Structure-activity relationships studies on weakly ...

Journal of Molecular Structure 1139 (2017) 362e370

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Structure-activity relationships studies on weakly basic N-arylsulfonylindoles with an antagonistic profile in the 5-HT6 receptor sar Morales-Verdejo b, Carlos F. Lagos c, d, Jaime Mella a, *, Francisco Villegas a, Ce e, ** Gonzalo Recabarren-Gajardo ~ a 1111, Playa Ancha, Valparaíso, Chile Instituto de Química y Bioquímica, Facultad de Ciencias, Universidad de Valparaíso, Casilla 5030, Av. Gran Bretan Universidad Bernardo OHiggins, Centro Integrativo de Biología y Química Aplicada, Laboratorio de Bionanotecnología, General Gana 1702, Santiago, Chile lica de Chile, Lira 85, 5th Floor, Santiago, Chile Department of Endocrinology, School of Medicine, Pontificia Universidad Cato d n, Lota 2465, Providencia 7510157, Santiago, Chile Facultad de Ciencia, Universidad San Sebastia e lica de Chile, Casilla 306, Avda. Vicun ~ a Mackenna 4860, Macul, Santiago, Departamento de Farmacia, Facultad de Química, Pontificia Universidad Cato Chile a

b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 1 March 2017 Received in revised form 13 March 2017 Accepted 17 March 2017 Available online 20 March 2017

We recently reported a series of 39 weakly basic N-arylsulfonylindoles as novel 5-HT6 antagonists. Eight of the compounds exhibited moderate to high binding affinities, with 2-(4-(2-Methoxyphenyl)piperazin1-yl)-1-(1-tosyl-1H-indol-3-yl)ethanol 16 showing the highest binding affinity (pKi ¼ 7.87). Given these encouraging results and as a continuation of our research, we performed an extensive step-by-step search for the best 3D-QSAR model that allows us to rationally propose novel molecules with improved 5-HT6 affinity based on our previously reported series. A comparative molecular similarity indices analysis (CoMSIA) model built on a docking-based alignment was developed, wherein steric, electrostatic, hydrophobic and hydrogen bond properties are correlated with biological activity. The model was validated internally and externally (q2 ¼ 0.721; r2pred ¼ 0.938), and identified the sulfonyl and hydroxyl groups and the piperazine ring among the main regions of the molecules that can be modified to create new 5-HT6 antagonists. © 2017 Elsevier B.V. All rights reserved.

Keywords: Serotonin 5HT6 receptor Antagonists Indole Obesity 3D-QSAR CoMSIA

1. Introduction The human serotonin (5-HT) type 6 receptor (5-HT6) belongs to the class A seven transmembrane G protein-coupled receptor (GPCR) [1]. Through positive coupling to Gas subunits, which induces cyclic adenosine monophosphate (cAMP) production through stimulation of adenylate cyclase activity, the 5-HT6 receptor is expressed almost exclusively within the mammalian central nervous system CNS, especially in the striatum, nucleus accumbens, olfactory tubercle, hippocampus and cortex. Expression has also been found in the amygdala, hypothalamus, thalamus, substantia nigra and cerebellum [2,3]. The mechanisms associated

* Corresponding author. ** Corresponding author. E-mail addresses: [email protected] (G. Recabarren-Gajardo).

(J.

http://dx.doi.org/10.1016/j.molstruc.2017.03.067 0022-2860/© 2017 Elsevier B.V. All rights reserved.

Mella),

[email protected]

with the activation/blocking of the 5-HT6 receptor are not completely understood, while gathered pharmacological data suggest that 5-HT6 receptor ligands have great potential for the treatment of neuropathological disorders, such as depression, anxiety, Alzheimer's disease and schizophrenia, as well as for the treatment of obesity and related metabolic syndromes [4e10]. Since the first selective 5-HT6 ligand was reported over a decade ago, a plethora of potent and selective ligands for the 5-HT6 receptor have been identified [11e18]. Among the most important structural classes of 5-HT6 receptor antagonists discovered to date, three main groups may be defined. One group comprises the indole and indole-like subclasses derived from the endogenous ligand 5HT [19e23]. The other main group comprises the monocyclic [24], bicyclic and tricyclic arylpiperazine subclasses [23,25]. Other antagonists comprise miscellaneous core structures that have an arylsulfonyl motif as a common structural feature [23,26]. All these reported antagonists share common pharmacophore features

J. Mella et al. / Journal of Molecular Structure 1139 (2017) 362e370

consisting of basic ionisable amine functionality, a sulfonamide or sulfone moiety as a hydrogen bond acceptor group connected to a hydrophobic site and a p-electron donor aromatic or heterocyclic ring [27]. Examples of ligands matching this pharmacophore are MS-245 [19,20], SAM-531 [21], and SB-271046 [24]. Accordingly, the large majority of these compounds are highly basic. However, the need for a basic side chain for effective interaction with the 5HT6 receptor has not been convincingly demonstrated. Although several reports have proven that the presence of a basic nitrogen atom, enabling formation of the interaction of its protonated form and residue Aspartate 106 (D3.32 in Ballesteros-Weinstein nomenclature [28]), is not indispensable for 5-HT6 receptor anchoring [29e31], the fraction of non-basic compounds within known 5-HT6 receptor ligands is low. On this basis, our group has recently reported a structure-based approach to design a series of weakly basic N-arylsulfonylindole compounds, which display moderate to high affinities and an antagonist profile at the 5-HT6 receptor [32]. In particular, compounds 2-(4-(2-Methoxyphenyl) piperazin-1-yl)-1-(1-tosyl-1H-indol-3-yl)ethanol 16e17, 1-(1-(4Iodophenylsulfonyl)-1H-indol-3-yl)-2-(4-(2-methoxyphenyl) piperazin-1-yl)ethanol 26e27 and 2-(4-(2-Methoxyphenyl)piperazin-1-yl)-1-(1-(naphthalen-1-ylsulfonyl)-1H-indol-3-yl)ethanol 32e33 were identified as potent receptor ligands. In addition,

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compound 32 (IC50 ¼ 32 nM), was disclosed as a highly potent antagonist in a functional assay of calcium mobilisation (Fig. 1). Several QSAR works have been made in the field of 5-HT6 antagonists. In a unique 3D-QSAR study, specifically on N1-arylsulfonylindoles as 5-HT6 antagonists, the authors collected a series of 20 compounds from the literature but did not perform all possible combinations of the field contributions [33]. In addition, the r2pred value was not given and the prediction for the test set showed a high dispersion. A 2D-QSAR study has been reported for a series of 1(azacyclyl)-3-arylsulfonyl-1H-pyrrolo [2,3-b]pyridines using topological descriptors [34]. Hao et al. reported receptor-based CoMFA and CoMSIA models for arylsulfonyl indoles, indazoles and benzoxazole derivatives, but with poor internal validation (q2 < 0.5) [35]. Other Hansch analyses have been made for indolyl and piperidinyl sulphonamides [36], as well as in epiminocyclohepta[b] indoles [37]. In order to rationalise the synthetic and biological results of our reported series [32], we herein report a comparative analysis of the molecular similarity index (CoMSIA) of a series of weakly basic Narylsulfonylindole derivatives with an antagonist profile at the 5HT6 receptor. We performed a 3D-QSAR model over these ligands highlighting the main structural requirements for the observed activity. The 3D-QSAR model has statistical parameters (q2, r2 and

Fig. 1. (A) Structure of 5-HT6 receptor ligands from different structural classes. MS-245 [19,20], SAM-531 [21] and SB-271046 [24]. (B) Schematic representation of the weakly basic N-arylsulfonylindole 5-HT6 receptor antagonist series studied. All compounds belong to the structural family of 2-(4-(aryl)piperazin-1-yl)-1-(1-(arylsulfonyl)-1H-indol-3-yl) ethanones or ethanols, except compounds 12, 13 and 36e39, in which morpholine is placed instead of the piperazine ring. (C) Most potent 5-HT6 receptor ligands identified by our group [32]. (D) Superposition between structurally related MS-245 (in red) and compound 16 (in green). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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r2pred) that suggest that it is reliable and predictive. A CoMSIA contour map analysis was performed to visualise the important regions in a 3D molecule representation, wherein it could be seen whether the steric, electrostatic, hydrophobic, donor and acceptor fields affect the affinity and selectivity of the studied compounds at the 5-HT6 receptor. 2. Experimental 2.1. 3D-QSAR study The 3D-QSAR study was performed on a set of 39 N-arylsulfonylindole derivatives previously reported by our group (Table 1). The affinity of the compounds at the 5-HT6 receptor was evaluated Table 1 Structure and biological activities of the studied compounds.

Entry

Z

Ar1

Ar2

pKi (nM)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

CO CO CO CO CO CO CO CO CO CO CO CO CO CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S) CHOH(R) CHOH(S)

4-Methylphenyl 4-Methylphenyl 4-Methylphenyl 4-Chlorophenyl 4-Fluorophenyl 4-Iodophenyl 4-Iodophenyl 4-Iodophenyl Naphthyl Naphthyl Naphthyl 4-Methoxylphenyl 3,5-Difluorophenyl 4-Methylphenyl 4-Methylphenyl 4-Methylphenyl 4-Methylphenyl 4-Methylphenyl 4-Methylphenyl 4-Chlorophenyl 4-Chlorophenyl 4-Fluorophenyl 4-Fluorophenyl 4-Iodophenyl 4-Iodophenyl 4-Iodophenyl 4-Iodophenyl 4-Iodophenyl 4-Iodophenyl Naphthyl Naphthyl Naphthyl Naphthyl Naphthyl Naphthyl 4-Methoxylphenyl 4-Methoxylphenyl 3,5-Difluorophenyl 3,5-Difluorophenyl

2-Piridyl 2-Methoxyphenyl Pyrimidin-2-yl Pyrimidin-2-yl Pyrimidin-2-yl 2-Piridyl 2-Methoxyphenyl Pyrimidin-2-yl 2-Piridyl 2-Methoxyphenyl Pyrimidin-2-yl e e 2-Piridyl 2-Piridyl 2-Methoxyphenyl 2-Methoxyphenyl Pyrimidin-2-yl Pyrimidin-2-yl Pyrimidin-2-yl Pyrimidin-2-yl Pyrimidin-2-yl Pyrimidin-2-yl 2-Piridyl 2-Piridyl 2-Methoxyphenyl 2-Methoxyphenyl Pyrimidin-2-yl Pyrimidin-2-yl 2-Piridyl 2-Piridyl 2-Methoxyphenyl 2-Methoxyphenyl Pyrimidin-2-yl Pyrimidin-2-yl e e e e

5.75 4.97 6.11 5.07 4.72 6.10 5.72 6.38 5.91 5.90 5.85 6.35 4.65 6.32 6.32 7.87 7.87 6.07 6.07 5.43 5.43 5.66 5.66 6.43 6.43 7.73 7.73 6.21 6.21 6.83 6.83 7.83 7.83 6.22 6.22 6.05 6.05 5.12 5.12

using HEK-293 cells expressing human 5-HT6 receptors using the iodinated specific radioligand [125I]-SB-258585 [32]. The compounds were randomly divided into training (31 compounds, 80%) and test sets (8 compounds, 20%), ensuring that both sets contained structurally diverse compounds with high, medium and low activities, and a uniform distribution to avoid possible problems during the external validation. The distribution of pKi values for the complete, training and test sets is shown in Fig. 2. In all three cases, the biological activity follows a gaussian distribution where all compounds lie in the pKi range between 4.65 and 7.87, spanning a 3.22 log unit order. 2.2. Selection of conformers and molecular alignment All ligands were constructed using Marvin Sketch v15.12.14 (ChemAxon Ltd.) and saved as SDF file. Multiconformer libraries of compounds were prepared using OMEGA v2.5.1.4 (OpenEye Scientific Software) [38,39]. QUACPAC v1.6.3.1 (OpenEye Scientific Software) was used to assign the AM1BCC charges to the libraries [40e42]. The libraries were docked in our previously reported model of the 5-HT6 receptor [32], using FRED v3.2.0.1 (OpenEye Scientific Software) [43e45]. Candidate poses of the ligands within the receptor sites (100) were obtained and optimized using the Chemgauss4 scoring function. Consensus structures of the poses returned from exhaustive docking and optimization were obtained by consensus scoring using the PLP, Chemscore and Chemgauss3 scoring functions [46]. Finally, the top ranked binding mode for each compound was minimized using the CHARMM22 force field in Discovery Studio v2.1 (Accelrys Inc.). The minimization protocol allowed the side chains of residue within 6 Å from the mass centroid of all docked ligands, using the conjugate gradient algorithm until convergence criteria of 0.001 kcal/mol/Å. Table S1 resumes the energy evaluation performed for each obtained complex using the PLP, LigScore, PMF, LUDI scoring functions, and the consensus scoring available with Discovery Studio v2.1 (Accelrys Inc.). All these protein-ligand complexes were structurally aligned and the obtained 3D final alignment of the ligands used for the 3DQSAR studies (Fig. 3). The final CoMSIA studies were performed using Sybyl X-1.2 software [47]. 2.3. CoMSIA field calculation To derive the CoMSIA descriptor fields, the aligned training set

Fig. 2. Histogram of frequency distribution data.


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Fig. 3. Docking-based molecular alignment of the compounds for the 3D-QSAR study.

Table 2 Sequential search for generation of the best 3D-QSAR models.a Model

q2

N

SEP

SEE

r2

F

Field contributions

CoMFA-S CoMFA-E CoMFA-SE CoMSIA-S CoMSIA-E CoMSIA-H CoMSIA-A CoMSIA-SE CoMSIA-SEH CoMSIA-SEHA CoMSIA-SEA CoMSIA-SH CoMSIA-SA CoMSIA-SHA CoMSIA-EH CoMSIA-EA CoMSIA-EHA CoMSIA-HA

0.311 0.411 0.475 0.66 0.49 0.264 0.213 0.503 0.448 0.721 0.488 0.49 0.315 0.399 0.42 0.466 0.451 0.306

8 3 3 14 2 20 3 4 4 8 4 20 5 20 4 4 3 20

0.814 0.697 0.658 0.639 0.639 1.086 0.805 0.65 0.684 0.682 0.659 0.904 0.774 0.981 0.701 0.673 0.673 1.055

0.101 0.404 0.255 0.094 0.435 0.012 0.541 0.299 0.296 0.05 0.304 0.008 0.342 0.014 0.312 0.325 0.363 0.02

0.989 0.802 0.921 0.993 0.764 1.000 0.645 0.895 0.897 0.907 0.891 1.000 0.866 1.000 0.886 0.876 0.84 1.000

347.658 47.341 136.755 230.575 58.25 10209.87 21.24 72.137 73.734 1021 69.607 21429.87 42.67 7510.773 65.772 59.992 61.375 3581.284

1

S

0.497 1

E

H

A

1 0.503 1 1 1

0.281 0.191 0.133 0.176 0.368 0.377 0.234

0.719 0.555 0.397 0.502

0.254 0.187

0.282 0.322

0.632

0.668 0.603 0.461

0.423 0.332 0.227 0.563

0.623 0.343 0.397 0.312 0.437

a q2 is the square of the LOO cross-validation (CV) coefficient, N is the optimum number of components, SEP is the standard error of prediction, SEE is the standard error of estimation of non-CV analysis, r2 is the square of the non-CV coefficient, F is the F-test value and S, E, H and A are the steric, electrostatic, hydrophobic and hydrogen bond acceptor contributions, respectively.

molecules were placed in a 3D cubic lattice with a grid spacing of 2 Å in the x, y and z directions so that the entire set was included. For the CoMSIA analysis, the standard settings (probe with charge þ1.0, radius 1 Å, hydrophobicity þ1.0, hydrogen bond donating þ1.0 and hydrogen bond accepting þ1.0) were used to calculate five different fields: steric, electrostatic, hydrophobic, acceptor and donor [48]. Gaussian-type distance dependence was used to measure the relative attenuation of the field position of each atom in the lattice. The default value of 0.3 was set for the attenuation factor, a. 2.4. Internal validation and partial least squares (PLS) analysis PLS analysis was used to construct a linear correlation between the CoMSIA descriptors (independent variables) and the activity values (dependent variables) [49]. To select the best model, the cross-validation analysis was performed using the leave-one-out (LOO) cross-validation method (and SAMPLS), which generates the square of the cross-validation coefficient (q2) and the optimum number of components (N). The non-cross-validation was performed with a column filter value of 2.0 in order to speed up the

analysis and reduce the noise. The q2, which is a measure of the internal quality of the models, was obtained according to the following equation:

P q2 ¼ 1

2 yi ypred P ðyi yÞ2

(1)

where yi , y, and ypred are the observed, mean and predicted activities in the training, respectively. 2.5. External validation of the CoMSIA model The predictive power of the models was assessed by the calculation of the predictive r2 (r2pred), r2pred measures the predictive performance of a PLS model and is defined according to the following equation [50,51]:

r2pred ¼

SD PRESS SD

(2)

where SD is the sum of the squared deviations between the

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Table 3 Experimental and predicted biological activity of the best CoMSIA model.a Molecule

Actual pKi (M)

Predicted pKi (M)

Residual

1 2a 3 4 5a 6 7 8 9a 10 11 12 13 14 15 16 17 18 19 20 21 22 23a 24 25a 26 27a 28 29 30 31 32a 33 34 35a 36 37 38 39

5.75 4.97 6.11 5.07 4.72 6.10 5.72 6.38 5.91 5.90 5.85 6.35 4.65 6.32 6.32 7.87 7.87 6.07 6.07 5.43 5.43 5.66 5.66 6.43 6.43 7.73 7.73 6.21 6.21 6.83 6.83 7.83 7.83 6.22 6.22 6.05 6.05 5.12 5.12

5.64 5.09 5.44 5.42 4.43 6.18 6.21 6.13 6.11 6.15 5.80 6.40 4.25 6.51 6.32 7.60 8.06 5.96 6.09 5.83 5.83 5.59 5.50 6.52 6.77 7.38 7.30 5.89 6.00 6.86 6.85 7.65 7.49 6.40 6.48 5.98 6.23 5.18 5.51

0.11 0.12 0.67 0.35 0.29 0.08 0.49 0.26 0.20 0.25 0.05 0.05 0.40 0.19 0.00 0.27 0.19 0.11 0.02 0.40 0.40 0.07 0.16 0.09 0.34 0.35 0.43 0.32 0.21 0.03 0.02 0.18 0.34 0.18 0.26 0.07 0.18 0.06 0.39

a

Test set compound.

biological activities of the test set compounds and the mean activity of the training set compounds, and PRESS is the sum of squared deviations between observed and predicted activities of the test set

Fig. 5. Residual plot between predicted and experimental values for CoMSIA.

compounds. 3. Results and discussion With the aim of finding the strongest models that contained the best set of descriptors, we performed a step-by-step calculation of 18 models assaying all possible field combinations (Table 2). The field contributions used in this search were the steric, electrostatic, hydrophobic and hydrogen bond acceptor contributions. The hydrogen bond donor properties did not correlate with affinity. The best model was selected based on the highest q2 values (0.721 for CoMSIA-SEHA). The best model herein contains the optimum number of independent variables to avoid overfitting and chance correlation [52,53], and to obtain the highest correlation coefficient. According to these models, the major contribution to biological activity was given by the steric, electrostatic, hydrophobic and hydrogen bond acceptor properties. In Table 3, we present the actual and predicted affinities of the compounds and the residual values, which represent the differences between the actual and predicted pKi values. The plot of the predicted pEC50 values versus the experimental

Fig. 4. (A) Plot of experimental versus predicted pKi values of the training and test set molecules for the CoMSIA model. (B) CoMSIA predictions for every molecule in the complete set.


ones for CoMSIA analyses is also shown in Fig. 4A, in which most points are well distributed along the line Y ¼ X, while in Fig. 4B, the prediction of the affinity for every molecule with regard to the experimental value is shown. In this plot, we can see that the model has good performance in the prediction of the activity for compounds with low, medium and high pKi values. These results suggest that the quality of the 3D-QSAR model is good and reliable. Fig. 5 shows the distribution of the residuals. A total of 17 compounds had positive deviations from the activity, while 21 compounds had negative deviations. Compound 15 presented a residual ¼ 0.00 (pKi ¼ 6.32). The range of residuals moved from 0.4 (compounds 20 and 21) to þ0.67 (compound 3). For the test set, four compounds showed positive deviations from the

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experimental value (compounds 5, 23, 27 and 32), and four compounds showed negative deviations (compounds 2, 9, 25 and 35). Therefore, the best model presented here exhibits an equilibrated behaviour regarding its internal and external predictability. 3.1. CoMSIA contour map analysis CoMSIA contour map analysis was performed to visualise the important regions in a 3D molecular representation, wherein it could be seen whether the steric, electrostatic, hydrophobic and acceptor fields affect the affinity and selectivity of the studied compounds on the 5-HT6 receptor. The contour maps for CoMSIA represent the significant interaction areas between the probe atom

Fig. 6. CoMSIA contour maps around the most active compound 16 (and its enantiomer 17. Maps A, C, E and G) and around the less active compound 13 (maps B, D, F and H). (A, B): Sterically favoured (green) and disfavoured (yellow). (C, D): Electropositive favoured (blue) and electronegative favoured (red). (E, F): Hydrophobic favoured areas are in yellow and unfavoured areas in white. (G, H): Hydrogen bond acceptor favoured areas are in magenta and unfavoured areas are in red. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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and the surface of the molecules and were developed using the field type StDev*Coefficient. The highly active compound 16 was used as the template ligand for all contour maps. 5-HT6 contour maps were generated from the binding affinity data of a series of indole ligands evaluated at a recombinant human 5-HT6 receptor. The contour maps of CoMSIA are displayed in Fig. 6. The major contribution to the activity is given by the electrostatic properties of the compounds. 3.2. CoMSIA steric contour map

electronegative atom to this polyhedra, like the oxygen atom of the morpholine ring or the methoxy group or nitrogen atom of indole. This suggests that an electronegative atom is essential in this region. Finally, all the compounds situate the N1 piperazinyl atom in a large blue polyhedron. Therefore, increasing the protonation ability of this nitrogen atom would be favourable, for example, through elimination of the carbonyl or hydroxyl group.

3.4. CoMSIA hydrophobic contour map

Two yellow and two green polyhedra surround the molecules (Fig. 6A and B). Almost all the molecules have a yellow polyhedron around the sulfonamide group, thus, we are planning the replacement of this linker for a smaller group, like methylene or carbonyl group. Surrounding the aryl group connected to the piperazine ring, there are green and a yellow polyhedra. This suggests there is a preferred conformation of the aryl with respect to the piperazine. For example, the less active compounds 2, 4 and 5 have both rings semi coplanar in a way that the aryl ring intersects the yellow polyhedron. While the most active compounds 16 and 32 have a perpendicular disposition for the two rings, allowing the aryl ring to reach the green region. On the other hand, compounds 6, 7, 8 and 24 showed a different orientation. They oriented the p-iodidebenzene through the yellow polyhedra, which is detrimental for activity. Finally, the presence of a green surface around the aromatic ring connected to the sulfonamide group means that the use of bulky groups or atoms (especially in the para position) is highly recommended. 3.3. CoMSIA electrostatic contour map In the electrostatic contour map (Fig. 6C and D), the benzene ring of the arylsulfonyl group is completely immersed in a red surface, so the use of electron donating groups in the benzene will be favourable. This again supports the idea that the replacement of this group for methylene will be favourable because of the improvement in the electronic enrichment of the benzene ring. Another two red polyhedra are at the end of the piperazine and benzene rings. In general, the compounds project an

In the hydrophobic contour map (Fig. 6E and F), there is a white polyhedron around the halogen atoms of the compounds, so to improve the affinity of ligands at the receptor, the halogen atoms should be eliminated from the benzene ring on the arylsulfonyl moiety of the molecules. However, a large yellow polyhedron is surrounding the pyridine ring linked to the piperazine, mainly at the meta and para positions. Therefore, these are appropriate positions to insert halogen atoms and alkyl chains. Alternatively, compounds less active like 13 orient the oxygen atom of the morpholine ring through the yellow polyhedral, which is detrimental for activity.

3.5. CoMSIA acceptor contour map Two large magenta polyhedra around the sulfonyl group and the chiral hydroxyl group suggest the use of hydrogen bond acceptor atoms in these positions (Fig. 6G and H). In the compound 16, the magenta polyhedron is over the hydrogen atom within the hydroxyl group, so the elimination of the hydroxyl group is a reliable option. On the other hand, at the end of pyridine group, a red polyhedron indicates that the use of the pyridine ring is detrimental to the activity, however as discussed above, electronegative atoms or groups are beneficial for activity in this region, so that good substituents in this part of the molecule would be nitro, halogens and methoxy groups. In Fig. 7, we summarise the structure-activity relationships found for the reported N-arylsulfonyindoles.

Fig. 7. Structure-affinity/selectivity relationships derived from the CoMSIA studies in this work.


4. Conclusions We carried out an exhaustive step-by-step search in order to find a 3D-QSAR model for our previously synthesised compounds with moderate to high affinity at the 5-HT6 receptor. A total of 18 models were generated, and the best model was selected based in the highest q2 parameter (0.721). In the final CoMSIA model, the steric, electrostatic, hydrophobic and hydrogen bond acceptor properties were found to be correlated with the 5-HT6 receptor affinity of the series. The external predictive capacity of the model was validated by a high r2pred (0.938) for the test set compounds. The range of residuals moved from 0.4 to þ0.67, therefore an equilibrated predictive capacity for compounds with high, medium and low affinities was achieved. Some favourable and important modifications found in the study are the elimination of the nitrogen atom in the pyridine ring, the insertion of lipophilic groups in the benzene ring linked to the piperazine fragment, the replacement of the sulphonyl groups by a methylene linker, and the insertion of bulky and electron donating groups in the benzene ring linked to the indole core. Therefore, based on the pharmacophoric features founded in this study, a new series of compounds can be proposed for synthesis.

[13]

[14] [15]

[16]

[17] [18]

[19]

[20]

[21]

Acknowledgements [22]

This work was supported by FONDECYT grants 11121418 (GRG) and 11130701 (JMR). ChemAxon and OpenEye Scientific Software for academic licenses of their software (CFL). SDG. [23]

Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.molstruc.2017.03.067.

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