A Novel Approach Using Pharmacophore ... - ACS Publications

Chem. Res. Toxicol. 2007, 20, 217-226

217

A Novel Approach Using Pharmacophore Ensemble/Support Vector Machine (PhE/SVM) for Prediction of hERG Liability Max K. Leong* Department of Chemistry, National Dong Hwa UniVersity, Shoufeng, Hualien 97401, Taiwan ReceiVed September 8, 2006

A novel approach by using a panel of plausible pharmacophore hypothesis candidates to constitute the pharmacophore ensemble (PhE) and subject them to regression by support vector machine (SVM) has been developed for predicting the liability of human ether-a-go-go-related gene (hERG). This PhE/SVM scheme takes into account the protein conformational flexibility while interacting with structurally diverse ligands, which is crucial yet often neglected by most of the analogue-based modeling methods. Thirtynine molecules were carefully selected and cross-examined from the literature data for this study, of which 26 and 13 molecules were deliberately treated as the training set and the test set to generate the model and to validate the generated model, respectively. The final PhE/SVM model gave rise to an r2 value of 0.97 for observed vs predicted pIC50 values for the training set, a q2 value of 0.89 by the 10-fold cross-validation and an r2 value of 0.94 for the test set. Thus, this PhE/SVM model provides a fast and accurate tool for predicting liability of hERG and can be utilized to guide medicinal chemistry to avoid molecules with an inhibition potential of this potassium channel. Introduction Drugs that inhibit the human ether-a-go-go-related gene (hERG) are at great risk to lead to a prolongation of the QT interval or Torsade de Pointes (TdP) in the worst case (1-3). It has become a crucial consideration for drug discovery and development for the past decade (4, 5). Experiments to determine the hERG liability for the potential drug molecules, nevertheless, are expensive, time-consuming, and labor-demending (6). Computational approaches seem to be better alternatives. In fact, numerous in silico models have been proposed to predict hERG blockade activity (7-30), including the traditional modeling tools CoMFA (8), pharmacophore (9), quantitative structure-activity relationship (QSAR) (10, 16), HQSAR (12), CoMSiA (14), pharmacophore/QSAR (19), and structure-based modeling (21, 23). There are a number of excellent reviews (5, 20, 28), which describe the latest development, and the brief summary can be found in Table 1 of the publication by Song and Clark (26). In addition to those traditional modeling methodologies mentioned above, several new modeling tools have also recently been introduced, trying to address the hERG liability from different approaches. Cianchetta et al., for example, used pharmacophorebased GRID descriptors to construct a QSAR model (19); Tobita et al. utilized a support vector machine (SVM) to classify hERG inhibition (22); Song and Clark developed new QSAR models by using fragment-based descriptors in conjunction with various statistical methods, including an SVM (26); Seierstad and Agrafiotis proposed another QSAR model, in which the regression calculation was done by neural network ensemble (25); Sun published a model by using Bayesian classification (29), and Yoshida and Niwa employed a genetic algorithm to select significant descriptors to generate QSAR models (27). Despite their impressive performances, there is one common characteristic shared by these proposed in silico models: They fail to take into account the protein plasticity, which is not * To whom correspondence should be addressed.E-mail: leong@ mail.ndhu.edu.tw.

uncommon especially when protein interacts with structurally diverse molecules (31). The importance of protein flexibility can be further demonstrated by Carlson et al. (32), in which a dynamic pharmacophore was developed for HIV-1 integrase based on the molecular dynamics calculations derived from the protein-inhibitor cocomplex structure. In fact, Rajamani et al. constructed two homology models of the hERG channel, namely, one for the open state and the other for the partially open state, to account for the protein flexibility (21). It will be a more realistic approach to construct a protein conformation ensemble to accommodate the fact that protein will adopt different conformations to interact with structurally diverse ligands provided that the protein or protein-ligand cocomplex structure is available and different protein conformations will yield different pharmacophore models. Little or no difference among those protein conformations or pharmacophore models can be expected, provided that those ligands appear to be structurally similar. On the other hand, the discrepancy among different protein conformations or pharmacophore models will be even more pronounced especially when protein interacts with structurally highly diverse ligands such as in the case of hERG. The pharmacophore ensemble (PhE), which consists of a panel of plausible pharmacophore hypotheses, can be constructed in lieu of a protein conformation ensemble in case the protein or protein-ligand cocomplex structure is not available or reliable enough to perform structure-based modeling. By doing so, the issue of protein plasticity can be taken into consideration without explicit protein structures. Furthermore, it is reasonable to assert that the true pharmacophore model is very close to at least one of the model candidates and only shows little deviation, suggesting that a true model can be the combination of all model candidates with different weights for a given protein-ligand interaction. In other words, different weights will be given to the corresponding pharmacophore model candidate and are governed by energy states. Upon completion of PhE construction, an appropriate datamining tool will be employed to construct the relationships among all of the model candidates for a given PhE. In this study,

10.1021/tx060230c CCC: $37.00 © 2007 American Chemical Society Published on Web 01/30/2007

218 Chem. Res. Toxicol., Vol. 20, No. 2, 2007

Leong

Table 1. Selected Compounds for This Study, Their IC50 (nM) Values or Average Values if Applicable, and References molecules

IC50 (nM)

refs

astemizole cisapride E-4031 dofetilide sertindole pimozide norastemizole haloperidol droperidol verapamil ziprasidone risperidone domperidone halofantrine loratadine clozapine olanzapine quinidine mesoridazine mizolastine bepridil azimilide vesnarinone desipramine mibefradil chlorpromazine ketoconazole alosetron imipramine granisetron cocaine dolasetron amitriptyline diltiazem glibenclamide grepafloxacin sildenafil moxifloxacin nicotine

0.9a 7a 8a 12a 14a 18a 28a 28a 32a 143a 152a 155a 162a 173b 174a 191a 231a 320a 320a 355b 550a 560b 1100a 1390a 1430c 1470b 1900a 3200a 3400a 3730a 5754a 5950a 10000a 17300a 74131d 77625b 100000a 117490b 244800a

8, 13 8, 9 8, 76 8, 77 8, 9, 78 8, 79 8, 80 8, 9 8, 81 8, 9 8, 9 8, 9 8, 80 8, 9, 80 8, 82 8, 83 9 9, 84 9 8, 10 8, 80, 85 8, 86 9, 87 9, 88 8, 89 8, 9, 80 9, 90 9, 29 8, 9, 80 8, 9, 80 8, 91 8, 80 8, 80 8, 9 8, 80, 92 8, 93 8, 80 8, 93 9, 29

a In human embryonic kidney (HEK) cells. b In Chinese hamster ovary (CHO) cells. c In African green monkey kidney-derived cell line COS. d In neuroblastoma cells.

an SVM, which was developed by Vapnik et al. in 1995 (33) and has been extensively used in a wide range of applications (22, 34-56), will be employed since a recent study by Yao et al. (57) showed that SVM performs no worse than any other regression/classification tools, such as multiple linear regression (58), partial least-squares regression (59), genetic algorithm (60), and artificial neural networks (61), if no better than the others. The main advantages of SVM over other regression/classification methods are its dimensional independence, limited number of freedom, excellent generalization capability, global optimum, and little effort to implement (62).

Computational Methods Data Collections. To construct quality data for this investigation, published IC50 values for hERG potassium channel blocking activity were selected from the literature and carefully cross-examined for consistency. If there were two or more available biological data for a given compound and in very close range, the average values were then taken in order to warrant better consistency. Molecular structures were cautiously inspected so that compounds without defined stereochemistry such as racemates were excluded. All molecules enlisted in this study, their biological data, and references to the literature are listed in Table 1.

Conformation Search. The conformational search calculations of all molecules were carried out by MacroModel (Schro¨dinger, Portland, OR) since a number of studies (6365) have demonstrated that the low-mode conformation search (66) together with the GB/SA hydration algorithm implemented in the MacroModel perform better than the Catalyst’s Best and Fast conformation generation algorithms. Additionally, the solvation effect was taken into consideration by using water as the solvent with constant dielectric constant and the MMFFs force field was selected in this study. Energy minimization was done by truncated-Newton conjugated gradient method (TNCG); mixed Monte Carlo multiple minimum/low mode conformational search was selected because of its speed and efficiency as compared with any other searching algorithms in general, as described in the MacroModel user’s manual; and the number of unique structures and energy windows were set to 255 and 83.7 kJ/mol (or 20 kcal/mol), respectively, in order to be accommodated by Catalyst. The generated conformer output files, stored in mae format, were then converted into MDL sd files, which can be recognized by Catalyst, by using the Schro¨dinger’s utility program sdconvert. Training Set Selection. Good pharmacophore hypotheses can only be generated from excellent selection of a training set, suggesting that any subtle flaw in choosing compounds to construct the training set, especially in the case of redundancy, highly possibly results in overfitted or overtrained models. More specifically, the critical factor to construct a perfect training set is to let the program “learn” new knowledge from the input. Structurally similar compounds with substantially different biological activities, for example, will serve as perfect entries for the training set. More detailed selection criteria can be found at a number of publications (67, 68) and in Catalyst’s user manual. Twenty-six compounds were selected to construct the training set for automatic pharmacophore generation and regression based on compounds’ activities and chemical structures in order to achieve statistic significance, and the remaining 13 compounds were placed in the test set, which was completely irrelevant to the model generations per se and only served to validate those hypotheses generated from the training set. Tables 2 and 3 list compounds used in the training set and the test set, respectively, and their corresponding negative logarithm IC50 values, namely, pIC50, since all of the calculations and analyses within Catalyst are carried out in a logarithm scale. Another critical factor needed to take into consideration for categorizing compounds into the training set and the test set was the balance of activity span between these two sets. Consequently, the IC50 values of compounds in the training set ranged from 0.9 to 120000 nM, spanning 6 orders of magnitude, while that of compounds in the test set ranged from 28 to 240000 nM or 5 log units. Pharmacophore Generation. Hydrogen bond donors, hydrogen bond acceptors, and hydrophobic, ring aromatic, and/or positive ionizable chemical features were selected for calculations with different feature combinations and minimum, maximum, and total numbers for each selected chemical feature and total features. A variety of combinations of variable weight and variable tolerance hypothesis generation options were employed to establish the hypothesis diversity. The costs of a generated hypothesis and the corresponding null hypothesis were retrieved from the log file, and the difference between these two values was calculated to survey the statistic quality of a hypothesis. All generated hypotheses were then employed to predict the biological activities of those compounds in the training set by

Prediction of hERG Liability

Chem. Res. Toxicol., Vol. 20, No. 2, 2007 219

Table 2. Experimentally Observed pIC50 Values of Compounds in the Training Set, Corresponding Predicted Values by Hypo A, Hypo B, and Hypo C, and Associated Statistic Numbers (Correlation Coefficient) r2, rmsd, Maximum Residual, Average Residual, and Standard Deviation of Residual observed

Hypo A

Hypo B

Hypo C

molecules

pIC50

pIC50 residual pIC50 residual pIC50 residual

astemizole cisapride E-4031 dofetilide sertindole pimozide haloperidol droperidol verapamil domperidone halofantrine loratadine mizolastine bepridil azimilide mibefradil chlorpromazine imipramine granisetron dolasetron amitriptyline diltiazem glibenclamide grepafloxacin sildenafil moxifloxacin

9.05 8.19 8.11 7.91 7.85 7.74 7.55 7.49 6.84 6.79 6.76 6.77 6.46 6.26 6.25 5.84 5.83 5.47 5.43 5.23 5.00 4.76 4.13 4.11 4.00 3.94

9.00 7.30 7.57 7.18 7.34 7.42 7.28 7.51 6.46 6.89 6.74 5.77 6.46 5.82 5.92 5.82 6.06 5.77 5.06 4.96 5.00 4.72 4.20 4.25 4.14 4.30

r2 RMSD max average SD

-0.05 -0.89 -0.54 -0.73 -0.52 -0.32 -0.28 0.02 -0.39 0.10 -0.02 -1.00 0.00 -0.44 -0.33 -0.02 0.22 0.30 -0.37 -0.27 0.00 -0.04 0.07 0.14 0.14 0.37

0.95

9.0 7.6 7.7 7.5 8.3 7.7 7.6 7.6 7.3 7.6 7.1 5.0 6.5 6.4 5.1 7.3 6.1 5.1 5.1 5.1 5.0 5.0 4.0 4.8 4.0 4.3

-0.09 -0.55 -0.44 -0.46 0.45 -0.05 0.09 0.15 0.44 0.85 0.31 -1.75 0.01 0.14 -1.15 1.48 0.28 -0.37 -0.33 -0.12 0.03 0.28 -0.13 0.66 0.02 0.41

0.83 1.31 1.00 0.29 0.27

9.64 7.55 7.57 7.30 7.92 7.85 7.57 7.51 7.01 7.00 6.82 7.02 6.44 6.28 5.16 6.89 5.92 5.19 5.16 5.16 5.09 5.12 4.36 4.85 4.15 3.74

0.59 -0.63 -0.54 -0.61 0.07 0.11 0.02 0.02 0.16 0.21 0.06 0.25 -0.01 0.02 -1.09 1.04 0.09 -0.27 -0.27 -0.06 0.09 0.36 0.23 0.74 0.15 -0.19

0.91 1.28 1.75 0.42 0.44

1.27 1.09 0.30 0.31

Table 3. Experimentally Observed pIC50 Values of Compounds in the Test Set, Corresponding Predicted Values by Hypo A, Hypo B, and Hypo C, and Associated Statistic Numbers (Correlation Coefficient) r2, rmsd, Maximum Residual, Average Residual, and Standard Deviation of Residual observed

Hypo A

Hypo B

Hypo C

molecules

pIC50


norastemizole ziprasidone risperidone clozapine cocaine quinidine ketoconazole desipramine mesoridazine nicotine alosetron olanzapine vesnarinone

7.55 6.82 6.79 6.72 5.24 6.49 5.72 5.86 6.49 3.61 5.49 6.64 5.96

7.52 6.82 6.85 7.14 4.82 5.49 5.55 5.77 6.08 3.05 5.00 6.85 5.08


-0.03 0.01 0.07 0.42 -0.42 -1.00 -0.17 -0.09 -0.42 -0.57 -0.49 0.22 -0.88

0.91

7.49 7.09 6.82 6.64 5.09 6.85 5.55 5.10 6.12 3.68 4.62 6.47 5.09

-0.06 0.27 0.04 -0.08 -0.15 0.36 -0.17 -0.75 -0.38 0.07 -0.88 -0.17 -0.87

0.88 1.78 1.00 0.37 0.32

7.43 6.92 6.80 6.32 5.16 6.77 6.16 5.16 6.13 3.70 4.80 6.21 5.15

-0.12 0.10 0.01 -0.40 -0.08 0.27 0.44 -0.70 -0.36 0.09 -0.70 -0.43 -0.80

0.86 1.77 0.88 0.33 0.31

1.77 0.80 0.35 0.26

the best fit and fast fit algorithms, followed by the evaluations of the correlation coefficient (r2) and root-mean-square deviation (rmsd), maximum residual, average residual, and standard deviation of residual between the observed and the predicted pIC50 values. Pharmacophore Evaluation. Only those hypotheses that showed good statistical significance, namely, cost differences between the generated hypothesis and the null hypothesis, r2, rmsd, and maximum residual values, were subject to further

evaluations by those compounds in the test set, which was constructed by the remaining 13 compounds. Those statistic parameters, employed to evaluate the quality of a hypothesis in the training set, were served again to examine the performance of a hypothesis in the test set by the same calculation schemes, namely, best fit and fast fit. Finally, only those pharmacophore hypotheses that functioned excellently in both training sets and test sets were eligible to construct the PhE. SVM Calculations. The regression calculations for the PhE were done by the SVM package LIBSVM (69), which consists of two modules for regression, namely, svm-train for producing SVM model based on the input data and options and svm-predict for predicting the new samples using a model previously built with svm-train. The predicted pIC50 values generated from the PhE in the training set were used as input for the svm-train calculations, while the predicted pIC50 values generated from the PhE in the test set were used as input for the svm-predict calculations. The regression modes, namely, -SVR and ν-SVR, were selected, and kernel type was set to be radial basis function, which is widely used among various kernels due to its simplicity and marked performance (70). To train the SVR, a perl script was written to systemically scan through those associated parameters, namely, cost C, the width of the RBF kernel γ, in case of -SVR, and ν in case of ν-SVR. The generated SVM models were further validated using a 10-fold cross-validation scheme, which was proven to work better than the widely used leave-one-out (71).

Results and Discussion PhE. Tables 2 and 3 list predicted pIC50 values along with their associated statistical numbers of three pharmacophore models, denoted by Hypo A, Hypo B, and Hypo C, selected to construct the PhE from all generated pharmacophore hypotheses using different combinations of chemical features and runtime conditions for the training set and the test set, respectively. These three models, consisting of the same four chemical features, namely, one hydrophobe, two aromatic rings, and one positive ionizable group, despite the fact that they display different topological relationships as shown in Figure 1, were selected because of their consistent and excellent performance in both the training set and the test set as shown in Tables 2 and 3, and their corresponding hypothesis characteristics, including weights, tolerances, and three-dimensional coordinates of chemical features and interfeature distances, are summarized in Tables 4-6, respectively. The distance between the chemical features hydrophobe and positive ionizable group in Hypo A, for example, is 5.874 Å, while that increases to 5.891 and 5.899 Å in Hypo B and Hypo C, respectively, as demonstrated in Figure 1. The angles centered at positive ionizable group and connecting to hydrophobe and two aromatic rings vary from 129.7 and 151.6° in Hypo A to 155.8 and 159.2° in Hypo B and 149.5 and 165.4° in Hypo C. The lengths between the positive ionizable group and the two aromatic rings show even greater differences, namely, 6.440 and 8.475 Å in Hypo A, 6.189 and 7.365 Å in Hypo B, and 6.448 and 7.426 Å in Hypo C. The angles constructed by the positive ionizable group and aromatic rings provide other evidence of topological discrepancies among these three models, namely, 66.1 and 89.0° in Hypo A, 94.1 and 114.3° in Hypo B, and 90.3 and 117.3° in Hypo C. Figure 2 illustrates the superposition of these three models, and it can be observed that the relative topological relationships are not only different but also the absolute coordinates in the space are different. The statistical significance of a hypothesis can be determined by cost, which is calculated based on the number of bits required


Leong

Figure 2. Superposition of three pharmacophore models Hypo A, Hypo B, and Hypo C, denoted in red, white, and light blue, respectively. Table 5. Weights, Tolerances, and Three-Dimensional Coordinates of Chemical Features and Interfeature Distances of Pharmacophore Model Hypo B hydrophobic posionizable ring aromatic weights tolerances X Y Z hydrophobic posionizable ring aromatic ring aromatic

2.63 1.60 1.41 -7.34 2.71

2.63 1.60 0.36 -1.75 1.18

2.63 1.60 0.24 4.42 1.56

1.60 3.22 4.69 1.63

5.9 11.9 12.2 13.0 14.2

6.2 7.1 7.4 9.0

3.0 5.2 7.4

6.8 7.9

ring aromatic 2.63 1.60 1.60 -1.19 0.67 3.78 3.98 -3.43 -5.78

3.0

Table 6. Weights, Tolerances, and Three-Dimensional Coordinates of Chemical Features and Interfeature Distances of Pharmacophore Model Hypo C hydrophobic posionizable ring aromatic weight tolerances X Y Z hydrophobic posionizable ring aromatic ring aromatic

Figure 1. Generated pharmacophore models (A) Hypo A, (B) Hypo B, and (C) Hypo C, consisting of hydrophobic (light blue), ring aromatic (orange), and positive ionizable (red) chemical features. The interfeature distances and angles among features, depicted in white, are measured in Ångstroms and degrees, respectively. Table 4. Weights, Tolerances, and Three-Dimensional Coordinates of Chemical Features and Interfeature Distances of Pharmacophore Model Hypo A hydrophobic posionizable ring aromatic weights tolerances X Y Z hydrophobic posionizable ring aromatic ring aromatic

2.54 1.75 2.51 -7.14 0.24

2.54 1.60 2.37 -1.28 0.62

5.9 11.9 11.9 13.0 13.0

6.4 7.1 8.5 7.8

ring aromatic

3.31 3.31 1.75 1.60 1.60 1.60 -0.73 -1.40 -3.59 -0.79 4.24 3.23 4.16 5.23 1.78 4.52 -1.98 -2.19

3.0 4.7 4.1

6.9 7.0

3.0

to completely describe a hypothesis. The larger the cost difference between a hypothesis and its corresponding null

2.43 1.60 2.10 -6.38 -2.18

2.43 1.60 2.14 -0.73 -0.49

2.43 1.60 3.85 5.91 2.37

1.60 4.30 6.06 5.33

5.9 13.2 14.7 11.9 11.9

7.4 9.2 6.4 7.1

3.0 5.3 7.1

7.0 7.8

ring aromatic 2.43 1.60 1.60 -1.10 -2.58 4.59 3.04 1.19 3.30

3.0

Table 7. Costs of Returned Hypotheses and Null Hypotheses and the Cost Differences (∆) between Returned and Null Hypotheses for the Pharmacophore Models Hypo A, Hypo B, and Hypo C cost

Hypo A

Hypo B

Hypo C

null hypothesis returned hypothesis ∆

245.68 182.26 63.43

245.68 160.15 85.53

231.36 156.21 75.15

hypothesis, which acts like a hypothesis without features and all molecules in the training set estimated having the mean activity, the more statistically significant a hypothesis is. If the cost difference is 60 bits or more, there is more than 90% chance that a hypothesis shows true correlation in the data (72). The returned costs of Hypo A, Hypo B, and Hypo C, their costs of null hypotheses, and cost differences are presented in Table 7. The smallest cost difference is slightly larger than 60 bits (Hypo A), and the biggest one is even larger than 85 bits (Hypo B), suggesting that all of these three hypotheses are qualified candidates to construct the PhE in terms of statistics point of view since their cost differences are greater than 60 bits. The predictions by all of the three pharmacophore models are, in general, in agreement with observed values for molecules


Figure 3. Observed pIC50 vs the pIC50 predicted by Hypo A, Hypo B, Hypo C, and SVM model for those molecules in the training set and their corresponding linear regression lines.

in both the training set and the test set, which can be asserted from the small deviations between the observed and the prediction pIC50 values as described in Tables 2 and 3 and can be further supported by the small rmsds, maximum residuals, average residuals, and standard residual deviations. Statistically, these three pharmacophore hypotheses are excellent models to predict the biological activity trend for those molecules in the training set since the square of correlation coefficients or the goodness of fit (r2) between the observed and the prediction values are 0.95, 0.83, and 0.91 for Hypo A, Hypo B, and Hypo

Chem. Res. Toxicol., Vol. 20, No. 2, 2007 221

C, respectively, as shown in Table 2, which can be further confirmed by inspecting the scatter plot of observed vs the predicted pIC50 values as illustrated in Figure 3. Similar assertion can be also applied to the test set as shown in Table 3 and Figure 4. The maximum prediction error of Hypo A in the training set was 1.00, which maintained the approximately same level for Hypo C with a value of 1.09 and slightly increased to 1.75 for Hypo B. The maximum residuals in the training set generated by Hypo A and Hypo B resulted from the prediction of loratadine, whose residual was only 0.25 by Hypo C, nevertheless. The prediction error of azimilide, on the other hand, was only 0.33 by Hypo A; yet, that was the worst prediction by Hypo C with a deviation of 1.09. Conversely, mizolastine was perfectly predicted by Hypo A, Hypo B, and Hypo C with only errors of 0.00, 0.01, and 0.01, respectively. When applied, these three models to astemizole, for example, Hypo A and Hypo B, precisely predicted the activity with the residuals of 0.05 and 0.09, respectively, and Hypo C showed more prediction deviation with an error of 0.59. However, all of these models adopted different conformations to exert the biological activities as illustrated in parts A-C of Figure 4, and this discrepancy becomes more pronounced by the overlay of these three conformations as depicted in part D of Figure 4. On the basis of the facts mentioned above, it clearly demonstrates the need to construct a PhE in order to address the conformation variations. Parameters r2, rmsd, maximum residual, average residual, and residual standard deviation in the training set suggest that Hypo A is the best model and Hypo B is the worst. Such observation is opposite to the result calculated from the cost differences in the training set as listed in Table 7, which suggests that Hypo

Figure 4. Pharmacophore models (A) Hypo A, (B) Hypo B, and (C) Hypo C fitted to astemizole and (D) overlay of these three models, which are color-coded by red, white, and light blue, respectively. The chemical features are denoted in Figure 1.


Leong Table 8. Optimal Runtime Parameters for the Final SVM Model parameter

value

SVM type kernel γ termination tolerance ν

-SVR radial basis function 0.015625 0.1 0.004 0.605905

Table 9. Experimentally Observed pIC50 Values of Compounds in the Training Set, Corresponding Predicted Values by SVM Model, Min Model and Avg Model, Associated Statistic Numbers (Correlation Coefficient) r2, rmsd, Maximum Residual, Average Residual, and Standard Deviation of Residual, and Cross-Validation Coefficient q2 observed

Figure 5. Observed pIC50 vs the pIC50 predicted by Hypo A, Hypo B, Hypo C, and SVM model for those molecules in the test set and their corresponding linear regression lines.

B is the best performer and Hypo A is the worst. In addition, it can be observed from Table 2 that these three pharmacophore models unanimously predicted some compounds in the training set perfectly, such as mizolastine as discussed previously, yet gave rise to some relatively large prediction errors in some other cases, implying that no single hypothesis model can flawlessly predict all molecules in the training set. As a result, a better solution can be achieved by utilizing various combinations of these hypotheses in the PhE. These three hypotheses in the PhE also show excellent correlations between the observed and the predicted pIC50 for those molecules in the test set as shown in Table 3 and Figure 5. In fact, they show very similar performance in both training set and test set by comparing their r2 values (Tables 2 and 3 as well as Figures 3 and 4), suggesting that these pharmacophore models were well-trained or no overtraining effect was observed, which usually results in the substantial correlation coefficient difference between the training set and the test set. Conversely, the r2 parameter calculated by Hypo B slightly increases from 0.83 in the training set to 0.88 in the test set, suggesting that Hypo B performed better in the test set than in the training set. The rmsd errors calculated for those molecules in the training set by Hypo A, Hypo B, and Hypo C increase marginally from 1.31, 1.28, and 1.27 to 1.78, 1.67, and 1.77 in the test set, respectively, indicating that these three models performed better in the training set. Hypo A yielded the same maximum residuals in the training set and the test set, while the maximum residuals obtained by Hypo B and Hypo C decreased from the training set to the test set, especially Hypo B, which showed dramatic reduction from 1.75 in the training set to 0.88 in the test set. Average residual and standard deviation of residual exhibit similar levels between the training set and the test set by these three candidate hypotheses. Moreover, risperidone, for example, is perfectly predicted by Hypo A, Hypo B, and Hypo C with residuals of 0.07, 0.04, and 0.01, respectively. Quinidine, oppositely, is the worst predicted molecule by Hypo A with an error of 1.00, which is merely 0.36 and 0.27 by Hypo B and Hypo C, respectively. The variation of prediction errors described above demonstrates the need for the construction of PhE using various hypothesis models.

SVM model

min model

avg model

molecules

pIC50


astemizole cisapride E-4031 dofetilide sertindole pimozide haloperidol droperidol verapamil domperidone halofantrine loratadine mizolastine bepridil azimilide mibefradil chlorpromazine imipramine granisetron dolasetron amitriptyline diltiazem glibenclamide grepafloxacin sildenafil moxifloxacin

9.05 8.19 8.11 7.91 7.85 7.74 7.55 7.49 6.84 6.79 6.76 6.77 6.46 6.26 6.25 5.84 5.83 5.47 5.43 5.23 5.00 4.76 4.13 4.11 4.00 3.94

9.14 7.65 7.85 7.43 7.75 7.91 7.64 7.78 6.74 6.89 6.86 6.87 6.56 6.11 5.64 6.22 6.02 5.57 5.17 5.12 5.09 5.00 4.06 4.57 3.90 3.84

r2 RMSD max average SD q2

0.10 -0.54 -0.26 -0.49 -0.10 0.17 0.09 0.28 -0.10 0.10 0.09 0.10 0.10 -0.15 -0.61 0.37 0.19 0.10 -0.26 -0.10 0.09 0.24 -0.07 0.46 -0.10 -0.10

0.97

9.64 7.64 7.68 7.46 8.30 7.85 7.64 7.64 7.28 7.64 7.07 7.02 6.47 6.40 5.92 7.33 6.11 5.77 5.16 5.16 5.09 5.12 4.36 4.85 4.15 4.35

0.59 -0.55 -0.44 -0.46 0.45 0.11 0.09 0.15 0.44 0.85 0.31 0.25 0.01 0.14 -0.33 1.48 0.28 0.30 -0.27 -0.06 0.09 0.36 0.23 0.74 0.15 0.41

0.91 1.27 0.61 0.21 0.16

9.11 7.47 7.60 7.30 7.68 7.62 7.46 7.55 6.78 7.08 6.86 5.42 6.46 6.09 5.27 6.25 6.02 5.27 5.10 5.07 5.04 4.93 4.16 4.54 4.10 4.04

0.06 -0.71 -0.51 -0.61 -0.18 -0.12 -0.09 0.06 -0.07 0.29 0.10 -1.35 0.00 -0.17 -0.98 0.41 0.19 -0.20 -0.32 -0.16 0.04 0.16 0.03 0.42 0.10 0.10

0.92 1.23 1.48 0.37 0.31

1.30 1.35 0.29 0.32

0.89

The excellent performance of Hypo A, Hypo B, and Hypo C in the training set and the test set can be plausibly attributed to (i) valid biological data, since only compounds demonstrating consistent assay data were selected and any inaccurate biological activity may give rise to false models, resulting in especially faulty predictions for the test set; (ii) defined chemical structures, which provide clear structure information to the pharmacophore generations and eliminate ambiguity during calculations; (iii) better conformation generations, resulting from better conformation search algorithm as well as solvation effect; and (iv) perfect selection of training compounds, providing statistically meaningful samples to yield valid models. SVM. Table 8 summarizes the optimal conditions for running SVM, which were chosen on the basis of the prediction results of those molecules in the training set and cross-validation as given in Table 9. It can be observed that the SVM model predicted those molecules in the training set better than all of those individual hypotheses in the PhE that can be further demonstrated by the scatter plot of observed vs the predicted pIC50 values as illustrated in Figure 3, in which those points obtained from the SVM model are generally closer to the


regression line than those obtained from the Hypo A, Hypo B, and Hypo C. As a result, the maximum residual calculated by the SVM model significantly declined to 0.61 from 1.00, 1.75 and 1.09 by Hypo A, Hypo B, and Hypo C, respectively, and came from the prediction of azimilide, which deviated from the observed value by 0.33, 1.15, and 1.09 by Hypo A, Hypo B, and Hypo C, respectively, indicating that the SVM model gave more weight to Hypo A than to Hypo B and Hypo C for azimilide. Additionally, loratadine was miscalculated by both Hypo A and Hypo B with residuals of 1.00 and 1.75, respectively, and was modestly overestimated by Hypo C with a residual of 0.25, yet was accurately predicted by the SVM model with a residual of 0.10, showing a substantial decrease of the prediction error by the SVM model. The 10-fold cross-validation of the SVM model yielded the correlation coefficient q2 of 0.89 as compared with an r2 of 0.97 for the training set as indicated in Table 9. This insignificant difference between these two parameters confirms that the SVM model shows a statistically true relation between the observed and the predicted values and that it is highly possible that this SVM model is an authentic model. It may be argued that the final model can be selected from the best fit model, which is defined as the model with the highest potency or the minimum biological activity among all predictions calculated by all candidates in the ensemble for any given molecule, or the minimum model, whose prediction results are listed in Table 9. It seems that the minimum model showed mixed results since the minimum model has an r2 value of 0.91, for example, obtained from the training set as compared with values of 0.95, 0.83, and 0.91 by Hypo A, Hypo B, and Hypo C, respectively, suggesting that the minimum model performed as good as Hypo C, yet better than Hypo B and worse than Hypo A. However, when compared with the SVM model (r2 ) 0.97), the minimum model was clearly outperformed by the SVM model. Because the minimum model is taken from the minimum biological activity for any given molecule, it can be expected that the maximum residual of the minimum model should fall in the range of the worst and the best ones. In fact, that of the minimum model (1.48) was smaller than that of Hypo B (1.75) and larger than that of Hypo A (1.00) and Hypo C (1.09). However, the predictions from the SVM model deviated from the observed values by no more than 0.61, strongly confirming that the predominance of SVM model to the minimum model in terms of the maximum residual. In general, all of the statistical numbers, listed in Table 9, suggest that the SVM model is superior to the minimum model for the performance of those molecules in the training set except rmsd, in which the minimum model showed slight improvement (1.23 vs 1.27). Similarly, it can be assumed that the final predicted values can be obtained from the average value of all predictions calculated by those hypotheses in the PhE for any given molecule to yield the consensus model or the average model. Table 9 lists the prediction results of those molecules in the training set calculated based on the average model. It can be seen that the average model shows better performance than that of the minimum model in terms of r2, maximum residual, and average residual and slightly worse in rmsd and residual standard deviation. The largest prediction deviation of those molecules in the training set by the average model comes from loratadine with an error of 1.35, which is merely 0.10 and 0.25 by the SVM model and the minimum model, respectively. On the other hand, mibefradil was worst, predicted by the minimum model with a residual of 1.48, and was only 0.37 and 0.41 by the SVM

Chem. Res. Toxicol., Vol. 20, No. 2, 2007 223 Table 10. Published and Predicted hERG pIC50 Values of Compounds in the Test Set by SVM, Average and Minimum Models, and Associated Statistic Numbers observed

SVM model

min model

avg model

molecules

pIC50


norastemizole ziprasidone risperidone clozapine cocaine quinidine ketoconazole desipramine mesoridazine nicotine alosetron olanzapine vesnarinone

7.55 6.82 6.79 6.72 5.24 6.49 5.72 5.86 6.49 3.61 5.49 6.64 5.96

7.76 6.98 6.98 6.90 5.07 6.27 6.16 5.55 6.20 2.76 4.90 6.65 5.18


0.21 0.16 0.19 0.18 -0.17 -0.22 0.44 -0.31 -0.29 -0.85 -0.59 0.01 -0.78

0.94

7.52 7.09 6.85 7.14 5.16 6.85 6.16 5.77 6.13 3.70 5.00 6.85 5.15

-0.03 0.27 0.07 0.42 -0.08 0.36 0.44 -0.09 -0.36 0.09 -0.49 0.22 -0.80

0.89 1.75 0.85 0.34 0.26

7.48 6.93 6.82 6.58 5.00 5.93 5.68 5.26 6.11 3.36 4.78 6.44 5.11

-0.07 0.11 0.04 -0.14 -0.24 -0.56 -0.04 -0.60 -0.39 -0.25 -0.72 -0.20 -0.85

0.93 1.71 0.80 0.29 0.22

1.79 0.85 0.32 0.27

model and the average model, respectively. Conversely, as mentioned above, the prediction of azimilide deviated from the observed value by 0.61, 0.33, and 0.98 by the SVM model, the minimum model, and the average model, respectively, which is the largest deviation by the SVM model. On the basis of the fact mentioned above, it can be stated that the SVM model is the best performer for those molecules in the training set among these three models. Table 10 lists the prediction and statistical results of those molecules in the test set obtained from the SVM model, the minimum model, and the average model. Among these three regression models, the SVM performed slightly better than the other two for those molecules in the test set in terms of the parameter r2 (0.94 vs 0.89 and 0.93). Other than r2, the minimum and average models when applied to the test set maintained the same performance level as the SVM model (Table 10), and both models even had slightly better average residuals than the SVM model (0.29 and 0.32 vs 0.34, respectively). In general, the prediction results obtained from these three models in the test set were slightly worse than in the training set, especially the rmsd values. The similar observation can also be found for those individual hypotheses in the PhE (Tables 2 and 3). However, the three regression models still demonstrated better results than those three individual models for those molecules in the test set (Tables 3 and 10). More importantly, the SVM model performed better than the other two regression models despite the fact that these two also yielded satisfactory results not only in the training set but in the test set, suggesting that the resultant SVM model can be applied to predict those molecules outside of the training set because of the performance consistency in both sets. As a result, it can be asserted that the SVM model is a superior model as compared with the others. In addition to the accurate predictions and excellent performance as discussed above, this novel PhE/SVM approach also has the flexibility advantage as compared with traditional modeling methods especially in case there are more biological activities that become available, in which the target protein adopts different conformations to interact with structurally distinct ligands, giving rise to discrepant pharmacophore hypotheses from those models in the PhE. The traditional modeling methods will demand to rebuild a new model in order to accommodate the variations of these new structures, or some level of deviations from those new molecules can be expected.


As a result, it will require more time to generate the new prediction model, and more importantly, the new model in turn can be expected to show more prediction errors for those molecules used in the previous calculations. Conversely, it will only take a fraction of time to build new pharmacophore hypotheses based on those new structures, which in turn will be implemented in the PhE to refine the SVM model. The new PhE/SVM model does not have to comprise its predictability for those molecules used in the previous calculations after adding structural diversity of molecules as in the case of loratadine mentioned above. Overall, this novel PhE/SVM scheme works perfectly to predict the hERG liability, which can be achieved by some other modeling tools within some limitations. Nevertheless, this PhE/ SVM approach is derived from the pharmacophore modeling, which has been proven to be a robust and efficient screening tool in terms of virtual screening speed (73, 74) since it only takes hours to screen millions of compounds on a descent machine (75) once the database is constructed, which, in turn, only demands one-time conformation generation and can be used for a variety of applications. As a result, pharmacophore-based screening was suggested to be used as primary screening among various virtual screening tools (68). In other words, this PhE/ SVM technique can be employed to virtually screen a compound library in order to filter out those compounds with potential hERG problem, resulting in expediting the drug discovery process. Additionally, this PhE/SVM scheme can not only be applied to the prediction of hERG liability but any ligand-based modeling. In fact, the PhE/SVM approach provides a fast, accurate, and versatile way to conduct analogue-based drug design.

Conclusion A novel approach, based on the combination of PhE, which addresses the issue of protein conformational flexibility while interacting with structurally diverse small molecules, and SVM, which provides robust and fast regression, has been developed to accurately predict the hERG liability values for those 26 and 13 compounds in the training set and test sets, respectively, with excellent predictability and statistical significance. Additionally, this PhE/SVM approach also provides flexibility and swiftness for model refining in case new molecules are augmented in the sample panel that will otherwise require model reconstructions like any traditional modeling approaches. It can be asserted, based on the facts mentioned above, that this PhE/SVM approach serves as a good model for hERG liability prediction and also provides a valuable tool for analogue-based modeling and drug design. Certainly, it can be expected that more quality data will be published in the literature, which in turn can be employed as sample pool to further verify this generated PhE/ SVM model or to improve the model in case some significant deviations are observed in the hopes of producing a reliable and useful model for prediction of hERG liability. Acknowledgment. This work was supported by the National Science Council, Taiwan. Parts of calculations were performed at the National Center for High-Performance Computing, Taiwan. I thank Dr. G. H. Hakimelahi for reading the manuscript and Kadir Liano for valuable discussions about the SVM.

References (1) Vandenberg, J. I., Walker, B. D., and Campbell, T. J. (2001) HERG K+ channels: Friend and foe. Trends Pharmacol. Sci. 22, 240-246.

Leong (2) Brown, A. M. (2004) Drugs, hERG and sudden death. Cell Calcium 35, 543-547. (3) Fermini, B., and Fossa, A. A. (2003) The impact of drug-induced QT interval prolongation on drug discovery and development. Nat. ReV. Drug DiscoVery 2, 439-447. (4) Pritchard, J. F., Jurima-Romet, M., Reimer, M. L. J., Mortimer, E., Rolfe, B., and Cayen, M. N. (2003) Making better drugs: Decision gates in non-clinical drug development. Nat. ReV. Drug DiscoVery 2, 542-553. (5) Finlayson, K., Witchel, H. J., McCulloch, J., and Sharkey, J. (2004) Acquired QT interval prolongation and HERG: Implications for drug discovery and development. Eur. J. Pharmacol. 500, 129-142. (6) Witchel, H. J., Milnes, J. T., Mitcheson, J. S., and Hancox, J. C. (2002) Troubleshooting problems with in vitro screening of drugs for QT interval prolongation using HERG K+ channels expressed in mammalian cell lines and Xenopus oocytes. J. Pharmacol. Toxicol. Methods 48, 65-80. (7) Mitcheson, J. S., Chen, J., Lin, M., Culberson, C., and Sanguinetti, M. C. (2000) A structural basis for drug-induced long QT syndrome. Proc. Natl. Acad. Sci. U.S.A. 97, 12329-12333. (8) Cavalli, A., Poluzzi, E., Ponti, F. D., and Recanatini, M. (2002) Toward a pharmacophore for drugs inducing the long QT syndrome: Insights from a CoMFA study of HERG K+ channel blockers. J. Med. Chem. 45, 3844-3853. (9) Ekins, S., Crumb, W. J., Sarazan, R. D., Wikel, J. H., and Wrighton, S. A. (2002) Three-dimensional quantitative structure-activity relationship for inhibition of human ether-a-go-go-related gene potassium channel. J. Pharmacol. Exp. Ther. 301, 427-434. (10) Roche, O., Trube, G., Zuegge, J., Pflimlin, P., Alanine, A., and Schneider, G. (2002) A virtual screening method for prediction of the hERG potassium channel liability of compound libraries. ChemBioChem. 3, 455-459. (11) Ekins, S. (2003) In silico approaches to predicting drug metabolism, toxicology and beyond. Biochem. Soc. Trans. 31, 611-614. (12) Keseru, G. M. (2003) Prediction of hERG potassium channel affinity by traditional and hologram QSAR methods. Bioorg. Med. Chem. Lett. 13, 2773-2775. (13) Pearlstein, R., Vaz, R., and Rampe, D. (2003) Understanding the structure-activity relationship of the human ether-a-go-go-related gene cardiac K+ channel. A model for bad behavior. J. Med. Chem. 46, 2017-2022. (14) Pearlstein, R. A., Vaz, R. J., Kang, J., Chen, X. L., Preobrazhenskaya, M., Shchekotikhin, A. E., Korolev, A. M., Lysenkova, L. N., Miroshnikova, O. V., Hendrix, J., and Rampe, D. (2003) Characterization of hERG potassium channel inhibition using CoMSiA 3D QSAR and homology modeling approaches. Bioorg. Med. Chem. Lett. 13, 1829-1835. (15) Mitcheson, J. S., and Perry, M. D. (2003) Molecular determinants of high-affinity drug binding to HERG channels. Curr. Opin. Drug DiscoVery DeV. 6, 667-674. (16) Aptula, A. O., and Cronin, M. T. D. (2004) Prediction of hERG K+ blocking potency: Application of structural knowledge. SAR QSAR EnViron. Res. 15, 399-411. (17) Zolotoy, A. B., Plouvier, B. P., Beatch, G. B., Hayes, E. S., Wall, R. A., and Walker, M. J. A. (2003) Physicochemical determinants for drug induced blockade of HERG potassium channels: Effect of charge and charge shielding. Curr. Med. Chem. CardioVasc. Hematol. Agents 1, 225-241. (18) Witchel, H. J., Dempsey, C. E., Sessions, R. B., Perry, M., Milnes, J. T., Hancox, J. C., and Mitcheson, J. S. (2004) The low-potency, voltage-dependent HERG blocker propafenonesMolecular determinants and drug trapping. Mol. Pharmacol. 66, 1201-1212. (19) Cianchetta, G., Li, Y., Kang, J., Rampe, D., Fravolini, A., Cruciani, G., and Vaz, R. J. (2005) Predictive models for hERG potassium channel blockers. Bioorg. Med. Chem. Lett. 15, 3637-3642. (20) Norinder, U. (2005) In silico modelling of ADMET-A minireview of work from 2000 to 2004. SAR QSAR EnViron. Res. 16, 1-11. (21) Rajamani, R., Tounge, B. A., Li, J., and Reynolds, C. H. (2005) Twostate homology model of the hERG K+ channel: Application to ligand binding. Bioorg. Med. Chem. Lett. 15, 1737-1741. (22) Tobita, M., Nishikawa, T., and Nagashima, R. (2005) A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors. Bioorg. Med. Chem. Lett. 15, 28862890. (23) Farid, R., Day, T., Friesner, R. A., and Pearlstein, R. A. (2006) New insights about HERG blockade obtained from protein modeling, potential energy mapping, and docking studies. Bioorg. Med. Chem. 14, 3160-3173. (24) Gepp, M. M., and Hutter, M. C. (2006) Determination of hERG channel blockers using a decision tree. Bioorg. Med. Chem. 14, 53255332.

Prediction of hERG Liability (25) Seierstad, M., and Agrafiotis, D. K. (2006) A QSAR model of hERG binding using a large, diverse, and internally consistent training set. Chem. Biol. Drug Des. 67, 284-296. (26) Song, M., and Clark, M. (2006) Development and evaluation of an in silico model for hERG binding. J. Chem. Inf. Model. 46, 392-400. (27) Yoshida, K., and Niwa, T. (2006) Quantitative structure-activity relationship studies on inhibition of HERG potassium channels. J. Chem. Inf. Model. 46, 1371-1378. (28) Recanatini, M., Poluzzi, E., Masetti, M., Cavalli, A., and De Ponti, F. (2005) QT prolongation through hERG K+ channel blockade: Current knowledge and strategies for the early prediction during drug development. Med. Res. ReV. 25, 133-166. (29) Sun, H. (2006) An accurate and interpretable Bayesian classification model for prediction of hERG liability. ChemMedChem. 1, 315-322. (30) Dubus, E., Ijjaali, I., Petitet, F., and Michel, A. (2006) In silico classification of hERG channel blockers: A knowledge-based strategy. ChemMedChem. 1, 622-630. (31) Zacharias, M. (2004) Rapid protein-ligand docking using soft modes from molecular dynamics simulations to account for protein deformability: Binding of FK506 to FKBP. Proteins: Struct., Funct., Bioinf. 54, 759-767. (32) Carlson, H. A., Masukawa, K. M., Rubins, K., Bushman, F. D., Jorgensen, W. L., Lins, R. D., Briggs, J. M., and McCammon, J. A. (2000) Developing a dynamic pharmacophore model for HIV-1 integrase. J. Med. Chem. 43, 2100-2114. (33) Cortes, C., and Vapnik, V. (1995) Support vector networks. Machine Learning 20, 273-293. (34) Burbidge, R., Trotter, M., Buxton, B., and Holden, S. (2001) Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Comput. Chem. 26, 4-15. (35) Chow, M. L., Moler, E. J., and Mian, I. S. (2001) Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. Physiol. Genomics 5, 99-111. (36) Ding, C. H. Q., and Dubchak, I. (2001) Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17, 349-358. (37) Hua, S., and Sun, Z. (2001) A novel method of protein secondary structure prediction with high segment overlap measure: Support vector machine approach. J. Mol. Biol. 308, 397-407. (38) Trotter, M., Burbidge, R., Buxton, B., and Holden, S. (2001) Drug design by machine learning: Support vector machines for pharmaceutical data analysis. Comput. Chem. 26, 5-14. (39) Cai, Y. D., Liu, X. J., Xu, X. B., and Chou, K. C. (2002) Prediction of protein structural classes by support vector machines. Comput. Chem. 26, 293-296. (40) Cai, Y. D., Liu, X. J., Xu, X. B., and Chou, K. C. (2002) Support vector machines for predicting HIV protease cleavage sites in protein. J. Comput. Chem. 23, 267-274. (41) Song, M., Breneman, C. M., Bi, J., Sukumar, N., Bennett, K. P., Cramer, S., and Tugcu, N. (2002) Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J. Chem. Inf. Comput. Sci. 42, 1347-1357. (42) Yuan, Z., Burrage, K., and Mattick, J. S. (2002) Prediction of protein solvent accessibility using support vector machines. Proteins 48, 566570. (43) Anderson, D. C., Li, W., Payan, D. G., and Noble, W. S. (2003) A new algorithm for the evaluation of shotgun peptide sequencing in proteomics: Support vector machine classification of peptide MS/ MS spectra and SEQUEST scores. J. Proteome Res. 2, 137-146. (44) Cai, Y.-D., Zhou, G.-P., and Chou, K.-C. (2003) Support vector machines for predicting membrane protein types by using functional domain composition. Bioinformatics 84, 3257-3263. (45) Lee, Y., and Lee, C.-K. (2003) Classification of multiple cancer types by multicategory support vector machines using gene expression data. Bioinformatics 19, 1132-1139. (46) Lind, P., and Maltseva, T. (2003) Support vector machines for the estimation of aqueous solubility. J. Chem. Inf. Comput. Sci. 43, 18551859. (47) Liu, H. X., Zhang, R. S., Luan, F., Yao, X. J., Liu, M. C., Hu, Z. D., and Fan, B. T. (2003) Diagnosing breast cancer based on support vector machines. J. Chem. Inf. Comput. Sci. 43, 900-907. (48) Liu, H. X., Zhang, R. S., Yao, X. J., Liu, M. C., Hu, Z. D., and Fan, B. T. (2003) QSAR study of ethyl 2-[(3-methyl-2,5-dioxo(3-pyrrolinyl))amino]-4-(trifluoromethyl) pyrimidine-5-carboxylate: An inhibitor of AP-1 and NF-κB mediated gene expression based on support vector machines. J. Chem. Inf. Comput. Sci. 43, 1288-1296. (49) Zernov, V. V., Balakin, K. V., Ivaschenko, A. A., Savchuk, N. P., and Pletnev, I. V. (2003) Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J. Chem. Inf. Comput. Sci. 43, 2048-2056.

Chem. Res. Toxicol., Vol. 20, No. 2, 2007 225 (50) Deng, W., Breneman, C., and Embrechts, M. J. (2004) Predicting protein-ligand binding affinities using novel geometrical descriptors and machine-learning methods. J. Chem. Inf. Comput. Sci. 44, 699703. (51) Yap, C. W., Cai, C. Z., Xue, Y., and Chen, Y. Z. (2004) Prediction of torsade-causing potential of drugs by support vector machine approach. Toxicol. Sci. 79, 170-177. (52) Jorissen, R. N., and Gilson, M. K. (2005) Virtual screening of molecular databases using a support vector machine. J. Chem. Inf. Model. 45, 549-561. (53) Kriegl, J. M., Arnhold, T., Beck, B., and Fox, T. (2005) A support vector machine approach to classify human cytochrome P450 3A4 inhibitors. J. Comput.-Aided Mol. Des. 19, 189-201. (54) Mu¨ller, K.-R., Ra¨tsch, G., Sonnenburg, S., Mika, S., Grimm, M., and Heinrich, N. (2005) Classifying ‘drug-likeness’ with kernel-based learning methods. J. Chem. Inf. Model. 45, 249-253. (55) Saeh, J. C., Lyne, P. D., Takasaki, B. K., and Cosgrove, D. A. (2005) Lead hopping using SVM and 3D pharmacophore fingerprints. J. Chem. Inf. Model. 45, 1122-1133. (56) Zhao, C. Y., Zhang, H. X., Zhang, X. Y., Liu, M. C., Hu, Z. D., and Fan, B. T. (2006) Application of support vector machine (SVM) for prediction toxic activity of different data sets. Toxicology 217, 105119. (57) Yao, X. J., Panaye, A., Doucet, J. P., Zhang, R. S., Chen, H. F., Liu, M. C., Hu, Z. D., and Fan, B. T. (2004) Comparative study of QSAR/ QSPR correlations using support vector machines, radial basis function neural networks, and multiple linear regression. J. Chem. Inf. Comput. Sci. 44, 1257-1266. (58) Egan, W. J., Merz, K. M., Jr., and Baldwin, J. J. (2000) Prediction of drug absorption using multivariate statistics. J. Med. Chem. 43, 38673877. (59) Kratochwil, N. A., Huber, W., Mu¨ller, F., Kansy, M., and Gerber, P. R. (2002) Predicting plasma protein binding of drugs: A new approach. Biochem. Pharmacol. 64, 1355-1374. (60) Wegner, J. K., and Zell, A. (2003) Prediction of aqueous solubility and partition coefficient optimized by a genetic algorithm based descriptor selection method. J. Chem. Inf. Comput. Sci. 43, 10771084. (61) Huuskonen, J. (1997) Neural network modeling for estimation of the aqueous solubility of structurally related drugs. J. Pharm. Sci. 450454. (62) Scho¨lkopf, B., and Smola, A. J. (2002) Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 1st ed., The MIT Press, Cambridge, Massachusetts. (63) Bo¨strom, J. (2001) Reproducing the conformations of protein-bound ligands: A critical evaluation of several popular conformational searching tools. J. Comput.-Aided Mol. Des. 15, 1137-1152. (64) Greenidge, P. A., and Weiser, J. (2001) A comparison of methods for pharmacophore generation with the catalyst software and their use for 3D-QSAR: Application to a set of 4-aminopyridine thrombin inhibitors. Mini ReV. Med. Chem. 1, 79-87. (65) Greenidge, P. A., Merette, S. A. M., Beck, R., Dodson, G., Goodwin, C. A., Scully, M. F., Spencer, J., Weiser, J., and Deadman, J. J. (2003) Generation of ligand conformations in continuum solvent consistent with protein active site topology: Application to thrombin. J. Med. Chem. 46, 1293-1305. (66) Kolossvary, I., and Guida, W. C. (1996) Low mode search. An efficient, automated computational method for conformational analysis: Application to cyclic and acyclic alkanes and cyclic peptides. J. Am. Chem. Soc. 118, 5011-5019. (67) Sprague, P. W. (1995) Automated chemical hypothesis generation and database searching with catalyst. Perspect. Drug DiscoVery Des. 3, 1-20. (68) Gu¨ner, O. F. (2000) Pharmacophore Perception, DeVelopment, and Use in Drug Design, International University Line, La Jolla, California. (69) Chang, C.-C., and Lin, C.-J. (2005) LIBSVM: A Library for Support Vector Machines, version 2.81. Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm. (70) Kecman, V. (2001) Learning and Soft Computing: Support Vector Machines, Neural Networks and Fuzzy Logic Models, MIT Press, Cambridge, MA. (71) Breiman, L., and Spector, P. (1992) Submodel selection and evaluation in regression: The X-random case. Int. Statist. ReV. 60, 291-319. (72) Catalyst, Version 4.11, Accelyrs, San Diego, CA. (73) Steindl, T., and Langer, T. (2005) Docking versus pharmacophore model generation: A comparison of high-throughput virtual screening strategies for the search of human rhinovirus coat protein inhibitors. QSAR Comb. Sci. 24, 470-479. (74) van Drie, J. H. (2003) Pharmacophore discoverysLessons learned. Curr. Pharm. Des. 9, 1649-1664. (75) Leong, M. K. Unpublished work. (76) Zhou, Z., Gong, Q., Ye, B., Fan, Z., Makielski, J. C., Robertson, G. A., and January, C. T. (1998) Properties of HERG channels stably


(77)

(78)

(79) (80) (81)

(82) (83) (84)

(85)

expressed in HEK 293 cells studied at physiological temperature. Biophys. J. 74, 230-241. Rampe, D., Roy, M.-L., Dennis, A., and Brown, A. M. (1997) A mechanism for the proarrhythmic effects of cisapride (propulsid): High affinity blockade of the human cardiac potassium channel HERG. FEBS Lett. 417, 28-32. Rampe, D., Murawsky, M. K., Grau, J., and Lewis, E. W. (1998) The antipsychotic agent sertindole is a high affinity antagonist of the human cardiac potassium channel HERG. J. Pharmacol. Exp. Ther. 286, 788793. Kang, J., Wang, L., Cai, F., and Rampe, D. (2000) High affinity blockade of the HERG cardiac K+ channel by the neuroleptic pimozide. Eur. J. Pharmacol. 392, 137-140. Bains, W., Basman, A., and White, C. (2004) HERG binding specificity and binding site structure: Evidence from a fragment-based evolutionary computing SAR study. Prog. Biophys. Mol. Biol. 86, 205-233. Drolet, B., Zhang, S., Deschenes, D., Rail, J., Nadeau, S., Zhou, Z., January, C. T., and Turgeon, J. (1999) Droperidol lengthens cardiac repolarization due to block of the rapid component of the delayed rectifier potassium current. J. CardioVasc. Electrophysiol. 10, 15971604. Crumb, W. J. J. (2000) Loratadine blockade of K+ channels in human heart: Comparison with terfenadine under physiological conditions. J. Pharmacol. Exp. Ther. 292, 261-264. Warner, B., and Hoffmann, P. (2002) Investigation of the potential of clozapine to cause torsade de pointes. AdVerse Drug React. Toxicol. ReV. 21, 189-203. Paul, A. A., Witchel, H. J., and Hancox, J. C. (2002) Inhibition of the current of heterologously expressed HERG potassium channels by flecainide and comparison with quinidine, propafenone and lignocaine. Br. J. Pharmacol. 136, 717-729. Uchino, T., Lee, T.-S., Kaku, T., Yamashita, N., Noguchi, T., and Ono, K. (2005) Voltage-dependent and frequency-independent inhibi-

Leong

(86)

(87)

(88)

(89)

(90)

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tion of recombinant Cav3.2 T-type Ca2+ channel by bepridil. Pharmacology 74, 174-181. Walker, B. D., Singleton, C. B., Tie, H., Bursill, J. A., Wyse, K. R., Valenzuela, S. M., Breit, S. N., and Campbell, T. J. (2000) Comparative effects of azimilide and ambasilide on the human ether-a-go-gorelated gene (HERG) potassium channel. CardioVasc. Res. 48, 4458. Katayama, Y., Fujita, A., Ohe, T., Findlay, I., and Kurachi, Y. (2000) Inhibitory effects of vesnarinone on cloned cardiac delayed rectifier K+ channels expressed in a mammalian cell line. J. Pharmacol. Exp. Ther. 294, 339-346. Haugan, K., Olsen, K. B., Hartvig, L., Petersen, J. S., Holstein-Rathlou, N.-H., Hennan, J. K., and Nielsen, M. S. (2005) The antiarrhythmic peptide analog ZP123 prevents atrial conduction slowing during metabolic stress. J. CardioVasc. Electrophysiol. 16, 537-545. Chouabe, C., Drici, M.-D., Romey, G., Barhanin, J., and Lazdunski, M. (1998) HERG and KvLQT1/IsK, the cardiac K+ channels involved in long QT syndromes, are targets for calcium channel blockers. Mol. Pharmacol. 54, 695-703. Ridley, J. M., Milnes, J. T., Duncan, R. S., McPate, M. J., James, A. F., Witchel, H. J., and Hancox, J. C. (2006) Inhibition of the HERG K+ channel by the antifungal drug ketoconazole depends on channel gating and involves the S6 residue F656. FEBS Lett. 580, 1999-2005. Ferreira, S., Crumb, W. J., Jr., Carlton, C. G., and Clarkson, C. W. (2001) Effects of cocaine and its major metabolites on the HERGencoded potassium channel. J. Pharmacol. Exp. Ther. 299, 220-226. Rosati, B., Rocchetti, M., Zaza, A., and Wanke, E. (1998) Sulfonylureas blockade of neural and cardiac HERG channels. FEBS Lett. 440, 125-130. Kang, J., Wang, L., Chen, X.-L., Triggle, D. J., and Rampe, D. (2001) Interactions of a series of fluoroquinolone antibacterial drugs with the human cardiac K+ channel HERG. Mol. Pharmacol. 59, 122-126.

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