Considering population variability of electrophysiological models improves the in silico assessment of drug-induced torsadogenic risk. (June 2022)

• Record Type: Journal Article
• Authors: Llopis-Lorente, Jordi; Trenor, Beatriz; Saiz, Javier
• Is Part Of: Computer methods and programs in biomedicine. Volume 221(2022)
• Publication Date: 2022-06
• Subjects: In-silico -- Proarrhythmic-risk -- Torsade de Pointes -- Cardiac safety -- Population of models; AP action potencial -- APDx action potential duration at x% of the repolarization -- BCL basic length cycle -- EAD early after depolarization -- EFTPc effective free therapeutic plasma concentration (peak) -- EMw electromechanical window -- IC50 half-maximal inhibitory concentration -- MCE mean classification error -- MCC Mathews correlation coefficient -- SVM support vector machines -- TdP Torsade de Pointes; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106934
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Inter-individual variability needs to be considered when predicting TdP-risk. TdP-risk classification accuracy improves when using population of models. Population of models identify ionic profiles more prone to develop TdP. Classification accuracy of the 16 CiPA validation drugs is 87.5%. TdP-risk prediction strategy is tested using 2 cell models and 2 drug data sets. Abstract: 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.