify dog–human differences in sensitivity to drug‐induced QTc prolongation (for dofetilide only). Scaling parameters for translational in vivo extrapolation of drug effects were derived based on the assumption of system‐specific myocardial ion channel densities and transduction of ion channel block: the operational efficacy (transduction of hERG block) in dogs was drug specific (1–19% hERG block corresponded to ≥10 msec ΔQTc). System‐specific maximal achievable ΔQTc was estimated to 28% from baseline in both dog and human, while %hERG block leading to half‐maximal effects was 58% lower in human, suggesting a higher contribution of hERG‐mediated potassium current to cardiac repolarization. These results suggest that differences in sensitivity to drug‐induced QTc prolongation may be well explained by drug‐ and system‐specific differences in operational efficacy (transduction of hERG block), consistent with experimental reports. The proposed scaling approach may thus assist the translational risk assessment of QTc prolongation in different species and patient populations, if mediated by the hERG channel.
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Affiliation(s)
- Verena Gotta
- Systems Pharmacology Leiden Academic Centre for Drug Research (LACDR) Leiden University Leiden The Netherlands; Pediatric Pharmacology and Pharmacometrics University of Basel Children's Hospital (UKBB) Basel Switzerland
| | - Zhiyi Yu
- Division of Medicinal Chemistry Leiden Academic Centre for Drug Research (LACDR) Leiden University Leiden The Netherlands
| | - Frank Cools
- Global Safety Pharmacology Janssen Research & Development Janssen Pharmaceutica NV Beerse Belgium
| | - Karel van Ammel
- Global Safety Pharmacology Janssen Research & Development Janssen Pharmaceutica NV Beerse Belgium
| | - David J Gallacher
- Global Safety Pharmacology Janssen Research & Development Janssen Pharmaceutica NV Beerse Belgium
| | - Sandra A G Visser
- Quantitative Pharmacology and Pharmacometrics/Merck Research Laboratories Merck & Co., Inc. Upper Gwynedd Pennsylvania
| | - Frederick Sannajust
- SALAR-Safety and Exploratory Pharmacology Department/Merck Research Laboratories Merck & Co., Inc. West Point Pennsylvania
| | - Pierre Morissette
- SALAR-Safety and Exploratory Pharmacology Department/Merck Research Laboratories Merck & Co., Inc. West Point Pennsylvania
| | - Meindert Danhof
- Systems Pharmacology Leiden Academic Centre for Drug Research (LACDR) Leiden University Leiden The Netherlands
| | - Piet H van der Graaf
- Systems Pharmacology Leiden Academic Centre for Drug Research (LACDR) Leiden University Leiden The Netherlands; Certara Quantitative Systems Pharmacology Canterbury United Kingdom
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39
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Knight-Schrijver V, Chelliah V, Cucurull-Sanchez L, Le Novère N. The promises of quantitative systems pharmacology modelling for drug development. Comput Struct Biotechnol J 2016; 14:363-370. [PMID: 27761201 PMCID: PMC5064996 DOI: 10.1016/j.csbj.2016.09.002] [Citation(s) in RCA: 64] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 09/08/2016] [Accepted: 09/19/2016] [Indexed: 01/01/2023] Open
Abstract
Recent growth in annual new therapeutic entity (NTE) approvals by the U.S. Food and Drug Administration (FDA) suggests a positive trend in current research and development (R&D) output. Prior to this, the cost of each NTE was considered to be rising exponentially, with compound failure occurring mainly in clinical phases. Quantitative systems pharmacology (QSP) modelling, as an additional tool in the drug discovery arsenal, aims to further reduce NTE costs and improve drug development success. Through in silico mathematical modelling, QSP can simulate drug activity as perturbations in biological systems and thus understand the fundamental interactions which drive disease pathology, compound pharmacology and patient response. Here we review QSP, pharmacometrics and systems biology models with respect to the diseases covered as well as their clinical relevance and applications. Overall, the majority of modelling focus was aligned with the priority of drug-discovery and clinical trials. However, a few clinically important disease categories, such as Immune System Diseases and Respiratory Tract Diseases, were poorly covered by computational models. This suggests a possible disconnect between clinical and modelling agendas. As a standard element of the drug discovery pipeline the uptake of QSP might help to increase the efficiency of drug development across all therapeutic indications.
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Affiliation(s)
| | - V. Chelliah
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK
| | | | - N. Le Novère
- Babraham Institute, Babraham Research Campus, Cambridge CB22 3AT, UK
- Corresponding author.
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Comprehensive in vitro Proarrhythmia Assay (C i PA): Pending issues for successful validation and implementation. J Pharmacol Toxicol Methods 2016; 81:21-36. [DOI: 10.1016/j.vascn.2016.05.012] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2016] [Revised: 05/21/2016] [Accepted: 05/23/2016] [Indexed: 12/29/2022]
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Gaspar D, Zeugolis DI. Engineering in vitro complex pathophysiologies for drug discovery purposes. Drug Discov Today 2016; 21:1341-1344. [DOI: 10.1016/j.drudis.2016.08.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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