Molecules 2014, 19(3), 2842-2861; doi:10.3390/molecules19032842

Article
3D-QSAR/CoMFA-Based Structure-Affinity/Selectivity Relationships of Aminoalkylindoles in the Cannabinoid CB1 and CB2 Receptors
Jaime Mella-Raipán 1,*, Santiago Hernández-Pino 1, César Morales-Verdejo 2 and David Pessoa-Mahana 3
1
Departamento de Química y Bioquímica, Facultad de Ciencias, Universidad de Valparaíso, Av. Gran Bretaña 1111, Playa Ancha, Valparaíso, Casilla 5030, Chile
2
Laboratorio de Bionanotecnología, Departamento de Ciencias Químico Biológicas, Universidad Bernardo O´Higgins, General Gana 1780, Santiago 8370993, Chile
3
Departamento de Farmacia, Facultad de Química, Pontificia Universidad Católica de Chile, Casilla 306, 22, Santiago 7820436, Chile
*
Author to whom correspondence should be addressed; E-Mail: jaime.mella@uv.cl; Tel.: +56-032-250-8067; Fax: +56-032-250-8017.
Received: 15 January 2014; in revised form: 25 February 2014 / Accepted: 25 February 2014 /
Published: 5 March 2014

Abstract

: A 3D-QSAR (CoMFA) study was performed in an extensive series of aminoalkylindoles derivatives with affinity for the cannabinoid receptors CB1 and CB2. The aim of the present work was to obtain structure-activity relationships of the aminoalkylindole family in order to explain the affinity and selectivity of the molecules for these receptors. Major differences in both, steric and electrostatic fields were found in the CB1 and CB2 CoMFA models. The steric field accounts for the principal contribution to biological activity. These results provide a foundation for the future development of new heterocyclic compounds with high affinity and selectivity for the cannabinoid receptors with applications in several pathological conditions such as pain treatment, cancer, obesity and immune disorders, among others.
Keywords:
cannabinoid; CB1/CB2; aminoalkylindole; molecular modelling; 3D-QSAR; Comparative Molecular Field Analysis (CoMFA); simulated annealing, structure-activity relationships

1. Introduction

The cannabinoid receptors are seven transmembrane domains proteins with two known subtypes: CB1 and CB2 [1]. They belong to the G protein-coupled receptors (GPCRs) and since the discovery of ∆9-THC [2], the main psychoactive component of Cannabis sativa, they have been the focus of several studies due to their implication in a variety of pathophysiological conditions [3,4]. There is a notable difference in the distribution of the cannabinoid receptors, as well as in the physiological functions they control [5]. However, there are some regions that can express both subtypes of receptors [6]. The CB1 receptors are expressed fundamentally in the central nervous system (CNS) and they are the most abundant GPCRs in the brain, with levels 10-fold higher than those of other GPCRs [7], indicating a highly significant functional role in a wide variety of circuits and neuronal systems [8]. The greater abundance of CB1 receptors in the CNS occurs in areas related to the control of motor activity, such as cortex, basal ganglia and cerebellum [9,10,11]. At a peripheral level the CB1 receptors are located in organs such as testes, vas deferens, bladder, ileum, eyes, liver, skeletal muscle, heart, pancreas and adipose tissue [12]. It is postulated that both central and peripheral distribution of CB1 receptors are integrated in the regulation of metabolic homeostasis [13], so compounds that target these receptors can be useful in the treatment of obesity, type 2 diabetes and other metabolic disorders. On the other hand, the distribution of the CB2 receptors is more bounded, as they are found almost exclusively in cells of the immune system, with particularly high levels in B lymphocytes and natural killer cells [14,15]. Other sites where CB2 receptors are found include thymus, tonsils, bone marrow, spleen, pancreas, peripheral nerve terminals, microglial cells, tumor cells (melanoma and glioma) and astrocytes [16,17,18,19]. Little is known about the physiological and pathophysiological role of CB2 receptors. When activated, they can modulate the migration of immune system cells, suppress the release of proinflammatory cytokines and increase the release of inflammatory cytokines [20,21]. Although the physiological role of CB2 receptors is still not completely understood, several preclinical studies support the utility of using CB2 ligands for the treatment of chronic pain, maintenance of bone density, halting the progression of atherosclerotic lesions, asthma, autoimmune and inflammatory diseases, and multiple sclerosis [22].

Among the diverse variety of compounds synthesized against the CB receptors, the aminoalkylindole family represents a versatile group of compounds that includes CB1 and CB2 ligands such as WIN55212, AM1235, AM630 and JWH-015 (Figure 1). However, despite their chemical similarity, there is no obvious chemical rationality to the distinct affinity they exhibit for cannabinoid receptors, so in order to obtain useful information about the three-dimensional requirements for their receptor selectivity, we have performed a CoMFA study on a wide range of selective CB2 aminoalkylindoles reported in recent literature [23,24,25]. Moreover, the information obtained in this study will provide a means for predicting the activity of related compounds, and help guide further structural modifications and synthesis of new potent and selective cannabinoid ligands.

From the work of Cramer et al. [26], CoMFA represent a useful methodology in understanding the pharmacological properties of a studied series of compounds. The steric and electrostatic maps obtained may help to: (a) understand the nature of the ligand-receptor interactions; (b) predict the biological affinity and (c) rationally design new promising compounds. Some Three-dimensional Quantitative Structure-Activity Relationships (3D-QSAR) studies have been made on the cannabinoid ligands for the CB1 or CB2 receptors [27,28,29,30,31] in the last years. As a continuation of our efforts aimed at exploring and understanding the cannabinoid system [32,33,34], and due the need to obtain useful SAR in the aminoalkylindole family, we present two CoMFA models carried out on a wide number of compounds of the recent literature [23,24,25] with a marked pKi ratio (CB1/CB2). The molecules have broad structural variability and the contour plots are vivid and clear, and offer valuable information about the structural requirements for cannabinoid affinity and selectivity.

Molecules 19 02842 g001 200
Figure 1. Representative aminoalkylindole ligands.

Click here to enlarge figure

Figure 1. Representative aminoalkylindole ligands.
Molecules 19 02842 g001 1024

2. Results and Discussion

2.1. Cannabinoid CB1 and CB2 CoMFA Models

3D-QSAR models were obtained from CoMFA analysis and its statistical parameters are listed in Table 1. For a reliable predictive model the square q2 of the cross-validation coefficient should be greater than 0.5 [35]. The models have high r2, r2pred and q2 suggesting that they are reliable and predictive. The steric and electrostatic contributions were found to be 71% and 29% respectively, in both cases, which is in agreement with the hydrophobic character of the cannabinoid ligands and the active site of the receptors.

Table 1. Statistical parameters of the CoMFA models a.

Click here to display table

Table 1. Statistical parameters of the CoMFA models a.
CoMFA CB1%Contribution
Nq2r2SEEFPRESSSDr2predStericElectrostatic
20.7220.8450.1971005.4615.220.64171.428.6
CoMFA CB2
100.6430.9990.02143762.5610.160.74871.228.8

a N is the optimum number of components, q2 is the square of the LOO cross-validation (CV) coefficient, r2 is the square of the non CV coefficient, r2pred is the predictive r2 based only on the test set molecules, SEE is the standard error of estimation of non CV analysis, SD is the sum of the squared deviation between the biological activity of molecules in the test set and the mean activity of the training set molecules, PRESS is the sum of the squared deviations between predicted and actual biological activity values for every molecule in the test set, and F is the F-test value.

The affinity values of molecules predicted by CoMFA models are listed in Table 2. In the CB1 model, the compounds have a residual range of ‒0.48 to 0.95 (including training and test set). While in the CB2 model, the compounds have a residual of ‒0.63 to 0.65 (including training and test set). The major deviations from activity were in compounds 38 and 39 in CB1 and 84 and 88 in CB2.

Table 2. Experimental and predicted activity for training and test set in the CB1 and CB2 models a.

Click here to display table

Table 2. Experimental and predicted activity for training and test set in the CB1 and CB2 models a.
CoMFA CB1
MoleculeKi CB1Actual pKiPredicted pKiResidual
(nM)(M)(M)
Training Set
116605.785.6640.12
24886.3126.2230.09
36986.1566.325−0.17
45306.2766.352−0.08
536175.4425.462−0.02
627915.5545.5260.03
736215.4415.531−0.09
81258.95.95.918−0.02
93896.416.3530.06
10245.56.617.022−0.41
111096.55.965.9580.00
12281.86.556.2160.33
132818.45.555.665−0.12
14776.26.115.9780.13
15851.16.076.0650.01
161288.25.895.911−0.02
171698.25.775.775−0.01
181862.15.735.808−0.08
19229.16.646.4170.22
20363.16.446.551−0.11
21616.66.216.070.14
22131.86.886.907−0.03
2361.77.217.0360.17
2472.47.146.9480.19
255376.276.1760.09
26562.36.256.130.12
2791.27.046.8590.18
28213.86.676.695−0.03
29281.86.556.921−0.37
30144.56.846.6050.24
31676.16.176.0830.09
32660.76.186.222−0.04
33380.26.426.723−0.30
34213.86.676.6350.04
35691.86.166.487−0.33
361258.95.96.12−0.22
Test Set
371828.15.7386.141−0.4
3812.37.916.9990.91
3913.27.886.930.95
4044.77.356.8860.46
4133.17.486.730.75
4228.27.556.7620.79
43100066.462−0.46
4431.67.56.7730.73
4525.17.66.8250.77
461621.85.796.218−0.43
47354.86.456.927−0.48
4847.97.326.8830.44
492238.75.656.124−0.47
Training Set
11106.9596.974−0.02
2987.0097.028−0.02
3677.1747.1690.01
4507.3017.33−0.03
510.37.9877.9640.02
76.48.1968.1740.02
103.28.4958.536−0.04
113.38.4818.4780.00
124.68.3378.348−0.01
154.48.3578.385−0.03
17267.5857.5220.06
20117.9597.972−0.01
21167.7967.811−0.02
232.68.5858.5790.01
242.18.6788.6470.03
255.98.2298.234−0.01
272.88.5538.5450.01
283.38.4818.5−0.02
301.88.7458.730.02
348.28.0868.0790.01
352.88.5538.5420.01
3717.87.757.7430.01
410.99.0569.060.00
432.98.5388.5210.02
451.48.8548.8470.01
49317.5097.4890.02
50507.3017.2990.00
511896.7246.7190.01
5212.77.8967.907−0.01
5325.47.5957.5670.03
5411.27.9517.98-0.03
5522.27.6547.6510.00
5611.67.9367.964−0.03
572.58.6068.6080.00
5828.6938.6910.00
5970.87.157.1340.02
6058.3018.2990.00
611.68.7968.806−0.01
6238.5238.4980.03
632.58.6028.635−0.03
643.58.4568.4520.00
658.58.0718.082−0.01
660.59.2929.2730.02
679.38.0328.0280.00
683.18.5098.5040.01
699.38.0328.039−0.01
70277.5697.5560.01
71327.4957.502−0.01
721.98.7218.743−0.02
735.88.2378.254−0.02
749.28.0368.010.03
7528.6998.6710.03
762.18.6788.6610.02
772.28.6588.6440.01
783.18.5098.532−0.02
792.98.5388.554−0.02
80207.6997.721−0.02
818.38.0818.0660.02
821.38.8868.8840.00
83357.4567.4550.00
Test Set
65.78.2428.2020.04
838.5238.553−0.03
93.98.4098.651−0.24
18587.2377.1640.07
262.38.6388.2050.43
321.38.8868.4940.39
3319.0048.6060.40
3611.57.9398.209−0.27
390.79.1878.6350.55
42198.530.47
483.78.4328.2640.17
8411.87.9287.2830.65
8520.57.6887.3530.34
86707.1557.346−0.19
876.68.188.353-0.17
881906.7217.35−0.63
893.88.428.662−0.24
900.49.3988.8630.53
911.88.7458.7150.03
921.88.7458.5570.19

a The structures of all the molecules are displayed on Table 3.

The plot of the predicted pKi values versus the experimental ones for CoMFA analysis is also shown in Figure 2, in which most points are well distributed along the line Y = X, suggesting that the quality of the 3D-QSAR model is good.

Molecules 19 02842 g002 200
Figure 2. Experimental versus predicted activity for A. CB1 CoMFA analysis and B. CB2 CoMFA analysis.

Click here to enlarge figure

Figure 2. Experimental versus predicted activity for A. CB1 CoMFA analysis and B. CB2 CoMFA analysis.
Molecules 19 02842 g002 1024

2.2. CoMFA Contour Maps Analysis

CoMFA contour maps analysis was performed to visualize the important region in 3D molecules where the steric and electrostatic fields may affect the affinity and selectivity of the studied compounds in the cannabinoid CB1 and CB2 receptors. The weight of StDev*Coeff was used to calculate the field energies for all fields in CoMFA models. The highly active compound 90 was shown as the template ligand for all contour maps. CB1 and CB2 contour maps were generated from binding affinity of series of indole ligands evaluated at recombinant human CB1 and CB2 receptors. The steric and electrostatic contour maps of CoMFA are displayed in Figure 3. In order to systematize the analysis and obtain useful information on SAR, we had divided the molecule into three regions:

Region I. Close examination of the CoMFA maps allows us to see in the steric CB1 model a large green polyhedron around this region, which continues along the entire region III. At a greater distance from the green polyhedron in region I, we can see a yellow region. This suggests that bulky substituents at any position of the benzene will increase biological activity and selectivity for this receptor. However, as it was already mentioned, the yellow region restricts the size and length of the substitutions to some extent in the position 5 of the indole system. In the steric CB2 model, the big restrictive contour map near the positions 4 and 5 of the indole core limits drastically the possibility of substitute these positions. In fact, the most active compounds in the CB2 receptor (compounds 33, 39, 41, 66 and 90) bear a hydrogen atom in positions 4 and 5. On the other hand, in the electrostatic contour maps, the CB1 model indicates a red area contiguous to the positions 4 and 5 of the indole nucleus. According to this, electronegative substituents can be connected to the benzene or it is possible to replace the ring by an isostere with electronegative properties such as a thiophene or pyrrole. In the CB2 model the data implicate an inverse situation. In this case would be favorable the insertion of electropositive groups or the exchange for rings capable of protonation at physiological pH, such as pyridine, pyrimidine, piperidine and aniline, among others. Region I is, therefore, from the electronic standpoint a key area for selectivity.

Molecules 19 02842 g003 200
Figure 3. A. Molecular Regions analyzed. B. CoMFA steric (left) and electrostatic (right) contour maps for CB1 and CB2 receptors around compound 90, the most active of the series. Sterically favored (green) and disfavored (yellow); electropositive (blue) and electronegative (red).

Click here to enlarge figure

Figure 3. A. Molecular Regions analyzed. B. CoMFA steric (left) and electrostatic (right) contour maps for CB1 and CB2 receptors around compound 90, the most active of the series. Sterically favored (green) and disfavored (yellow); electropositive (blue) and electronegative (red).
Molecules 19 02842 g003 1024

Region II. The steric contour maps show that region II is inside a yellow polyhedron in CB1, while it is inside a green contour map surrounded by two yellow areas in CB2. This noteworthy difference suggests that a controlled increase of the size of the ring substituents or the replacement of this ring for bulkier systems like naphthoyl (for example in compounds 82 and 83), will raise the selectivity for the CB2 receptor. In fact, the comparative lower CB2 receptor Ki values for compounds 3842 (Ki < 20 nM), are consistent with the existence of bulky rings in region II, and, as expected, they displayed less affinity for the CB1 receptor (Ki > 60 nM in all cases). On the other hand, there are not electrostatic surfaces around the region II in the CB1 model. The absence of information in this particular region is consequence of the similarity of the rings occupying that area which are electrically neutral: cyclopropyl, adamantyl, cyclohexyl, and phenyl. This, however, rather than being a limitation in the design, provides the ability to evaluate multiple electronic options. However, the CoMFA for CB2 shows the region II completely immersed in a red polyhedron. This suggests that when adding electronegative atoms or groups such as oxygen, halogens, sulfur, and no protonable nitrogens in this region, the CB2 affinity is boosted. As an example, compound 25, bearing an oxaadamantyl moiety in this region, and compound 36, with a 2-iodo-5-nitrophenyl, have 90 and 110 times greater affinity for the CB2 receptor than CB1, respectively.

Region III. As can be seen for compound 90, there is a green surface along the N-alkyl chain in the CB1 model but only a terminal green polyhedron in the CB2 model. This suggests that a branched chain would increase the affinity for the CB1 receptor, and the substitution with bulky groups at the end of it would be better for the CB2 affinity. Analogously to region II, in region III there are not any electrostatic contour maps in CB1 CoMFA model, while a blue region is observed in the CB2 receptor. This suggests that the introduction of protonable or electropositive groups would increase CB2 affinity. For example compounds 49, 72, 73 and 80 share the same structure but differ in the type of nitrogen at the end of the N-alkyl chain. Compounds 49 and 80 have the less basic nitrogen, whereas compounds 72 and 73 have strong basic sp3 nitrogen displaying 10 times higher CB2 affinity than compounds 49 and 80. This may suggests the presence of a hydrogen bond or ionic interaction in the stabilization of the complex ligand-receptor.

3. Experimental

3.1. Data Set

A structurally diverse and homogeneous data set of 92 aminoalkylindole ligands with binding affinities (expressed as Ki) spanning about 4 log orders of magnitude (12.3–3,621 nM for CB1 and 0.4–190 nM for CB2), was selected from literature [23,24,25] for the construction of the CoMFA models (Table 3). The data set was classified into training set (36 compounds in CB1 and 60 compounds in CB2) and test set (13 compounds in CB1 and 20 compounds in CB2) in such a way to avoid any redundancy in terms of structural features or activity range and to assess the predictive ability of the models.

Table 3. Molecular structures of the molecules. Molecules 19 02842 i004

Click here to display table

Table 3. Molecular structures of the molecules. Molecules 19 02842 i004
Comp.R1R2R3R4R5R6R7
1 Molecules 19 02842 i005H Molecules 19 02842 i006H Molecules 19 02842 i007HH
2CH3-H Molecules 19 02842 i008H Molecules 19 02842 i009HH
3CH3CH2-H Molecules 19 02842 i008H Molecules 19 02842 i010HH
4 Molecules 19 02842 i011H Molecules 19 02842 i008H Molecules 19 02842 i010HH
5 Molecules 19 02842 i011H Molecules 19 02842 i012H Molecules 19 02842 i007HH
6 Molecules 19 02842 i013H Molecules 19 02842 i014H Molecules 19 02842 i007HH
7 Molecules 19 02842 i015H Molecules 19 02842 i014H Molecules 19 02842 i016HH
8 Molecules 19 02842 i017H Molecules 19 02842 i018HHCH3SO2-H
9 Molecules 19 02842 i017H Molecules 19 02842 i018OH-HHH
10 Molecules 19 02842 i017H Molecules 19 02842 i018HHHOH-
11 Molecules 19 02842 i017H Molecules 19 02842 i018CH3O-HHH
12 Molecules 19 02842 i017H Molecules 19 02842 i018HCH3O-HH
13 Molecules 19 02842 i019H Molecules 19 02842 i020H Molecules 19 02842 i021CH3O-H
14 Molecules 19 02842 i019H Molecules 19 02842 i020HOH-CH3O-H
15 Molecules 19 02842 i022H Molecules 19 02842 i020HHHH
16 Molecules 19 02842 i022H Molecules 19 02842 i020HHCH3O-H
17 Molecules 19 02842 i023H Molecules 19 02842 i020HCH3OCH2-HH
18 Molecules 19 02842 i023H Molecules 19 02842 i020H Molecules 19 02842 i024HH
19 Molecules 19 02842 i023H Molecules 19 02842 i020HH Molecules 19 02842 i024H
20 Molecules 19 02842 i025H Molecules 19 02842 i026HHHH
21 Molecules 19 02842 i025H Molecules 19 02842 i027HHHH
22 Molecules 19 02842 i028H Molecules 19 02842 i029HHHH
23 Molecules 19 02842 i028H Molecules 19 02842 i030HHHH
24 Molecules 19 02842 i028H Molecules 19 02842 i031HHHH
25 Molecules 19 02842 i028H Molecules 19 02842 i032HHHH
26 Molecules 19 02842 i033H Molecules 19 02842 i034HHHH
27 Molecules 19 02842 i035H Molecules 19 02842 i034HHHH
28 Molecules 19 02842 i036H Molecules 19 02842 i034HHHH
29 Molecules 19 02842 i037H Molecules 19 02842 i034HHHH
30 Molecules 19 02842 i038H Molecules 19 02842 i034HHHH
31 Molecules 19 02842 i039H Molecules 19 02842 i034HHHH
32 Molecules 19 02842 i040H Molecules 19 02842 i041HHHH
33 Molecules 19 02842 i042H Molecules 19 02842 i041HHHH
34 Molecules 19 02842 i043H Molecules 19 02842 i041HHHH
35 Molecules 19 02842 i044H Molecules 19 02842 i041HHHH
36 Molecules 19 02842 i045H Molecules 19 02842 i046HHHH
37 Molecules 19 02842 i047H Molecules 19 02842 i048HHHH
38 Molecules 19 02842 i049H Molecules 19 02842 i018HHHH
39 Molecules 19 02842 i017H Molecules 19 02842 i018HHBr-H
40 Molecules 19 02842 i017H Molecules 19 02842 i018HHHCH3O-
41 Molecules 19 02842 i017H Molecules 19 02842 i018HH Molecules 19 02842 i050H
42 Molecules 19 02842 i017H Molecules 19 02842 i018H Molecules 19 02842 i051H
43 Molecules 19 02842 i052H Molecules 19 02842 i018HH Molecules 19 02842 i053H
44 Molecules 19 02842 i017H Molecules 19 02842 i018HHCN-H
45 Molecules 19 02842 i017H Molecules 19 02842 i018HHCH3OCO-H
46 Molecules 19 02842 i054H Molecules 19 02842 i055HHHH
47 Molecules 19 02842 i056H Molecules 19 02842 i020HHHH
48 Molecules 19 02842 i057H Molecules 19 02842 i020HHHH
49 Molecules 19 02842 i058H Molecules 19 02842 i020HHHH
50 Molecules 19 02842 i059H Molecules 19 02842 i060H Molecules 19 02842 i007HH
51 Molecules 19 02842 i061H Molecules 19 02842 i060H Molecules 19 02842 i062HH
52 Molecules 19 02842 i063H Molecules 19 02842 i064H Molecules 19 02842 i065HH
53 Molecules 19 02842 i066H Molecules 19 02842 i064H Molecules 19 02842 i065HH
54 Molecules 19 02842 i063H Molecules 19 02842 i067H Molecules 19 02842 i065HH
55 Molecules 19 02842 i068H Molecules 19 02842 i069H Molecules 19 02842 i070HH
56 Molecules 19 02842 i071H Molecules 19 02842 i072H Molecules 19 02842 i073
57 Molecules 19 02842 i071H Molecules 19 02842 i074H Molecules 19 02842 i007HH
58 Molecules 19 02842 i071H Molecules 19 02842 i075HH Molecules 19 02842 i076H
59HH Molecules 19 02842 i077H Molecules 19 02842 i073HH
60 Molecules 19 02842 i049H Molecules 19 02842 i034F-F-F-F-
61 Molecules 19 02842 i049H Molecules 19 02842 i034HF-HH
62 Molecules 19 02842 i049H Molecules 19 02842 i034HCl-HH
63 Molecules 19 02842 i049H Molecules 19 02842 i034HHCl-H
64 Molecules 19 02842 i049H Molecules 19 02842 i034HHCF3-H
65 Molecules 19 02842 i049H Molecules 19 02842 i041HHOH-H
66 Molecules 19 02842 i049H Molecules 19 02842 i041HHCH3O-H
67 Molecules 19 02842 i049H Molecules 19 02842 i041 Molecules 19 02842 i078HHH
68 Molecules 19 02842 i049H Molecules 19 02842 i041HHH Molecules 19 02842 i079
69 Molecules 19 02842 i049H Molecules 19 02842 i041HNH2-HH
70 Molecules 19 02842 i080CH3- Molecules 19 02842 i018HHHH
71 Molecules 19 02842 i080H Molecules 19 02842 i018NH2-HHH
72 Molecules 19 02842 i081H Molecules 19 02842 i018HHHH
73 Molecules 19 02842 i082H Molecules 19 02842 i018HHHH
74CH3(CH2)2-H Molecules 19 02842 i018HHHH
75CH3(CH2)3-H Molecules 19 02842 i018HHHH
76OH(CH2)3-H Molecules 19 02842 i018HHHH
77OH(CH2)4-H Molecules 19 02842 i018HHHH
78CH3O(CH2)2-H Molecules 19 02842 i018HHHH
79 Molecules 19 02842 i083H Molecules 19 02842 i018HHHH
80 Molecules 19 02842 i084H Molecules 19 02842 i020HHHH
81 Molecules 19 02842 i085H Molecules 19 02842 i020HHHH
82 a Molecules 19 02842 i086
83 bCH3(CH2)2-CH3- Molecules 19 02842 i087HHHH
84CH3(CH2)2-H Molecules 19 02842 i060H Molecules 19 02842 i065HH
85CH3(CH2)3-H Molecules 19 02842 i088H Molecules 19 02842 i065HH
86CH3(CH2)4-H Molecules 19 02842 i088H Molecules 19 02842 i065HH
87 Molecules 19 02842 i028H Molecules 19 02842 i034HBr-HH
88 Molecules 19 02842 i089H Molecules 19 02842 i090HHHH
89 Molecules 19 02842 i091H Molecules 19 02842 i034HHHH
90 Molecules 19 02842 i092H Molecules 19 02842 i034HHHH
91 Molecules 19 02842 i093H Molecules 19 02842 i034HHHH
92 Molecules 19 02842 i094H Molecules 19 02842 i034HHHH

a (R)-(+)-WIN 55,212-2; b JWH-015.

3.2. Generation of CoMFA and Partial Least Squares (PLS) Analysis

CoMFA studies were performed using SYBYL-X 1.2 molecular modeling software (Tripos Inc., St. Louis, MO, USA) running on a PC platform with Intel core i7 CPU. The compounds were subjected to a preliminary minimization to remove close atom contacts by 1,000 cycles of minimization using standard Tripos force field [36] (with 0.005 kcal/mol energy gradient convergence criterion). The structures were next subjected to molecular dynamic simulation to heat the molecule at 700 K for 1,000 fs followed by annealing the molecule to 200 K for 1,000 fs. Gasteiger-Hückel charges [37] were assigned to all the molecules. Finally the minimized structures were superimposed by the atom fit method choosing the indole nucleus as the common scaffold for alignment (Figure 4).

Molecules 19 02842 g004 200
Figure 4. The superimposed structure of all compounds used in the CoMFA models.

Click here to enlarge figure

Figure 4. The superimposed structure of all compounds used in the CoMFA models.
Molecules 19 02842 g004 1024

PLS analysis was used to construct a linear correlation between the CoMFA descriptors (independent variables) and the activity values (dependent variables) [38]. To select the best model, the cross-validation analysis was performed using the LOO method (and SAMPLS), which generates the square of the cross-validation coefficient (q2) and the optimum number of components N. The optimum number of components analysis is shown in Table 4.The non-cross-validation was performed with a column filter value of 2.0 to speed up the analysis and reduce the noise.

Table 4. Optimum number of components analysis a.

Click here to display table

Table 4. Optimum number of components analysis a.
CoMFA CB1
SEP0.2680.2630.2730.2770.2840.2970.3050.3110.3180.3220.3290.3350.3410.3480.355
q20.7040.7220.7090.7090.7030.6850.6770.6740.6710.6730.6720.6730.6720.6720.672
N123456789101112131415
CoMFA CB2
SEP0.4320.3920.3880.3740.3730.3690.3660.3700.3740.3750.3770.3800.3830.3870.391
q20.4400.5460.5630.6000.6110.6250.6380.6390.6370.6430.6470.6490.6500.6500.651
N123456789101112131415

a SEP = standard error of prediction; q2 = the square of the LOO cross-validation (CV) coefficient; N = the optimum number of components.

To assess the predictive ability of the models, the pKi values of the test sets were predicted and then was calculated the predictive r2 (r2pred) [39,40] for both CoMFA models. r2pred, which measures the predictive performance of a PLS model, is defined by equation 1 as follows:

Molecules 19 02842 i096
where yi is the predicted biological activity value of every molecule in the test set, xi is the actual biological activity value of every molecule in the test set, and x is the mean activity of the training set molecules.

4. Conclusions

In summary, a 3D-QSAR study was performed on a wide series of 92 aminoalkylindoles with the aim of understanding and rationalizing their affinity and selectivity for the cannabinoid receptors CB1 and CB2. We have defined clear differences in the steric and electrostatic requirements for each receptor subtype. In Figure 5 we summarize the structure-activity relationships found for aminoalkylindoles. This work provides valuable information for the design of new cannabinoid ligands, allowing us to save time and resources by directing the synthesis toward obtaining the most promising molecules. Further functional studies are required to evaluate agonism/ antagonism activity.

Molecules 19 02842 g005 200
Figure 5. Structure-affinity/selectivity relationships derived from CoMFA studies developed in this work.

Click here to enlarge figure

Figure 5. Structure-affinity/selectivity relationships derived from CoMFA studies developed in this work.
Molecules 19 02842 g005 1024

Acknowledgments

This research work was financially supported by the Chilean National Science and Technology Research Fund FONDECYT (1100493 and 11130701). S. D. G.

Author Contributions

Santiago Hernández-Pino and Jaime Mella-Raipán participated in designing the study. César Morales-Verdejo and David Pessoa-Mahana collected and processed data. Manuscipt was written by Jaime Mella-Raipán and David Pessoa-Mahana. Jaime Mella-Raipán conducted the study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Howlett, A.C. The cannabinoid receptors. Prostag. Other Lipid Mediat. 2002, 68–69, 619–631, doi:10.1016/S0090-6980(02)00060-6.
  2. Gaoni, Y.; Mechoulam, R. Isolation, structure and partial synthesis of an active constituent of hashish. J. Am. Chem. Soc. 1964, 86, 1646–1647, doi:10.1021/ja01062a046.
  3. Adams, I.B.; Martin, B.R. Cannabis: Pharmacology and toxicology in animals and humans. Addiction 1996, 91, 1585–1614, doi:10.1111/j.1360-0443.1996.tb02264.x.
  4. Lambert, D.M. Medical use of cannabis through history. J. Pharm. Belg. 2001, 56, 111–118.
  5. Pacher, P.; Mechoulam, R. Is lipid signaling through cannabinoid 2 receptors part of a protective system? Prog. Lipid Res. 2011, 50, 193–211, doi:10.1016/j.plipres.2011.01.001.
  6. Sheng, W.S.; Hu, S.; Min, X.; Cabral, G.A.; Lokensgard, J.R.; Peterson, P.K. Synthetic cannabinoid win55,212-2 inhibits generation of inflammatory mediators by il-1beta-stimulated human astrocytes. Glia 2005, 49, 211–219, doi:10.1002/glia.20108.
  7. Cinar, R.; Szucs, M. Cb1 receptor-independent actions of sr141716 on g-protein signaling: Coapplication with the mu-opioid agonist tyr-d-ala-gly-(nme)phe-gly-ol unmasks novel, pertussis toxin-insensitive opioid signaling in mu-opioid receptor-chinese hamster ovary cells. J. Pharmacol. Exp. Ther. 2009, 330, 567–574, doi:10.1124/jpet.109.152710.
  8. Di Marzo, V.; Matias, I. Endocannabinoid control of food intake and energy balance. Nat. Neurosci. 2005, 8, 585–589, doi:10.1038/nn1457.
  9. Herkenham, M.; Lynn, A.B.; de Costa, B.R.; Richfield, E.K. Neuronal localization of cannabinoid receptors in the basal ganglia of the rat. Brain Res. 1991, 547, 267–274, doi:10.1016/0006-8993(91)90970-7.
  10. Herkenham, M.; Lynn, A.B.; Johnson, M.R.; Melvin, L.S.; de Costa, B.R.; Rice, K.C. Characterization and localization of cannabinoid receptors in rat brain: A quantitative in vitro autoradiographic study. J. Neurosci. 1991, 11, 563–583.
  11. Herkenham, M.; Lynn, A.B.; Little, M.D.; Johnson, M.R.; Melvin, L.S.; de Costa, B.R.; Rice, K.C. Cannabinoid receptor localization in brain. Proc. Natl. Acad. Sci. USA 1990, 87, 1932–1936, doi:10.1073/pnas.87.5.1932.
  12. Mackie, K. Cannabinoid receptors: Where they are and what they do. J. Neuroendocrinol. 2008, 20 (Suppl. 1), 10–14, doi:10.1111/j.1365-2826.2008.01671.x.
  13. Di Marzo, V. Cb(1) receptor antagonism: Biological basis for metabolic effects. Drug Discov. Today 2008, 13, 1026–1041, doi:10.1016/j.drudis.2008.09.001.
  14. Bouaboula, M.; Rinaldi, M.; Carayon, P.; Carillon, C.; Delpech, B.; Shire, D.; le Fur, G.; Casellas, P. Cannabinoid-receptor expression in human leukocytes. Eur. J. Biochem. 1993, 214, 173–180, doi:10.1111/j.1432-1033.1993.tb17910.x.
  15. Galiegue, S.; Mary, S.; Marchand, J.; Dussossoy, D.; Carriere, D.; Carayon, P.; Bouaboula, M.; Shire, D.; le Fur, G.; Casellas, P. Expression of central and peripheral cannabinoid receptors in human immune tissues and leukocyte subpopulations. Eur. J. Biochem. 1995, 232, 54–61, doi:10.1111/j.1432-1033.1995.tb20780.x.
  16. Carrier, E.J.; Kearn, C.S.; Barkmeier, A.J.; Breese, N.M.; Yang, W.; Nithipatikom, K.; Pfister, S.L.; Campbell, W.B.; Hillard, C.J. Cultured rat microglial cells synthesize the endocannabinoid 2-arachidonylglycerol, which increases proliferation via a cb2 receptor-dependent mechanism. Mol. Pharmacol. 2004, 65, 999–1007, doi:10.1124/mol.65.4.999.
  17. Di Marzo, V.; Bifulco, M.; de Petrocellis, L. The endocannabinoid system and its therapeutic exploitation. Nat. Rev. Drug Discov. 2004, 3, 771–784, doi:10.1038/nrd1495.
  18. Nunez, E.; Benito, C.; Pazos, M.R.; Barbachano, A.; Fajardo, O.; Gonzalez, S.; Tolon, R.M.; Romero, J. Cannabinoid cb2 receptors are expressed by perivascular microglial cells in the human brain: An immunohistochemical study. Synapse 2004, 53, 208–213, doi:10.1002/syn.20050.
  19. Walter, L.; Franklin, A.; Witting, A.; Wade, C.; Xie, Y.; Kunos, G.; Mackie, K.; Stella, N. Nonpsychotropic cannabinoid receptors regulate microglial cell migration. J. Neurosci. 2003, 23, 1398–1405.
  20. Berdyshev, E.V. Cannabinoid receptors and the regulation of immune response. Chem. Phys. Lipids 2000, 108, 169–190, doi:10.1016/S0009-3084(00)00195-X.
  21. Molina-Holgado, E.; Guaza, C.; Borrell, J.; Molina-Holgado, F. Effects of cannabinoids on the immune system and central nervous system: Therapeutic implications. BioDrugs 1999, 12, 317–326, doi:10.2165/00063030-199912050-00001.
  22. Mackie, K. Cannabinoid receptors as therapeutic targets. Annu. Rev. Pharmacol. Toxicol. 2006, 46, 101–122, doi:10.1146/annurev.pharmtox.46.120604.141254.
  23. Frost, J.M.; Dart, M.J.; Tietje, K.R.; Garrison, T.R.; Grayson, G.K.; Daza, A.V.; El-Kouhen, O.F.; Miller, L.N.; Li, L.; Yao, B.B.; et al. Indol-3-yl-tetramethylcyclopropyl ketones: Effects of indole ring substitution on cb2 cannabinoid receptor activity. J. Med. Chem. 2008, 51, 1904–1912, doi:10.1021/jm7011613.
  24. Frost, J.M.; Dart, M.J.; Tietje, K.R.; Garrison, T.R.; Grayson, G.K.; Daza, A.V.; El-Kouhen, O.F.; Yao, B.B.; Hsieh, G.C.; Pai, M.; et al. Indol-3-ylcycloalkyl ketones: Effects of n1 substituted indole side chain variations on cb(2) cannabinoid receptor activity. J. Med. Chem. 2010, 53, 295–315, doi:10.1021/jm901214q.
  25. Pasquini, S.; Mugnaini, C.; Ligresti, A.; Tafi, A.; Brogi, S.; Falciani, C.; Pedani, V.; Pesco, N.; Guida, F.; Luongo, L.; et al. Design, synthesis, and pharmacological characterization of indol-3-ylacetamides, indol-3-yloxoacetamides, and indol-3-ylcarboxamides: Potent and selective cb2 cannabinoid receptor inverse agonists. J. Med. Chem. 2012, 55, 5391–5402, doi:10.1021/jm3003334.
  26. Cramer, R.D.; Patterson, D.E.; Bunce, J.D. Comparative molecular field analysis (comfa). 1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 1988, 110, 5959–5967, doi:10.1021/ja00226a005.
  27. Chen, J.Z.; Han, X.W.; Liu, Q.; Makriyannis, A.; Wang, J.; Xie, X.Q. 3D-QSAR studies of arylpyrazole antagonists of cannabinoid receptor subtypes CB1 and CB2. A combined NMR and CoMFA approach. J. Med. Chem. 2006, 49, 625–636, doi:10.1021/jm050655g.
  28. Cichero, E.; Cesarini, S.; Mosti, L.; Fossa, P. CoMFA and CoMSIA analyses on 1,2,3,4-tetrahydropyrrolo[3,4-b]indole and benzimidazole derivatives as selective CB2 receptor agonists. J. Mol. Model. 2010, 16, 1481–1498, doi:10.1007/s00894-010-0664-1.
  29. Durdagi, S.; Kapou, A.; Kourouli, T.; Andreou, T.; Nikas, S.P.; Nahmias, V.R.; Papahatjis, D.P.; Papadopoulos, M.G.; Mavromoustakos, T. The application of 3D-QSAR studies for novel cannabinoid ligands substituted at the C1' position of the alkyl side chain on the structural requirements for binding to cannabinoid receptors CB1 and CB2. J. Med. Chem. 2007, 50, 2875–2885, doi:10.1021/jm0610705.
  30. Durdagi, S.; Papadopoulos, M.G.; Mavromoustakos, T. An effort to discover the preferred conformation of the potent amg3 cannabinoid analog when reaching the active sites of the cannabinoid receptors. Eur. J. Med. Chem. 2012, 47, 44–51, doi:10.1016/j.ejmech.2011.10.015.
  31. Durdagi, S.; Papadopoulos, M.G.; Zoumpoulakis, P.G.; Koukoulitsa, C.; Mavromoustakos, T. A computational study on cannabinoid receptors and potent bioactive cannabinoid ligands: Homology modeling, docking, de novo drug design and molecular dynamics analysis. Mol. Diver. 2010, 14, 257–276, doi:10.1007/s11030-009-9166-4.
  32. Alvarez-Figueroa, M.J.; Pessoa-Mahana, C.D.; Palavecino-Gonzalez, M.E.; Mella-Raipan, J.; Espinosa-Bustos, C.; Lagos-Munoz, M.E. Evaluation of the membrane permeability (pampa and skin) of benzimidazoles with potential cannabinoid activity and their relation with the biopharmaceutics classification system (bcs). AAPS PharmSciTech 2011, 12, 573–578, doi:10.1208/s12249-011-9622-1.
  33. Araya, K.A.; David Pessoa Mahana, C.; Gonzalez, L.G. Role of cannabinoid CB1 receptors and Gi/o protein activation in the modulation of synaptosomal Na+,K+-ATPase activity by WIN55,212-2 and delta(9)-THC. Eur. J. Pharm. 2007, 572, 32–39, doi:10.1016/j.ejphar.2007.06.013.
  34. Mella-Raipan, J.A.; Lagos, C.F.; Recabarren-Gajardo, G.; Espinosa-Bustos, C.; Romero-Parra, J.; Pessoa-Mahana, H.; Iturriaga-Vasquez, P.; Pessoa-Mahana, C.D. Design, synthesis, binding and docking-based 3d-qsar studies of 2-pyridylbenzimidazoles—a new family of high affinity cb1 cannabinoid ligands. Molecules 2013, 18, 3972–4001, doi:10.3390/molecules18043972.
  35. Golbraikh, A.; Tropsha, A. Beware of q2! J. Mol. Graph. Model. 2002, 20, 269–276, doi:10.1016/S1093-3263(01)00123-1.
  36. Vinter, J.G.; Davis, A.; Saunders, M.R. Strategic approaches to drug design. I. An integrated software framework for molecular modelling. J. Comput.-Aided Mol. Des. 1987, 1, 31–51, doi:10.1007/BF01680556.
  37. Gasteiger, J.; Marsilili, M. Iterative partial equalization of orbital electronegativity—a rapid access to atomic charges. Tetrahedron 1980, 36, 3219–3228, doi:10.1016/0040-4020(80)80168-2.
  38. Clark, M.; Cramer, R.; Opdenbosch, N. Validation of the general purpose tripos 5.2 force field. J. Comp. Chem. 1989, 10, 982–1012, doi:10.1002/jcc.540100804.
  39. Oprea, T.I.; Waller, C.L.; Marshall, G.R. Three-dimensional quantitative structure-activity relationship of human immunodeficiency virus (i) protease inhibitors. 2. Predictive power using limited exploration of alternate binding modes. J. Med. Chem. 1994, 37, 2206–2215, doi:10.1021/jm00040a013.
  40. Waller, C.L.; Oprea, T.I.; Giolitti, A.; Marshall, G.R. Three-dimensional qsar of human immunodeficiency virus (i) protease inhibitors. 1. A comfa study employing experimentally-determined alignment rules. J. Med. Chem. 993, 36, 4152–4160.
  • Sample Availability: Not Available.
Molecules EISSN 1420-3049 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert