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Prediction of hERG Liability - Using SVM Classification, Bootstrapping and Jackknifing
Hongmao Sun1, Ruili Huang1, Menghang Xia1
1National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, Bethesda, MD 20892, USA.
Insights
Predicting drug-induced cardiotoxicity is crucial. Support vector classification models effectively predict human ether-à-go-go-related gene (hERG) channel inhibition, aiding early drug discovery and preventing costly failures.
Area of Science:
- Computational chemistry
- Drug discovery
- Cardiovascular pharmacology
Background:
- Drug-induced QT prolongation causes life-threatening cardiotoxicity, primarily via blockage of the human ether-à-go-go-related gene (hERG) potassium channels.
- The hERG channel is a critical antitarget in early drug discovery to prevent late-stage development failures.
Purpose of the Study:
- To develop and validate predictive models for hERG channel inhibition using quantitative high-throughput screening (qHTS) data.
- To assess the impact of dataset rebalancing techniques on model performance and identify optimal strategies for predicting hERG liabilities.
Main Methods:
- Screening of 4,323 molecules for hERG channel inhibition using a thallium flux assay in a qHTS format.
- Development of support vector classification (SVC) models to predict hERG channel inhibition.
- Application of Jackknifing and bootstrapping for dataset rebalancing and analysis of their impact on model performance.
Main Results:
- SVC models achieved an averaged area under the receiver operator characteristics curve (AUC-ROC) of 0.93 on the tested compounds.
- Dataset rebalancing techniques did not improve predictive power; optimal cutoffs were found to restore classifier sensitivity and specificity.
- An external validation set of 66 drug molecules showed an AUC-ROC of 0.86 for the SVC model.
Conclusions:
- SVC models are effective tools for predicting hERG channel inhibition early in the drug discovery process.
- Optimal cutoff selection is more critical than dataset rebalancing for binary classifiers predicting hERG liabilities.
- This modeling approach demonstrates significant utility in identifying potential cardiotoxicity risks associated with drug candidates.
Abstract:
Drug-induced QT prolongation leads to life-threatening cardiotoxicity, mostly through blockage of the human ether-à-go-go-related gene (hERG) encoded potassium ion (K+ ) channels. The hERG channel is one of the most important antitargets to be addressed in the early stage of drug discovery process, in order to avoid more costly failures in the development phase. Using a thallium flux assay, 4,323 molecules were screened for hERG channel inhibition in a quantitative high throughput screening (qHTS) format. Here, we present support vector classification (SVC) models of hERG channel inhibition with the averaged area under the receiver operator characteristics curve (AUC-ROC) of 0.93 for the tested compounds. Both Jackknifing and bootstrapping have been employed to rebalance the heavily biased training datasets, and the impact of these two under-sampling rebalance methods on the performance of the predictive models is discussed. Our results indicated that the rebalancing techniques did not enhance the predictive power of the resulting models; instead, adoption of optimal cutoffs could restore the desirable balance of sensitivity and specificity of the binary classifiers. In an external validation set of 66 drug molecules, the SVC model exhibited an AUC-ROC of 0.86, further demonstrating the utility of this modeling approach to predict hERG liabilities.
