Additivity of Molecular Fields: CoMFA Study on Dual Activators of

J. Med. Chem. 2005, 48, 3015-3025

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Additivity of Molecular Fields: CoMFA Study on Dual Activators of PPARr and PPARγ Smriti Khanna, M. E. Sobhia, and Prasad V. Bharatam* Department of Medicinal Chemistry, National Institute of Pharmaceutical Education and Research (NIPER), Sector-67, S. A. S. Nagar, 160 062, Punjab Received August 2, 2004

Recent trends in drug discovery include methods to identify dual and triple activating drugs. This approach is being successfully employed in malaria, cancer, asthma, insulin resistance, etc. Molecular field analysis has been employed in correlating pharmacological data and field parameters. In this paper we introduce the concept of additivity of molecular fields to correlate molecular fields of dual activators and their pIC50 values. PPARR and PPARγ dual activators, which affect hypertriglyceridemia and hyperglycemia, have been chosen to validate the molecular field additivity concept. Three CoMFA models namely R-model, γ-model and dualmodel have been developed. The validity of this concept has been ascertained by (a) comparing contour maps, (b) by comparing CoMFA results with FlexX docking results and (c) by analyzing newly designed molecules. Introduction Designing multiple activating drugs is one of the modern approaches in medicinal chemistry. Such drugs act at more than one biological target and produce a synergistic effect. Most often the biological targets involved in multiple activity are quite similar to each other and belong to the same family of receptors. A few recent examples of dual activating NCEs are given below. (i) SKI-606, a 4-anilino-3-quinolinenitrile dual inhibitor of Src and Abl kinases, is a potent antiproliferating agent against chronic mylogenous leukemia (CML) and causes regression of K562 xenografts in nude mice.1 (ii) Prolactin antagonist-endostatin fusion protein has been developed to combine the anti-breast cancer tumor effects of human prolactin antagonist G129R that acts through the induction of apoptosis via the regulation of bcl-2 gene expression and the angiogenesis inhibitor endostatin that acts through the inhibition of endothelial cells as a dual functional therapeutic agent for breast cancer.2 (iii) β2-Adrenoceptors agonists are the most commonly prescribed antiasthmatic agents. Another approach to control the hyperreactivity of the lung is based on activation of D2 receptors. Since stimulation of D2 receptors leads to the inhibition of afferent nerve activity in the lungs, dual D2/β2 receptor agonists are a better choice.3 (iv) Artemesinin is the most rapid-acting class of antimalarial drugs that acts at all sites of the parasitic life cycle such as asexual stages and gametocytes and also blocks sporogony.4 (v) NBI-3001 (IL-4 PE) is a recombinant protein, composed of a portion of IL-4 joined to the Pseudomonas exotoxin. The IL-4 portion binds to the IL-4 receptor on the cancer cell, delivering the exotoxin directly into the cell.5 (vi) NO-aspirin and NO-paracetamol, etc., are nitric oxide (NO) releasing drugs, in which case the released NO prevents the side effects of the drug.6 Special efforts to design multiple acting molecules can be successfully made using com* To whom correspondence [email protected].

should

be

addressed.

E-mail:

putational methods. Knowledge of 3D structures of all the targeted receptors is an added advantage. An important class of compounds currently under focus in the same category of dual activators is peroxisome proliferator activated receptors (R, γ, β or δ) that are members of the nuclear receptor superfamily.7 Each of these subtypes appears to be differentiated in a tissue-specific manner and to play a pivotal role in glucose and lipid homeostasis. PPARγ agonists enhance insulin action and promote glucose utilization in peripheral tissues. PPARR agonists improve insulin sensitivity associated with obesity and mediate their effects on lipid metabolism. Therefore PPARR/γ dual activators provide superior profile toward the control of hyperglycemia and hypertriglyceridemia. Thiazolidinedione derivatives (glitazones)8,9 and other classes of insulin sensitizers such as oxazolidinediones,10,11 isoxazolidinediones,12 tetrazoles13 and tyrosine derivatives14-16 are found to specifically sensitize PPARγ. The fibrate class of antilipidemic agents act as agonists for PPARR. Propionic acid derivatives (ragaglitazar) have been found to be dual activators for PPARR and PPARγ.17-22 Xu et al. converted GW409544 (Chart 1), an analogue of farglitazar, into a dual activator of PPARR and PPARγ by suitably modifying the side chain of farglitazar through careful analysis of the subtle differences in the active site of PPARR and PPARγ.23 Carboxylic acid ligands have been found to activate both receptors whereas the conventional glitazones activate PPARγ only. However, recent studies from Merck showed that a series of thiazolidinedione (I) and oxazolidinedione (II) derivatives show PPARR/γ dual activity.24,25 This series has been derived from the lead KRP-297.26 These compounds differ from standard glitazones mainly in two ways: (i) the CH2 linker between the acidic unit and phenoxy ring of glitazones is removed and (ii) alkoxy unit and the acidic unit are meta-linked rather than para-linked. This set of compounds has been used in this work to define ‘additivity’ of molecular fields.

10.1021/jm049383s CCC: $30.25 © 2005 American Chemical Society Published on Web 03/18/2005

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Chart 1

3D-QSAR methodologies have been effectively employed to recognize pharmacophoric units with crucial interactions for the ligand. Apex-3D, CoMFA, CoMSIA, SoMFA, CoMMA, AFMoC, etc.27-33 are some of the successful 3D-QSAR methods. In these methods physiochemical properties of molecules are represented in the form of molecular fields, which can be effectively correlated to the in vivo and in vitro activity represented in numerical form. The most successful attempt was the evolution of Comparative Molecular Field Analysis (CoMFA) module by Cramer in 1988.29 It is based upon the calculated energies of steric and electrostatic interactions between the compound and the probe atom placed at the various intersections of a regular 3-D lattice, a structure-activity relationship developed by partial least-squares analysis. This method is being successfully employed in the prediction of biological activity of known and unknown compounds, designing new molecules, prediction of toxic parameters of lead compounds, etc. Utilization and predictivity of CoMFA itself have been improved sufficiently in accordance with the objectives to be achieved by applying different techniques such as the ‘divide and conquer’ strategy for alignment.34,35 Zefirov et al. addressed the problem of pairwise selectivity by using the difference between biological activities expressed as -log Ki of receptor subtypes as a dependent variable to develop the CoMFA model.36 The resulting selectivity fields indicated ways to increase the binding selectivity of the either receptor. In this paper we present the concept of ‘additivity’ of fields in order to design dual activators. We have explored this concept by modeling dual activators of PPARR and PPARγ, by developing the dual CoMFA model. Biological activities (pIC50) for the individual receptors have been added to get the combined activity for both receptors on which CoMFA has been performed. The fields resulting from this CoMFA model represent

‘additivity fields’. The additivity CoMFA model developed as described above has been validated to possess the additivity properties by (1) comparing the individual CoMFA models for PPARR and PPARγ and (2) by carrying out docking studies. This kind of additivity analysis will definitely go a long way in designing molecules, which demand receptor subtype selectivity or duality in action once validated with a few more examples. Materials and Methods Data Sets. A dataset consisting of a series of 5-aryl thiazolidinedione and oxazolidinedione derivatives acting as PPARR and PPARγ dual activators (Table 1) has been chosen to develop three CoMFA models: (i) R-model, (ii) γ-model, and (iii) dual-model. These molecules are composed of four parts: part A constitutes the acidic fragment, part B refers to the phenoxy ring next to the acidic fragment, part C is composed of a (CH2)n linker and the last part D is composed of a phenoxy side chain. The dataset of 34 molecules were sorted randomly into training set and test set comprising of 27 and 5 molecules respectively in the process of model refinement for all three CoMFA models reported herein. Two molecules (20 and 25) did not fit well into either the training or the test set and in any of the three models and were dropped. The reported SAR studies also include compounds for which the IC50 values in PPARR could not be specifically ascertained, but a minimum of a range was mentioned (e.g. 3, 5, 6 etc. in Table 1). For such molecules, the reported minimum value was employed in building the models. The biological activities have been reported as the binding affinities measured as IC50 values in µM using radiolabeled TZD AD-5075 and recombinant PPARs by Desai et al.37 These have been converted to pIC50 (-log IC50) values in molar terms and are used as the dependent variables in the CoMFA analysis (Table 1). In the dual model development, the pIC50 values (dual) have been defined as the sum of pIC50 values for PPARR and PPARγ. Minimization and Alignment. The ligands under study were built employing the SKETCH module of the SYBYL6.938 molecular modeling package installed on a Silicon Graphics Octane2 workstation with IRIX 6.5 operating system. Since

Additivity of Molecular Fields

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Table 1. Data Set Used for CoMFA Analysis with Their IC50 (µM) and pIC50 (M) Values in the R, γ and Dual (d)-Models

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Table 1 (Continued)

#

) without propyl group. *n ) 2.

the crystal structure of rosiglitazone with PPARγ is known, the basic skeleton and conformation for the most active molecule, 1, from the first series was modeled after the structure of the ligand rosiglitazone extracted from the complex (pdb code 2PRG) and minimized using PM3 method while maintaining the U-shape essential for the activity.39 The rest of the molecules were built taking 1 as the template and changing the required substituents for the subsequent set of molecules and minimized similarly. Mulliken charges40 were assigned to all the molecules. The alignment criterion plays an important role in CoMFA studies, and it is preferable to choose an alignment which maintains bioactive conformation. The alignment of molecules in these data have been carried out, maintaining the U-shape of the ligands, considering a set of eight atoms (as shown below) for the ‘fit atom’ function of SYBYL6.9. Figure 1 depicts alignment of the training set molecules.

CoMFA 3D-QSAR Models. The standard Tripos settings were used to carry out the CoMFA analysis. To derive the CoMFA fields, a 3D cubic lattice was created, and the steric and electrostatic parameters were calculated at each lattice intersection of regularly spaced grid of 2.0 Å in all three dimensions within the defined region. The van der Waals potential and the Coulombic term representing the steric and electrostatic fields were calculated using standard Tripos force fields. An sp3 carbon atom was used as a probe atom to generate steric (Lennard-Jones potential) field energies and a charge of +1.0 to generate electrostatic (Coulombic potential) field energies. A distance dependent dielectric constant of 1.00 was used. The steric and electrostatic fields were truncated at +30.00 kcal/mol. Partial Least Squares (PLS). This statistical method was used to linearly correlate the CoMFA fields to the binding affinity values. The cross-validation analysis was performed using leave-one-out (LOO) method. The cross-validated r2 (r2cv) that resulted in optimum number of components and lowest standard error of prediction were taken. Equal weights were assigned to steric and electrostatic fields using CoMFA_STD scaling option. To speed up the analysis and reduce noise, a



Figure 1. Alignment of training set molecules; hydrogens are not shown. Fit atom alignment has been carried out with the central aromatic ring as the template. The thiazolidinedione ring and the oxazolidinedione rings are oriented in a slightly different way from each other that result in some of the contours in the acidic region. minimum filter value σ of 2.00 kcal/mol was used. Final analysis was performed to calculate conventional r2 (r2ncv) using the optimum number of components obtained from the crossvalidation analysis. Cross-validation runs with varying number of groups were also performed to improve the confidence limits of the derived model. Since the statistical parameters were found to be the best for the model from the LOO method, it was employed for the further predictions of the designed molecules. Molecular Docking Studies. Docking studies were carried out using the FlexX program41 interfaced with SYBYL6.9. FlexX is a fast-automated program based on incremental construction procedure. In this method the flexibility of the ligands is considered by including several conformations of ligands while maintaining a rigid structure for the biomolecule. A selective set of molecules were docked into the active sites of the PPARR and PPARγ. The 3D coordinates of the active sites were taken from the X-ray crystal structures of the PPARR and PPARγ reported as complexes with their corresponding agonists, rosiglitazone and GW409544, deposited in the Brookhaven Protein Databank (PDB codes: 2PRG39 and 1K7L23 respectively). While creating the receptor description file (rdf), the active site was defined as the area within 6.5 Å around the cocrystallized ligand, and customization was done for the histidine, serine and tyrosine residues so as to represent the reported H-bonding interactions. Formal charges were assigned to all the molecules and FlexX run was submitted. The CScore module of Sybyl6.9 is employed to estimate docking score using G_score, D_Score, PMF_Score, chemscore methods.42

Results and Discussion Statistical Analysis. Three independent CoMFA models were built: (1) R-model, based on the binding affinity to PPARR as the dependent variable, (2) γ-model, based on the binding affinity of the ligands to the PPARγ receptor and (3) dual-model, based on the addition of binding affinities of the ligands for both receptors. These additive values of pIC50 represent the combined fields or the ‘additivity fields’ for both receptors. These CoMFA models were chosen after a rigorous cycles model-development and validation based on internal predictions of the training set and the external predictions of the test set. The statistical parameters for the three models developed are shown in Table 2. The dual model shows the best statistical results with cross-validated r2 (r2cv) ) 0.782 with six components and noncross-validated r2 (r2ncv) ) 0.985 while the corresponding values for the other two models are 0.589 with six components and 0.965 for the R-model and 0.687 with four components and 0.951 for the γ-model. The statistical errors in these models are also reasonably low amounting to 0.197 for R-model, 0.126 for γ-model

Table 2. PLS Statistics of CoMFA Models parameters no. of molecules in the training set no. of molecules in the test set r2 cv no. of components r2 ncv SEE F value steric field contributions electrostatic field contributions

R-model γ-model dual-model 27 5 0.589 6 0.965 0.197 93.3 55.8 44.2

27 5 0.687 4 0.951 0.126 103.9 52.8 47.2

27 5 0.782 6 0.985 0.187 212.5 53.5 46.5

and 0.187 for the dual-model as indicated by the internal and external predictions of the training and the test set molecules in Table 3 and 4 and Figure 2. The steric and the electrostatic field contributions for the R-model are 0.558 and 0.442 respectively while for the γ-model these values are 0.528 and 0.472. The dual model achieves a balance between these values of the R and the γ-models, making the steric field contribution of 0.535 and the electrostatic field contribution of 0.465. The R-model is expected to predict the biological activities of ligands for PPARR, and the γ-model is expected to predict the biological activities of the ligand specifically for PPARγ. However, the dual model is much more advantageous in being able to predict the combined biological activities for both receptors. To further cross validate the models built, we performed group based cross validation studies - in all cases, the conventional r2 was found to be greater than 0.95 and the r2cv was found to be greater than 0.6, erasing the doubts regarding the possibility of chance correlation in the developed models. The two individual models are expected to be useful in predicting molecules for PPARR and PPARγ activities, the dual model is expected to identify molecules which act at both PPARR and PPARγ i.e., any molecule which shows activity in the dual model should also be able to show activity in both the R and the γ-models. An inverse logic should be able to suggest molecules with selectivity in PPARR and PPARγ. Contour Analysis. CoMFA is a model of the relationship between molecular field differences of a set of molecules and differences in their biological activity. Molecular fields are defined in terms of the interaction energies of some probe atom placed at the nodes of a grid surrounding the molecules. A ‘field fit’ of the molecular fields with known biological activities leads to colored contour maps, after partial least squares (PLS) analysis, which indicates the required field changes while designing new molecules. The steric

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Table 3. Actual (Act) and Predicted (Pred) pIC50 Values and the Residuals (∆) of the Training Set Molecules for the R, γ and Dual (d)-Models compd

act. (R)

pred

∆

act. (γ)

pred

∆

act. (d)

pred

∆

1 3 4 5 6 8 10 11 12 14 15 16 17 18 19 21 22 23 24 26 27 28 30 31 32 33 34

7.55 5.30 5.68 5.00 5.00 5.00 7.55 5.59 6.02 6.79 6.95 5.85 6.68 7.38 7.05 7.43 6.92 7.21 7.34 6.54 5.57 6.21 4.82 4.82 6.23 5.48 5.54

7.47 5.11 5.76 5.03 5.05 5.07 7.25 5.42 6.43 6.92 7.06 6.10 6.93 7.09 7.04 7.57 6.93 7.13 7.31 6.36 5.78 6.35 4.67 4.85 6.01 5.33 5.72

0.079 0.192 -0.083 -0.029 -0.048 -0.069 0.305 0.175 -0.410 -0.133 -0.111 -0.253 -0.013 0.282 0.015 -0.144 -0.01 0.085 0.026 0.177 -0.210 -0.141 0.152 -0.034 0.220 0.151 -0.178

7.24 6.71 6.77 6.47 6.54 6.65 7.11 6.52 7.52 7.25 7.11 7.43 7.07 7.20 7.72 7.52 7.19 6.82 7.23 6.31 6.35 6.19 5.94 6.74 6.27 5.75 6.12

7.37 6.78 6.72 6.49 6.50 6.54 7.15 6.75 7.22 7.13 7.22 7.27 7.18 7.26 7.66 7.56 7.10 6.94 7.14 6.17 6.38 6.12 5.86 6.88 6.35 5.80 6.18

-0.125 -0.070 0.051 -0.024 0.038 0.109 -0.042 -0.232 0.299 0.121 -0.107 0.160 -0.106 -0.062 0.062 -0.044 0.088 -0.120 0.090 0.137 -0.034 0.070 0.078 -0.144 -0.082 -0.055 -0.055

14.79 12.01 12.45 11.47 11.54 11.65 14.66 12.11 13.54 14.04 14.06 13.28 13.75 14.58 14.77 14.95 14.11 14.03 14.57 12.85 11.92 12.40 10.76 11.56 12.50 11.23 11.66

14.76 11.82 12.46 11.53 11.57 11.73 14.34 12.14 13.71 14.08 14.29 13.46 13.82 14.28 14.78 15.11 14.10 13.98 14.51 12.52 12.18 12.45 10.69 11.57 12.31 11.13 11.93

0.026 0.195 -0.005 -0.063 -0.033 -0.082 0.318 -0.034 -0.165 -0.035 -0.231 -0.176 -0.070 0.304 -0.014 -0.155 0.007 0.054 0.056 0.333 -0.264 -0.053 0.073 -0.011 0.191 0.104 -0.269

Figure 2. Scatter graph of the predicted vs actual activities of the training and the test set molecules. (a) Shows the graph of the R-model, (b) shows the graph of the γ-model and (c) shows the graph of the dual-model. The blue color indicates the training set molecules, and the magenta color indicates the test set molecules.

contour maps are represented in green and yellow while the electrostatic contour maps are represented in red and blue. The green contours are indicative of favorable regions for sterically bulkier groups and the yellow contours are indicative of regions that are sterically less favorable. In a similar way the red contours represent regions that lead to the enhancement of activity with electron rich groups, and contrary to that the blue regions represent electron deficient regions and can lead to an increase in the activity of molecules by similar substitutions. A careful comparison of the contours of the dual-model with the R-model and the γ-model reveals that the contours of the dual-model incorporates features of both individual models and thus represents a combination of field deviations of the R and γ models. Most of the contours are found in the variable side chain region (part D) while very few are found in the acidic part (part A). The steric and the electrostatic contour maps for the three models are represented in Figures 3 and 4, respectively. In the γ-model the green contours form a continuous streak around the substituted phenoxy side

chain (part D) of the molecules. In the R-model, the green contours are broken and do not show continuity. In the dual-model, these sterically favorable contours occur in the same shape as found in the γ-model contours but appear much more refined. On the other hand, yellow contours are distributed in three broken units in all three models. The difference lies only in their sizes. In the R-model they are much smaller in size but similar in shape as compared to those in the γ-model. The same pattern of occurrence is observed in the dualmodel also, except that there is further gradual increase in size of these contours from R to γ to the dual-model. The electrostatic fields also appear to add the features of both individual models and represent addition of fields in the dual-model. The red contours around the phenoxyphenyl side chain (part D) in the R-model are much bulkier and occur in three parts. In the γ-model there is only one central contour found at the same place, of the three. In the dual model all three red fragments (part D) are retained and the central one shows a larger size and a greater coverage than the other two models, which reflects the addition of these



Figure 3. Steric contour maps of (a) R-model, (b) γ-model and (c) dual-model.

Figure 4. Electrostatic contour maps of (a) R-model, (b) γ-model and (c) the dual-model.

Figure 5. Steric and electrostatic contour map for the dualmodel showing the contributions from each model. A depicts the contributions made by the R-model and G depicts the contributions made by the γ-model.

fields from the two models. The blue contours in the R-model are comparatively larger than the γ-model. In the dual-model they are more refined and split into two fragments, showing contributions made by individual models. The acidic unit has a green, blue and a red contour in close vicinity to each other in the γ-model. Out of these, only the red contour is retained in the R and the dual-model and appear very similar in shape. Thus it is seen that both steric and electrostatic contours in the dual model incorporate the characteristic features of both R and the γ-models (Figure 5). The contour maps clearly show that the electrostatic contributions are relatively more in R-model and the steric fields play an important role in the γ-model. In the dual-model, however, a proper balance between these field contributions has been noted, confirming that the dual model indeed represents the desired dual character.

The additivity concept has been further validated by comparing the molecular fit of several molecules into the contour maps of the three CoMFA models. A set of molecules which are high affinity dual activators for PPARR and PPARγ are showing very good fit into the contour maps of all three models. For example 1, 10, 19, 21, etc. show comfortable fit into the contour maps of all three CoMFA models, and no destabilizing overlaps are noticeable. On the other hand, compounds 5, 6 and 30 are inactive for PPARR and have weak affinity for PPARγ. In 5 and 6, the terminal alkyl substituents enter into unfavorable yellow regions in all three CoMFA models. Compound 30 shows very unfavorable electrostatic overlap because the carbonyl group of the cyclohexanone ring enters into the blue region in the R and the dual-models. Contrary to compound 30, compound 32 shows comparable affinity for both receptors. In this molecule, the two fluorine atoms (in place of cyclohexanone ring in 30) avoid the blue region, showing better ‘field fit’, in all three CoMFA models. These examples clearly indicate that a comparative analysis of contour maps provide clues regarding the substituent preferences. This comparative analysis is useful in validating dual model also because those molecules, which find proper field fit in dual model, also find proper field fit in both the R and the γ-models. This set of CoMFA models can also be used to identify features required to show selectivity. For example 8 and 31 are active in PPARγ but not in PPARR. Contour analysis in the presence of 8 and 31 revealed that the terminal rings do not show favorable overlap with the contours in the R-model but show favorable overlap with the contours in both dual model and the γ-model. This validates the representative character of the dual model and also indicates that this set of models can be effectively used to design molecules, which can show selectivity.

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Khanna et al. Table 4. Actual (act.) and Predicted (pred) pIC50 Values and the Residuals (∆) of the Test Set Molecules for the R, γ and Dual (d)-Models compd 2 7 9 13 29

Figure 6. Aligned active site residues of PPARR (pdb code 1K7L) and PPARγ (pdb code 2PRG) with the radius of 6.5 Å around their cocrystallized ligands GW409544 and rosiglitazone, respectively.

Docking Studies. The molecular docking studies were carried out on two proteins, viz. PPARγ and PPARR, using FlexX method. The X-ray crystal structures of PPARγ reveal a large Y-shaped binding pocket in PPARR and PPARγ. The three-dimensional structures of the active sites of PPARR and PPARγ are very much comparable. Figure 6 shows protein backbone alignment of the active sites of PPARR and PPARγ. In both receptors the ligands adopt a U-shaped conformation wrapping around the helix. The ligands bind by forming strong hydrogen bonds between the acidic fragment and a set of residues in the active site: Tyr314, Tyr464, His440 and Ser280 in PPARR, and His323, Tyr473, His449 and Ser289 in PPARγ. Xu et al. reported that the major difference between the active sites of PPARR and PPARγ arises due to the replacement of Tyr314 in PPARR with His323 in PPARγ. Tyr314 in PPARγ is larger in size and pushes the bound ligand 1.5 Å deeper in relation to His323 in PPARγ. 23 Molecular docking studies (using FlexX method) of rosiglitazone in the two active sites clearly reveal the differences in the interactions of the ligand in the active sites of the PPARR and PPARγ. The results are quite comparable to the results described by Xu et al.23 The FlexX docking scores of rosiglitazone in PPARR and PPARγ are -19.8 and -31.4 kcal/mol respectively, clearly reflecting the poor binding of rosiglitazone in PPARR. To validate this docking experiment, we also estimated the G_score, PMF_Score, D-score and Chemscore for the docking of rosiglitazone in PPARγ and PPARR using the CScore module of SYBYL6.9. As shown in Table 5, all the scores for docking rosiglitazone in PPARR (1K7L) are weaker than that in PPARγ (2PRG) (Table 5). These docking scores indicate that the score values can be used to distinguish the strengths of the interactions of ligands in the active sites; however, such clear indications are seldom found in the literature. To unveil the differences in the binding affinity of these molecules with the two receptors, we have performed docking studies on some of the molecules and have

act. (R) pred 7.33 5.70 7.00 7.17 6.20

6.70 5.37 7.02 7.34 6.05

∆ 0.63 0.33 0.02 0.17 0.15

act. (γ) pred 7.12 6.48 7.14 7.19 5.97

6.94 6.60 7.08 7.09 6.26

∆

act. (d)

pred

∆

0.18 0.12 0.06 0.10 0.29

14.45 12.18 14.14 14.36 12.17

14.21 11.97 14.08 14.39 12.33

0.24 0.21 0.06 0.03 0.16

analyzed the reasons for their selectivity and dual activity. Two of the molecules 8 and 31 do not show good binding affinity for PPARR but are found to be PPARγ activators. The docking studies indicate unfavorable orientation of 8 in the active site of PPARR; it lies perpendicular to the conformation in which GW409544, the original ligand of 1K7L, is placed in the same active site. This prevents the molecule from making the prime H-bonds essential for the activity in PPARR. However, in PPARγ the docking of this molecule allows it a better fit into the active site, maintaining all the important H-bonds (Figure 7). Similarly the acidic group of 31 also does not form proper H-bonding interactions in PPARR but finds the required H-bonding interactions in PPARγ according to FlexX docking. Dual active compounds 19 and 21 show satisfactory docking in both PPARR and PPARγ. Both molecules dock well in PPARγ, showing all three H-bonding interactions originating from the thiazolidinedione ring. In PPARR, however, the acidic ring shows only one H-bonding interaction with both 19 and 21. In both cases, a compensating stabilizing interaction was noticed between ethereal oxygen (of part D) and Ala333 and Thr279 of PPARR. Most of the docking scores of 19 and 21 are quite comparable in PPARR and PPARγ. The results obtained from the docking of 8, 19, 21 and 31 using the FlexX method and the results obtained from the field fit of the same molecules in the three CoMFA models are quite comparable, thus validating the additivity concept introduced in this work. Such collective analysis by CoMFA and docking methods is a very useful approach in dual activator design to further validate the additivity concept. We have designed several compounds using three CoMFA models and docked the molecules in PPARR and PPARγ, the results of which are given below. Design of New Molecules. The designing of new molecules using the dual model posed a few challenges and taught some lessons, which are described below. Initially the dual CoMFA model was employed to design new molecules. Compound 21, which is the most preferred dual agonist, has been chosen for further improvement in dual activity. Taking the clues from the contour maps of the dual CoMFA model, attempts were made to improve predicted IC50 values in PPARR and PPARγ activity for the designed molecules. All the attempts proved to improve PPARR activity but not PPARγ activity. One major clue obtained was that replacement of the phenoxy ring in part D with a bicyclic ring improved the PPARR activity significantly. Since the dual CoMFA model was of limited help in improving PPARγ activity, we employed the γ-model. This led to the suggestions that the replacement of the n-propyl



Table 5. Predicted (pred) pIC50 Values and the CScore Energies in kcal/mol in 2PRG and 1K7L for Rosiglitazone, the Designed Set of Molecules (D1-D5) and Molecule 21 (the most active in the dual-activity series) for the R, γ and Dual (d)-Models Flex-score

G-score

PMF-score

D-score

Chem-score

ID

pred (R)

pred (γ)

pred (d)

2PRG

1K7L

2PRG

1K7L

2PRG

1K7L

2PRG

1K7L

2PRG

1K7L

rosi 21 D1 D2 D3 D4 D5

4.91 7.44 7.57 7.57 7.62 7.69 7.63

6.18 7.53 7.78 7.77 7.76 7.77 7.77

10.57 14.92 15.30 15.30 15.34 15.42 15.35

-31.4 -26.2 -37.4 -42.0 -38.6 -30.6 -42.7

-19.1 -21.1 -33.0 -30.5 -33.9 -31.2 -36.5

-204.9 -265.7 -297.4 -303.6 -307.1 -317.8 -301.1

-197.4 -273.5 -329.9 -251.4 -340.2 -383.9 -336.9

-44.9 -43.4 -41.8 -39.9 -39.4 -63.2 -49.1

-40.21 -31.5 -66.9 -66.7 -64.9 -59.7 -69.1

-128.4 -170.5 -198.6 -200.2 -203.4 -207.2 -203.7

-127.9 -168.5 -201.3 -170.9 -204.9 -223.9 -219.0

-39.9 -48.1 -56.7 -58.7 -57.9 -57.6 -58.4

-31.3 -39.3 -52.3 -52.3 -53.9 -58.6 -52.2

Figure 7. Aligned H-bonding residues of active sites of PPARR and PPARγ along with the docked ligand. This molecule shows the formation of prime H-bonds with PPARγ and is thus a PPARγ agonist. However, it is devoid of these interactions due to unbefitting orientation in the PPARR active site and hence is an inactive PPARR agonist.

Figure 8. A set of designed dual activators.

side chain aromatic ring improves PPARγ activity. Employing this modification and replacing the phenoxy unit of part D with a benzofuran ring led to an overall improvement in dual activity of the designed molecules. Using the dual CoMFA model, further modifications led to the suggestion that compounds D1-D5 (Figure 8) potentially show improved dual agonistic activity. The predicted pIC50 values for the newly designed molecules are listed in Table 5. Thus the CoMFA model built using the additivity concept has been found to be useful in designing many new molecules with dual activity.

The newly designed molecules, D1-D5, were docked into the active sites of PPARR and PPARγ using FlexX. All of them were found to dock well into both active sites. The FlexX and other docking scores are also better than those of 21, i.e. the most active molecule in the dual-agonist series as well as rosiglitazone (Table 5). On the basis of these docking results, we can conclude that the molecules designed from the additivity CoMFA model indeed possess the improved dual characteristics. Thus the combination of additivity CoMFA and molecular docking studies provide useful tools to understand multiple activities of drugs.

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As discussed above, the additivity model presented for the dual activators gave satisfactory results. A possible limitation of this approach may be envisaged as follows. The dual CoMFA model appears to be effective for the chemical species which show comparable activity in both targets (for example pIC50 values of 6.0 and 6.0 in both targets, the total being 12.0). However, if the numerical values for another chemical species are widely different in the two targets, but leading to the same total (for example pIC50 values 9.0 of in one target and 3.0 in another, total being 12.0), the developed model should be showing selectivity, which is not possible with the dual CoMFA model. Hence, it is important to employ the dual CoMFA model along with individual CoMFA models. A comparative analysis arising out of the three models provide clues for dual activity vs selectivity. Employing only one of them can lead to misleading conclusions. Hence, designing new molecules should be carried out only after considering dual as well as individual models both for dual activity enhancement as well as selectivity prediction.

As a final validation criterion, new molecules were designed using the three CoMFA models by making suitable substitutions in the ‘activity-enhancing’ regions of the contour maps. Docking of the newly designed molecules in the active sites of PPARR and PPARγ was found to be quite satisfactory in terms of molecular fit as well as FlexX and other docking scores. The additivity molecular fields concept presented in this paper can be employed in designing dual activator as exemplified in the case of PPARR and PPARγ. This opens a wide window of opportunity for the rational design of molecules, which act at multiple sites positively.

Conclusions Molecular field analysis, which became popular in the form of CoMFA, is a very useful method that has been employed for the prediction of biological activities of new compounds. Molecular fields have been constructively added to define dual CoMFA model. Three different CoMFA models have been developed, R-model, γ-model and the dual-model, to represent the molecular fields associated with lead compounds acting at PPARR, PPARγ and both receptors, respectively. The molecular fields developed using the additivity concept incorporate the features of both receptors. Clearly the contour maps of the dual-model represent the fields of both receptors and therefore can be effectively employed for the design of dual activators. The validity of the dual-model has been verified using different approaches. In the first place the predictability of the dual-model has been well established from the statistical parameters obtained, which are found quite satisfactory. The predictive ability of the three CoMFA models is also found to be well within the acceptable range. The second type of validation was done by analyzing the behavior of some of the molecules within the contour maps of individual and the dual-model. Those molecules, which are active at both receptors, show specific favorable field fit in each of the individual models while maintaining the same trend in the dualmodel, and the molecules, which are inactive at the PPARR receptor, have actually shown similar unfavorable placement in both the R model and the dual-model. As a third step of validation, docking studies were carried out to complement the 3D-QSAR analysis to determine the differences in the selectivity of a few molecules for a particular receptor and the dual activity of a few others. Some representative molecules were docked into the active sites of both PPARR and PPARγ using the FlexX method. The results indicated that the dual activators show all the important essential interactions, besides some specific ones, with the side chains of each receptor, while the R inactive molecules are devoid of even the most primary essential interactions leading to their inactivity.

References

Acknowledgment. The authors thank the reviewers for bringing the attention to group validations, CScores and other related important points incorporated in this paper. The authors also thank the Council of Scientific Industrial Research (CSIR) and Department of Science and Technology (DST), New Delhi, for financial support.

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Additivity of Molecular Fields: CoMFA Study on Dual Activators of

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