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Predicting the potency of hERG K+ channel inhibition by combining 3D-QSAR pharmacophore and 2D-QSAR models

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Abstract

Blockade of the hERG K+ channel has been identified as the most important mechanism of QT interval prolongation and thus inducing cardiac risk. In this work, an ensemble of 3D-QSAR pharmacophore models was constructed to provide insight into the determinants of the interactions between the hERG K+ channel and channel inhibitors. To predict hERG inhibitory activities, the predicted values from the ensemble of models were averaged, and the results thus obtained showed that the predictive ability of the combined 3D-QSAR pharmacophore model was greater that those of the individual models. Also, using the same training and test sets, a 2D-QSAR model based on a heuristic machine-learning method was developed in order to analyze the physicochemical characters of hERG inhibitors. The models indicated that the inhibitors have certain key inhibitory features in common, including hydrophobicity, aromaticity, and flexibility. A final model was developed by combining the combined 3D-QSAR pharmacophore with the 2D-QSAR model, and this final model outperformed any other individual model, showing the highest predictive ability and the lowest deviation. This model can not only predict hERG inhibitory potency accurately, thus allowing fast cardiac safety evaluation, but it provides an effective tool for avoiding hERG inhibitory liability and thus enhanced cardiac risk in the design and optimization of new chemical entities.

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Acknowledgments

The authors gratefully acknowledge financial support from National Major Science and Technology Project of China (grant no. 2009ZX09501-003), the National Natural Science Foundation of China (grant no. 21072231) and the Qing Lan Project of Jiangsu Province. We also thank Semichem Company for providing the complete CODESSA software for our evaluation.

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  1. Department of Medicinal Chemistry, China Pharmaceutical University, 24 Tongjiaxiang, JiangSu, 210009, People’s Republic of China

    Yayu Tan, Qidong You, Haopeng Sun & Manhua Li

  2. School of Basic Science, China Pharmaceutical University, 24 Tongjiaxiang, JiangSu, 210009, People’s Republic of China

    Yayu Tan & Yadong Chen

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  1. Yayu Tan
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  2. Yadong Chen
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Correspondence to Yadong Chen.

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Tan, Y., Chen, Y., You, Q. et al. Predicting the potency of hERG K+ channel inhibition by combining 3D-QSAR pharmacophore and 2D-QSAR models. J Mol Model 18, 1023–1036 (2012). https://doi.org/10.1007/s00894-011-1136-y

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