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Blockage of some ion channels and in particular, the hERG cardiac potassiumchannel delays cardiac repolarization and can induce arrhythmia. In some casesit leads to a potentially life-threatening arrhythmia known as Torsade dePointes (TdP). Therefore recognizing drugs with TdP risk is essential.Candidate drugs that are determined not to cause cardiac ion channel blockageare more likely to pass successfully through clinical phases II and III trials(and preclinical work) and not be withdrawn even later from the marketplace dueto cardiotoxic effects. The objective of the present study is to develop an SARmodel that can be used as an early screen for torsadogenic (causing TdParrhythmias) potential in drug candidates. The method is performed usingdescriptors comprised of atomic NMR chemical shifts and correspondinginteratomic distances which are combined into a 3D abstract space matrix. Themethod is called 3D-SDAR (3 dimensional spectral data-activity relationship)and can be interrogated to identify molecular features responsible for theactivity, which can in turn yield simplified hERG toxicophores. A dataset of 55hERG potassium channel inhibitors collected from Kramer et al. consisting of 32drugs with TdP risk and 23 with no TdP risk was used for training the 3D-SDARmodel.An ANN model with multilayer perceptron was used to define collinearitiesamong the independent 3D-SDAR features. A composite model from 200 randomiterations with 25% of the molecules in each case yielded the following figuresof merit: training, 99.2 %; internal test sets, 66.7%; external (blindvalidation) test set, 68.4%. In the external test set, 70.3% of positive TdPdrugs were correctly predicted. Moreover, toxicophores were generated from TdPdrugs. A 3D-SDAR was successfully used to build a predictive model fordrug-induced torsadogenic and non-torsadogenic drugs.

Development of models for predicting Torsade de Pointes cardiac arrhythmias using perceptron neural networks