Identifying the structural features of pyrazolo[4,3-c]quinoline-

3-ones as inhibitors of phosphodiesterase 4: An exploratory

CoMFA and CoMSIA study

Anand Gaurav*, Vertika Gautam

School of Pharmaceutical Sciences, Shobhit University,

Modipuram, Meerut 250110, India

* Author to whom correspondence should be addressed: Anand

Gaurav, School of Pharmaceutical Sciences, Shobhit University,

Meerut, 250110, India. Email: anand.pharma@gmail.com. Tel.: +91-

9458272353; Fax: +91-121-2575724

Abstract:

Phosphodiesterases (PDEs) are responsible for the hydrolysis of

cyclic nucleotides (cAMP and c-GMP). Cyclic nucleotides are

important intracellular secondary messengers in cell function,

relaying the signals from hormones at specific cell-surface

receptors. An increase of cAMP due to the stimulation of adenylyl

cyclase or the inhibition of PDEs affects the activity of immune

system and inflammatory cells. Thus, PDE4, a cAMP specific PDE,

received much attention as a target for the treatment of the

diseases like asthma and Chronic Obstructive Pulmonary Disease

(COPD). Pyrazolo[4,3-c]quinoline-3-one nucleus has attracted

considerable attention recently as PDE4 receptor antagonists

which have shown remarkable therapeutic potential in the

treatment of asthma and Chronic Obstructive Pulmonary Disease

(COPD). In the present study, three dimensional quantitative

structure activity relationship (3D QSAR) approach using CoMFA

and CoMSIA was applied to a series of 2, 5-dihydropyrazolo [4, 3-

c] quinoline-3-ones as PDE4 receptor antagonists. For the

purpose, 22 compounds from the series were used to develop and

validate models. The robustness of the model was confirmed with

the help of leave one out cross-validation method, while the

predictive ability of models was tested using a test set

containing three molecules. Novel compounds were designed on the

basis of results of CoMFA and CoMSIA studies. Designed compounds

were evaluated by Docking and Lipinski filters. 3D-QSAR models

with high squared correlation coefficient of up to 0.9590 for

CoMFA and 0.9740 for CoMSIA were established. Robustness of the

models is demonstrated by R2cv values of up to 0.8600 and 0.8230

for CoMFA and CoMSIA, respectively. Predictive ability of the

models is reflected by R2pred values of 0.865 and 0.926 for CoMFA

and CoMSIA respectively. Predicted activity of the designed

molecules correlated well with the docking scores and the

molecules also passed the Lipinski filters. Developed models

highlighted the importance of steric, electrostatic and

hydrophobic properties of the molecules for PDE 4 receptor

affinity. The designed compounds may serve as lead for the

development of newer PDE4 inhibitors based on the 2, 5-

dihydropyrazolo [4, 3-c] quinoline-3-one scaffold.

Keywords: Phosphodiesterase; CoMFA; CoMSIA; K-means clustering;

Enhanced replacement method; Replacement method; cAMP; cGMP

Introduction

Phosphodiesterases (PDEs), a super family of 11 isozymes, are

responsible for the hydrolysis of cyclic nucleotides (cAMP and c-

GMP) [1]. Cyclic nucleotides are important intracellular

secondary messengers in cell function, relaying the signals from

hormones at specific cell-surface receptors [2]. An increase of

cAMP due to the stimulation of adenylyl cyclase or the inhibition

of PDEs affects the activity of immune system and inflammatory

cells [3]. Thus, PDE4, a cAMP specific PDE, received much

attention as a target for the treatment of the diseases like

asthma and Chronic Obstructive Pulmonary Disease (COPD) [4].

Since the first report of rolipram as a selective inhibitor of

PDE4, a number of compounds have been studied to increase the

activity and to reduce the side effects such as nausea, vomiting,

psychotropic activity, and increased gastric secretion [5-7].

PDE4 inhibitors based on benzofuran, indazole, pyrazolo[3, 4-

c]pyridine and pyrazolo[4, 3-c]quinoline-3-one nucleus have been

the subject of study in the recent past [8-11]. Research efforts

have also been directed towards optimization of PDE4 inhibition

activity of pyrazolo[4, 3-c]quinoline-3-ones [11]. However, the

task still remains largely unaccomplished. Thus, there has been a

need for developing newer, selective, and potent inhibitors of

PDE4 was always felt.

A successful QSAR model not only helps in better understanding of

the structure–activity relationship of any class of compounds,

but also provides the researcher an in-depth analysis about the

lead compounds to be used in further studies [12]. 3D QSAR

employs three dimensional descriptors of molecules to gain

insights into the structural requirements for activity.

Comparative molecular field analysis (CoMFA) and comparative

molecular similarity indices analysis (CoMSIA) are most widely

used 3D QSAR methods by the medicinal chemist. CoMFA and CoMSIA

involve the alignment of molecules in a structurally and

pharmacologically reasonable manner on the basis of the

assumption that each compound acts via a common target binding

site [13, 14]. They can give critical information of the

interactions between the ligand and the putative receptors. Using

these methods, it is possible to predict the biological activity

of molecules and represent the relationships between molecular

properties (steric, electrostatic, hydrophobic and hydrogen

bonding) and biological activity in the form of contour maps.

Consequently, statistical data in the form of contour maps for

PDE4 inhibitors can be generated to obtain an understanding of

the steric, electrostatic, hydrophobic and hydrogen bonding

requirements for biological activity. 3D-QSAR studies of PDE4

inhibitors have been reported by several groups in past, however,

such study for 2, 5-dihydropyrazolo [4, 3-c] quinoline-3-one

derivatives as PDE4 inhibitors remains unplumbed till now [15-

18]. Hence, our research group initiated 3D QSAR studies (CoMFA

and CoMSIA) as validated tools for QSAR analysis of 2, 5-

dihydropyrazolo [4, 3-c] quinoline-3-ones as PDE4 inhibitors.

Material and methods

Data sets

A series of 2, 5-dihydropyrazolo [4, 3-c] quinoline-3-one

derivatives, with reported activities, was used for the present

study [11]. The experimental PDE4 inhibitory activity of the data

set was measured on isolated guinea pig ventricular PDE4. The 50%

inhibitory concentration (IC50) is defined as the micro molar

concentration of PDE4 inhibitor necessary to reduce PDE4 activity

by 50%, relative to a reaction mixture containing PDE4 but no

inhibitor [11]. Table 1 summarizes the molecular structures,

experimental IC50 and -log IC50 (pIC50) of the above mentioned 2,

5-dihydropyrazolo [4, 3-c] quinoline-3-one derivatives.

Molecular modeling

Molecular modeling and CoMFA, CoMSIA analyses were performed

using SYBYL-X 1.2 (Tripos, Inc., St. Louis, MO) running on a

Pentium Dual Core CPU with the Windows XP operating system [19].

Structures of the compounds were drawn in SYBYL-X 1.2 using

compound 25 as the template since it showed highest activity, and

assigned the gasteiger–marselli charges. The structures were then

energy minimized using the Tripos force field with a distance

dependent dielectric constant and the Powell minimizer till a RMS

gradient of 0.001 kcal/(mole A) was achieved.

Training and Test Set Selection

An essential characteristic of a training set is that the

molecules must be orthogonal (i.e., dissimilar from each other).

Surflex-Sim is able to provide the most orthogonal and diverse

set of molecules to be included in the training set [20, 21].

Thus, Surflex-Sim was used to select the training set and test

set. For 2, 5-dihydropyrazolo [4, 3-c] quinoline-3-one

derivatives the 25 molecules were segregated into training and

test set having 22 and 3 molecules, respectively.

Molecular alignment for 3D-QSAR analysis

The common 2, 5-dihydropyrazolo [4, 3-c] quinoline-3-one ring

system of the derivatives was chosen to align all molecules,

while the most active, compound 25 was used as the template for

superimposition (Fig. 1). Each analog was aligned to the template

by rotation and translation so as to minimize the RMSD between

atoms in the template and the corresponding atoms in the analog

using the DATABASE ALIGN option in SYBYL. The aligned compounds

are displayed in Fig. 1. To refine the model, region focusing was

also performed on the best CoMFA and CoMSIA models [22]. This

procedure led us to two different sets of alignment models.

Calculation of CoMFA and CoMSIA descriptors

The initial CoMFA and CoMSIA models were calculated using the

SYBYL-X 1.2 molecular modeling software [19]. For CoMFA, steric

and electrostatic interactions were calculated using a sp3 carbon

atom and a +1 charge as steric and electrostatic probes,

respectively, and Tripos force field with a distance-dependent

dielectric constant at all intersections in a regularly spaced

grid (2 Å). The default value of 30 kcal/mol was set as the

maximum steric and electrostatic energy cutoff. The minimum

column filtering was set to 2.0 kcal/mol to improve the signal-

to-noise ratio by omitting those lattice points where energy

variation was below this threshold. Initially regression analysis

was performed using the cross-validation by leave-one-out (LOO)

method [13]. The non-cross-validated conventional analysis was

done with the optimal number of components equal to that yielding

the highest R2cv, and the corresponding conventional correlation

coefficient R2. Its standard error, and the F ratio were also

calculated. To further assess the robustness and the statistical

confidence of the derived models, boot strapping analysis was

performed [23]. A number of cross-validations, for example, two

and five were carried out and were confirmed thereafter by 100

runs for each cross-validation and average values were found.

Bootstrapping involves the generation of many new datasets from

the original dataset and obtained by randomly choosing samples

from the original dataset. The predictive ability of each 3D QSAR

model was determined from a set of three molecules that were not

included in the model generation. The activity of the test-set

was predicted by the CoMFA model using the predict command. CoMFA

coefficient maps were generated by interpolation of the pair-wise

products between the PLS coefficients and the standard deviations

of the corresponding CoMFA descriptor values.

Five CoMSIA similarity index fields (steric, electrostatic,

hydrophobic, H-bond donor and H-bond acceptor) were evaluated

using the sp3 carbon probe atom with a radius of 1 Å and a +1

charge placed at the lattice points of the same region of grid as

it was used for the CoMFA calculations. A distance-dependent

Gaussian type was used between the grid point and each atom of

the molecule.

The default value of 0.3 was used as the attenuation factor. The

minimum column filtering was set to 1.0 kcal/mol. The statistical

evaluation, predictive ability evaluation and coefficient map

generation for the CoMSIA analysis were carried out in the same

way as described in CoMFA.

Design of ligands and Docking studies

Finally, based on above suggestions, two virtual libraries were

designed by Legion in SYBYL and screened by the optimal CoMFA and

CoMSIA models. Legion is a technique for creating virtual

combinatorial libraries for efficient storage in a UNITY

database, for retrieving them from such databases, and for

generating lists of the individual compounds included in the

combinatorial structure [19]. Thus, Legion is used to generate

new analogues. First, a database of more than three hundreds of

mono-valent substitutes was generated, thereupon virtual

combinatorial library of 2, 5-dihydropyrazolo [4, 3-c] quinoline-

3-one derivatives was created by replacing the mono-valent

substitutes at different positions. Subsequently, the virtual

library was screened by the optimal 3D QSAR models.

For the purpose of further validating the rationality of designed

molecules, docking studies were performed by Surflex-Dock on the

2, 5-dihydropyrazolo [4, 3-c] quinoline-3-one derivatives.

Surflex-Dock uses an empirical scoring function and a patented

search engine to dock ligands into a protein's binding site. It

is particularly successful in eliminating false positive results

and thus provides with docking scores which are more reliable

than those provided by earlier docking tools [21]. Structure of

catalytic domain of human Phosphodiesterase 4d in complex with

cilomilast (1XOM) was downloaded from Protein Data Bank [24]. The

protein structure was prepared using the Biopolymer module with

default parameters. From the protein, all water molecules were

removed and hydrogen atoms were added. The structure was then

optimized using AMBER force field with Kollman atom charges.

Designed molecules were then docked using the standard protocol

for Surflex-Dock.

Results and Discussion

Results of CoMFA and CoMSIA models are shown in Table 2, 3 and 4

respectively. Experimental and predicted pIC50 values of the

training and test set (labeled with an asterisk) compounds are

shown in Table 5.

CoMFA analyses

The structures and biological activities of all the compounds in

the training and test-set are shown in Table 1. The study was

restricted to a limited set of training and test compounds since

these are the only reported 2, 5-dihydropyrazolo [4, 3-c]

quinoline-3-one derivatives as PDE 4 inhibitors. It is worthwhile

to note that the results of Leave-One-Out (LOO), group cross-

validation, test-set prediction, and effectiveness in designing

new analogues; point towards the robustness of the underlying

QSAR model.

The QSAR models are required to be refined based on new

experimental structure–activity relationship (SAR) as and when

made available in the scientific literature. Thus the 3D QSAR

model described here can also be refined depending upon the

availability of additional experimental SAR on PDE4 inhibitors.

Two CoMFA models were generated, model 1 using the molecular

alignment obtained by DATABASE ALIGN option in SYBYL and model 2

by applying region focusing. Detailed statistical results of the

model 1 and 2 are shown in Table 2.

The steric and electrostatic CoMFA fields for model 1 yielded a

cross validated R2 (R2cv) of 0.759 with 4 components; non-cross-

validated R2 of 0.927; SEE 0.314 and F value of 110.257. Region-

focusing (model 2) showed improvement in statistical significance

with R2 (R2cv) of 0.860 with 4 components, non-cross-validated R2

of 0.959; SEE 0.246 and F value of 127.358. The contribution of

steric and electrostatic fields is 55.4% and 44.6%, respectively.

The predictive ability of model 2 is justified by r2pred value of

0.865. Graph of experimental versus predicted pIC50 values

obtained using model 2 for the training and test set compounds is

depicted in Fig. 2. The plot of experimental pIC50 versus

residual for CoMFA (Fig. 3) shows normal distribution of

residuals steric and electrostatic field contour maps are shown

in Fig. 4 with compound No. 25 as reference molecule.

In CoMFA steric map (Fig. 4), green contours indicate regions

where groups with steric bulk increase the activity, while yellow

contours indicate regions where groups with steric bulk decrease

the activity. There is one green contour surrounding the

cyclopentyl group at R position of molecule 25, suggesting that

steric bulk is favored at this site. The presence of green

contour around R is justified by the higher activity of compound

10 (pIC50 = 6.1549) in which the substituent at R position is

cyclopentyl, as compared to compound 1 (pIC50 = 4.4318) having

hydrogen as substituent at R. Also, a yellow contour is seen on

one side of R suggesting steric hindrance due to active site

residues at the location. Thus the orientation of R towards the

green contour and away from yellow contour as seen in molecule 25

and 19 is favorable for activity.

The yellow contour near the R1 position depicts that the steric

occupancy with more bulky groups in this region will decrease the

activity. This finding is justified by higher activity of

compound 14 (pIC50 = 4.0458) having hydrogen at R1 as compared to

compound 16 (pIC50 = 3.7959), having phenethyl group at R1.

For CoMFA electrostatic map (Fig. 4), blue contours indicate

regions where electron positive groups increase activity, and red

contours indicate regions where electron negative groups increase

activity. The blue contour around the R1 position suggests that

substitution with electropositive group at this position will

lead to more active compounds. This is vindictive by the higher

activity of compounds 5 (pIC50 = 5.6778), which possess benzyl

group at the R1 position, as compared to compound 21 (pIC50 =

4.9208) with m-nitrobenzyl group at R1 position. The red

contour located at R position, explains that electronegative

substitution at R position will lead to increase in the activity.

Another red contour near the 3-carbonyl group suggests that the

presence of electronegative atom is essential at this site.

CoMSIA analyses

Several models were generated in case of CoMSIA analysis using

the molecular alignment obtained by DATABASE ALIGN option in

SYBYL and by applying region focusing. In order to obtain the

best statistical results, the combination of the five fields

(steric, electrostatic, hydrophobic, hydrogen bond donor, and

hydrogen bond acceptor) was systemically altered. The model with

best cross-validation and non-cross-validation, smallest error,

and largest F value was chosen.

Detailed statistical results of the generated models are shown in

Table 3. The statistical parameters are summarized in Tables 3

and 4. Best Models were obtained with the combinations of steric,

electrostatic, hydrophobic, hydrogen-bond donor and acceptor

fields. Model 3 yielded R2cv (0.7730) with 5 components, and R2

(0.9170) with SEE of 0.232 and F value of 131.21. Application of

region-focusing (model 3a) resulted in improvement of statistical

significance with R2cv (0.8230) with 5 components, and R2 (0.9740)

with SEE of 0.122 and F value of 145.65. The predictive ability

of model 3a is justified by R2pred value of 0.926. The

contributions of steric, electrostatic, hydrophobic, hydrogen-

bond donor and acceptor fields are 36.3%, 20.0%, 25.4%, 8.3% and

10.0%, respectively. This high contributions of steric and

hydrophobic fields indicate that they play most important role in

PDE4 receptor affinity of 2, 5-dihydropyrazolo [4, 3-c]

quinoline-3-one derivatives.

Fig. 5 shows the experimental versus predicted pIC50 values

obtained using model 3a for the training and test set compounds.

The plot of experimental pIC50 versus residual for CoMSIA (Fig.

6) shows normal distribution of residuals. CoMSIA steric,

electrostatic, hydrophobic, hydrogen-bond donor and acceptor

field contour maps are shown in Fig. 7 with compound 25 as

reference molecule. Since the steric and electrostatic contour of

CoMSIA (Fig. 7) are very similar with that of CoMFA, only

hydrophobic and hydrogen-bond fields are discussed in the

following paragraphs.

The presence of yellow contour at a location in CoMSIA map

suggests that a hydrophobic substituent at that location may

favor activity. On the other hand white contours at a location

indicate that a hydrophobic group at the location will diminish

activity. A big yellow contour near R suggests that hydrophobic

substitutes at R would favor the PDE 4 inhibitory activity. This

finding is well supported by the higher activity of compound 8

(pIC50 = 5.1549) where R is substituted by hydrophobic benzyl

group, as compared to compound 9 (pIC50 = 5.000) where R is

substituted by a lesser hydrophobic group i.e. phenethyl.

The presence of yellow contour near R1 positions suggests the

importance of hydrophobic group at this location. The higher

activity of compound 23 (pIC50 = 5.5686) bearing thienylmethyl

group, over compound 21 (pIC50 = 4.9208) bearing m-aminobenzyl

group justifies the above finding.

H-bond donor and acceptor contour maps (Fig. 7) demonstrated that

in addition to steric, electrostatic and hydrophobic properties,

H-bond donors and acceptors in 2, 5-dihydropyrazolo [4, 3-c]

quinoline-3-one derivatives can also play important role in PDE 4

inhibition. These hydrogen bonds can help in keeping the tri-

cyclic ring, R and R1 substituent in proper orientation to

optimize other type (steric, electrostatic and hydrophobic) of

interaction with the active site residues. In Fig. 7, there is a

big cyan contour covering the region around R which indicates

that hydrogen-bond donors in this region favor the PDE 4

inhibitory activity. In Fig. 7, magenta contours surrounding the

region near the 3-carbonyl oxygen and R1 suggest that the

presence of hydrogen-bond acceptor at these two positions favors

the PDE 4 inhibitory activity.

Design of Ligands and Docking

Ligand-based method such as 3D QSAR (CoMFA and CoMSIA) is widely

used not only because it is computationally less intensive but it

can also lead to the rapid generation of QSARs from which the

biological activity of newly designed molecules can be predicted.

In contrast, an accurate prediction of activity of untested

molecules based on the computation of binding free energies is

both complicated and lengthy.

The developed 3D QSAR models could be a clear indicator to

intuitionist medicinal chemist for predicting novel molecules

with enhanced PDE4 inhibitory activity. Also, a consistent

observation is that, minor changes in the tricyclic substitutions

produces significant differences in activity. Hence, further

design of new molecules was a challenging task. Critical

interpretation of the 3D QSAR models resulted in the

identification of key structural features which could be

exploited for improving the potency of the reference molecules

i.e., 19 and 25.

The importance of cyclopentyl at R; and cyclohexylmethyl and 2-

thienylmethyl groups at R1 is clearly evident by the higher

activity of molecules 19 and 25. Taking into consideration the

information obtained from CoMFA and CoMSIA models and by applying

the principles of bioisosterism; substitution were proposed on

the tricyclic ring system at R and R1 positions which may

increase the activity. Thus, we can predict few molecules (S1-S6)

that may be the next synthetic targets (Table 6). The cyclopentyl

group at R position of molecule 25 and 19 was replaced by

tetrahydro-pyran-2-yl, tetrahydro-pyran-3-yl and tetrahydro-

pyran-4-yl groups with the intent to increase steric bulk,

lipophilicity and electronegativity (oxygen). On the other hand

2-Thienylmethyl and cyclohexylmethyl groups at R1 position of

molecule 19 and 25 were replaced with 1,1-dimethyl-silinan-4-yl

group and 1,1-dimethyl-silinan-3-yl group with the objective of

reducing steric bulk, increasing lipophlicity and

electropositivity (Silicon).

To validate the results of 3D QSAR studies and design of

molecules based on them, docking studies were performed by

Surflex-Dock on the designed derivatives. The molecules S1-S6

were docked into the active site of phosphodiesterase 4d occupied

by cilomilast in the crystal structure (1XOM). The results of

docking studies (docking scores) were found to be in agreement

with the activities predicted by CoMFA and CoMSIA models (Table

6). Fig. 8 illustrates the docked pose of molecule S1, the oxygen

of pyran ring and 3-carbonyl is involved in hydrogen bonding

interactions with Asn321 and Tyr159 respectively. The additional

hydrogen bonding interaction due to pyran oxygen may be

responsible for higher activity of S1, apart from other factors

already discussed. Similar binding interactions are displayed by

other designed compounds also.

Moreover, the proposed molecules also fulfill the conditions of

Lipinski’s rule of five for oral bioavailability. Predicted -

log1/IC50 for these proposed molecules against PDE4 along with

their ClogP, molecular weight, and the number of hydrogen bond

acceptors and donors (conditions of Lipinski’s “rule of five”)

are given in Table 6.

Conclusions

Predictable and statistically significant CoMFA and CoMSIA models

of 2, 5-dihydropyrazolo [4, 3-c] quinoline-3-one derivatives as

PDE4 inhibitors were developed. The analysis of developed CoMFA

models revealed that PDE4 binding affinity of this class of

molecules is greatly influenced by the functional groups attached

to different positions of the basic skeleton. For the dataset of

25 molecules steric factors along with conformation of the

molecules appears to be the major governing factor for PDE4

receptor binding. Electrostatic features also seem to play

important role. Evaluation of CoMSIA models revealed the

importance of hydrophobicity apart from the steric and

electrostatic fields as seen in CoMFA. Information obtained

from CoMFA and CoMSIA was used to design novel molecules. The

predicted PDE4 inhibitory activity of the newly designed

molecules was found to be quite similar based on both the CoMFA

and CoMSIA models. Docking studies of the designed compounds

revealed how they can make additional interactions with the PDE 4

active site residues. The docking scores also agreed considerably

with the results of CoMFA and CoMSIA. Further the present study

succeeded in designing CoMFA and CoMSIA models that will guide

synthetic medicinal chemist to design and synthesize new

molecules with increased biological activity than existing

compounds.

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[23] Vong, R.; Geladi, P.; Wold, S.; Esbensen, K.J. Source

contributions to ambient aerosol calculated by discriminat

partial least squares regression (PLS). J.Chemom. 1988, 2, 281-

296.

[24] RCSB Protein Data Bank [http://www.rcsb.org/pdb].

Figure Captions

Figure 1 Database alignment used in 3D-QSAR studies with compound

25 as the template ligand

Figure 2 Graphical representation of comparison between

experimental and predicted activities using CoMFA for training

and test sets respectively.

Figure 3 Dispersion plot of residuals for CoMFA

Figure 4 CoMFA ‘STDEV*COEFF’ contour maps a: steric: green and

yellow polyhedra indicate regions where steric bulk will enhance

and reduce the binding affinity respectively. b: Blue and red

polyhedra indicate regions where positive charge or negative

charge respectively will enhance the affinity. For ease of

visualization compound 7 was displayed in the maps.

Figure 5 Graphical representation of comparison between observed

and predicted activities using CoMSIA for training and test sets

respectively.

Figure 6 Dispersion plot of residuals for CoMSIA

Figure 7 CoMSIA contour maps a: steric: Compound 20 was shown

inside the field, Green and yellow polyhedra indicate regions

where more steric bulk or less steric bulk, respectively, will

enhance the affinity. b: electrostatic: Blue and red polyhedra

indicate regions where positive charge or negative charge

respectively will enhance the affinity. c: hydrophobic: Yellow

and white polyhedra indicate regions where hyrophobicity and

hydrophilicity respectively will enhance the affinity. For ease

of visualization compound 20 was displayed in the maps. d: H-bond

donor: cyan contour indicates regions where hydrogen-bond donor

groups increase activity e: H-bond acceptor: Magenta contour

indicates regions where hydrogen-bond acceptor groups increase

activity.

Figure 8 Surfelx Dock-predicted binding mode of compound S1 PDE

4d active site. For clarity, only the polar hydrogens are shown.

Hydrogen bonds are shown as dotted red lines. Active site amino

acid residues are represented as lines with the atoms colored as

(carbon: white, hydrogen: cyan, nitrogen: blue, oxygen: red and

sulfur: yellow) while the inhibitor is shown as stick with same

color scheme as above.

Table 1. Structure and experimental pIC50 values for the dataset

Cpd R R1 IC50 (µM) Experimental pIC50

1 H Benzyl 37.00 4.43182 Methyl Benzyl 14.00 4.85393 Ethyl Benzyl 3.00 5.52294 Isopropyl Benzyl 1.20 5.92085 Butyl Benzyl 2.10 5.67786 tert. Butyl Benzyl 2.10 5.67787 Pentyl Benzyl 6.40 5.19388 Benzyl Benzyl 7.00 5.15499 Phenenthyl Benzyl 10.00 5.000010 Cyclopentyl Benzyl 0.70 6.154911 Norbornyl Benzyl 1.40 5.853912 Cyclobutylmethyl Benzyl 0.80 6.096913 Cyclohexylmethyl Benzyl 24.00 4.619814 Butyl H 90.00 4.0458

15 Butyl Phenyl 18.00 4.744716 Butyl Phenenthyl 160.00 3.7959

17 Butyl Cyclohexylmethyl 0.70 6.1549

18 tert. Butyl Cyclohexylmethyl 0.50 6.3010

19 Cyclopentyl Cyclohexylmethyl 0.40 6.3979

20 Butyl m-Nitrobenzyl 11.00 4.958621 Butyl m-Aminobenzyl 12.00 4.920822 Butyl m-Chlorobenzyl 2.30 5.6383

23 Butyl 2-thienylmethyl 2.70 5.5686

24 tert. Butyl 2-thienylmethyl 0.80 6.0969

25 Cyclopentyl 2-thienylmethyl 0.40 6.3979

Table 2. Summary of CoMFA results. R2cv, LOO cross-validated correlation

coefficient; R2, non-cross-validated correlation coefficient; N, number of

components used in the PLS analysis; SEE, standard error of estimate; F value,

F-statistic for the analysis; RF, Region focusing; r2bs, bootstrapping

correlation coefficient; SEEbs, bootstrapping standard error of estimate

CoMFA (Model 1) CoMFA (RF) (Model 2)

R2cv 0.7590 0.8600

R2 0.9270 0.9590

N 4 4

SEE 0.314 0.2460

F 110.257 127.358

Steric 57.0 55.4

Electrostatic 43.0 44.6

Hydrophobic - -

Donor - -

Acceptor - -

R2bs 0.8029 0.8658

SEEbs 0.325 0.294

R2pred - 0.865

25

Table 3. Summary of CoMSIA results. A, Steric field; B, Electrostatic field;

C, Hydrophobic field; D, Donor field; E, Acceptor field; R2cv, LOO cross-

validated correlation coefficient; R2, non-cross-validated correlation

coefficient; N, number of components used in the PLS analysis; SEE: standard

error of estimate; F value, F-statistic for the analysis.

No. CoMSIA Field R2cv N R2 SEE F

3 A/B/C/D/E 0.8230 5 0.9740 0.122 145.65

4 A/B/C/D 0.7634 6 0.8476 0.2299 109.22

5 A/B/C/E 0.7816 6 0.8742 0.1862 110.76

6 A/C/D/E 0.7691 6 0.8536 0.2067 114.34

7 A/B/D/E 0.7217 5 0.8197 0.2974 98.73

8 B/C/D/E 0.6826 5 0.7817 0.3324 94.66

9 A/B/C 0.7485 5 0.8215 0.2742 101.53

10 A/B/D 0.6944 6 0.7506 0.3492 93.19

11 B/C/D 0.6745 5 0.7287 0.3726 84.51

12 B/C/E 0.6282 5 0.6947 0.4127 82.19

13 A/C/D 0.6451 6 0.7102 0.3813 89.34

14 A/C/E 0.6492 5 0.7312 0.3891 82.61

26

Table 4. Summary of CoMSIA models’ results. R2cv, LOO cross-validated

correlation coefficient; R2, non-cross-validated correlation coefficient; N,

number of components used in the PLS analysis; SEE, standard error of

estimate; F value, F-statistic for the analysis; RF, Region focusing; r2bs,

bootstrapping correlation coefficient; SEEbs, bootstrapping standard error of

estimate

CoMSIA (Model 3) CoMSIA (RF) (Model 3a)

R2cv 0.7730 0.8230

R2 0.9170 0.9740

N 5 5

SEE 0.232 0.122

F 131.21 145.65

Steric 32.1 36.3

Electrostatic 19.5 20.0

Hydrophobic 29.2 25.4

Donor 8.9 8.3

Acceptor 10.3 10.0

R2bs 0.8159 0.8528

SEEbs 0.243 0.176

27

R2pred - 0.926

Table 5. Experimental, predicted pIC50 and residuals of training and test set

(marked with asterisk) compounds.

Cpd ExperimentalCoMFA CoMSIA

Predicted Residual Predicte

d Residual

1* 4.4318 4.4836 -0.0518 4.4736 -0.04182 4.8539 4.9552 -0.1013 4.7352 0.11873 5.5229 5.6738 -0.1509 5.6068 -0.08394 5.9208 5.7770 0.1438 5.8577 0.06315 5.6778 5.5234 0.1544 5.5764 0.10146 5.6778 5.6149 0.0629 5.6880 -0.01027* 5.1938 4.9822 0.2116 5.0806 0.11328 5.1549 5.2062 -0.0513 5.0069 0.14809 5.0000 5.0877 -0.0877 5.0757 -0.075710 6.1549 6.0300 0.1249 6.2234 -0.068511 5.8539 5.8966 -0.0427 5.8506 0.003312 6.0969 6.0436 0.0533 6.1435 -0.046613 4.6198 4.6615 -0.0417 4.5906 0.0292

28

14 4.0458 4.1703 -0.1245 4.0954 -0.049615 4.7447 4.5511 0.1936 4.6101 0.134616 3.7959 4.0115 -0.2156 3.8670 -0.071117 6.1549 6.0528 0.1021 6.1344 0.020518 6.3010 6.5823 -0.2813 6.5809 -0.279919 6.3979 6.7266 -0.3287 6.7296 -0.331720 4.9586 5.1105 -0.1519 5.0205 -0.061921* 4.9208 4.9908 -0.0700 5.0228 -0.102022 5.6383 5.5029 0.1354 5.5089 0.129423 5.5686 5.4073 0.1613 5.3673 0.201324 6.0969 6.0179 0.0790 6.0167 0.080225 6.3979 6.4247 -0.0268 6.4978 -0.0999

*Test set compounds

Table 6. Structure, Predicted pIC50 and rule of five criteria for the proposed

molecules.

No. R R1 pIC50

(Predicted

Eq. 3)

pIC50

(Predicted

HQSAR)

Clog

P

MW HBD HBA

S1 Tetrahydro-

pyran-3-yl

1,1-dimethyl-

silnan-4-yl

6.7110 6.8370 5.03

58

395 0 5

S2 Tetrahydro-

pyran-3-yl

1,1-dimethyl-

silnan-3-yl

6.4135 6.4875 5.03

58

395 0 5

29

S3 Tetrahydro-

pyran-4-yl

1,1-dimethyl-

silnan-4-yl

6.6870 6.7320 4.88

58

395 0 5

S4 Tetrahydro-

pyran-4-yl

1,1-dimethyl-

silnan-3-yl

6.5687 6.6627 4.88

58

395 0 5

S5 Tetrahydro-

pyran-2-yl

1,1-dimethyl-

silnan-4-yl

6.8152 6.7934 4.88

58

395 0 5

S6 Tetrahydro-

pyran-2-yl

1,1-dimethyl-

silnan-3-yl

6.5362 6.4678 4.88

58

395 0 5

Figure 1.

30

Figure 2.

Figure 3.

31

Figure 4.

32

Figure 5.

Figure 6.

33

Figure 7.

Figure 8.

34