Key result
An in silico classifier combining Tx, TqNet, and Ttriang achieved an accuracy of 94.5% for predicting drug-induced torsade de pointes risk, compared to 78.9% for the standard hERG IC50 test.
Why the study?
Currently used TdP risk-assessment methods are effective but expensive and prone to false positives, prompting evaluation of biomarkers and development of an in silico risk classifier.
An in silico risk classifier combining multiple action potential biomarkers can accurately predict drug-induced torsade de pointes risk, outperforming the standard hERG IC50 test.
High Resolution Image Download MS PowerPoint Slide 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 (IC 50 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 IC 50 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 https://riunet.upv.es/handle/10251/136919 .
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Llopis‐Lorente et al. (2020) studied Drug-induced torsade de pointes (TdP) risk (n=109). In silico risk classifier combining Tx, TqNet, and Ttriang vs. hERG IC50 test was evaluated on Accuracy of TdP risk classification. An in silico classifier combining Tx, TqNet, and Ttriang achieved an accuracy of 94.5% for predicting drug-induced torsade de pointes risk, compared to 78.9% for the standard hERG IC50 test.
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