Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk

[EN] Background and Objective In silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In additio...

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Autores: Llopis-Lorente, Jordi, Trenor Gomis, Beatriz Ana|||0000-0001-9166-6112, Saiz Rodríguez, Francisco Javier|||0000-0002-9850-0825
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/192412
Acceso en línea:https://riunet.upv.es/handle/10251/192412
Access Level:acceso abierto
Palabra clave:In-silico
Proarrhythmic-risk
Torsade de Pointes
Cardiac safety
Population of models
TECNOLOGIA ELECTRONICA
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
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oai_identifier_str oai:riunet.upv.es:10251/192412
network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk
title Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk
spellingShingle Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk
Llopis-Lorente, Jordi
In-silico
Proarrhythmic-risk
Torsade de Pointes
Cardiac safety
Population of models
TECNOLOGIA ELECTRONICA
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
title_short Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk
title_full Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk
title_fullStr Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk
title_full_unstemmed Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk
title_sort Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic risk
dc.creator.none.fl_str_mv Llopis-Lorente, Jordi
Trenor Gomis, Beatriz Ana|||0000-0001-9166-6112
Saiz Rodríguez, Francisco Javier|||0000-0002-9850-0825
author Llopis-Lorente, Jordi
author_facet Llopis-Lorente, Jordi
Trenor Gomis, Beatriz Ana|||0000-0001-9166-6112
Saiz Rodríguez, Francisco Javier|||0000-0002-9850-0825
author_role author
author2 Trenor Gomis, Beatriz Ana|||0000-0001-9166-6112
Saiz Rodríguez, Francisco Javier|||0000-0002-9850-0825
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Ingeniería Electrónica
Escuela Técnica Superior de Ingeniería Aeroespacial y Diseño Industrial
Escuela Técnica Superior de Ingeniería Industrial
Centro de Investigación e Innovación en Bioingeniería
GENERALITAT VALENCIANA
European Commission
Universitat Politècnica de València
Ministerio de Ciencia, Innovación y Universidades
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv In-silico
Proarrhythmic-risk
Torsade de Pointes
Cardiac safety
Population of models
TECNOLOGIA ELECTRONICA
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
topic In-silico
Proarrhythmic-risk
Torsade de Pointes
Cardiac safety
Population of models
TECNOLOGIA ELECTRONICA
03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades
description [EN] Background and Objective In silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In addition, classification tools are usually binary and are not validated using an external data set. Here we analyze the role of incorporating electrophysiological variability in the prediction of drug-induced arrhythmogenic-risk, using a ternary classification and two external validation datasets. Methods The effects of the 12 training CiPA drugs were simulated at three different concentrations using a single baseline model and an electrophysiologically calibrated population of models. 9 biomarkers related with action potential (AP), calcium dynamics and net charge were measured for each simulated concentration. These biomarkers were used to build ternary classifiers based on Support Vector Machines (SVM) methodology. Classifiers were validated using two external drug sets: the 16 validation CiPA drugs and 81 drugs from CredibleMeds database. Results Population of models allowed to obtain different AP responses under the same pharmacological intervention and improve the prediction of drug-induced TdP with respect to the baseline model. The classification tools based on population of models achieve an accuracy higher than 0.8 and a mean classification error (MCE) lower than 0.3 for both validation drug sets and for the two electrophysiological action potential models studied (Tomek et al. 2020 and a modified version of O'Hara et al. 2011). In addition, simulations with population of models allowed the identification of individuals with lower conductances of IKr, IKs, and INaK and higher conductances of ICaL, INaL, and INCX, which are more prone to develop TdP. Conclusions The methodology presented here provides new opportunities to assess drug-induced TdP-risk, taking into account electrophysiological variability and may be helpful to improve current cardiac safety screening methods.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-06-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/192412
url https://riunet.upv.es/handle/10251/192412
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv European Commission https://doi.org/10.13039/501100000780 H2020 101016496 Simulation of Cardiac Devices & Drugs for in-silico Testing and Certification
Ministerio de Universidades MIU FPU18%2F01659 DESARROLLO DE MODELOS MULTI-ESCALA DE CORAZON HUMANO Y HERRAMIENTAS COMPUTACIONALES PARA LA EVALUACION DE LA CARDIOTOXICIDAD DE FARMACOS EN CONDICIONES SANAS Y DE INSUFICIENCIA CARDIACA
Generalitat Valenciana https://doi.org/10.13039/501100003359 PROMETEO%2F2020%2F043 MODELOS IN-SILICO MULTI-FISICOS Y MULTI-ESCALA DEL CORAZON PARA EL DESARROLLO DE NUEVOS METODOS DE PREVENCION, DIAGNOSTICO Y TRATAMIENTO EN MEDICINA PERSONALIZADA (HEART IN-SILICO MODELS)
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Considering population variability of electrophysiological models improves the assessment of drug-induced torsadogenic riskLlopis-Lorente, JordiTrenor Gomis, Beatriz Ana|||0000-0001-9166-6112Saiz Rodríguez, Francisco Javier|||0000-0002-9850-0825In-silicoProarrhythmic-riskTorsade de PointesCardiac safetyPopulation of modelsTECNOLOGIA ELECTRONICA03.- Garantizar una vida saludable y promover el bienestar para todos y todas en todas las edades[EN] Background and Objective In silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In addition, classification tools are usually binary and are not validated using an external data set. Here we analyze the role of incorporating electrophysiological variability in the prediction of drug-induced arrhythmogenic-risk, using a ternary classification and two external validation datasets. Methods The effects of the 12 training CiPA drugs were simulated at three different concentrations using a single baseline model and an electrophysiologically calibrated population of models. 9 biomarkers related with action potential (AP), calcium dynamics and net charge were measured for each simulated concentration. These biomarkers were used to build ternary classifiers based on Support Vector Machines (SVM) methodology. Classifiers were validated using two external drug sets: the 16 validation CiPA drugs and 81 drugs from CredibleMeds database. Results Population of models allowed to obtain different AP responses under the same pharmacological intervention and improve the prediction of drug-induced TdP with respect to the baseline model. The classification tools based on population of models achieve an accuracy higher than 0.8 and a mean classification error (MCE) lower than 0.3 for both validation drug sets and for the two electrophysiological action potential models studied (Tomek et al. 2020 and a modified version of O'Hara et al. 2011). In addition, simulations with population of models allowed the identification of individuals with lower conductances of IKr, IKs, and INaK and higher conductances of ICaL, INaL, and INCX, which are more prone to develop TdP. Conclusions The methodology presented here provides new opportunities to assess drug-induced TdP-risk, taking into account electrophysiological variability and may be helpful to improve current cardiac safety screening methods.This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 101016496 (SimCardioTest). This workwas alsopartially supported by the Direccion General de Politica Cientifica de la Generalitat Valenciana (PROMETEO/2020/043). JL is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the Formacion de Profesorado Universitario (grant reference: FPU18/01659). Funding for open access charge: Universitat Politecnica de Valencia.ElsevierDepartamento de Ingeniería ElectrónicaEscuela Técnica Superior de Ingeniería Aeroespacial y Diseño IndustrialEscuela Técnica Superior de Ingeniería IndustrialCentro de Investigación e Innovación en BioingenieríaGENERALITAT VALENCIANAEuropean CommissionUniversitat Politècnica de ValènciaMinisterio de Ciencia, Innovación y UniversidadesRepositorio Institucional de la Universitat Politècnica de València Riunet20222022-06-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/192412reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengEuropean Commission https://doi.org/10.13039/501100000780 H2020 101016496 Simulation of Cardiac Devices & Drugs for in-silico Testing and CertificationMinisterio de Universidades MIU FPU18%2F01659 DESARROLLO DE MODELOS MULTI-ESCALA DE CORAZON HUMANO Y HERRAMIENTAS COMPUTACIONALES PARA LA EVALUACION DE LA CARDIOTOXICIDAD DE FARMACOS EN CONDICIONES SANAS Y DE INSUFICIENCIA CARDIACAGeneralitat Valenciana https://doi.org/10.13039/501100003359 PROMETEO%2F2020%2F043 MODELOS IN-SILICO MULTI-FISICOS Y MULTI-ESCALA DEL CORAZON PARA EL DESARROLLO DE NUEVOS METODOS DE PREVENCION, DIAGNOSTICO Y TRATAMIENTO EN MEDICINA PERSONALIZADA (HEART IN-SILICO MODELS)open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1924122026-06-13T07:49:27Z
score 15.301603

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