Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features

• Published in: Frontiers in pharmacology Vol. 8; p. 816
• Main Authors: Parikh, Jaimit, Gurev, Viatcheslav, Rice, John J
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 14.11.2017
• Subjects: cardiac modeling; classification and prediction; early afterdepolarization; machine-learning; Pharmacology; Torsades de Pointes; early afterdepolarization; machine-learning; classification and prediction; Torsades de Pointes; cardiac modeling
• ISSN: 1663-9812; 1663-9812
• DOI: 10.3389/fphar.2017.00816

While pre-clinical Torsades de Pointes (TdP) risk classifiers had initially been based on drug-induced block of hERG potassium channels, it is now well established that improved risk prediction can be achieved by considering block of non-hERG ion channels. The current multi-channel TdP classifiers can be categorized into two classes. First, the classifiers that take as input the values of drug-induced block of ion channels (direct features). Second, the classifiers that are built on features extracted from output of the drug-induced multi-channel blockage simulations in the models (derived features). The classifiers built on derived features have thus far not consistently provided increased prediction accuracies, and hence casts doubt on the value of such approaches given the cost of including biophysical detail. Here, we propose a new two-step method for TdP risk classification, referred to as Multi-Channel Blockage at Early After Depolarization (MCB@EAD). In the first step, we classified the compound that produced insufficient hERG block as non-torsadogenic. In the second step, the role of non-hERG channels to modulate TdP risk are considered by constructing classifiers based on direct or derived features at critical hERG block concentrations that generates EADs in the computational cardiac cell models. MCB@EAD provides comparable or superior TdP risk classification of the drugs from the direct features in tests against published methods. TdP risk for the drugs highly correlated to the propensity to generate EADs in the model. However, the derived features of the biophysical models did not improve the predictive capability for TdP risk assessment.