Drug-induced torsade de pointes (TdP)
is a life-threatening ventricular
arrhythmia responsible for the withdrawal of many drugs from the market.
Although currently used TdP risk-assessment methods are effective,
they are expensive and prone to produce false positives. In recent
years, in silico cardiac simulations have proven
to be a valuable tool for the prediction of drug effects. The objective
of this work is to evaluate different biomarkers of drug-induced proarrhythmic
risk and to develop an in silico risk classifier.
Cellular simulations were performed using a modified version of the
O’Hara et al. ventricular action potential model and existing
pharmacological data (IC50 and effective free therapeutic
plasma concentration, EFTPC) for 109 drugs of known torsadogenic risk
(51 positive). For each compound, four biomarkers were tested: T
x (drug concentration leading to a 10% prolongation
of the action potential over the EFTPC), T
qNet (net charge carried by ionic currents when exposed to 10 times the
EFTPC with respect to the net charge in control), T
triang (triangulation for a drug concentration of 10 times
the EFTPC over triangulation in control), and T
EAD (drug concentration originating early afterdepolarizations
over EFTPC). Receiver operating characteristic (ROC) curves were built
for each biomarker to evaluate their individual predictive quality.
At the optimal cutoff point, accuracies for T
x, T
qNet, T
triang, and T
EAD were 89.9, 91.7,
90.8, and 78.9% respectively. The resulting accuracy of the hERG IC50 test (current biomarker) was 78.9%. When combining T
x, T
qNet and T
triang into a classifier based on decision trees,
the prediction improves, achieving an accuracy of 94.5%. The sensitivity
analysis revealed that most of the effects on the action potential
are mainly due to changes in I
Kr, I
CaL, I
NaL and I
Ks. In fact, considering that drugs affect only
these four currents, TdP risk classification can be as accurate as
when considering effects on the seven main currents proposed by the
CiPA initiative. Finally, we built a ready-to-use tool (based on more
than 450 000 simulations), which can be used to quickly assess
the proarrhythmic risk of a compound. In conclusion, our in
silico tool can be useful for the preclinical assessment
of TdP-risk and to reduce costs related with new drug development.
The TdP risk-assessment tool and the software used in this work are
available at .