Development and Evaluation of an in Silico Model for hERG Binding

It has been recognized that drug-induced QT prolongation is related to blockage of the human ether-a-go-go-related gene (hERG) ion channel. Therefore, it is prudent to evaluate the hERG binding of active compounds in early stages of drug discovery. In silico approaches provide an economic and quick method to screen for potential hERG liability. A diverse set of 90 compounds with hERG IC(50) inhibition data was collected from literature references. Fragment-based QSAR descriptors and three different statistical methods, support vector regression, partial least squares, and random forests, were employed to construct QSAR models for hERG binding affinity. Important fragment descriptors relevant to hERG binding affinity were identified through an efficient feature selection method based on sparse linear support vector regression. The support vector regression predictive model built upon selected fragment descriptors outperforms the other two statistical methods in this study, resulting in an r(2) of 0.912 and 0.848 for the training and testing data sets, respectively. The support vector regression model was applied to predict hERG binding affinities of 20 in-house compounds belonging to three different series. The model predicted the relative binding affinity well for two out of three compound series. The hierarchical clustering and dendrogram results show that the compound series with the best prediction has much higher structural similarity and more neighbors of training compounds than the other two compound series, demonstrating the predictive scope of the model. The combination of a QSAR model and postprocessing analysis, such as clustering and visualization, provides a way to assess the confidence level of QSAR prediction results on the basis of similarity to the training set.

[1]  Matthew Clark,et al.  Generalized Fragment-Substructure Based Property Prediction Method , 2005, J. Chem. Inf. Model..

[2]  Brian B. Goldman,et al.  A model for identifying HERG K+ channel blockers. , 2004, Bioorganic & medicinal chemistry.

[3]  Antranig Basman,et al.  HERG binding specificity and binding site structure: evidence from a fragment-based evolutionary computing SAR study. , 2004, Progress in biophysics and molecular biology.

[4]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[5]  Jules C Hancox,et al.  Troubleshooting problems with in vitro screening of drugs for QT interval prolongation using HERG K+ channels expressed in mammalian cell lines and Xenopus oocytes. , 2002, Journal of pharmacological and toxicological methods.

[6]  M. Sanguinetti,et al.  Molecular Genetic Insights into Cardiovascular Disease , 1996, Science.

[7]  Roy J. Vaz,et al.  Characterization of HERG potassium channel inhibition using CoMSiA 3D QSAR and homology modeling approaches. , 2003, Bioorganic & medicinal chemistry letters.

[8]  Robert P. Sheridan,et al.  Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR , 2004, J. Chem. Inf. Model..

[9]  Anton J. Hopfinger,et al.  Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships , 1994, J. Chem. Inf. Comput. Sci..

[10]  W. L. Jorgensen,et al.  Prediction of Properties from Simulations: Free Energies of Solvation in Hexadecane, Octanol, and Water , 2000 .

[11]  Gisbert Schneider,et al.  A Virtual Screening Method for Prediction of the hERG Potassium Channel Liability of Compound Libraries , 2002, Chembiochem : a European journal of chemical biology.

[12]  Michael J A Walker,et al.  Physicochemical determinants for drug induced blockade of HERG potassium channels: effect of charge and charge shielding. , 2003, Current medicinal chemistry. Cardiovascular and hematological agents.

[13]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[14]  A. Cavalli,et al.  Toward a pharmacophore for drugs inducing the long QT syndrome: insights from a CoMFA study of HERG K(+) channel blockers. , 2002, Journal of medicinal chemistry.

[15]  Gabriele Cruciani,et al.  Three-Dimensional Quantitative Structure-Properties Relationships , 2003 .

[16]  W. Crumb,et al.  Three-dimensional quantitative structure-activity relationship for inhibition of human ether-a-go-go-related gene potassium channel. , 2002, The Journal of pharmacology and experimental therapeutics.

[17]  Jinbo Bi,et al.  Dimensionality Reduction via Sparse Support Vector Machines , 2003, J. Mach. Learn. Res..

[18]  Kristin P. Bennett,et al.  Duality, Geometry, and Support Vector Regression , 2001, NIPS.

[19]  G. Keserü.  Prediction of hERG potassium channel affinity by traditional and hologram qSAR methods. , 2003, Bioorganic & medicinal chemistry letters.

[20]  Robert P. Sheridan,et al.  Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling , 2003, J. Chem. Inf. Comput. Sci..

[21]  Manfred Kansy,et al.  Predicting plasma protein binding of drugs: a new approach. , 2002, Biochemical pharmacology.

[22]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[23]  M. Sanguinetti,et al.  A structural basis for drug-induced long QT syndrome. , 2000, Proceedings of the National Academy of Sciences of the United States of America.