Quantitative Structure Relaxant Activity Relationship of Benzopyran Derivatives: A GA-PLS-MLR Approach

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1 BI Biomedicine International 2011; 2:. ORIGINAL ARTICLE Quantitative Structure Relaxant Activity Relationship of Benzopyran Derivatives: A GA-PLS-MLR Approach Behzad Jafari, 1 Somaieh Soltani, 2 Hossein Babaei, 3 Abolghasem Jouyban 4 1 Biotechnology Research Center, Tabriz University of Medical Sciences, Tabriz, Iran 2 Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran 3 Drug Applied Research Center and Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran 4 Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences, Tabriz, Iran ABSTRACT Quantitative structure relaxant activity relationships of 56 benzopyran derivatives were developed and validated on the basis of a linear model using two descriptors. The mean percentage deviation (MPD) of the prediction data set was used as an accuracy criterion and the validity of the developed model was evaluated by correlation coefficient (R) and variance ratio (F) values. The developed model was able to predict the vasorelaxant activity (pec 50) by 6.4 (± 5.2)% MPD value. Biomed. Int. 2011; 2: Biomedicine International, Inc. Key words: Benzopyran derivatives, vasorelaxant, QSAR INTRODUCTION Adenosine triphosphate (ATP)-sensitive potassium channels (K ATP-channels) are ionic channels with functions that can be regulated by changing the intracellular levels of ATP. These K ATP-channels are known to reduce the intracellular calcium concentration by blocking voltage-dependent calcium channels and inhibiting intracellular calcium release, resulting in smooth muscle relaxation and antispasmodic actions. The channels are closed when intracellular ATP levels are elevated and are opened when they decline, thereby linking the membrane potential to the metabolic state of the cell. Opening the channels allows potassium ions to leave the cell, thus causing trans-membrane hyperpolarization and re-polarization. 1 KATP-channels are widely distributed among various organs, and the open channels play significant roles by acting on secretory cells (β cells of the pancreas), smooth muscles (vascular and non-vascular), cardiomyocytes, and neurons. 2,3 These channels are heterooctameric complexes composed of pore-forming subunits of four inwardly rectifying potassium channels (Kir) and four regulatory sulfonylurea receptors (SUR). The KATPchannels differ from other rectifiers owing to the inclusion of the latter subunit. 1 Since K ATP-channels are involved in major physiological actions in different organs, they could be considered excellent drug targets. Antagonists (anti-diabetic sulfonylurea) as well as activators or openers of KATP-channels (KCO) have been described in the literature. 4,5 The KCOs include a very heterogeneous chemical family comprising benzopyrans, Address correspondence to Somaieh Soltani, PhD, Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran. somaieh.s@gmail.com Submitted May 31, 2011; accepted in revised form June 10, Advance Access Publication 1 July 2011 (see

2 Jafari et al. / Biomed Int (2011) 2:55 benzothiazins, and cyanoguanidines. Among these, benzopyrans (cromakalim derivatives) have been studied owing to their vasorelaxant activity and recently as pancreatic β-cell KCOs. 3 Substititions at different positions (C2, C3, C4, and C6) of benzopyran derivatives and their effect on vasorelaxant activity have been investigated. 5 It is known from the literature that 6-substituents have an outstanding impact on modulating the biological activity of benzopyran KCO. 6 Owing to the wide distribution of K ATP-channels, the development of a novel KCO must be linked to the expression of high selectivity for a particular channel subtype located on a specific target tissue. In order to develop such selective compounds, researchers have attempted to evaluate the relationship between the structural features and biological activities of developed benzopyrans including 6-substituted derivatives. In 1999, Lemoine et al. 7 reported the synthesis, vasorelaxant activity, and structureactivity relationships of 24 6-substituted benzopyrans (bimakalim derivatives (C6-CN)); these compounds are numbered 1-24 in Table 1. They concluded that 6-substitution influences biological activity by a direct receptor interaction of its own and not indirectly by withdrawing electrons from the benzopyran backbone. 7 The SAR of a set of substituted benzopyrans as KCO drug candidates including 10 newly synthesized compounds (compounds numbered 25-27, 30, 32, 34, and in Table 1) was also reported. 6 The mean RSD value obtained for the experimental pec 50 values was 1.1 (±0.5). The methods of principal component analysis (PCA) and partial least square (PLS) analysis were used to investigate the structure-activity relationship of these candidates and excluded three molecules (compound numbers 1 (C6-H), 25 (C6-OH), and 30 (C6-OCO CH 3)) as outliers. By employing the remaining 31 data points, a 3-component PLS model was introduced for explaining 81% of the variance in biological activity, which was in agreement with the findings of a previous study. 6 Subsequently, the first quantitative structure activity relationship of these chemicals was studied by Mannhold. 5 On the impact of 6-substitution on the biological activity of 50 benzopyran derivatives (Figure 1, Table 1, compound numbers 1-51), the QSAR equation they developed represented a correlation between relaxant potency and the direction of the dipole vector of the ligands. The physicochemical parameters and the structural features (such as Dragon derived molecular descriptors) were not considered in the previous study. The only outlier in that study was the negatively charged compound number 33. No statistical property of the model was reported except the significance of the selected parameter and the R value of 0.67 for 49 compounds. N O R O CH 3 CH 3 Figure 1: Molecular structure of benzopyran derivatives. P a g e 56

3 Biomed Int (2011) 2:55 / Benzopyran Derivativ erivatives es A set of 34 derivatives of 6-substituted benzopyran were employed by Agrawal and coworkers 8 for developing QSAR models from distance-based topological indices. After excluding 6 data points (compound numbers 1, 6, 9, 16, 24, and 34) as outliers, four different QSAR equations were built for the 28 remaining molecules. The best model (containing four descriptors) was used for back-calculating the biological activity with a mean percentage deviation (MPD) of 3.9 (±2.9)%. The model was not tested on the external data set; however, cross-validation results were reported showing that the model is able to predict the biological activity of the data set. A general ANN model was reported for explaining the QSAR of 137 diverse vasorelaxant compounds including 51 substituted benzopyran derivatives (compounds numbered 1-51). The proposed ANN (6 variables selected using the GA-PLS method from 1479 descriptors of the Dragon software) could predict the biological activity of a prediction data set with an MPD of 15.0 % with no outlier. 9 The literature review indicated that there is no single, general, validated, and applicable model for evaluating the QSAR of 6-substituted benzopyran compounds. The aim of this study was to develop a general, validated, and applicable QSAR method for a diverse set of 6-substituted benzopyran derivatives. Table 1: The experimental biological activity data. No. R pec50 Reference No. R pec50 Reference 1 H OCO-Ph CO CH OCO CH * CO C2H OCH2 o,o F-Ph CO C6H * OCH2 CO-Ph CO-Ph OSO CO poh-ph OSO2F * CO-3-furyl OSO2CF CO-3-thienyl OSO2Ph * CO poch3-ph OSO2CH CO pno2-ph * OSO2NH CO of-ph OSO2Cl CO ono2-ph * OCH2SO2Ph CO och3-ph SO2NH C2H * CO ocf3-ph SO2NH CH2-Ph CHO * SO2NH C(=NOH)-NH SO2N (CH3) CH=C(CN) SO2NH-Ph N-2,5-(CH3) 2-pyrrolyl SO2N (CH2CH2)2 O * CO o,o -F-Ph SO2NH CH(CH3) CN * SO2NH CH Pyridyl SO2-Ph * Br SO2F CF SO2N * CS NH OCO-o-F-Ph * OH OCONH-o-F-Ph OCH * OCONH-p-F-Ph OCONH-Ph OCONH-o-CF3-Ph OCH2-Ph OCONH-p-CF3-Ph * Test data points. P a g e 57

4 Jafari et al. / Biomed Int (2011) 2:55 EXPERIMENTAL DATA AND COMPUTATIONAL METHODS Data set The pec 50 values (log (1/EC 50) of half maximal potency of relaxant activity on rat aorta) of 56 C6-substituted benzopyran derivatives were collected from the literature. 5-7 The details of the selected molecules are summarized in Table 1. It can be seen from the data that the pec 50 values of vasorelaxant activity of these compounds were in the range 4.50 to Molecular modeling methods Descriptor calculation The 2D structure of each compound was drawn and converted to 3D using HyperChem 7.0 software, and subsequently energy-minimized by Polak-Ribiere geometry optimization using MM + force field. The energy-optimized molecules were used for computing molecular descriptors using Dragon 5.4 software. The total of 1479 different 1D, 2D, and 3D molecular descriptors included constitutional descriptors, topological descriptors, molecular walk counts, BCUT descriptors, Galveze topological charge indices, 2D autocorrelations, charge descriptors, aromaticity indices, Randic molecular profiles, geometrical descriptors, RDF descriptors, 3D-MoRSE descriptors, WHIM descriptors, GETAWAY descriptors, functional groups, atom-centered fragments, and empirical descriptors. The highly inter-correlated (R 2 >0.95), constant, and near constant descriptors were excluded and the remaining 583 were used for descriptor selection analysis. Selection of test and training sets Subsequent to sorting the data set on the basis of biological activity, 20 % of the data points were selected as the prediction set and the remainder were used for training data procedures (feature selection and model development). The test and training data points are shown in Table 1. Variable selection and model building The purpose of variable selection is to select the variables that contribute significantly to the prediction and to discard the others. Stepwise multiple linear regression (MLR) is one of the most widely used methods for variable selection. In the stepwise MLR method, a multiple linear equation is built step by step. The procedure includes identifying an initial model; then adding and removing the parameters according to the stepping criteria; and finally building the model. The stepping procedure is iterative and ends when the maximum number of steps is reached. This simple method is limited by its ability to search for just one single model; therefore, other models with the same number of parameters cannot be investigated using this method and consequently several significant and relevant parameters are ignored owing to their intercorrelation. Genetic algorithms (GA) have been developed to mimic some of the processes observed in natural evolution, which is an efficient strategy for searching for the global optima of solutions. Recently, these GAs have been successfully applied to feature selection in regression analysis. 10 Moreover, an approach combining GA with PLS (GA-PLS) has also been proposed for variable selection in QSAR and QSPR studies. 11,12 This hybrid method, which integrates GA as a powerful optimization tool and PLS as a robust statistical tool, could lead to a significant improvement in multivariate calibration, QSAR, and QSPR models. It is also capable of selecting the most significant and highly correlated parameters with dependent variable; however, its limitation is that the use of the first two or three P a g e 58

5 Biomed Int (2011) 2:55 / Benzopyran Derivativ erivatives es parameters as the best predictors will not lead to the best MLR equation and thus a final selection analysis must be performed within the selected parameters. In this study, a combination of stepwise regression with GA-PLS was used in order to select suitable predictors. The selected parameters were used for building a QSAR model. Statistical approach The quality of the proposed model was judged using the values of correlation coefficient (R), variance ratio (F), and Mean Percentage Deviation (MPD) calculated using Eq. 1, for the training and prediction sets. (1) 100 MPD = N Y pred. Y Y exp. exp. where N denotes the number of data points, and Y pred. and Y exp. represent the predicted and experimental normalized pec 50 values. The robustness and validity of the developed models was evaluated using the 9-fold leave-many-out cross-validation method. Y randomization was performed in order to reject the chance correlation probability of the selected parameters. To investigate the individual behavior of data points, the individual percentage deviation (IPD) was calculated using Eq. 2: (2) Y IPD = 100 pred. Y Y exp. exp. The effect of outlier data on the developed method was evaluated by comparing the statistical properties of the developed method before and after exclusion of the outlier molecules. RESULTS Evaluation of outliers A common feature of QSAR models is the presence of outliers or other anomalous compounds. Various studies have identified the outliers using numerical methods, and they are then removed from the dataset. 13 As mentioned in introduction, some data were identified as outliers in the published articles, 4,5,7 and hence excluded. The excluded data points in previous articles were 1, 25, 30, 6 33, 5 1, 6, 9, 16, 24, and The outliers were excluded as a result of outlier behavior observed during the preliminary analysis. In order to investigate the possible outliers, PCA analysis and standard score analysis were carried out. None of the molecules were found to show different behavior owing to their structural features and pec 50 values; however, the primary MLR analysis indicated that some data points produced high IPD values and reduced R in all developed MLR equations. Nevertheless, the selected parameters were not changed. In fact, the variable selection resulted in the same parameter with and without outlier data points. Thus, no data point was excluded as an outlier. Also, it was found that placing all the outliers in the training or the prediction set did not affect the selected parameters. The data points that led to the highest IPD values were compound numbers 24, 33, 38, and 40. P a g e 59

6 Jafari et al. / Biomed Int (2011) 2:55 Variable MSD HATS8v Table 2: The selected parameters and their description. Description Mean square distance index (Balaban) (topological descriptors) Leverage-weighted autocorrelation of lag 8 / weighted by atomic van der Waals volumes (GETAWAY descriptors) Variable selection The calculated variables were divided into three subgroups and each subgroup together with the corresponding pec 50 values (dependent variable) was introduced into the algorithm as input. The output was produced after 20 runs as scored variables. The top 10 % scores of each subgroup were selected, stepwise regression was conducted to discard the variables with low relevance, and the remainder was cross-correlated using SPSS software. The significant parameters with R 2 >0.4 were selected. The 17 highly correlated parameters were studied through their intercorrelation, and the most highly correlated parameters with low intercorrelation and significant variables were selected for developing MLR equations. As seen from Table 2, the selected parameters are MSD (distance based topological parameters) and HATS8v, which belong to the set of GETAWAY descriptors. Table 3: Observed (obs.) and calculated (calc.) pec50 values using Eq. 3. No. Obs. calc. IPD No. obs calc. IPD * * * * * * * * * * * * * * *Prediction data points. The outliers are shown in bold. QSAR analyses The MLR equation of the two developed parameters for the training set containing outlier data points (after excluding prediction set data points) is as follows: P a g e 60

7 Biomed Int (2011) 2:55 / Benzopyran Derivativ erivatives es pec 50 = 22.6( ± 3.3) 83.1( ± 14.4)MSD+ 14.0( ± 3.4)HATS8v N = 42, R = 0.8, F= 35.8, S.E. = 0.6, MPD= 6.7( ± 7.3) where N denotes the number of data points, R is the correlation coefficient, F is the variance ratio, and S.E. is the standard error of estimation. It was found that the highest IPD value (=38.9%) was produced by compound number 33 in the training set and by compound numbers 24 (=24.5%), 38 (=46.3%), and 40 (=30.4%) in the prediction set. This equation was used to predict the pec 50 values of the prediction set (N=8) by an MPD of 12.2 (±13.3)%, where the IPD of 71% of prediction data points was below 10%. The frequency of IPD for both training and prediction sets was found to be the same. It must be noted that after excluding the outliers, the MPD values for the training and the prediction sets were 5.9 (±5.3)% and 6.4 (±5.2)%, respectively. No significant difference was found in the correlation properties before and after the exclusion of outliers except for the R value of the prediction set, which increased from 0.4 to 0.9. This was obviously owing to the contribution of 3 of 4 outliers in the prediction set. The details of the predicted and back calculated pec 50 values are listed in Table 3. As mentioned in the introduction, no similar model in previous studies could be compared with the proposed one. In comparison with a previously developed model, 8 the proposed model is found to be more general and applicable to evaluating the QSAR of a part of the studied database (number of data points=28, number of descriptors=4, and MPD =3.8 (±2.9)%). Cross-validation A 9-fold leave-many-out cross-validation analysis was conducted in order to test the validity and stability of the proposed model. The results of this analysis are shown in Table 4. The similar MPD values for all the training and prediction sets along with acceptable Q 2 values indicated that the model is robust and valid and does not depend on the training set. Table 4: Cross validation analysis results Group Q 2 IPD (min) IPD (max) MPD (±SD) (±9.4) (±9.0) (±9.2) (±9.1) (±8.9) (±8.8) (±8.9) (±8.5) (±8.1) (3) Chance correlation A Y randomization chance correlation analysis was performed in order to test the probability of the random selected parameters. Different sets of random values of pec 50 were used as dependent variables and the resulting R values were found to be in the range , thus revealing that there is no chance correlation. Structure activity evaluation P a g e 61

8 Jafari et al. / Biomed Int (2011) 2:55 The selected descriptors and their definitions are shown in Table 2. The GETAWAY (GEometry, Topology, and Atomic Weights AssemblY) descriptors were developed to match 3D molecular geometry derived from the Molecular Influence Matrix (MIM) and atom relatedness by topology with chemical information using different atomic weighting schemes (unit weights, mass, polarizability, electronegativity). In fact, they are indicative of molecular complexity (since they are sensitive to the whole molecule structure including hydrogen atoms). It was found that HATS descriptors belonged to this family and that the greater the HATS8v, the greater the pec 50 values (low potency). The MSD reflects only the carbon backbone and neglects connected hydrogens. It is based on the molecular structure according to graph theory and the distance matrix and reflects the relative connectivity and effective size of the carbon chain to which multiple methyl groups are attached. The magnitude of this descriptor is found to increase with (i) an increase in branching, and (ii) an increase in the number of atoms in the molecule. The most potent molecules are found to have higher MSD values. By grouping the pec 50 values in the following order: <5, 5-6, 6-7, >7, it was seen that there is a decrease in the MSD/HATS8v ratio average (2.2, 1.9, 1.8, 1.4, 1.3). By considering these facts, it can be concluded that the flexibility of residues is important for these benzopyrans and that steric limitations lower the potency. The interpretation of dragon descriptors proved not to be straightforward and the potency cannot be predicted using one of them, so a combination of these two descriptors should be considered. CONCLUSIONS In this study, an attempt was made to develop an applicable and simple QSAR for the purpose of filtering and prediction. It is hoped that the developed model would be helpful to researchers for selecting and synthesizing more potent lead compounds that exert a vasorelaxant effect. ACKNOWLEDGMENT The authors are thankful to the Drug Applied Research Center for providing partial financial support under grant No REFERENCES 1. Triggle DJ, Gopalakrishnan M, Rampe D, Zheng W. Voltage-Gated Ion Channels as Drug Targets. In: Mannhold R, Kubinyi H, Folkers G (Eds.). Methods and Principles in Medicinal Chemistry. Weinheim, Wiley-VCH, 2006: p Carosati E, Lemoine H, Spogli R, et al. Binding studies and GRIND/ALMOND-based QSAR analysis of benzothiazine type K(ATP) channel openers. Bioorg Med Chem 2005; 13: Alam SM, Samanta S, Halder AK, Basu S, Jha T. QSAR modelling of pancreatic β-cell KATP channel openers R/S-3,4-dihydro-2,2-dimethyl-6-halo-4-(substituted phenylaminocarbonylamino)-2h-1- benzopyrans using MLR FA techniques. Eur J Med Chem 2009; 44: Mannhold R. KATP channel openers: structure-activity relationships and therapeutic potential. Med Res Rev 2004; 24: Uhrig U, Höltje HD, Mannhold R, Weber H, Lemoine H. Molecular modeling and QSAR studies on K(ATP) channel openers of the benzopyran type. J Mol Graph Model 2002; 21: Mannhold R, Cruciani G, Weber H, et al. 6-Substituted benzopyrans as potassium channel activators: synthesis, vasodilator properties, and multivariate analysis. J Med Chem 1999; 42: P a g e 62

9 Biomed Int (2011) 2:55 / Benzopyran Derivativ erivatives es 7. Lemoine H, Weber H, Derix A, Uhrig U, Höltje HD, Mannhold R. Relaxant activity in rat aorta and trachea, conversion to a muscarinic receptor antagonist and structure activity relationships of new KATP activating 6-varied benzopyrans. Eur J Pharmacol. 1999;378(1): Agrawal VK, Singh J, Gupta M, Jaliwala YA, Khadikar PV, Supuran CT. QSAR studies on benzopyran potassium channel activators. Eur J Med Chem 2006; 41: Soltani S, Babaei H, Asadpour-Zeynali K, Jouyban A. Modeling vasorelaxant activity of some drugs/drug candidates using artificial neural networks. J Pharmacol Toxicol. 2007;2: Leardi R, Lupiáñez González A. Genetic algorithms applied to feature selection in PLS regression: how and when to use them. Chemom Intell Lab Sys 1998; 41: Leardi R. Application of genetic algorithm-pls for feature selection in spectral data sets. J Chemom 2000; 14: Tang K, Li T. Combining PLS with GA-GP for QSAR. Chemom Intell Lab Sys 2002; 64: Guha R. On the interpretation and interpretability of quantitative structure-activity relationship models. J Comput Aided Mol Des 2008; 22: P a g e 63

Coefficient Symbol Equation Limits

1 Coefficient Symbol Equation Limits Squared Correlation Coefficient R 2 or r 2 0 r 2 N 1 2 ( Yexp, i Ycalc, i ) 2 ( Yexp, i Y ) i= 1 2 Cross-Validated R 2 q 2 r 2 or Q 2 or q 2 N 2 ( Yexp, i Ypred, i