Welcome to the UPF Digital Repository

Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models

Show simple item record

dc.contributor.author Kopańska, Karolina
dc.contributor.author Rodríguez-Belenguer, Pablo
dc.contributor.author Llopis-Lorente, Jordi
dc.contributor.author Trenor, Beatriz
dc.contributor.author Saiz, Javier
dc.contributor.author Pastor Maeso, Manuel
dc.date.accessioned 2023-09-29T07:33:26Z
dc.date.available 2023-09-29T07:33:26Z
dc.date.issued 2023
dc.identifier.citation Kopańska K, Rodríguez-Belenguer P, Llopis-Lorente J, Trenor B, Saiz J, Pastor M. Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models. Arch Toxicol. 2023 Oct;97(10):2721-40. DOI: 10.1007/s00204-023-03557-6
dc.identifier.issn 0340-5761
dc.identifier.uri http://hdl.handle.net/10230/57991
dc.description.abstract In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.
dc.description.sponsorship Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors received funding from the eTRANSAFE project, Innovative Medicines Initiative 2 Joint Undertaking under Grant Agreement No. 777365, supported from European Union’s Horizon 2020 and the EFPIA. Authors declare that this work reflects only the author’s view, and that IMI-JU is not responsible for any use that may be made of the information it contains. Also, this project received funding from the European Union’s Horizon 2020 Research and Innovation programme under Grant Agreement No. 964537 (RISK-HUNT3R), which is part of the ASPIS cluster. We also received funding from the SimCardioTest supported by European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 101016496. J.L.L. is being funded by the Ministerio de Ciencia, Innovacion y Universidades for the “Formacion de Profesorado Universitario” (Grant Reference: FPU18/01659). The work was also partially support by the Dirección General de Política Científica de la Generalitat Valenciana (PROMETEO/2020/043).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartof Arch Toxicol. 2023 Oct;97(10):2721-40
dc.rights © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1007/s00204-023-03557-6
dc.subject.keyword Drug-induced ventricular arrhythmia
dc.subject.keyword In silico toxicology
dc.subject.keyword Machine learning
dc.subject.keyword Uncertainty
dc.subject.keyword Variability
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/777365
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/964537
dc.relation.projectID info:eu-repo/grantAgreement/EC/HE/101016496
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


This item appears in the following Collection(s)

Show simple item record

Search DSpace

Advanced Search


My Account


In collaboration with Compliant to Partaking