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.

Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation

“…Annotations with complete attention maps are provided in Supplementary File S1. The table reveals that MoLFormer-XL was used to report SAs associated with DIQT in previous studies, such as amines, ethers, and aromatic halogens [13]. Furthermore, Aniline structures, such as pyridine in disopyramide phosphate and the benzimidazole ring in quinidine gluconate and vandetanib, were also identified as crucial structural features associated with DIQT [38][39][40].…”

“…We compared the performance of our proposed MoLFormer-XL-CNN model with state-of-the-art methods reported in previous studies (Figure 1). In previous studies, Wulin Long et al indicated that SVM achieved the highest MCC value and recall rate (mean MCC = 0.591, mean recall rate = 0.870), predicting the high risk of inducing QT interval prolongation of marketed drugs [13]. Liyuan Kang et al indicated that SVM performed better in detecting the marketed drugs with high teratogenic risk (mean MCC = 0.312) [12].…”

“…These methodologies offer pivotal approaches to correlate the chemical structure of drugs with their propensity for ADRs, representing indispensable tools for predicting drug-induced adverse reactions [9,10]. Previous research has demonstrated the utility of SAR models employing conventional machine-learning methods, including Support Vector Machine (SVM) [11][12][13][14], Random Forest (RF) [15][16][17], and Decision Forest (DF) [18,19]. Notably, these models have exhibited promising performance in predicting liver toxicity [15,[18][19][20], cardiotoxicity [13,14,17,21], and other adverse reactions.…”

“…Previous research has demonstrated the utility of SAR models employing conventional machine-learning methods, including Support Vector Machine (SVM) [11][12][13][14], Random Forest (RF) [15][16][17], and Decision Forest (DF) [18,19]. Notably, these models have exhibited promising performance in predicting liver toxicity [15,[18][19][20], cardiotoxicity [13,14,17,21], and other adverse reactions. In our previous investigation, we constructed datasets encompassing three distinct ADRs based on FDA-approved drug labeling information: the drug-induced QT prolongation atlas (DIQTA) [22], drug-induced teratogenicity dataset (DITD) [12], and drug-induced rhabdomyolysis atlas (DIRA) [23].…”

“…In our previous investigation, we constructed datasets encompassing three distinct ADRs based on FDA-approved drug labeling information: the drug-induced QT prolongation atlas (DIQTA) [22], drug-induced teratogenicity dataset (DITD) [12], and drug-induced rhabdomyolysis atlas (DIRA) [23]. Leveraging conventional machine-learning algorithms in conjunction with molecular descriptors, we predicted the risk associated with these three types of ADRs [12,13,16].…”