• anti-obesity agents;
  • bioactive ligands;
  • cannabinoid receptors;
  • CB1;
  • CoMFA;
  • hologram QSAR


  1. Top of page
  2. Abstract
  3. Methodology
  4. Results and Discussion
  5. Conclusions
  6. Acknowledgments
  7. References

Two-dimensional and 3D quantitative structure–activity relationships studies were performed on a series of diarylpyridines that acts as cannabinoid receptor ligands by means of hologram quantitative structure–activity relationships and comparative molecular field analysis methods. The quantitative structure–activity relationships models were built using a data set of 52 CB1 ligands that can be used as anti-obesity agents. Significant correlation coefficients (hologram quantitative structure–activity relationships: r 2 = 0.91, q 2 = 0.78; comparative molecular field analysis: r 2 = 0.98, q 2 = 0.77) were obtained, indicating the potential of these 2D and 3D models for untested compounds. The models were then used to predict the potency of an external test set, and the predicted (calculated) values are in good agreement with the experimental results. The final quantitative structure–activity relationships models, along with the information obtained from 2D contribution maps and 3D contour maps, obtained in this study are useful tools for the design of novel CB1 ligands with improved anti-obesity potency.

The recreational and medicinal use of cannabis-derived preparations have been known for centuries. Cannabinoids are the main chemical constituents of marijuana plant (Cannabis sativa L.), of which the main psychoactive ingredient is known as Δ9-tetrahydrocannabinol (Δ9-THC) (1). Marijuana has remained one of the most widely used drugs in the world and is the third most common drug of abuse used after tobacco and alcohol. It has been investigated for the treatment of a large number of medical illnesses, including glaucoma, hypertension, nausea associated with chemotherapy, pain, migraine and many other disorders (2). Several studies have been performed with the aim to understand possible molecular mechanisms responsible for medicinal and psychoactive properties of cannabinoid compounds (3–7). The pharmacological effects of cannabinoid compounds are mediated by at least two receptors, termed CB1 and CB2, which are two subtypes of G-protein-coupled cannabinoid receptors (8).

CB1 receptor was discovered in the rat cortex, in 1990 (9), and is found predominantly in the central nervous system (10,11), located in the brain areas that mediate the effects of Δ9-THC, where it is coupled to several signal transduction mechanisms, including activation of potassium channels, inhibition of voltage-dependent calcium channels and adenylyl cyclase enzyme (12–14). This receptor is responsible for most of the overt pharmacological effects of the cannabinoid compounds (8). Also, it is expressed strongly in the basal ganglia, cerebellum, and hippocampus, which accounts for the well-known effects of cannabis on motor coordination and short-term memory processing (15). A second cannabinoid receptor, CB2, was identified in 1993 and has been found mainly in peripheral tissues (16). CB2 inhibits adenylyl cyclase similarly to CB1; however, it has not been shown to affect ion channels as CB1 does. Furthermore, CB2 is implicated in the immune system, and it is thought to have immunosuppressive and anti-inflammatory activities (17).

CB1 agonists, such as Δ9-THC, are known to stimulate food intake, whereas CB1 receptor knockout mice are lean and resistant to diet-induced obesity. Initially, the search for CB1 antagonists/inverse agonists was based on the structure of known agonists such as Δ9-THC (18), but the first potent and selective CB1 inverse agonist, SR141716 (Figure 1), belongs to a new family of CB1 ligands based on a 1,5-diphenylpyrazole structure. It has been shown that SR141716 produces a marked and sustained reduction of adiposity in diet-induced obese mice, which was more than what is expected from the reduction of food intake (19). This suggests that CB1 and its endogenous ligands modulate the energy balance via a dual mechanism of intake reduction and increased energy expenditure. So, cannabinoid-based therapies that are devoid of unwanted side effects are being designed, and a large effort has been directed to the discovery of new, potent, and selective CB1 antagonists as anti-obesity drugs (12).


Figure 1.  Structure of SR141716.

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Discovery and development of novel anti-obesity agents based on the CB1 hypothesis can be aided by ligand-based approaches, which have become vital components of modern drug design. On the other hand, the lack of X-ray 3D structure of CB1 hampers the design of new ligands using structure-based approaches. In previous ligand-based studies, several modifications were made on the parent structure of SR141716, either by changing substituent in the two phenyl rings or by trying different scaffolds as an alternative to the pyrazole core (20–25). For most of the SR141716 analogs, many studies have reported three main structural requirements for high affinities to the CB1 receptor: (i) a 2,4-dichlorophenyl substituent at position 1, (ii) a para-substituted phenyl ring at position 5 and (iii) a carboxamide group at position 3 of the pyrazole ring (20–27). Other quantitative structure–activity relationships (QSAR) and molecular modeling studies were performed for pyrazole derivatives (28–32). Nevertheless, as far as we know, there is no reported QSAR study on SR141716 derivatives where the pyrazole ring is replaced by a pyridine moiety. So, in this work, a series of diarylpyridines acting at CB1 receptor were investigated by means of QSAR, employing a combination of hologram QSAR (HQSAR) and comparative molecular field analysis (CoMFA). The predictive 2D and 3D QSAR models generated can be useful in the design of new CB1 antagonists or inverse agonists with improved potency.


  1. Top of page
  2. Abstract
  3. Methodology
  4. Results and Discussion
  5. Conclusions
  6. Acknowledgments
  7. References

Data set

The data set employed in the HQSAR and CoMFA analyses is constituted by 52 CB1 ligands selected from the literature (23), and these compounds consist of 2-benzyloxy-5-(4-chlorophenyl)-6-(2,4-dichlorophenyl) pyridines having either a 3-cyano or a 3-carboxamide moiety. The chemical structures and biological properties for the complete set of compounds are listed in Table 1. The IC50 values vary from 1.3 to 2800 nm (Table 1) and were measured under the same experimental conditions, which is a fundamental requirement for successful QSAR studies. The IC50 values were converted to the corresponding pIC50 (−log IC50) and used as dependent variables in the QSAR investigations. The pIC50 values span approximately three orders of magnitude, and the property values are acceptably distributed across the range of values. From the original data set of 52 CB1 ligands, 40 compounds (1–40, Table 1) were selected as members of the training set for model construction, and the other 12 compounds (41–52, Table 1) as members of the test set for external model validation. Hierarchical cluster analyses performed with Pirouette 3.10 (Infometrix Inc., Bothell, WA, USA) was used to guide an appropriate compound selection. Training and test sets were selected in such a way that structurally diverse molecules, possessing a wide range of activities, were included in both sets. Thus, the data set is suitable for QSAR model development. Generation of ligand structures, as well as QSAR modeling analyses, calculations, and visualizations were performed using the sybyl 8.0 package (Tripos Inc., St. Louis, Missouri, USA).

Table 1.   Chemical structures and IC50 values for the CB1 ligands studied
Training set
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CompoundX1X2X3X4IC50 (nm)
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CompoundRR′NR1R2IC50 (nm)
26CH2 (cyclohexyl)NHMe3.4
27CH2 (cyclohexyl)NH(n-Pr)5.2
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CompoundR4NR1R2IC50 (nm)
inline image
CompoundNR1R2IC50 (nm)
Test set
inline image
CompoundR4NR1R2IC50 (nm)
inline image
CompoundNR1R2IC50 (nm)
inline image
CompoundRR′NR1R2IC50 (nm)
48CH2 (cyclohexyl)Me2.9
inline image
CompoundX1X2X3X4IC50 (nm)

HQSAR analyses

Quantitative structure–activity relationships studies have been successfully applied to optimize different properties for a considerable amount of hit compounds in drug design projects. Two-dimensional QSAR methods permit not only the investigation of several biological properties for a huge number of compounds but can also be employed in those cases when 3D biological target information is not available (33–37). One of such methods is HQSAR, which requires only 2D structures and the corresponding biological activity as input, allowing the investigation of a wide variety of bioactive compounds (38,39). An interesting feature of HQSAR is that it typically produces fast statistical correlations that are comparable in quality to 3D QSAR techniques, such as CoMFA (40), but avoids the time-consuming step of 3D model generation and mutual alignment in 3D space. Hologram QSAR explains differences in the observed activity in a series of molecules by quantifying variations within their calculated molecular holograms and using the partial least squares (PLS) method. The main steps involved in HQSAR analyses are summarized in Figure 2.


Figure 2.  Main steps involved in hologram quantitative structure–activity relationships analyses.

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In the substructural fragmentation process, each molecule is hashed to a molecular fingerprint that encodes the frequency of occurrence of various molecular fragment types using a predefined set of rules. Next, to construct a molecular hologram, the fingerprint is cut into strings at a fixed interval as specified by a hologram length (HL) parameter. The strings are then aligned, and the sum of each column constitutes the individual component of the molecular hologram of a particular length. The progress of incorporating information about each fragment, and each of its constituent subfragments, implicitly encodes 3D structural information (e.g., hybridization, chirality). So, the final HQSAR models can be affected by a number of parameters concerning hologram generation, such as hologram length, fragment size, and fragment distinction (38,39,41). In our studies, holograms were generated using the standard parameters implemented in sybyl 8.0, and all models were investigated using full cross-validated (q 2), PLS and leave-one-out (LOO) methods.

CoMFA analyses

Comparative molecular field analysis studies were carried out with the aim to better understand and explore the contributions of electrostatic and steric fields of the CB1 ligands under study for their biological activity. To build predictive 3D QSAR models, an important step is the generation of a molecular alignment. A large variety of useful approaches have been described in the literature for this purpose (42). The 3D alignment approach used in this work is based on molecular conformations obtained from a procedure where a single optimized conformation of each molecule in the data set was energetically minimized, employing the atom-centered partial charge AM1-ESP calculations implemented in mopac 6.0 (43). This choice for the molecular alignment can be justified because there is no crystallographic structure determined for the CB1 receptor. Figure 3 shows the alignment obtained from the optimized geometries for the complete set of CB1 ligands studied here.


Figure 3.  Molecular alignment for the complete set of CB1 ligands.

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After the alignment of all molecular conformations, the next step was the calculation of steric and electrostatic properties according to Lennard–Jones and Coulomb potentials, respectively. For this, the 40 aligned training set molecules were placed in a 3D grid box of 2.0 Å in the x, y, and z directions, and the grid region was automatically generated by the CoMFA routine to encompass all molecules with an extension of 4.0 Å in each direction. Comparative molecular field analysis steric and electrostatic fields were generated at each grid point with Tripos force field using a sp3 carbon atom probe carrying a +1 net charge. An important option used in this work was the region-focusing method, which was applied to increase the resolution of CoMFA models. The default value of 30 kcal/mol was set as the maximum steric and electrostatic energy cutoff. Minimum sigma (column filtering) was set as 2.0 kcal/mol to improve the signal-to-noise ratio, by omitting those lattice points where energy variation is below the threshold.

Results and Discussion

  1. Top of page
  2. Abstract
  3. Methodology
  4. Results and Discussion
  5. Conclusions
  6. Acknowledgments
  7. References

HQSAR results

First of all, it is necessary to vary some parameters to construct the HQSAR models. For this, the generation of molecular fragments for each compound was carried out using the following fragment distinctions: atoms (A), bonds (B), connections (C), hydrogen atoms (H), chirality (Ch), and donor and acceptor (DA). Besides, to assess the hologram generation, several combinations of these parameters were considered using the fragment size default (4–7) as follows: AB, ABC, ABCH, ABCHCh, ABCHChDA, ABH, ABCCh, ABDA, ABCDA, ABHDA, ABCChDA, ABCHDA, and ABHChDA. Another considered option during the HQSAR analyses was the screening of the 12 default series of hologram length values, which ranged from 53 to 401 bins. From the patterns of fragment counts, for the training set compounds and the measured biological activity (IC50), several HQSAR models were generated and investigated using full cross-validated r 2 (q 2) PLS and LOO methods. The predictive capability of all models obtained was assessed by analyzing their q 2 values. The statistical results from the PLS analyses for the 40 training set compounds using several fragment distinction combinations are presented in Table 2.

Table 2.   HQSAR results using several fragment distinctions (default fragment size = 4–7)
ModelFragment distinctionq 2SEPr 2SEEHLN
  1. q2, cross-validated correlation coefficient; SEP, cross-validated standard error; r2, non-cross-validated correlation coefficient; SEE, non-cross-validated standard error; HL, hologram length; N, optimal number of components. Fragment distinction: A, atoms; B, bonds; C, connections; H, hydrogen atoms; Ch, chirality; DA, donor and acceptor; HQSAR, hologram quantitative structure–activity relationships.


Analyzing Table 2, we can see that the best statistical results among all of the 13 generated models were obtained for models 2 and 7 (q 2 = 0.776 and r 2 = 0.911), which were derived using A/B/C and A/B/C/Ch, respectively, and with four being the optimum number of PLS components. The only difference among the two better models is the absence of one distinction (chirality, Ch) in model 2. To investigate the importance of this fragment distinction among the two models (models 2 and 7, Table 2), we decided to study the influence of different fragment sizes in the main statistical parameters. Fragment size parameters control the minimum and maximum length of fragments to be included in the hologram fingerprint. The results obtained after the variation of the fragment sizes are displayed in Table 3. From the results presented in Table 3, it is possible to notice that there is no statistical improvement in the generated HQSAR models (q 2 = 0.776 and r2 = 0.911, for both models). These results also indicate that the inclusion of one fragment distinction (chirality, model 7) did not improve the statistical quality of the model. So, from these results, we decided to use model 2 (A/B/C), with the fragment size default (4–7), in the future analyses.

Table 3.   Influence of various fragment sizes for the two models selected (models 2 and 7)
Fragment sizeq 2SEPr 2SEEHLN
Model 2 (A/B/C)
Model 7 (A/B/C/Ch)

After the selection of the best HQSAR model, the next stage is to validate this model. Two strategies can be employed for this purpose: internal and external validation. In this work, an internal cross-validation (using LOO technique) was performed. A second process of validation (external validation) involves the use of the HQSAR model obtained with the training set to predict the biological activity of new molecules (test compounds). This is possible as the structure encoded within a 2D fingerprint is directly related to biological activity of molecules within the training set, and the HQSAR model can be used to predict the activity of new related molecules from its fingerprint. The external validation process can be considered the most valuable validation method, as the test compounds are completely excluded during the training of the model. The results from internal validation demonstrate a good correlation between experimental and predicted values, as can be seen from Figure 4, which displays the experimental versus predicted pIC50 values for training set molecules.


Figure 4.  Predicted versus experimental values of pIC50 for the 52 CB1, inverse agonists (training and test sets) obtained with hologram QSAR method.

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The predictive power of the best HQSAR model derived using the 40 training set molecules was assessed by predicting pIC50 values for 12 test set molecules (compounds 41–52, Table 1), which were not included in the training set. The results of the external validation are presented in Table 4 and can also be seen in Figure 4.

Table 4.   Experimental and predicted activities (pIC50), along with the residual values, for a test series of CB1 ligands using HQSAR and CoMFA methods
ExperimentalPredicted HQSARResidual HQSARPredicted CoMFAResidual CoMFA
  1. CoMFA, comparative molecular field analysis; HQSAR, hologram quantitative structure–activity relationships.


In Figure 4 and Table 4, it is possible to see a good agreement between experimental and predicted values for the test set compounds, which indicates the high reliability of the constructed HQSAR model. From the low residual values, we can observe that the HQSAR model obtained can be used to predict the biological activity of novel compounds within this structural class. The predicted values fall close to the experimental pIC50 values, deviating by 0.33 log units on average.

Finally, another important application of a QSAR model is to provide hints about what molecular fragments are directly related to biological activity. This information could help in the synthesis of new molecules with improved properties. From the HQSAR analyses, it is possible to generate atomic contribution maps, in which a color code that discriminates the positive and negative contributions to the biological activity is used (colors at the red end of the spectrum indicate poor contributions; colors at the green end correspond to favorable contributions; atoms with intermediate contributions are colored white). Therefore, to analyze the most relevant atomic contributions to CB1 affinity, we selected three compounds of the data set: two high-affinity compounds (7 and 19, Table 1) and one compound with moderate affinity (41, Table 1). Figure 5 displays the individual atomic contributions for these three compounds.


Figure 5.  Individual atomic contributions for the biological activity of three CB1 ligands.

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According to HQSAR color code, the molecular fragments making the strongest contribution to CB1 affinity are the pyridine ring, along with the phenyl ring attached to C-6 and also the one in the O-benzyl moiety. This indicates the importance of the benzyl-pyridine scaffold into this series of compounds. The favorable contribution of the O-benzyl fragment is in accordance with IC50 data presented by compounds lacking this group (compounds 28–40, see Table 1), which show only moderate affinity to CB1. On the other hand, the most active compounds have a phenyl ring with different patterns of substitution at this location. Furthermore, it is worth noting that the presence of small groups at C-3 is more suitable for high CB1 affinities, while the bulkier piperidinyl fragment in compound 41 makes an unfavorable contribution to the biological property (IC50). These findings suggest that C-3 in the pyridine ring is an important target for molecular modification and additional SAR studies.

CoMFA results

Using the pIC50 values and the electrostatic and steric properties calculated by CoMFA method, and based on the molecular alignment performed with all optimized structures (see Figure 3), several PLS analyses were carried out to correlate these CoMFA fields (electrostatic and steric) with the biological property (pIC50). The main statistical results obtained are displayed in Table 5.

Table 5.   CoMFA statistical results for the training set
 Region focusing
Now = 0.3w = 0.5w = 0.8w = 1.2
  1. The region focusing was weighted by standard deviation coefficient values (w).

  2. q2, cross-validated correlation coefficient; SEP, cross-validated standard error; N, optimal number of components; CoMFA, comparative molecular field analysis.

Grid spacing0.

From Table 5, it can be seen that an initial analysis without using region focusing (an advanced method of noise reduction) produced a low cross-validated correlation coefficient (q 2 = 0.569, with four components). Thus, we have applied the region-focusing procedure, weighted by standard deviation coefficient values ranging from 0.3 to 1.2, with a grid spacing varying from 0.5 to 1.5. The best statistical results were obtained when region focusing was weighted by a standard deviation coefficient of 0.8, along with a grid spacing of 1.0 (r 2 = 0.980, SEE = 0.142, q 2 = 0.769, SEP = 0.458 and four components). Within this CoMFA model, the contributions of steric and electrostatic fields correspond to 54.2% and 45.8% of the total variance, respectively. The internal validation was again performed using the LOO methodology. Figure 6 displays the experimental versus predicted values of pIC50, where it is possible to observe the good correlation between experimental and predicted values of pIC50 for the training set compounds.


Figure 6.  Predicted versus experimental values of pIC50 for the 52 CB1 inverse agonists (training and test sets) obtained with the comparative molecular field analysis model.

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After the model construction and internal validation, the predictive capability of the most significant CoMFA model, obtained with the 40 training set molecules, was assessed by predicting pIC50 values for the 12 test set molecules (compounds 41–52, Table 1). These test set compounds were submitted to the same alignment and descriptor generation procedures as the training set compounds, as previously described. The results of the external validation are listed in Table 4 and Figure 6. Analyzing these results, we can observe a good agreement between experimental and predicted values for the test set compounds, which indicates the reliability of our best CoMFA model. The predicted pIC50 values for the test set compounds fall close to the experimental values, with a few exceptions.

Going further in the CoMFA analysis, the visualization of steric and electrostatic interaction fields is an important tool to guide further molecular modifications in the search toward improved CB1 ligands. Graphical CoMFA results can be analyzed considering steric and electrostatic fields, and favorable and unfavorable regions for substitution by bulky substituents are represented in green and yellow, respectively. Electrostatic features are characterized in such a way that red contours represent regions in which electronegative substituents may increase the biological activity, whereas blue contours indicate regions in which electropositive groups would contribute to enhance activity. Figure 7 displays the CoMFA contour map for the steric and electrostatic fields, in which one of the most potent CB1 ligands (compound 7) is represented.


Figure 7.  Steric and electrostatic contour maps for the most potent CB1 ligand (compound 7)

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According to the CoMFA/PLS analysis, the steric and electrostatic field properties contribute in a 54.2/45.8 ratio to the total variance, meaning that both kind of interactions should be considered as equally important to CB1 affinity within this series of compounds. Figure 7 shows a huge region surrounding the pyridine nitrogen where substitution for electropositive groups can favor the biological activity. This finding emphasizes that the C-2 side chain can contribute not only as a hydrophobic functionality but also as a region where polar interactions can take place (23). Considering steric properties, our model shows very clearly that sizable groups as X2 and X3 substituents (see general structure in Table 1) can improve biological activity, as represented by the green contours. On the other hand, our CoMFA analysis shows that some steric restrictions can be expected in the CB1 receptor active site portion interacting with the X1-phenyl ring, as suggested by the yellow contours. Thus, our CoMFA model suggests that the SAR of these compounds can be further explored by modifying X1, X2, and X3 substituents, keeping in mind that X1 must be a small group, while X2 and X3 can be exchanged by bulkier moieties.

Molecular modeling studies performed on SR141716 and some analogs interacting with homology models of the CB1 receptor (29,44) predicted that the antagonists bind to this receptor in a hydrophobic region located within the transmembrane (TM) region formed by helices TM3, TM5, TM6, and TM7 (45). Specifically, hydrogen bond interactions with Lys192 and Ser383 are considered to be crucial for antagonism (46). Additionally, it is well accepted that hydrophobic contacts are important for stabilization of the interaction, along with π–π stacking interactions between the aromatic system formed by the phenyl groups attached to the pyrazole ring and Tyr275, Trp279, Trp356, and Phe379 side chains (45,46). All of these observations are in good agreement with our findings, suggesting that antagonist affinity can be improved as long as a balance between electrostatic and hydrophobic functions are maintained in the substituents neighboring the pyridine nitrogen.


  1. Top of page
  2. Abstract
  3. Methodology
  4. Results and Discussion
  5. Conclusions
  6. Acknowledgments
  7. References

Ligand-based approaches, as the ones employed here, are very useful in the search for new compounds acting at receptors with unknown 3D structure. In this study, reliable and predictive 2D and 3D QSAR models were built for a series of CB1 ligands that can be used as anti-obesity agents. Both models have presented good statistical results, as well as internal and external consistency. HQSAR and CoMFA methodologies were successfully employed to provide useful insights into the chemical and structural requirements for CB1 affinity within this class of compounds. Two-dimensional contribution maps and 3D contour maps have demonstrated the molecular determinants for biological activity and emphasized important regions in 3D space, where modifications of steric and electrostatic fields might be strongly favorable to improve CB1 affinity. Integration of the information obtained with both approaches (HQSAR and CoMFA) should be useful in the design of new CB1 ligands with improved anti-obesity profile.


  1. Top of page
  2. Abstract
  3. Methodology
  4. Results and Discussion
  5. Conclusions
  6. Acknowledgments
  7. References

We gratefully acknowledge financial support from FAPESP (The State of São Paulo Research Foundation), CNPq (The National Council for Scientific and Technological Development) and CAPES (Coordination for the Improvement of High Education Personnel), Brazil.


  1. Top of page
  2. Abstract
  3. Methodology
  4. Results and Discussion
  5. Conclusions
  6. Acknowledgments
  7. References
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