sample 1

ORIGINAL ARTICLE

Study of physicooxide synthase ranalogs: Pharma

M a, S

a hilyab hodhac , Devi

Received 25 September 201

KEYWORDS

Group based QSAR;

K nearest neighbor (KNN);

Genetic algorithm (GA);

Simulated annealing (SA)

Abstract A series of substituted quinazolinamine derivatives with potent highly selective inhibitors

logical, and electrotopological descriptors and calculated from the Molecular Design Suite Software

(V-life MDS 3.5). The partial least square (PLS) regression methods coupled with various feature

structure and activity and offers clues for structural modications that can improve the activity.2

shown cross validation (q ) and external validation (pred_r ) values of 0.7966 and 0.7587, respec-

ative (electron-donating) and electropositive groups (electron-withdrawing) substituent at position

* Corresponding author.E-mail addresses: [email protected] (M.C. Sharma),

[email protected] (S. Sharma).

Peer review under responsibility of King Saud University.

Production and hosting by Elsevier

Arabian Journal of Chemistry (2013) xxx, xxxxxx

King Saud University

Arabian Journal of Chemistry

www.ksu.edu.sawww.sciencedirect.comtively. The steric and electrostatic descriptors at the grid points S_479, S_402, E_285 and E_636

play an important role in the design of new molecule. It also suggests the importance of electroneg-Statistically signicant equations with high correlation coefcient (R = 0.8604) and low standard

deviation (SD= 0.281) were developed by GA-PLS and best Group based QSAR (GQSAR) model

high correlation coefcient (R2 = 0.779) and low standard deviation (SD= 0.326) were developed

by GA-PLS method. k nearest neighbor molecular eld analysis combined with various selection

procedures was performed. The 3D-QSAR model built with the selected variables by GA method

resulted in better prediction incase of 3D-QSAR modeling as compared to other two methods. The

best GA-PLS model with good external and internal predictivity for the training and test set has2 2Partial least square;

Stepwise (SW);selection methods, viz., stepwise (SW), genetic algorithm (GA) and simulated annealing (SA) were

applied to derive QSAR models. 2D-QSAR study provides details on the ne relationship linking1,2-Dihydro-4-quinazolin-

amines;

Nitric oxide synthase;

of inducible nitric oxide synthase (i-NOS) were subjected to two dimensional, Group-based QSAR,

k-nearest neighbor molecular eld analysis, and pharmacophore approach. Structural features

responsible for the activity of the compounds were characterized by using physicochemical, topo-18

ht

P

oukesh C. Sharma

School of Pharmacy, Devi ADepartment of Chemistry, CSchool of Chemical Sciences78-5352 2013 Productiontp://dx.doi.org/10.1016/j.arab

lease cite this article in press as:

linamines analogs: Pharmacoph2; accepteand hosti

jc.2013.0

Sharma,

ore identichemical properties-inducible nitricelationship of substituted quinazolinaminescophore identication and QSAR studies

mita Sharma b,*, Pratibha Sharma c, Ashok Kumar c

University, Takshila Campus, Khandwa Road, Indore 452 017, Indiary Dilip Singh Kanya Mahavidyalya Bhind 477001, IndiaAhilya University, Takshila Campus, Khandwa Road, Indore 452 017, India

d 20 January 2013ng by Elsevier B.V. on behalf of King Saud University.

1.018

M.C. et al., Study of physicochemical properties-inducible nitric oxide synthase relationship of substituted quinaz-

cation and QSAR studies. Arabian Journal of Chemistry (2013), http://dx.doi.org/10.1016/j.arabjc.2013.01.018

R1 to increase the activity. The 3D-QSAR study indicates less bulky (at R4 and R5) and more

ups

rom

Th

lin

duc

Pharmacophore modeling correlates activities with the spatialarrangement of various chemical features (Liedl et al., 1996).T evto predict the biological activity o

pounds such as anti-tubercular age2011), antimalarial agents (Sahu etTRPV1 antagonists, anti-Alzheimer

2.2. Dataset division into training and test sets

2 M.C. Sharma et al.

Please cite this article in press as: Sharma,

olinamines analogs: Pharmacophore identieloped a fewQSARmodelsf different groups of com-

nts (Sharma and Sharma,al., 2010a, 2011b, 2012c),s agents (Bhadoriya et al.,

Compounds were sketched using the 2D drawing applicationand converted to 3D structures. Energy minimization and

geometry optimization were conducted using Merck molecularforce eld (Halgren and Nachbar, 1996) and atomic charges,he present group of authors has dhydrophobic substituent gro

hydrogen bond donor, and a

tributing toward the activity.

tural requirements of quinazo

2013 Pro

1. Introduction

Nitric oxide synthases (NOS) (Moncada et al., 1991) are a class

of enzymes found in mammals and other species that utilize L-arginine to generate nitric oxide (NO). NO is an importantsignaling molecule involved in a wide range of physiological

functions, as well as pathophysiological states (Nathan,1997).Three isforms of the enzyme have been characterized.Two of these, endothelial NOS (e-NOS) and neuronal NOS

(n-NOS), are constitutive and calcium dependent. The third iso-form, inducible NOS (i-NOS), is formed in response to patho-logical challenges. It is not dependent on calcium andproduces much higher concentrations of nitric oxide than the

others. Over expression of i-NOS has been implicated in a num-ber of inammatory diseases, for example, septic shock andrheumatoid arthritis. Inhibition of i-NOS should be a useful ap-

proach to treatment of these conditions (Cochran et al., 1996).Nitric oxide acts as a second messenger molecule through theactivation of its main target, soluble guanylatecyclase. In the

cardiovascular system, it is implicated in the regulation of vascu-lar tone, platelet aggregation, and leukocyte adhesion on theendothelial surface. In the central nervous system and in the

peripheral non-adrenergic non-cholinergic nerves, NO is apotent neurotransmitter involved in long-term potentiation, mi-graine, and gastric motility. Elsewhere, Nitric oxide is a cyto-toxic and cytostatic agent associated with phagocytic cells in

the immune system (Kerwin et al., 1995; Pfeiffer et al., 1999;Davis et al., 2001). Nitric oxide is synthesized by oxidation ofthe amino acid L-arginine (L-arg) catalyzed by three distinct iso-

forms of hemeproteins called NO synthases (NOS). The neuro-nal (nNOS) and endothelial NOS (eNOS) are constitutivelyexpressed and are Ca++ and calmodulin (CaM)-dependent en-

zymes, whereas inducible NOS (i-NOS) is expressed in responseto an immune challenge (Alderton et al., 2001; Li and Poulos,2005; Masters et al., 1996). Quantitative structure activity rela-tionships (QSAR) are the most important applications of

chemometrics giving useful information for the design of newcompounds acting on a specic target. A good QSAR modelboth enhances our understanding of the specics of drug action

and provides a theoretical foundation for lead optimization(Kubinyi, 1997).The pharmacophore modeling is a well-estab-lished approach to quantitatively explore common chemical fea-

tures among a considerable number of structures, and aqualied pharmacophore model could also be used as a queryfor searching chemical databases to nd new chemical entities.M.C. et al., Study of physicochemical

cation and QSAR studies. Arabian J(at R3) can improve the activity. The hydrogen bond accepter,

atic carbon, parameter are the important features which are con-

e QSAR models may lead to a better understanding of the struc-

amines compounds and also help in the design of novel molecules.

tion and hosting by Elsevier B.V. on behalf of King Saud University.

2012a,b,2013), antimicrobial activity, antibacterial activity,COX inhibitors (Dhakad et al., 2009; Sharma et al.,

2009a,b,c,d), and angiotensin II receptor antagonists (Sharmaet al., 2009e; Sharma and Kohli, 2011ac; Sharma et al.,2011d,e; Sharma and Kohli, 2011fk; Sharma, 2012; Sharmaand Kohli, 2012a; Sharma et al., 2012al; Sharma et al.,

2013a), Protein Kinase CK2 Inhibitors (Sharma et al., 2013b)and 5-lipoxygenase inhibitors (Sharma et al., 2013c), antitumoractivity (Sharma et al.,2013d). In the literature, there are QSAR

studies addressed to inducible nitric oxide synthase (Nagpal andTiwari, 2005).

The aim of the present work is to derive some statistically

signicant QSAR methods two dimensional, Group based-QSAR, k-nearest neighbor and pharmacophore approachallowing exibility to the study of molecular substitution sites

of interest and statistically signicant QSAR models for 2-substituted 1,2-dihydro-4-quinazolinamines for their nitricoxide synthase (i-NOS) inhibitors. The developed model pro-vides insight into the inuence of various interactive elds on

the activity and, thus, can help in designing and forecastingthe nitric oxide synthase (i-NOS) inhibitors activities of 2-substituted 1,2-dihydro-4-quinazolinamines. In combination

with QSAR studies, Group based, k-nearest neighbor and Phar-macophore studies were done using VLife MDS molecularmodeling suite (VLife MDS 3.5, 2008).

2. Materials and method

All computations and molecular modeling studies (3D-QSAR)

were carried out on a Windows XP workstation using themolecular modeling software package VLife Molecular DesignSuite (V-Life MDS) version 3.5.

2.1. Dataset and biological activity for analysis

A series of twenty-two compounds substituted 1,2-dihydro-4-quinazolinamines potent highly selective inhibitors of induc-ible nitric oxide synthase (i-NOS) which show anti-inamma-

tory activity in vivo and described in the literature wereconsidered to perform the QSAR study (Tinker et al., 2003).The molar inhibitory concentrations of the compounds were

converted into pIC50 before being correlated with the struc-tural features (physicochemical descriptors). Table 1 showsthe structure of Twenty-two such compounds along with theirbiological activity values.properties-inducible nitric oxide synthase relationship of substituted quinaz-

ournal of Chemistry (2013), http://dx.doi.org/10.1016/j.arabjc.2013.01.018

e de

R2

R1

Com

H5

Study of physicochemical properties-inducible nitric oxide synthase relationship of substituted quinazolinamines analogs: 3Table 1 Structures of substituted 1,2-dihydro-4-quinazolinamin

N

HN

R3

R4

R2

R1 NH2

Compound 1-14

Comp. NO R1 R2 R3 R4

1 H H H C6maximum number of cycles were 1000, convergence criteria(RMS gradient) was 0.01 and mediums dielectric constant of

1 by batch energy minimization method. Conformationalsearch of each energy-minimized structure was performedusing stochastic approach. Stochastic conformational search

method is similar to the RIPS method, which generates newmolecular conformation by randomly perturbing the positionof each coordinate of each atom in the molecule followed by

energy minimization. Selection of the training and test setfor the QSAR model was done by considering the fact thatthe test set compounds should represent structural diversityand a range of biological activities similar to those of the train-

ing set. The sphere exclusion method (Golbraikh and Tropsha,2003) was adopted for the division of training and test data set

2 H H H H

3 H H H CH34 H H H C2H55 H H H c-prop

6 H H H c-buty

7 H H H c-pent

8 H H H 2-fura

9 H H H 2-thien

10 H H CH3 CH311 H H CH3 C2H512 F H H c-buty

13 F F H 2-fura

14 F H H FPh

15 F H H H

16 Cl H H H

17 F H H H

18 F H H H

19 F H H H

20 F F H H

21 F F H H

22

SN

HN

NH2

N

O

OEt

a pIC50 to generate equation.b Indicates the compounds considered in the training and test set for Q

Please cite this article in press as: Sharma, M.C. et al., Study of physicochemical

olinamines analogs: Pharmacophore identication and QSAR studies. Arabian Jrivatives with activities.

N

HN

NH2

N

O

R5

pound 15-21

R5 IC50 pIC50a QSAR setb

H 2.5 0.397 Trainingcomprising 22 molecules, with a dissimilarity value of 3.8where the dissimilarity value gives the sphere exclusion radius

Table 1. The dataset was split randomly into a training set (17compounds) for generation of models and a test set (ve com-pounds) for validation of the developed model.(see Table 2)

2.3. Molecular modeling

2.3.1. 2D-QSAR studies and calculation of 2D descriptor

2D-QSAR study requires the calculation of molecular descrip-tors; almost 239 physicochemical descriptors were calculatedby QSAR Plus module within VLife MDS. The invariable

descriptors (descriptors that are constant for all the molecules)were removed, as they do not contribute to the QSAR. Variable

H 40 1.602 Training

H 9.8 0.991 Test

H 0.9 0.045 Trainingyl H 1.1 0.041 Training

l H 1.1 0.041 Test

yl H 4.5 0.653 Training

nyl H 0.2 0.698 Trainingyl H 0.4 0.397 Training

H 52 1.716 Test

H 32 1.505 Training

l H 0.02 1.698 Trainingnyl H 0.002 2.698 Training

H 0.05 1.301 TrainingOEt 0.035 1.455 TestOEt 0.047 1.327 TrainingO(CH2)2SMe 0.039 1.408 Training3-thienyl 0.067 1.173 Training4-CNPh 0.027 1.568 Test4-CNPh 0.048 1.318 Training6-CN3-pyridyl 0.037 1.431 Training

0.3 0.522 Training

SAR study.

properties-inducible nitric oxide synthase relationship of substituted quinaz-


Table 2 Selected physico-chemical parameters of substituted 1,2-di

36

636

614

465

423

435

464

556

486

141

076

482

045

194

856

929

599

660

691

645

640

036

187

4 M.C. Sharma et al.SsClE T_C_O_1 SdsNE E_6

0 1 9.474691 0.0

2 2 10.1818 0.0

2 2 10.8365 0.1

3 3 12.69831 0.0

2 0 13.40541 0.1

3 2 10.8365 0.1

1 2 10.8365 0.0

3 2 13.17336 0.1

1 0 11.82806 0.2

0 1 13.40541 0.1

3 2 11.66493 0.0

1 2 14.27566 0.03 3 17.69831 0.0

0 2 7.681798 0.0

3 0 8.552042 0.0

2 3 9.259149 0.0

2 0 10.8365 0.0

3 1 10.67336 0.0

2 0 12.25071 0.0

2 1 11.75915 0.0

3 3 10.95782 0.1

2 1 9.36987 0.2selection was performed using Genetic algorithmmethodology.2D descriptors were calculated which encoded different aspects

of molecular structure consisting of electronic, thermodynamic,spatial and structural descriptors, e.g., retention index (chi),atomic valence connectivity index (chiV), path count, chain path

count, cluster, path cluster, element count, estate number, semi-empirical, molecular weight, molecular refractivity, slogP, andtopological index. The various alignment-independent (AI)

descriptors (Baumann, 2002) were also calculated. In this studyto calculate AI descriptors, we have used the following attri-butes, 2 (double bonded atom), 3 (triple bonded atom), C, N,O, S, H, F, Cl, Br and I and the distance range of 07.

The QSAR models are considered acceptable (Golbraikhand Tropsha, 2002) if they satisfy all of the following condi-tions: (i) Q2 > 0.5, (ii) R2 > 0.6. The Q2 value provided the

statistical signicance and predictability of the models, beingused as a criterion for both robustness and predictive abilityof the model. The high Q2 value (for instance Q2 > 0.5) sug-

gests that the models will be appropriate for meaningful pre-dictions (Gramatica, 2007; Moorthy et al., 2011). Standarderror of estimate (smaller is better) indicates how well theregression function predicts observed data. The low standard

error of pred_r2se, q2se and r2se shows the absolute qualityof tness of the model. The generated QSAR model was vali-dated for predictive ability inside the model by using cross val-

Table 3 Correlation matrix between descriptors present in the 2D

Parameter SsClE-index SdsNE-

SsClE-index 1.0000

SdsNE-index 0.7622 1.0000

HOMO energy 0.4116 0.6398

T_C_O_1 0.4992 0.7941


olinamines analogs: Pharmacophore identication and QSAR studies. Arabian Jhydro-4-quinazolinamine derivatives.

E_285 S_479 H_183

75 3.73928 0.08157 0.1234116 3.36961 0.0892 0.14784355 0.240786 0.06873 0.15139375 7.3999 0.09992 0.16106479 2.189978 0.08432 0.17110983 0.450164 0.07769 0.10705702 0.495674 0.07258 0.13344493 2.609555 0.09124 0.14407871 0.418825 0.08233 0.14946356 2.510523 0.09233 0.13941129 1.90702 0.08265 0.1576163 7.161841 0.10938 0.15478175 8.109874 0.22703 0.22045908 3.35415 0.05987 0.07343307 4.83301 0.08557 0.09909664 2.9282 0.08128 0.12291597 2.6743 0.08076 0.15586774 4.12694 0.06942 0.16544602 2.37852 0.08765 0.20112317 2.96916 0.09315 0.18286332 4.42591 0.08004 0.14168341 4.14896 0.04128 0.265874idation (LOO) for q2 and external validation pred_r2, which isa more robust alternative method used by dividing the data

into training set and test set and calculating pred_r2. The highpred_r2 and low pred_r2se showed the high predictive ability ofthe model. The values (pred_r2 and pred_r2se) related to exter-

nal validation are shown in all developed models. Furtheranalysis shows that for all compounds the error (residual va-lue) is very small. Residuals values (difference between actual

and predicted activities) were found to be minimal. Smallervalues of residuals show the high predictive power of QSARmodels.

2.3.2. Group based (G-QSAR) QSAR

Group based QSAR allows establishing a correlation of chem-ical group/fragment variation at different molecular sites ofinterest with the biological activity. Fragmentation can be

done by applying specic chemical rules for breaking the mol-ecules along the specic bonds and/or bonds on ring fusionand/or any Pharmacophoric feature such as hydrogen bond

acceptor, hydrogen bond donor, hydrophobic group, chargedgroup etc. The suggested important fragments can be used asbuilding blocks to design novel molecules (Ajmani et al.,

2009; Ajmani et al., 2010).For Group based QSAR method, various 2D descriptors (as

discussed in 2D QSAR), were calculated for various groups

QSAR model.

index HOMO energy T_C_O_1

1.0000

0.8163 1.0000

properties-inducible nitric oxide synthase relationship of substituted quinaz-


2006) using the most active molecule as reference Quinazolin-amines as a template. Fig. 1b shows alignment of training set

Study of physicochemical properties-inducible nitric oxide synthase relationship of substituted quinazolinamines analogs: 5Figure 1a Quinazolinamines (template).present at different substitution sites of the molecules (i.e.

Fragment R1 and R2) and applied on a selected set of 360descriptors which include group-based and interaction termdescriptors. Various 2D descriptors (as discussed in 2D

QSAR), like element counts, molecular weight, molecularrefractivity, topological index, electro-topological index, Bau-mann alignment independent topological descriptors, etc. were

calculated for various groups present at different substitutionsites of the molecules. Since the same descriptors are calculatedfor various groups at different sites the following nomenclatureis used for naming a descriptor at a particular position, for

example R1_smr represents the molar refractivity of the grouppresent at substitution site R1(Sahu et al., 2011).

2.3.3. Three dimensional QSAR

The molecular alignment utility can be used to study the shapevariation with respect to the base structure selected for align-ment. The alignments dene the putative pharmacophore for

the series of ligands. Alignment of all 22 compounds was doneusing the template-based alignment. In the template-basedalignment, a template structure was dened and used as a basis

for the alignment of a set of molecules. The reference moleculeis chosen in such a way that it is the most active among the ser-ies of molecules considered. Compound 13 possessed very

highly selective inhibitors of inducible nitric oxide synthase(i-NOS) which made it a valid lead molecule and, therefore,was chosen as a reference molecule. Optimized molecules werealigned Fig. 1a, by template based method (Ajmani et al.,

Figure 1b 3D view of aligned Quinazolinamines molecules.


olinamines analogs: Pharmacophore identication and QSAR studies. Arabian Jmolecules used in 3D-QSAR models.

For the purpose of 3D QSAR analysis, chemical structuresof all the compounds have been drawn using the builder mod-ule in Molecular Design Suite 3.5 software package. Molecular

descriptors such as steric, electrostatic and hydrophobic eldshave been calculated utilizing MDS 3.5 software which allowsthe user to choose probe, grid size, and grid interval for the

generation of descriptors. In this study, we have generated acommon rectangular grid around the molecules, interactionenergies are computed at the lattice points of the grid usinga methyl probe of charge +1.

For calculation of 3D eld descriptor values, using Triposforce eld (Clark et al.,1989) and Gasteiger and Marsili chargetype (Gasteiger and Marsili, 1980), electrostatic, steric and

hydrophobic eld descriptors were calculated. The dielectricconstant was set to 1.0 considering the distance dependentdielectric function. The software produces more than 6000

descriptors and prior to model development descriptors havingzero values or same values are removed.

2.3.4. Pharmacophore approach

3D pharmacophore modeling is a technique for designing theinteraction of a small molecule ligand with a macromoleculartarget. VLife MDS Mol Sign Module is used for the identica-

tion, generation and analysis of pharmacophore by aligningsmall organic molecules based on their 3D pharmacophorefeatures.

In the present study, we have generated pharmacophoremodel using mol sign software for a diverse set of moleculesas 2-substituted 1,2 dihydroquinazolinamines with an aim toobtain pharmacophore model that would provide a hypothet-

ical picture of the chemical features responsible for activity. All22 aligned molecules were taken for pharmacophore develop-ment and the most active molecule was selected to set it as ref-

erence. The reference molecule is the molecule on which theother molecules of the align dataset get aligned. For ve pointpharmacophore identication tolerance limit was set up to

30 A and max distance allowed between two features, settingthe value to 5 A. This abstract model, containing chemicalfunctionalities (such as hydrogen bond donor, hydrogen bondacceptor and aromatic carbon center) can serve as an effective

search lter for virtual screening.

2.3.5. Model validation

Internal validation was carried out using leave-one-out (q2)method. To calculate q2, each molecule in the training setwas sequentially removed, the model ret using same descrip-tors, and the biological activity of the removed molecule pre-

dicted using the ret model.Models generated by 2D and 3D-QSAR studies were cross

validated using the standard LOO procedure (Cramer et al.,

1988; Assefa et al., 2003). The cross validated r2 (q2) valuewas calculated using equation-1, where yi and yi are the ac-tual and predicted activities of the ith molecule, respectively,

and ymean is the average activity of all molecules in the train-ing set. Because of the calculation of the pair-wise molecularsimilarities, predictions were based upon the current trial

solution, the q2 obtained is indicative of the predictive powerproperties-inducible nitric oxide synthase relationship of substituted quinaz-


of the current kNNMFA model. The q2 was calculatedusing Eq. (1).

q2 1Pyi yî2Pyi ymean2

1

All cross-validation studies were performed by considering thefact that a value of q2 is above 0.5 indicating high predictivepower of generated QSAR models.External validation of gen-

erated models was carried out by predicting the activity of testset of compounds. The predicted r2 (pred_r2) value was calcu-lated using Eq. (2), where yi and yi are the actual and predictedactivities of the ith molecule in test set, respectively, and ymeanis the average activity of all molecules in the training set. Thepred_r2 value is indicative of the predictive power of the cur-rent kNNMFA model for external test set. The pred_r2 value

is calculated as follows (Eq. (2))

Pred r2 1Pyi yî2Pyi ymean2

2

A model is built using this random descriptor selection withLOO cross-validation (Oloff et al., 2005), where each com-

pound is eliminated from the training set and its biologicalactivity is predicted as the average activity of its k most similarmolecules (usually k= 1 4). The value k is optimized duringthe model building process to give the best prediction for thetraining set. The similarity is characterized by the Euclideandistance between compounds in the multidimensional space

of selected descriptors.

yî

Xkj1

ajwij

Xkj1

wij

;

where aj was the observed activity of the jth compound, and

weights wij are dened as

Wij 1 dijXkj1

dij

0BBBB@

1CCCCA

,and dij was the distance between compound i and its jth near-est neighbor.

3. Results and discussion

Molecular modeling and Pharmacophore studies of the 1,2-

dihydro-4-quinazolinamines series resulted in several QSARequations. Some statistically signicant QSAR models werechosen for discussion. The predicted (LOO) activities of the

compounds by the above models are shown in Table 4. TheQSAR models for inducible nitric oxide synthase which showanti-inammatory activity are the following.

3.1. Interpretation of 2D QSAR model

itut

Gr

Pr

0

1

1

00

0

0

001

1

12111111110

6 M.C. Sharma et al.Table 4 Comparative observed and predicted activities of subst

Com pIC50 2D based QSAR model

Pred. Res.

1 0.397 0.447 0.052 1.602 1.545 0.057

3 0.991 1.046 0.0554 0.045 0.115 0.075 0.041 0.014 0.027

6 0.041 0.111 0.077 0.653 0.599 0.054

8 0.698 0.822 0.1249 0.397 0.314 0.08310 1.716 1.792 0.07611 1.505 1.433 0.072

12 1.698 1.747 0.04913 2.698 2.748 0.0514 1.301 1.248 0.05315 1.455 1.375 0.0816 1.327 1.266 0.06117 1.408 1.374 0.03418 1.173 1.070 0.10319 1.568 1.485 0.08320 1.318 1.256 0.06221 1.431 1.362 0.06922 0.522 0.489 0.33Res. = Obs. pIC50Pred. pIC50.Please cite this article in press as: Sharma, M.C. et al., Study of physicochemical

olinamines analogs: Pharmacophore identication and QSAR studies. Arabian JpIC50 =+0.2917(0.0286) SsClE-index +0.8346(0.0734)

SdsNE-index 0.4067(0.1753) HOMO energy+0.4997(0.0631) T_C_O_1 + 2.8631 [2D QSAR Model-1]

ed 1,2-dihydro-4-quinazolinamine derivatives.

oup based QSAR model Genetic algorithm-PLS

ed. Res. Pred. Res.

.423 0.026 0.363 0.034

.531 0.071 1.549 0.053

.016 0.025 0.887 0.104

.078 0.033 0.118 0.073

.062 0.021 0.083 0.042

.097 0.056 0.157 0.116

.701 0.048 0.748 0.095

.582 0.116 0.575 0.123

.358 0.039 0.276 0.121

.632 0.084 1.696 0.02

.478 0.027 1.461 0.044

.612 0.086 1.629 0.069

.608 0.09 2.632 0.066

.272 0.029 1.216 0.085

.394 0.061 1.385 0.07

.311 0.016 1.308 0.019

.354 0.054 1.297 0.111

.093 0.08 1. 112 0.141

.503 0.065 1.589 0.021

.303 0.015 1.293 0.025

.341 0.09 1.334 0.097

.504 0.018 0.474 0.048properties-inducible nitric oxide synthase relationship of substituted quinaz-


Ntraining = 17, Ntest = 5, r2 = 0.8604, q2 = 0.7617, F

test = 48.526, r2_se = 0.4281, q2_se = 0.3784, pred_r

2 =0.8128, pred_r2se = 0.3578.

Substantial tetra parametric expression is presented inEq. (1).The statistically signicant 2D-QSAR model usingthe GA-PLS analysis method having 0.8604 as the coef-

cient of determination (r2) was considered. This equationshows a correlation coefcient (r2 = 0.8604) with the overallinternal statistically signicant level being better than 95%

as the calculated variance ratio, i.e., the Fischer value (F)exceeded the tabulated value F= 48.526. It shows an inter-nal predictive power (q2 = 0.761) of 76% and a predictivityfor the external test set (pred_r2 = 0.812) of about 81%.

The correlation matrix is shown in Table 3 which showsgood correlation of selected parameters with biological

gests the methoxy group to be attached to thequinazolinamine moiety. Moreover of additional descriptors,

one is R1-T_C_F_1 which is the count of the number of car-bon atoms (single, double or triple bonded) separated fromuorine by a single bond and showed positive contribution

with a value of 35% revealing the increases in activity withthe presence of uorine atoms on quinazolinamine fragmentR1 as indicated in compounds 1215 and 1721. The most

contributing descriptor in part-R2 is R2-SsCH3E-index(20%) which signies the number of eCH3 group connectedwith one single bond. This descriptor showing positive contri-bution in the selected model revealed the increase of Inducible

Nitric Oxide Synthase of quinazolinamines with the presenceof CH3 group such as in compounds 3, 10 and 11.The descrip-tors R2-StNE-indexare directly contributing to the activity,

Study of physicochemical properties-inducible nitric oxide synthase relationship of substituted quinazolinamines analogs: 7activity. Model-1 reveals that the descriptor T_C_O_1 plays

the most important role (26%) in determining the potentinducible nitric oxide synthase. Descriptor T_C_O_1 havingpositive contribution in the QSAR model reveals that for

maximal activity, O-Me, hydroxy group should be directlyattached with substituted quinazolinamines and an increasein R3 position on quinazolinamines moiety and O-Me, hy-

droxy group will increase the activity. The next descriptorSsClE-index (10.3%), contribution which represents elec-trotopological state indices for a number of chlorine atomsconnect with one single bond. The positive coefcient of the

descriptor suggests that the inducible nitric oxide synthaseactivity of quinazolinamine derivatives may be increasedby increasing the number of chlorine atoms present in the

nucleus. The above results are in close agreement with theexperimental observations where compounds 16 substitu-entCl group at the R1 produce a high activity. Another es-

tate contribution descriptor SdsNE-index (17.8%) whichrepresents electro-topological state indices for a number ofnitrogen atom connected at two double bond and one single

bond is directly proportional to the activity. It shows thatthe nitro group in substituted quinazolinamine derivativesis essential for the activity. This type of descriptor showsthe importance of the presence of electron environment on

substituted quinazolinamine derivatives increasing the activ-ity. Lastly HOMO energy contributes negatively to theactivity. An electron-donating substituent, such as hydroxy,

or methoxy group, on the ring increases the energy of theHOMO orbital. Electron-withdrawing substituents, such ashalogens, lower the energy of HOMO. The contribution

chart for 2D-QSAR model is shown in Fig. 2a and plotsof observed vs. predicted values of pIC50 are shown in

Figure 2a Plot of contribution chart 2D QSAR model.Please cite this article in press as: Sharma, M.C. et al., Study of physicochemical

olinamines analogs: Pharmacophore identication and QSAR studies. Arabian JFig. 2b. The predicted activities of the compounds by theabove model are shown in Table 4.

3.2. Interpretation of group based QSAR model

pIC50 = 0.8411(0.3761)R1-SssOE-index+0.3621(0.0380)R2-SsCH3E-index0.6864(0.0973) R2-StNE-index+0.4216(0.0651) R1-T_C_F_1 [Group based QSAR Model-2]

Ntraining = 17, Ntest = 5, r2 = 0.7795, q2 = 0.6916, F

test = 27.5431, r2 se = 0.2172, q2 se = 0.3758, pred_r2 =

0.7281, pred_r2se = 0.4173, ZScore Q2 = 1.3271, Best RandQ2 = 0.6328.

The statistically best signicant Group based QSAR model

using the GA-PLS method having 0.779 as the coefcient ofdetermination (r2) was considered. Model 2 can explain77.95% of the variance in the observed activity values. It

shows good internal predictive power (q2 = 0.6916) of 69%and a predictivity for the external test set (pred_r2 = 0.7281)of about 72%.The conventional QSAR model indicates thesignicance of basic molecular properties such as SssOE-index,

SsCH3E-index, StNE-index and T_C_F_1. The most contrib-uting descriptor in part-R1 is the positive coefcient of R1-SssOE-index, and R1-T_C_F_1showed that increases in the

values of these descriptors is benecial for the activity. Thepresence of descriptor R1-SssOE-index which is the electrotopological state index for the number of oxygen atoms con-

nected with two single bonds showed a positive contributionof 27% activity. Such positive effect indicated that the potentinducible nitric oxide synthase was increased with the presenceof methoxy groups in compounds. This indicates that in-

creased SssOE-index of fragment R1 shows the role and sug-

Figure 2b Graph of observed versus predicted activity of QSAR

model ( -training set, -test set).properties-inducible nitric oxide synthase relationship of substituted quinaz-


suggested that an increase in these descriptors 4-cyanophenyl-group at R5 position (like in compounds 19 and 20) of frag-ment R2 may lead to an increase in the activity. The

contribution chart for 2DQSAR model is shown in Fig. 2cand plots of observed vs. predicted values of pIC50 are shownin Fig. 2d. The predicted activities of the compounds by the

above model are shown in Table 4.

3.3. Interpretation of 3D QSAR models

For a better understanding of the QSAR models of these qui-nazolinamine compounds, an attempt to generate 3D-QSARmodels has also been made. Comparing the three different fea-

ture selection methods, it is implicit that the model built withthe selected variables by genetic algorithm (GA) method givesbetter prediction in the case of 3D QSAR modeling. This supe-riority of the genetic algorithm based QSAR model leads us to

explain the effect of steric, electrostatic and hydrophobic eldson different substituents of quinazolinamine moiety. The train-ing and test sets selected for such study are the same as has

been considered in 2D QSAR models for an effective compar-ison between genetic algorithms (GA), simulated annealing(SA), and stepwise (SW) selection methodologies.

pIC50 = 0.0875 S_479 (0.7168, 0.0415) S_402(0.1229, 0.1069) E_285 (1.6053, 1.1781) + E_636(0.0430, 0.1476). [Genetic Algorithms-PLS 3]

k Nearest Neighbor= 4, Ntraining = 17, Ntest = 5, q2 =

0.7966, q2 se = 0.2176, pred_r2 = 0.7587, pred_r2se = 0.2437,ZScore Q2 = 1.1764, Best Rand Q2 = 1.762

Here, n represents the number of observations, df is thedegree of freedom, r is the square root of the multiple R-squared for regression, q2 is the cross-validated r2, and F is

the F-statistic for the regression model. The statistically bestsignicant model (Eq. (3) using the GA-PLS analysis methodwith 0.8208 as the coefcient of determination (r2) and stan-

dard error of 0.2276 was considered. The variance in the ob-served activity values is 82.08%. The best q2 of PLS analysiswas found to be 0.7966, which suggests that the model could

be useful for predicting inducible nitric oxide synthase activ-ity for such quinazolinamine derivatives. S_479, S_402, E_285and E_636 are the steric and electrostatic eld energy ofinteractions between probe (CH3) and compounds at their

corresponding spatial grid points of 479, 402, 285 and 636.3D data points were generated that contribute to GA-PLS3D-QSAR model, and are shown in Fig. 3a. The range of

property values for the generated data points helped in thedesign of potent inducible nitric oxide synthase molecules.The range was based on the variation of the eld values at

the chosen points using the most active molecule and its near-est neighbor set. The plot of observed versus predicted activ-ities for the test compounds is represented in Fig. 3b. From

Table 4 it is evident that the predicted activities of all thecompounds in the test set are in good agreement with theircorresponding experimental activities and optimal t isobtained.

Similarly, 3D-QSAR model using PLS regression by simu-lated annealing (Eq. (4)) and stepwise (Eq. (5) based descriptor

8 M.C. Sharma et al.Figure 2d Graph of observed versus predicted activity of

GQSAR model ( -training set, -test set).

Figure 2c Plot of contribution chart Group based QSAR model.Please cite this article in press as: Sharma, M.C. et al., Study of physicochemical

olinamines analogs: Pharmacophore identication and QSAR studies. Arabian JFigure 3a Contribution plot for steric and electrostatic interac-

tions of GA-PLS model.

Figure 3b Graph of observed versus predicted activity of GA-

PLS model ( -training set, -test set).properties-inducible nitric oxide synthase relationship of substituted quinaz-


selection method was developed with the following statisticalparameters:

pIC50 = 0.87643 E_175 (0.2826, 0.9240) S_475(1.0128, 0.4945) + H_414 (0.1720, 0.3115) + E_456(0.2364, 0.5047) [Simulated Annealing -PLS -4 ]

k Nearest Neighbor= 4, Ntraining = 17, Ntest = 5,

q2 = 0.7215, q2 se = 0.1865, pred_r2 = 0.6821, pred_r2se =0.4653,ZScore Q2 = 0.9762, Best Rand Q2 = 0.7632.

pIC50 = S_560 (0.0635, 0.0537) E_269 (10.0000,10.0000) + H_183 (0.2788, 0.3994) E_227 (0.2333,0.3586) [Stepwise Selection -PLS -5 ]

k Nearest Neighbor= 4, Ntraining = 17, Ntest = 5,q2 = 0.6934, q2 se = 0.2652, pred_r2 = 0.6327, pre-

d_r2se = 0.4372, ZScore Q2 = 1.0431, Best Rand Q2 =1.2872For simulated annealing Fig. 3c predictive R2 is found to be

0.6821 with an estimated standard error of 0.4163 whereas

these parameters are estimated as 0.6327 and 0.2418 respec-tively in the case of model developed by stepwise based modelFig. 3d. The contribution plot of steric and electrostatic eld

interactions indicates relative regions of the local elds (stericand electrostatic) around the aligned molecules, leading toactivity variation in the model (Kubinyi, 1993). Green, blue

and yellow balls represent steric, electrostatic and hydrophobiceld effects, respectively. In the QSAR model, steric descrip-tors with positive coefcients represent regions of high sterictolerance; bulky substituent is favorable in this region. Steric

descriptors with negative coefcients indicate that negative ste-ric potential is favorable for increase in the activity and hence arelatively less bulky substituent group is preferred in that re-

gion. Electrostatic eld descriptors with negative coefcientsindicate that negative electrostatic potential is favorable for

near from the R1 position of the quinazolinamine moiety ring.

Study of physicochemical properties-inducible nitric oxide synthase relationship of substituted quinazolinamines analogs: 9Figure 3c Contribution plot for steric, electrostatic and hydro-

phobic interactions of SA-kNN model.

Figure 3d Contribution plot for steric, electrostatic and hydro-

phobic interactions of SW-kNN model.Please cite this article in press as: Sharma, M.C. et al., Study of physicochemical

olinamines analogs: Pharmacophore identication and QSAR studies. Arabian JThis indicates that electronegative groups are favorable on thissite and the presence of electronegative (electron-donating)groups increases the activity of quinazolinamine compounds

(Sahu et al., 2011; Nandi and Bagchi, 2010).These results arein close agreement with the experimental observations that com-pounds 1220 and 21 have chlorine, and uorine groups at R1,

R2, R3 and R4 -positions. These compounds produce a greateractivity due to electronegative substituents on the R1, R2, R3and R4 -positions of the quinazolinamine ring (Tinker et al.,

2003). Presence of electrostatic eld descriptors like E_636 withpositive values is near from the R1 position of the quinazolin-amine ring. This indicates that electropositive groups are favor-

able on this site and the presence of electropositive groupsincreases the activity of quinazolinamine derivatives. Most ofthe compounds (compounds 3, 10, 11, 19 and 20) with a higheractivity having electropositive substitution (CH3) at the R3 and

R4 positions and 4-cyanophenyl group of the quinazolinaminering strongly support the above statement. The positive valuesof electrostatic descriptors suggested the requirement of electro-

positive groups likemethyl, ethyl, propyl, isopropyl and butyl atthe position of the generated data point E_636 around Quinaz-olinamine analogs for maximum activity. This is also well sup-

ported by group based QSAR study. The presence of stericdescriptors S_479, and S_402 with negative values is also nearfrom the R4 and R5 positions of the quinazolinamine moiety

which indicates that less steric or less bulky substituents arefavorable on this site and the presence of less steric substituentsincreases the inducible nitric oxide synthase activity of quinazo-linamine compounds. Negative values in the steric eld descrip-

tor indicated the requirement of negative steric potential forenhancing the biological activity of quinazolinamine analog.

Furthermore, Fig. 3c and d compares the most signicant

favorable and unfavorable features at the position near R3which indicated that electronegative groups at position-3 en-hance the activity, while the presence of electron-withdrawing

groups decreases the activity. From 3D-QSAR model Eq. (5)and Fig. 3d it is observed that the presence of hydrophobicdescriptor H_183 with positive values indicates that less hydro-phobic substituents such as CH3, C2H5, C6H5 etc. are favor-able on this site and the presence of less hydrophobicsubstituents increases the activity of quinazolinaminemolecules.

3.3.1. Interpretation of pharmacophore models

Pharmacophore model for inducible nitric oxide synthaseactivity was generated using the mole sign module of Vlife

3.5 choosing the biophore with the lowest RMSD and generat-ing the pharmacophore. It starts generating properties of mol-ecules and nds the common three Dimensional map of ve to

maximum common properties Fig. 4a. The best aligned Fig. 4bmodel includes ve features viz. three hydrogen bond donorthe increase in the activity and hence a relatively more electro-negative substitution is preferred in that region. Electrostaticeld descriptors with positive coefcients representing regions

indicate that positive electrostatic potential is favorable for anincrease in the activity and hence a relatively less electronega-tive substitution is preferred in that region (Bhatia et al.,2012;

Choudhari and Bhatia, 2012; Sharma and Kohli, 2011acd,e).From 3D-QSARmodel Eq. (3) and Fig. 3a it is observed that

electrostatic descriptors like E_285 with negative coefcient areproperties-inducible nitric oxide synthase relationship of substituted quinaz-


10 M.C. Sharma et al.Figure 4a Pharmacophore features.(HBD-magenta color), one hydrogen bond acceptor (HBA-buff color), and one aromatic region of the structure (orange

color). In hydrogen bond acceptor eld, the magenta (favor-able) is seen near quinazolinamine ring suggests that acceptorgroups in this area are preferred for activity. Compounds with

one or more acceptor groups in this area result in higher induc-ible nitric oxide synthase activity. Furthermore, comparing themost signicant favorable and unfavorable features at R1, R3

positions- near hydrogen bond acceptor indicated that electrondonating groups at positions-R3 and R5 enhance the activity,while the presence of electron-withdrawing groups increasesthe activity. Hydrogen bond donor shows that hydrogen bond

donor makes the highest contribution in the model. The aver-age RMSD of the pharmacophore alignment of each of twomolecules is 0.8361 A. The distances between the pharmaco-

phore sites Fig. 4c were measured in order to conrm their sig-nicance to the activities. The results reveal that the acceptor(acc), donor (don), and aromatic pharmacophore properties

f the GA-based QSAR model leads us to explain the effect ofteric and electrostatic elds on different substituents of qui-azolinamine moiety. The contribution plot of steric, electro-

tatic and hydrophobic eld interactions generated by 3D-SAR shows that electronegative groups at quinazolinaminesoiety are favorable. This nding is in close agreement with

e structures of these compounds, where the presence of elec-opositive groups is found in the quinazolinamine moiety. Itlso suggests that bulky electronegative (electron-donating)roups are favorable at R1-position of the quinazolinamine

mplate. This nding supports the experimental observations,here the presence of bulky electronegative groups at R1 and2-position signies an increase in activities of compounds.

hree-dimensional features, steric, electrostatic and hydropho-ic elds, can be easily identied from the map developed fore best model. Signicant predictive ability of the model ob-

erved for the external test set molecules supports that the de-ived model can be used for the designing of the novel

Figure 4b Aligned Pharmacophoric features.


olinamines analogs: Pharmacophore identication and QSAR studies. Arabian Josn

sQm

thtrag

tewR

Tbth

srare favorable contour sites for both the activities. Distance(33HDr- 31HAc) = 3.4259 A, 31HAc Atom selected- 1CAtom selected, Distance (31HAc-1C) = 4.9099 A; 31HAc

Atom selected 32-HDr Atom selected Distance (31HAc32HDr) = 5.1672 A, 31HAc Atom selected-34HDr Atom se-lected Distance (31HAc 34HDr) = 4.8481 A.

Hence, these results reveal the requirements on the struc-

tural properties and the distances between the pharmacophorecontour sites of the molecules responsible for their induciblenitric oxide activities.

4. Conclusion

The present studies were aimed at deriving predictive QSAR

model and pharmacophore studies capable of elucidating thestructural requirements for novel 1,2-dihydro-4-quinazolinam-ines as inducible nitric oxide synthase which show anti-inam-

matory activity. The best 2DQSAR model (model 1) resultedin r2 = 0.8604 and pred_r2 = 0.8128 by GA-PLS conrms apositive contribution of SsClE-index, SdsNE-index and

T_C_O_1 to the inducible nitric oxide synthase activity. The3D-QSAR model built with the selected variables by the GAmethod resulted in better prediction in the case of 3D-QSARmodeling as compared to other two methods. This superiority

Figure 4c Best Pharmacophoric features with distance A.properties-inducible nitric oxide synthase relationship of substituted quinaz-


inhibitors. Furthermore, we hope that the current study pro-

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Acknowledgments

The author wishes to express gratitude to V-life Science Tech-

nologies Pvt. Ltd Pune (M.H) India for providing the trial ver-sion software for the study.

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12 M.C. Sharma et al.Please cite this article in press as: Sharma, M.C. et al., Study of physicochemical

olinamines analogs: Pharmacophore identication and QSAR studies. Arabian Jproperties-inducible nitric oxide synthase relationship of substituted quinaz-


Study of physicochemical properties-inducible nitric oxide synthase relationship of substituted quinazolinamines analogs: Pharmacophore identification and QSAR studies1 Introduction2 Materials and method2.1 Dataset and biological activity for analysis2.2 Dataset division into training and test sets2.3 Molecular modeling2.3.1 2D-QSAR studies and calculation of 2D descriptor2.3.2 Group based (G-QSAR) QSAR2.3.3 Three dimensional QSAR2.3.4 Pharmacophore approach2.3.5 Model validation

3 Results and discussion3.1 Interpretation of 2D QSAR model3.2 Interpretation of group based QSAR model3.3 Interpretation of 3D QSAR models3.3.1 Interpretation of pharmacophore models

4 ConclusionAcknowledgmentsReferences

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