Comparative Molecular Similarity Indices Analysis of 1-(Naphthylalky1)-1H-imidazole Analogs with Antiepileptic Activity

A three-dimensional quantitative structure-activity relationship (3D QSAR) of 44 structurally and functionally diverse series of 1- (Naphthylalkylimidazoles) as antiepileptic agents was studied using the Comparative molecular similarity indices analysis (CoMSIA) method. A training set containing 34 molecules served to establish the models. The optimum CoMSIA model obtained for the training set were all statistically significant, with cross-validated coefficients (q2) of 0.725 and conventional coefficients (r2ncv) of 0.998. The predictive capacities of the model were successfully validated by using a test set of 10 molecules that were not included in the training set. CoMSIA model (Model 1) obtained from the hydrophobic and Hbond acceptor field was found to have the best predictivity, with a predictive correlation coefficient (r2pred) of 0.67. The information obtained from this 3D-QSAR model can be used to guide the development of imidazoles as novel antiepileptic agents.


INTRODUCTION
includes additional molecular fields, such as, the lipophillic and hydrogen bond potential. CoMSIA introduces the Gaussian function for the distance dependence between the molecular atoms and the probe atoms, in order to avoid some of the inherent deficiencies arising from the Lennard-Jones and Coulomb potential functional forms. In CoMSIA, five different similarity fields, namely, steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor fields were calculated. These fields were selected to cover the major contributions to ligand binding and have several advantages over CoMFA, such as, development of a more robust 3D QSAR model, with no arbitrary cut-offs and more intuitively interpretable contour maps. This article describes the Comparative Molecular Similarity Indices Analysis (CoMSIA) of a series of 1-(Naphthylalky1)-1H-imidazole analogs [ Table 1]. On the basis of the CoMSIA model generated by us we attempted to elucidate a structure-activity relationship to provide useful information for the design of more selective and potent antiepileptic imidazole analogs.

Data set
Reported data on a series of 44 1-(Naphthylalky1)-1Himidazole derivatives [5] were used [ Table 1]. The ED 50 data for a Maximal Electroshock induced seizure (MES test) were used for QSAR analysis, as a dependent parameter, after converting the reciprocal of the logarithm of ED 50 (p ED 50 ) values. ED 50 was the dose of a drug that was pharmacologically effective for 50% of the population exposed to the drug. The total set of the imidazole analogs were segregated into the training set and the test set, in an approximately 4:1 ratio, each containing 34 and 10 molecules, respectively. The division was made at random with bias given to structural diversity in both the training set and the test set.

Molecular modeling
The CoMSIA studies reported herein were performed using   [10] molecular modeling software installed on a Dell computer, with Red Hat Linux Enterprise Version 3.0 (with 512 MB of memory). All the compounds were built from fragments in the SYBYL database. Each structure was fully geometry-optimized using the standard Tripos force field with a distance-dependent dielectric function, until a Root Mean Square (RMS) deviation of 0.001 kcal mol -1 Å -1 was achieved. All the compounds were then subjected to simulated dynamic annealing with 100 cycles. The least energy conformations were selected for each compound and further energy minimized using Powell (100 iterations) and Conjugation gradient (10,000 iterations) methods. Gasteiger-Huckel charges were computed for all molecules after energy minimization.

Alignment
Molecular conformation and orientation is one of the most sensitive input areas in 3D-QSAR studies. In the present study, superimposition of the molecules was carried out by DATABASE ALIGNMENT, using compound 26 [ Table 1] as a template structure. The molecules were aligned to the template molecule by using a common substructure labeled with *, as shown in Figure 2. The aligned molecules are shown in Figure 3.

CoMSIA interaction energy calculation [8]
In CoMSIA field energy calculation, the probe atom with radius 1 Å, charge + 1.0, hydrogen bond donating + 1.0, hydrogen bond accepting + 1.0, and hydropobicity + 1.0 were used. An attenuation factor of 0.3 was used to estimate the steric, electrostatic, hydrophobic, hydrogen bond donor, and acceptor fields in CoMSIA.
Partial least square (PLS) analysis [11] PLS regression technique is especially useful in common cases where the number of descriptors (independent variables) is comparable to or greater than the number of compounds (data points) and / or there exist other factors leading to correlations between variables. The column filtering value(s) were set to 2.0 kcal/mol, to improve the signal-to-noise ratio. Cross-validations were performed by the Leave-One-Out (LOO) procedure, to determine the optimum number of components (N). The cross-validated r 2 that resulted in optimum number of components and lowest standard error of prediction were considered for further analysis. The final analysis was performed to calculate conventional r 2 using the optimum number of components. Bootstrapping analysis for 100 runs was performed.
Predictive correlation coefficient [8,12] The predictive power of the model, predictive correlation coefficient (r 2 pred ), based on molecules of the test set was calculated by the following equation.

RESULTS
Based on the predictive correlation coefficient (r 2 pred = 0.67), the combination of hydrophobic and hydrogen bond  Table 2. A plot of predicted (CoMSIA) versus the actual activity for the training set molecules is shown in Figure 5. Figure 4 represents the plot of the cross-validated correlation coefficient versus all the CoMSIA models. The actual and predicted activity of training and test set for all CoMSIA models are given in Tables 3 and 4, respectively. The 3DQSAR contour maps revealing the contribution of the CoMSIA fields is shown in Figure 6. The contributions of the hydrophobic and hydrogen bond acceptor fields of CoMSIA are in the ratio 4:6 [ Table 2].

DISCUSSION
Considering the hydrophobic contours of CoMSIA (Model 1), the yellow(Y) contours denote regions where hydrophobic groups are favored, while white (W) contours indicate regions where hydrophilic groups can be incorporated. Figure 6a shows that the 2-methoxyethyl substituent at the first position of the imidazole nucleus    is embedded in a big yellow (Y) contour, indicating that the substitution with hydrophobic groups will increase the activity. The second position of the imidazole ring and the eighth position of the naphthalene ring are embedded in white (W) contour, indicating that introduction of hydrophilic groups at these positions will increase the activity. Similarly the third position of the napthyl group is surrounded by a white (W) contour indicating that addition of hydrophobic groups will decrease the activity. In the H-bond acceptor field [ Figure 6b], magenta (M) contours represent the favored region, while red (R) contours show the disfavored region. The second position of the imidazole ring is embedded in big red (R) contour, indicating that substitution with the hydrogen bond donor group may increase the activity, while the fourth and fifth position of imidazole is embedded in large magenta (M) contours, indicating introduction of hydrogen bond acceptor groups may result in increased activity. The 2-methoxy group attached to the CH2CH2 side chain at the first position of the imidazole nucleus is embedded in a red (R) contour, indicating that activity may increase if this position is substituted with hydrogen bond donor groups.

CONCLUSION
The CoMSIA analysis has been successfully applied to a series of 1-(Naphthylalky1)-1H -imidazole derivatives with anticonvulsant activity. The CoMSIA model (Model 1) was very well validated both internally and externally and proved to be the best of all the models developed. The robustness of the HA model was verified by the bootstrapping method. This model with a combination hydrophobic and H-bond acceptor fields (HA) indicates that hydrophobic and hydrogen bond acceptor groups may be important for the design of more potent imidazole analogs, as antiepileptic agents. Results of this study may be utilized for future drug design studies and synthesis of more potent antiepileptic agents with the arylalkylimidazole scaffold.