Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block
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 ac...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-01-01
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| Series: | Frontiers in Physiology |
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| Online Access: | http://journal.frontiersin.org/article/10.3389/fphys.2017.01096/full |
| _version_ | 1818217071513698304 |
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| author | Eliott Tixier Eliott Tixier Fabien Raphel Fabien Raphel Damiano Lombardi Damiano Lombardi Jean-Frédéric Gerbeau Jean-Frédéric Gerbeau |
| author_facet | Eliott Tixier Eliott Tixier Fabien Raphel Fabien Raphel Damiano Lombardi Damiano Lombardi Jean-Frédéric Gerbeau Jean-Frédéric Gerbeau |
| author_sort | Eliott Tixier |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-12T07:02:02Z |
| format | Article |
| id | doaj.art-5023db891b514ba98e2a309addd16331 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-12T07:02:02Z |
| publishDate | 2018-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-5023db891b514ba98e2a309addd163312022-12-22T00:33:49ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2018-01-01810.3389/fphys.2017.01096298182Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel BlockEliott Tixier0Eliott Tixier1Fabien Raphel2Fabien Raphel3Damiano Lombardi4Damiano Lombardi5Jean-Frédéric Gerbeau6Jean-Frédéric Gerbeau7Inria Paris, Paris, FranceSorbonne Universités, Université Pierre et Marie Curie—Paris 6, UMR 7598 LJLL, Paris, FranceInria Paris, Paris, FranceSorbonne Universités, Université Pierre et Marie Curie—Paris 6, UMR 7598 LJLL, Paris, FranceInria Paris, Paris, FranceSorbonne Universités, Université Pierre et Marie Curie—Paris 6, UMR 7598 LJLL, Paris, FranceInria Paris, Paris, FranceSorbonne Universités, Université Pierre et Marie Curie—Paris 6, UMR 7598 LJLL, Paris, FranceThe 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.http://journal.frontiersin.org/article/10.3389/fphys.2017.01096/fullcardiac electrophysiologynumerical simulationsbidomain modelmicro-electrode arrayclassificationdrug safety evaluation |
| spellingShingle | Eliott Tixier Eliott Tixier Fabien Raphel Fabien Raphel Damiano Lombardi Damiano Lombardi Jean-Frédéric Gerbeau Jean-Frédéric Gerbeau Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block Frontiers in Physiology cardiac electrophysiology numerical simulations bidomain model micro-electrode array classification drug safety evaluation |
| title | Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block |
| title_full | Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block |
| title_fullStr | Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block |
| title_full_unstemmed | Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block |
| title_short | Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block |
| title_sort | composite biomarkers derived from micro electrode array measurements and computer simulations improve the classification of drug induced channel block |
| topic | cardiac electrophysiology numerical simulations bidomain model micro-electrode array classification drug safety evaluation |
| url | http://journal.frontiersin.org/article/10.3389/fphys.2017.01096/full |
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