Unraveling Structural Alerts in Marketed Drugs for Improving Adverse Outcome Pathway Framework of Drug-Induced QT Prolongation

Abstract

In pharmaceutical treatment, many non-cardiac drugs carry the risk of prolonging the QT interval, which can lead to fatal cardiac complications such as torsades de points (TdP). Although the unexpected blockade of ion channels has been widely considered to be one of the main reasons for affecting the repolarization phase of the cardiac action potential and leading to QT interval prolongation, the lack of knowledge regarding chemical structures in drugs that may induce the prolongation of the QT interval remains a barrier to further understanding the underlying mechanism and developing an effective prediction strategy. In this study, we thoroughly investigated the differences in chemical structures between QT-prolonging drugs and drugs with no drug-induced QT prolongation (DIQT) concerns, based on the Drug-Induced QT Prolongation Atlas (DIQTA) dataset. Three categories of structural alerts (SAs), namely amines, ethers, and aromatic compounds, appeared in large quantities in QT-prolonging drugs, but rarely in drugs with no DIQT concerns, indicating a close association between SAs and the risk of DIQT. Moreover, using the molecular descriptors associated with these three categories of SAs as features, the structure-activity relationship (SAR) model for predicting the high risk of inducing QT interval prolongation of marketed drugs achieved recall rates of 72.5% and 80.0% for the DIQTA dataset and the FDA Adverse Event Reporting System (FAERS) dataset, respectively. Our findings may promote a better understanding of the mechanism of DIQT and facilitate research on cardiac adverse drug reactions in drug development.

Keywords: adverse outcome pathway; drug safety evaluation; drug-induced prolongation of QT interval; machine learning; structure–activity relationship.

Figure 1

(a) Distribution of drugs containing tertiary amines among different therapeutic categories of QT-prolonging drugs; (b) distribution of drugs containing alkylarylethers among different therapeutic categories of QT-prolonging drugs; (c) distribution of drugs containing aryl halides among different therapeutic categories of QT-prolonging drugs.

Figure 2

MCCs and recall rates achieved by SMV, LR, RF, and XGBoost with 1000 iterations of five-fold cross-validation and by SVM with permutation dataset. (a) The distribution of MCCs achieved by four models and SVM with permutation dataset. (b) The distribution of recall rates achieved by four models and SVM with permutation dataset.

Figure 3

The predictive results achieved by SVM model with full features and ten features related to the 24 structural alerts.

Figure 4

The trend of recall rate changed when the number of drugs increased in the dataset. The Y-axis indicates the recall rate, and the X-axis is the number of drugs used for the prediction.

Figure 5

Workflow of our study.