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(and preclinical work) and not be withdrawn even later from the marketplace due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