Multidimensional estimation of cardiac arrhythmia potential across space and time
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Euan A. Ashley | Ellen Kuhl | Francisco Sahli Costabal | Kinya Seo | F. S. Costabal | E. Ashley | E. Kuhl | Kinya Seo
[1] E. Sobie,et al. Improved Prediction of Drug‐Induced Torsades de Pointes Through Simulations of Dynamics and Machine Learning Algorithms , 2016, Clinical pharmacology and therapeutics.
[2] Natalia Trayanova,et al. From genetics to smart watches: developments in precision cardiology , 2018, Nature Reviews Cardiology.
[3] Robert B. Gramacy,et al. Particle Learning of Gaussian Process Models for Sequential Design and Optimization , 2009, 0909.5262.
[4] Euan A Ashley,et al. Early somatic mosaicism is a rare cause of long-QT syndrome , 2016, Proceedings of the National Academy of Sciences.
[5] Gary R. Mirams,et al. Simulation of multiple ion channel block provides improved early prediction of compounds’ clinical torsadogenic risk , 2011, Cardiovascular research.
[6] Jiang Yao,et al. Predicting critical drug concentrations and torsadogenic risk using a multiscale exposure-response simulator. , 2019, Progress in biophysics and molecular biology.
[7] Hongbin Yang,et al. Corrigendum: In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts , 2018, Front. Chem..
[8] Kousik Krishnan,et al. Current concepts in the mechanisms and management of drug-induced QT prolongation and torsade de pointes. , 2007, American heart journal.
[9] S. Polak,et al. Am I or am I not proarrhythmic? Comparison of various classifications of drug TdP propensity. , 2017, Drug discovery today.
[10] Zhilin Qu,et al. Stochastic initiation and termination of calcium-mediated triggered activity in cardiac myocytes , 2017, Proceedings of the National Academy of Sciences.
[11] Ellen Kuhl,et al. The Living Heart Project: A robust and integrative simulator for human heart function. , 2014, European journal of mechanics. A, Solids.
[12] Yoram Rudy,et al. Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation , 2011, PLoS Comput. Biol..
[13] A. Garfinkel,et al. Early afterdepolarizations in cardiac myocytes: beyond reduced repolarization reserve. , 2013, Cardiovascular research.
[14] Gary R. Mirams,et al. Computational assessment of drug-induced effects on the electrocardiogram: from ion channel to body surface potentials , 2013, British journal of pharmacology.
[15] S. Göktepe,et al. Computational modeling of cardiac electrophysiology: A novel finite element approach , 2009 .
[16] Amy Maxmen,et al. Busting the billion-dollar myth: how to slash the cost of drug development , 2016, Nature.
[17] F. S. Costabal,et al. Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification. , 2019, Computer methods in applied mechanics and engineering.
[18] A. Camm,et al. Relationships between preclinical cardiac electrophysiology, clinical QT interval prolongation and torsade de pointes for a broad range of drugs: evidence for a provisional safety margin in drug development. , 2003, Cardiovascular research.
[19] S. Göktepe,et al. Computational modeling of chemo‐electro‐mechanical coupling: A novel implicit monolithic finite element approach , 2013, International journal for numerical methods in biomedical engineering.
[20] Gary R. Mirams,et al. Hierarchical Bayesian inference for ion channel screening dose-response data , 2016, Wellcome open research.
[21] Gary R. Mirams,et al. Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator , 2017, bioRxiv.
[22] T. Colatsky,et al. The Comprehensive in Vitro Proarrhythmia Assay (CiPA) initiative - Update on progress. , 2016, Journal of pharmacological and toxicological methods.
[23] D. Strauss,et al. An evaluation of 30 clinical drugs against the comprehensive in vitro proarrhythmia assay (CiPA) proposed ion channel panel. , 2016, Journal of pharmacological and toxicological methods.
[24] Jiang Yao,et al. Predicting drug‐induced arrhythmias by multiscale modeling , 2018, International journal for numerical methods in biomedical engineering.
[25] N. Trayanova,et al. Computational models in cardiology , 2018, Nature Reviews Cardiology.
[26] E. Kuhl,et al. Predicting the cardiac toxicity of drugs using a novel multiscale exposure–response simulator , 2018, Computer methods in biomechanics and biomedical engineering.
[27] Jaimit Parikh,et al. Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features , 2017, Front. Pharmacol..
[28] R. W. Hansen,et al. The price of innovation: new estimates of drug development costs. , 2003, Journal of health economics.
[29] D. Noble,et al. A model for human ventricular tissue. , 2004, American journal of physiology. Heart and circulatory physiology.
[30] N. Stockbridge,et al. Dealing with Global Safety Issues , 2013, Drug Safety.
[31] George E Karniadakis,et al. Omics, big data and machine learning as tools to propel understanding of biological mechanisms and to discover novel diagnostics and therapeutics. , 2018, Metabolism: clinical and experimental.
[32] D. Noble,et al. Mathematical models of the electrical action potential of Purkinje fibre cells , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.