Design, synthesis, molecular modeling and anti-hyperglycemic evaluation of quinazolin-4(3H)-one derivatives as potential PPARγ and SUR agonists

Article abstract of DOI:10.1016/j.bmc.2017.07.015

Peroxisome proliferator-activated receptor gamma (PPARγ) and sulfonylurea receptor (SUR) play crucial roles in management of type-2 diabetes mellitus. In this study, a series of novel quinazoline-4(3H)-one-sulfonylurea hybrids were designed and synthesized as dual PPARγ and SUR agonists. The synthesized compounds were evaluated for their in vivo anti-hyperglycemic activities against STZ-induced hyperglycemic rats. Four compounds (19_a, 19_d, 19_f and 25g) demonstrated potent activities with reduction in blood glucose levels of 40.43, 46.42, 41.23 and 42.50 %, respectively. The most active ten compounds were further evaluated in vitro for their PPARγ binding affinities and insulin-secreting abilities. Compounds 19_b, 19_d, 19_f, 25_f and 25g exhibited the highest affinities against PPARγ with IC₅₀ values of 0.371, 0.350, 0.369, 0.408 and 0.353 μM, respectively. In addition, compounds 19_d, 19_f, and 25_d showed the highest insulin-secreting activities with EC₅₀ values of 0.97, 1.01 and 1.15 μM, respectively. Furthermore, molecular docking and pharmacophore generation techniques were carried out to investigate binding patterns and fit values of the designed compounds with PPARγ and SUR, respectively. Also, two QSAR models were generated to explore the structural requirements controlling the different biological activities of the synthesized compounds against PPARγ and SUR.

Full text of DOI:10.1016/j.bmc.2017.07.015

Accepted Manuscript
Design, synthesis, molecular modeling and anti-hyperglycemic evaluation of
quinazolin-4(3H)-one derivatives as potential PPARγ and SUR agonists
Mohamed.K. Ibrahim, Ibrahim H. Eissa, Mohamed S. Alesawy, Ahmed M.
Metwaly, Mohamed M. Radwan, Mahmoud A. ElSohly
PII:
S0968-0896(17)31152-5
DOI:
http://dx.doi.org/10.1016/j.bmc.2017.07.015
BMC 13851
Reference:
Bioorganic & Medicinal Chemistry
To appear in:
Received Date:
Revised Date:
Accepted Date:
30 May 2017
1 July 2017
6 July 2017
Please cite this article as: Ibrahim, Mohamed.K., Eissa, I.H., Alesawy, M.S., Metwaly, A.M., Radwan, M.M.,
ElSohly, M.A., Design, synthesis, molecular modeling and anti-hyperglycemic evaluation of quinazolin-4(3H)-one
derivatives as potential PPARγ and SUR agonists, Bioorganic & Medicinal Chemistry (2017), doi: http://dx.doi.org/
10.1016/j.bmc.2017.07.015
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Design, synthesis, molecular modeling and anti-hyperglycemic evaluation of
quinazolin-4(3H)-one derivatives as potential PPARγ and SUR agonists
Mohamed. K. Ibrahima, Ibrahim H. Eissaa*, Mohamed S. Alesawya, Ahmed M. Metwalyb,
Mohamed M. Radwanc and Mahmoud A. ElSohly*c,d
a Pharmaceutical Chemistry department, Faculty of Pharmacy (Boys), Al-Azhar University,
Cairo, 11884, Egypt
b
Pharmacognosy department, Faculty of Pharmacy (Boys), Al-Azhar University, Cairo,
Egypt
c
National Center for Natural Products Research, University of Mississippi, MS 38677,
USA
d Department of Pharmaceutics and Drug Deliveray University of Mississippi, University,
MS 38677, USA
* Corresponding authors:
Ibrahim H. Eissa
Pharmaceutical Chemistry department, Faculty of Pharmacy (Boys), Al-Azhar University,
Cairo, 11884, Egypt
Email: Ibrahimeissa@azhar.edu.eg
Mahmoud. A. ElSohly
National Center for Natural Products Research, University of Mississippi, University, MS
38677, USA
Email: msohly@olemiss.edu

Abstract
Peroxisome proliferator-activated receptor gamma (PPARγ) and sulfonylurea
receptor (SUR) play crucial roles in management of type-2 diabetes mellitus. In this study,
a series of novel quinazoline-4(3H)-one-sulfonylurea hybrids were designed and
synthesized as dual PPARγ and SUR agonists. The synthesized compounds were evaluated
for their in vivo anti-hyperglycemic activities aganist STZ-induced hyperglycemic rats.
Four compounds (19a, 19d, 19f and 25g) demonstrated potent activities with reduction in
blood glucose levels of 40.43, 46.42, 41.23 and 42.50 %, respectively. The most active ten
compounds were further evaluated in vitro for their PPARγ binding affinities and insulin-
secreting abilities. Compounds 19b, 19d, 19f, 25f and 25g exhibited the highest affinities
against PPARγ with IC50 values of 0.371, 0.350, 0.369, 0.408 and 0.353 µM, respectively.
In addition, compounds 19d, 19f, and 25d showed the highest insulin-secreting activities
with EC50 values of 0.97, 1.01 and 1.15 µM, respectively. Furthermore, molecular docking
and pharmacophore generation techniques were carried out to investigate binding patterns
and fit values of the designed compounds with PPARγ and SUR, respectively. Also, two
QSAR models were generated to explore the structural requirements controlling the
different biological activities of the synthesized compounds against PPARγ and SUR.
Key words: Quinazolin-4(3H)-one, PPARγ, Sulfonylurea, Docking, Pharmacophore,
QSAR, Anti-hyperglycemic
1. Introduction:
Diabetes mellitus is a metabolic disorder associated with three basic
pathophysiological abnormalities: impaired insulin secretion, excessive hepatic glucose
production and insulin resistance in skeletal muscle, liver, and adipose tissue [1]. WHO
estimated that more than 180 million people worldwide are afflicted with diabetes at 2015,
and the prevalence is expected to be more than double by the year 2030 [2]. Diabetic
patients are at high risk for developing long-term complications including nephropathy,
neuropathy, retinopathy and cardiovascular disease [3, 4].
Most of the current used therapies for treatment of type-2 diabetes were discovered
and developed in the absence of defined biological molecular targets [5]. Moreover, they
have a number of adverse effects compromising the patients, lives [6]. The emerging
knowledge of the key pathogenic mechanisms has led to identification of a number of
molecular drug targets [7]. Considering these facts, exploring of new potent anti-diabetic
drugs is extremely crucial.

Two main molecular drug targets (receptors) are involved in the treatment of type-2
diabetes mellitus; sulfonylurea receptors (SUR) and peroxisome proliferator-activated
receptors (PPARs) [8].
Sulfonylurea receptors present in pancreatic β cells can be stimulated by
sulfonylureas to increase the insulin secretion [9]. It was reported that second and third
generation sulfonylureas such as glipizide 1 and glimepiride 2, respectively, interact with
the A and B sites in SUR (Fig. 1A) [10]. Moreover, they exert clinical efficacy by acting
through not only SUR but also PPARγ [11-13]. Pharmacophore model for sulfonylurea
acting drugs was designed to contain three lipophilic centers separated by amidic and
anionic linkers (fig. 1A).
Peroxisome proliferator-activated receptors (PPARs) are ligand activated
transcription factors belonging to the nuclear hormone receptor superfamily [14]. Three
PPAR isotypes have been identified, PPARα, δ and γ which are involved in the regulation
of glucose and lipid homeostasis [15, 16]. PPARγ is the well-characterized member of the
PPAR subfamily [17]. The central role in glucose metabolism and fatty acid storage has
made PPARγ the most desirable pharmacological target for the development of insulin
sensitizing anti-hyperglycemic agents [18].
The basic structural features of PPARγ agonist are an acidic head, a linker attached
to an aromatic scaffold (spacer group), another linker, and a hetero-aromatic lipophilic tail
(Fig. 1B) [7]. Thiazolidinediones (TZDs) such as rosiglitazone 3, pioglitazone 4,
darglitazone 5 and troglitazone 6 showed high-affinities toward PPARγ (Fig. 1B) [19, 20] .
Thiazolidinediones are the only clinically available agents controlling hyperglycemia by
improving insulin resistance [21, 22]. Unfortunately, TZDs exhibited serious adverse
effects related to cardiovascular system [23, 24]. A large number of non-TZDs synthetic
ligands have been discovered to modulate PPARγ activity such as compounds 7-10 [25-
27].
Several reports suggested that non-TZDs have more potential to reduce glucose and
to improve dyslipidemia than rosiglitazone or pioglitazone. So that, they may be superior
for the treatment of type-2 diabetes mellitus with abnormal dyslipidemia compared with
rosiglitazone and pioglitazone [28]. In addition, the non-TZD PPARγ agonists exhibited
beneficial effects similar to that of TZDs in animal models, but without the associated
adverse effects [29].
Some synthetic quinazolin-4(3H)-one derivatives were reported to have anti-
hyperglycemic activities as exemplified by compounds 11 and 12 (Fig. 1C) [30, 31]

In recent years, the philosophy of drug design has been transformed from “one drug
one target” to “one drug multiple targets”, coined as polypharmacology.
Polypharmacological phenomena includes single drug acting on multiple targets of a
unique disease pathway [32]. Drugs designed to act against individual molecular targets
cannot usually combat multigenic diseases such as cancer, or diseases that affect multiple
tissues or cell types such as diabetes and immune-inflammatory disorders. Drugs that
impact multiple targets simultaneously are better at controlling complex disease systems,
are less prone to drug resistance and are the standard of care in many important therapeutic
areas [33]. In this review, we here in report the synthesis of new non-TZDs quinazolin-
4(3H)-one ligands bearing sulfonylurea moieties, aiming to obtaine a dual agonistic
activities against PPARγ and SUR .

Fig. 1: A) Pharmacophoric model for sulfonylurea agonists and some selective
sulfonylureas for treatment of type-2 diabetes. B) The essential structural features of
PPARγ agonists and some reported compounds. C) Reported anti-hyperglycemic
quinazolin-4(3H)-one derivatives.
1.1. Rationale of molecular design
There are considerable structural similarities between the features of SUR and
PPARγ agonists, accordingly, we designed and synthesized new compounds contain both
features (Fig. 2).
The sulfonylurea moiety acted as an acidic head required for PPARγ agonistic
activity through forming pivotal hydrogen bonds with serine289, tyrosine473, histidine323
and histidine449 of the PPARγ. Aromatic and aliphatic substitutions on sulfonylurea
moieties acted as lipophilic centers required for SUR agonists. Moreover, NH group of
sulfonamide moiety is weak organic acids (pKa = 5.2 – 6.2) and is largely ionized at
physiological pH [34] forming an anionic linker which contributes significantly to SUR
agonistic affinity. On the other hand, SO2 group acted as one atom spacer between the
acidic head and the aromatic group, giving the optimal length for PPARγ agonistic activity.
Para-disubstituted phenyl group played as a spacer group and a lipophilic center required
for PPARγ and SUR agonists, respectively. An acetamido (-CH2CONH-) group was used
as a linker between the lipophilic tail and the spacer group. The amido (-CONH-) group
gave our compounds the opportinunity to interact with sites A and B of SUR. In addition,
these linkers were synthesized with different lengths and different attachment positions on
quinazolin-4(3H)-one nucleus in order to study SAR of our compounds.
Finally, quinazolin-4(3H)-one neuclus acted as a cyclic tail and a lipophilic center
required for PPARγ and SUR agonistic activities, respectively. Three different
considerations drove us to choose quinazolin-4(3H)-one neuclus; first, the reported anti-
hyperglycemic activity of some quinazolin-4(3H)-one derivatives [35-37]. Second, bicyclic
structure of quinazolin-4(3H)-one nucleus is convenient to the large sized hydrophobic arm
II of PPARγ. Third, the nitrogen atoms of quinazolin-4(3H)-one may increase the
agonistic activity by serving as hydrogen-bond acceptors.

Fig. 2: The synthesized compounds 19a-f and 25a-i with basic structural pharmacophoric
features of SUR and PPARγ.
2. Results and discussion
2.1. Chemistry
The starting compounds, 3-(4-substitutedphenyl)-2-sulfanylquinazoline-4(3H)-one,
15a,b were prepared by one pot reaction of anthranilic acid 13 with 4-methylaniline 14a
and 4-methoxylaniline 14b in refluxing methanol in the presence of carbon disulfide and
potassium hydroxide. Reaction of compounds 15a,b with alcoholic potassium hydroxide
afforded the corresponding potassium salt derivatives 16a,b. Heating of the produced

potassium salt with 2-chloro-N-(4-sulfamoylphenyl)acetamide 17 [38] in dry DMF with
catalytic amount of KI afforded the key intermediates 18a,b in relatively high yields (81%
and 79 %, respectively). More over the treatment of compounds 18a,b with the appropriate
isocynate derivatives namely, cyclohexyl isocyanate, butyl isocyanate and
phenyl
isocyanate in dry acetone in the presence of anhydrous potassium carbonate as the base,
produced the target sulfonylurea derivatives 19a-f, respectively (Scheme 1).
The reaction of anthranilic acid 20a with acetic anhydride and propionic anhydride,
afforded the corresponding benzoxazine derivatives 21a,b, respectively The 4-
.
bromoanthranilic acid 20b with acetic anhydride gave the corresponding benzoxazine
derivative 21c. Subsequent reaction of 21a-c with formamide produced 2-alkyl-6-
substitutedquinazoline-4(3H)-one derivatives 22a-c, respectively . The reaction of
compounds 22a-c with alcoholic potassium hydroxide afforded the corresponding
potassium salt derivatives 23a-c, respectively. Heating of the obtained potassium salt with
2-chloro-N-(4-sulfamoylphenyl)acetamide 17 [38] in dry DMF with catalytic amount of KI
afforded the key intermediates 24a-c in relatively good yields (76% and 74 %, respectively).
Reaction of compounds 24a-c with appropriate isocynates namely, cyclohexyl isocyanate,
butyl isocyanate and phenyl isocyanate in dry acetone in the presence of anhydrous
potassium carbonates afforded the corresponding sulfonylureas 25a-i, respectively (Scheme
2).
With regard to the key compounds 18a,b and 24a-c, the IR spectra revealed the
presence of two characteristic NH and NH2 absorption bands of 4-
aminobenzenesulfonamide moiety around 3229 - 3352 cm-1, in addition to absorption
bands due to C=O groups of quinoxaline and amide moieties around 1680 -1673 cm-1. The
1H NMR spectra revealed the presence of D2O exchangeable singlet peak of NH2 of
sulfonamide moiety at about 7.2 ppm, and a singlet peak for the methylene group of
acetamido group at about 4.1 ppm. The DEPT spectra of compound 18a differentiated 13C
signals to be; one CH3 at 21.4 ppm and one CH2 at 37.4.
For the target compounds 19a-f, and 25a-i , the IR spectra showed disappearance of
sulfonamide NH2 peak and appearance of more deshielded singlet peak for NH of
sulfonylurea moiety. At the same time, they showed sharp absorption bands of 3NH groups
1
of 4-aminobenzenesulfonylurea moiety in the region 3357 - 3309 cm-1. The H NMR
spectra showed disappearance of sulfonamide NH2 peak and appearance of more
deshielded two singlet peaks for 2NH of sulfonylurea. The DEPT spectra of compound 19d
showed additional 5 CH2 signals characteristic for cyclohexyl moiety.

Scheme 1: Synthesis of target compounds 19a-f

Scheme 2: Synthesis of target compounds 25a-i
2.2. Biological testing
2.2.1. In vivo anti-hyperglycemic activity
The in vivo anti-hyperglycemic activity of compounds 19a-f and 25a-i was evaluated
against streptozotocin-induced hyperglycemic rats using anti-hyperglycemic assay [39].
Rosiglitazone and glimepiride as two of the most active anti-diabetic drugs, were used as
positive controls. The results were reported as % reduction of blood glucose level (Table
1). The tested compounds exhibited good, moderate or weak anti-hyperglycemic activities.
Compound 19d was found to be the most potent member (% reduction in blood glucose
level of 46.42). Besides, compounds 19a, 19f and 25g, displayed strong anti-hyperglycemic
activities (% reduction of 40.43, 41.23 and 42.50, respectively). Some compounds showed
moderate anti-hyperglycemic activities as 19b, 19c, 25a, 25d and 25f (% reduction of 33.42,

37.76, 38.64, 34.25 and 38.44, respectively). Moreover, compounds 19e, 25b, 25c, 25e, 25h
and 25i were weakly active with % reduction values ranging from 19.66 to 29.22.
2.2.2. In vitro PPARγ- ligand binding assay
The most active compounds (19a, 19b, 19c, 19d, 19f, 25a, 25c, 25d, 25f and 25g) were
further evaluated to assess their in vitro PPARγ binding affinities, using Fluorescence
Polarization Assay technique [40]. Rosiglitazone was used as a positive control.
Compounds 19b, 19d, 19f, 25f and 25g exhibited the highest affinities against PPARγ with
IC50 values of 0.371, 0.350, 0.369, 0.408 and 0.353 µM, respectively. It was noted that the
PPARγ binding affinities of the tested compounds are less than that of rosiglitazone (IC50 =
0.298 µM). On the other hand, compounds 19a, 19c, 25a and 25d showed moderate PPARγ
binding affinities with IC50 values ranging from 0.402 to 0.590 µM. Moreover, compound
25c exhibited a weak affinity with IC50 value of 0.980 µM (Table 1).
2.2.3. In vitro insulin assay
In vitro insulin-secreting activities of the most active compounds (19a, 19b, 19c,
19d, 19f, 25a, 25c, 25d, 25f and 25g) were evaluated against isolated pancreatic islets of rats
via quantitative sandwich enzyme immunoassay technique [41]. Glimepiride was used as a
positive control. The results were reported as the EC50 values (Table 1).
The tested compounds displayed good, moderate to weak insulin-secreting
activities. Compounds 19d, 19f, and 25d exhibited the highest insulin-secreting activities
with EC50 values of 0.97, 1.01 and 1.15 µM, respectively. The positive control, glimiperide
showed EC50 of 0.79 µM. In addition, compounds 19a, 19c, and 25g have demonstrated
moderate insulin-secreting activities with EC50 values of 1.31, 1.53 and 1.68 µM,
respectively, while compounds 19b, 25a, 25c and 25f exhibited weak activities with EC50
values of 3.09, 2.69, 2.91 and 2.51 µM, respectively.

Table 1: In vivo reduction in blood glucose levels, in vitro PPARγ binding affinities and in
vitro insulin secreting activities of synthesized compounds and reference drugs
In vivoa
in vitroa
Insulin Secreting
Comp.
Reduction in blood
glucose level (%)
PPARγ binding
affinity IC50 (µM)b
activity
EC50 (µM)c
40.43
33.42
37.76
46.42
24.58
41.23
38.64
19.66
29.22
34.25
21.55
38.44
42.50
0.402
0.371
0.434
0.350
NTd
1.31
3.09
1.53
0.97
NTd
1.01
2.69
NTd
2.91
1.15
NTd
2.51
1.68
19a
19b
19c
19d
19e
19f
25a
25b
25c
25d
25e
25f
25g
0.369
0.464
NTd
0.980
0.590
NTd
0.408
0.353
21.33
28.25
37.95
31.89
NTd
NTd
0.298
NTd
NTd
NTd
NTd
0.79
25h
25i
Rosiglitazone
Glimiperide
a All data within 10 % (n = 3)
b Concentration of the test compound required to displace 50% of titrated ligand
c Concentration required to produce 50% of maximal effect.
d NT: Compounds not tested

2.3. Molecular modeling
Docking, pharmacophore generation, QSAR and ADMET studies were performed
using the Discovery Studio 2.5 software (Accelrys Inc., San Diego, CA, USA). Structure
based drug design (docking) technique used as aguid to investigate the binding mode of
the target compounds with PPARγ [42]. On the other hand, the crystal structure for SUR is
not available, as a result, ligand based drug design (pharmacophore) technique was used as
a guide to predict the sulfonylurea agonistic activities of the synthesized compounds. Both
docking and pharmacophore modeling gave an idea about the interaction that would exist
between the ligand and the receptor [43].
2.3.1. Docking studies
Molecular docking studies of the titled compounds were performed in order to
rationalize the obtained biological results as well as to help in understanding the various
interactions between the ligands and the active site of PPARγ in details [44]. The PPARγ
cavity is Y-shaped and consists of an entrance, arm I and arm II. Arm I contains four polar
residues, Tyr473, His323, Ser289 and His449. These residues involved in hydrogen bond
network between the receptor and ligands. Arm II is formed from Ile281, Ile341, Val339
and Leu353, while the entrance consists of Arg288, Leu330, Ser342 and Leu333 [45] (Fig.
3).
Fig. 3: PPARγ cavity composed of 3 main parts arm I, arm II and entrance [45]

The proposed binding mode of the reference ligand, rosiglitazone, exhibited an
affinity value of -47.44 kcal/mol. It showed important different interactions with the
essential residues at the active site of PPARγ. The acidic polar thiazolidinedione moiety
was directed into the polar arm I of the receptor. At the same time, the imide group formed
a hydrogen bond with a distance of 2.35 A˚ with Tyr473 (OH) through its acidic hydrogen.
Moreover, the hydrophobic tail occupied the hydrophobic pocket (arm II and the
hydrophobic part of the entrance). These results were found to be consistent with the
reported data [45] (Fig. 4).
Results of docking study in Table 2 indicated that all studied ligands exhibited a
similar position and orientation inside the essential binding site of PPARγ. All designed
molecules displayed good binding energies ranging from – 35.01 to -58.60 kcal/mol.
Compound 19d showed an affinity value of - 51.42 kcal/mol with 4 H-bonds (Fig.
5). The acidic polar head (sulfonylurea moiety) was oriented in the polar arm I. It formed
two hydrogen bonds with the polar residues Ser289 and Cys285 in arm I. The acidic
hydrogen of the sulfamoyl group formed a hydrogen bond of 2.00 A˚ with Ser289 (OH),
and the oxygen of the carbamoyl group formed a hydrogen bond of 2.30 A˚ with Cys285.
Quinazolin-4(3H)-one nucleus was oriented in the hydrophobic entrance. The hydrogen of
amide linker formed a hydrogen bond of 2.3 A˚ with Leu340. The fourth hydrogen bond is
2.2 A˚ and was formed between the sulfur atom of linker and Ser342. These bonds explain
the high binding energy of compound 19d with PPARγ, and the biological activity of this
compound.
The proposed binding mode of compound 25f (affinity value of -55.38 kcal/mol and
2 H-bonds) was virtually the same as that of rosiglitazone, where its acidic polar head
(sulfonylurea moiety) occupied the polar arm I. It formed two hydrogen bonds with the
polar residues Tyr473 and Ser289 in arm I. The oxygen atom of the SO2 group formed a
hydrogen bond of 2.29 A˚ with Tyr473 (OH). The hydrogen atom of the carbamoyl group
formed a hydrogen bond of 1.97 A˚ with Ser289 (OH) (Fig. 6). These bonds and correct
binding mode explain the high energy of binding of compound 25f.

Table 2: The docking binding free energies of the synthesized compounds
Binding free energy
(kcal/mol)
- 46.28
Binding free energy
Comp.
Comp.
(kcal/mol)
- 51.69
- 37.60
- 54.88
- 55.38
- 53.48
- 50.08
- 39.13
-47.44
19a
19b
19c
19d
19e
19f
25a
25b
25c
- 39.35
25d
- 43.53
25e
- 51.42
25f
- 49.60
25g
25h
- 46.71
- 49.92
25i
- 47.67
Rosiglitazone
Fig. 4: Binding mode of rosiglitazone docked into the active site of PPARγ forming
hydrogen bond (green dot) between its polar head and tyr473

Fig. 5: Binding mode of compound 19d docked into the active site of PPARγ, forming
hydrogen bonds (green) with the polar residues (Ser289 & Cys285) in arm I. while linker
formed hydrogen bonds with (Leu340 & Ser342) residue of entrance
Fig. 6: Binding mode of compound 25f docked into the active site PPARγ, forming
hydrogen bonds (green) with the influencing polar residues Tyr473 and Ser289 in arm I
2.3.2. Pharmacophore studies
2.3.2.1. Generation of 3D-pharmacophore model
The principle goal of this procedure was to produce 3D pharmacophore models in light
of the known SU agonists, which can effectively reflect the SAR of the current SU
agonists. A set of thirty compounds containing a sulfonylurea scaffold were selected from
the literature to generate and validate the 3D pharmacophore model [46, 47] (Fig. 16). The
produced model was utilized as 3D look inquiry for assessing the SUR agonistic activities
for the synthesized compounds. As shown in Table 3, ten hypotheses were generated.

All of them have cost differences more than 60 bits and correlation coefficient values
higher than 0.90. If the cost difference is higher than 60 bits, there is a great possibility
(more than 90 %) that model represents a true correlation. Hypo-1 was selected for further
analyses due to its total cost is the closest to the fixed cost, its RMS is the smallest and the
correlation coefficient is the highest. The null cost, fixed cost and total cost values of hypo-
1 are 127.30, 46.26 and 50.43 bits, respectively. Hypo-1 cost difference is 76.87, RMS is
0.72 and correlation coefficient is 0.98, demonstrating a good prediction ability. The vast
majority of hypotheses involved the following features; Hydrogen bond acceptors (HBA),
hydrogen bond donor (HBD), hydrophobic aliphatic (HA) and ring aromatic (RA) (Table
3).
Table 3. Information of statistical significance and predictive power of top 10
hypotheses as a result of applying of HypoGen pharmacophore generation to the training
set
Total
cost differencea
Cost
RMSb Correlation
Featuresc
Max. fit
Hypo.
Hypo 1
Hypo 2
Hypo 3
Hypo 4
Hypo 5
Hypo 6
Hypo 7
Hypo 8
Hypo 9
Hypo 10
50.43
58.10
61.92
62.96
63.12
64.04
64.42
65.24
65.76
65.83
76.87
69.20
65.38
64.34
64.18
63.26
62.88
62.06
61.54
61.47
0.72
1.43
1.68
1.63
1.80
1.86
1.87
1.88
1.85
1.97
0.98
0.95
0.93
0.93
0.91
0.91
0.91
0.91
0.91
0.90
HBA, HBD, HA, RA
HBA, HBA, HA, RA
HBA, HBA, HBA, HA
HBA, HBA, HBA, HA
HBA, HBA, HBA, HA
HBA, HBA, HBD, HA
HBA, HBA, HBD, HA
HBA, HBA, HA, RA
HBA, HBA, HA
10.14
10.16
10.11
11.08
9.34
9.08
9.37
9.91
7.97
HBA, HBA, HA, RA
8.12
a Cost difference is the difference between null cost and total cost. Null cost = 127.30,
fixed cost = 46.26, configuration cost = 21.46, cost unit is bit.
b
RMS, root mean square deviation, the deviation of log estimated activities from log
experimental activities normalized by log uncertainty.
c
Abbreviation used for features: HBA, hydrogen bond acceptor, HBD, hydrogen bond
donor, HA, hydrophobic aliphatic, RA, ring aromatic.

Hypo-1 comprised hydrophobic aliphatic (HA), hydrogen bond donor (HBD)
hydrogen bond acceptors (HBA) and ring aromatic (RA) (Fig 7). The spatial arrangement
and the distances between the pharmacophore features of hypo one are demonstrated in fig
8. These study confirmed the reliability of the generated pharmacophore model.
Fig 7: The best hypothetical 3D-pharmacophore geometry (Hypo 1), with four features;
green (H-bond acceptor), blue (hydrophobic aliphatic), purple (H-bond donor) and brown
(ring aromatic).
Fig. 8: 3D spatial relationship and geometric parameters of Hypo1. The distance between
pharmacophore features is reported in angstroms.
The predictability of Hypo-1 was assessed by using compounds of training set divided
on the basis of their activity scale in to; highly active (+++, 1/C < 10 µM), moderately
active (++, 10 µM ≤ 1/C < 100 µM) and inactive (+, 1/C ≥ 100 µM). The results of

validation indicated that there is marked consistency between experimental and predicted
activity values for all test compounds.
Table 4. Experimental and estimated activities of the training set compounds based on
pharmacophore model hypo 1.
Exp.
scalec
+++
Est.
scalec
Fit
valued
Comp.
Exp.a (µM) Estb (µM)
Errore
+++
26a
26b
26c
26d
26e
27a
27b
27c
27d
27e
1.60
1.34
9.71
8.90
9.32
9.14
9.48
8.20
8.15
7.12
8.21
8.22
-1.18
-1.46
-1.03
1.60
++
+++
+++
+++
+++
++
12.50
3.40
8.55
+++
+++
+++
++
3.27
3.10
4.96
2.39
2.23
-1.07
1.30
33.30
50
43.31
48.09
520.75
42.26
41.00
++
++
-1.03
-1.92
1.26
+
+
1000.00
33.30
33.30
++
++
++
++
1.23
a Experimental activity value
b Estimated activity value.
c
Activity scale: 1/C < 10 µM = +++ (highly active); 10 µM ≤ 1/C -- 100 µM = ++
(moderately active); 1/C ≥ 100 µM = + (low active).
d
Fit value calculated by geometry fitting between the hypothesis and the compound; the
higher value, the better fit.
e
Error factor calculated as the ratio of the measured activity to the estimated activity or
the inverse if the estimated activity is greater than the measured activity. If measured
activity is greater than the estimated activity, the error factor is negative and vice versa.
2.3.2.2. Validation of Hypo-1 pharmacophore model
This progression was done to examine the ability of the created model to distinguish active
structures and anticipate their activities precisely. Two methods were applied to validate
the hypo-1; test set activity prediction and mapping of glimepiride as an external reference
of the generated pharmacophore.

2.3.2.2.1. Test set activity prediction
The same protocol of training set was carried out for test set of eleven compounds (fig. 14)
to verify the ability of the selected pharmacophore model (hypo-1) to predict the activities
of the new synthesized compounds. The reported results demonstrated that hypo-1 is able
to predict and differentiate between the different activities of active and inactive
compounds efficiently. The experimental and estimated activity values of the test set
compounds were listed in Table 5.
Table 5: Experimental and estimated activities of the test set compounds based on
pharmacophore model hypo 1.
Comp.
26f
Exp.a (µM)
7.14
Est.b(µM)
3.99
Fit valuec
9.04
2.40
1.85
8.90
26g
NAd
601.55
602.22
797.88
4.49
8.00
27f
NA
8.20
27g
NA
7.47
27h
4.00
8.58
(28)
a Experimental activity value
b Estimated activity value.
c
Fit value indicates how well the features in the pharmacophore overlap the chemical
features in the molecule.
d NA: not active
2.3.2.2. 2. Mapping of glimepiride
Glimepiride as an external reference was mapped with the generated pharmacophore
(hypo-1) and demonstrated a high Fit value of 8.58. the cyclohexyl moiety completely
fitted with HA, the carbonyl group of urea moiety fitted with HBA, the central phenyl ring
fitted with RA and the NH of amide linker fitted with HBD (fig. 9).

Fig. 9: Mapping of glimepiride on the generated high ranked pharmacophore (Hypo1)
2.3.2.3. Estimation of designed molecules
The valid 3D pharmacophore (Hypo-1) was used to assess the activities of designed
molecules. The activity designed molecules was estimated according to the degree of
fitting with Hypo-1. Estimated activities and fit values of the designed compounds were
reported in Table 6. It was observed that most of the designed compounds have high Fit
values toward Hypo1, indicating promising SUR agonistic activities. The fitted compounds
were arranged according to their Fit values in descending order as; 19d, 19a, 25g, 25a, 19f,
19c, 25d and 25f.
Mapping of four designed compounds (19a, 19c, 19f and 25g) on the generated
pharmacophore (Hypo1) was demonstrated in Fig. 10, 11, 12 and 13, respectively. It was
noted that the mapping pattern of these compound is as the same as that of glimepiride.
Where, the alkyl groups (cyclohexyl and butyl) completely fitted with HA, the carbonyl
groups of urea moieties fitted with HBA, the central phenyl rings fitted with RA and the
NH groups of amide linkers fitted with HBD

Table 6: Expected activities and fit values of designed compounds based on the Hypo 1
pharmacophore model
Comp.
19a
Est.a (µM)
Fit valueb
8.09
Comp.
25c
Est.a (µM)
37.00
Fit valueb
7.70
2.93
132.91
12.61
2.55
5.80
15.55
8.12
19b
19c
25d
25e
8.33
133.32
20.75
6.73
7.86
7.89
19d
19e
25f
109.70
5.53
6.42
2.98
7.48
25g
25h
25i
8.24
50.05
6.93
19f
4.66
7.22
34.13
8.34
25a
124.04
6.42
25b
a Estimated activity value.
b
Fit value indicates how well the features in the pharmacophore overlap the chemical
features in the molecule.
Fig. 10: Mapping of compound 19a (fit value = 8.09) on the generated high ranked
pharmacophore (Hypo1)

Fig. 11: Mapping of compound 19c (fit value = 8.33) on the generated high ranked
pharmacophore (Hypo1)
Fig. 12: Mapping of compound 19f (fit value = 8.24) on the generated high ranked
pharmacophore (Hypo1)

Fig. 13: Mapping of compound 25g (fit value = 7.48) on the generated high ranked
pharmacophore (Hypo1)
2.3.3. QSAR studies
In order to identify the substituents effect on the PPARγ binding affinity and insulin-
secreting ability, quantitative structure–activity relationship (QSAR) studies were carried
out using the multiple linear regression (MLR) analysis [48] . In the present study, a
training set of 18 sulfonylurea derivatives was subjected to MLR analysis for model
generation. This taining set includes the in vitro examined new compounds (19a-d, 19f, 25a,
25c,d and 25f,g) as well as eight reported compounds ( 28a,b, 28d,e, 29a,b and 30a,b) [47].
Biological activity data were transformed from IC50 and EC50 values into pIC50 and
pEC50 values (i.e. -log IC50 and -log EC50), respectively and used as dependent variables in
the QSAR studies. The used molecular descriptors (independent variables) are: i)
molecular properties as partition coefficient (Alog P), molar refractivity (MR), pKa,
molecular solubility (MS), molecular polar surface area (MPSA) and molecular volume
(MV), ii) topological descriptors like Balaban indices (JX and JY), Kier’s molecular
connectivity (CHI0, CHI1, CHI2), and Graph-Theoretical InfoContent descriptor (IC), iii)
3D electronic descriptors such as dipole moment (µ), Jurs descriptors (shape and electronic
descriptors) as surface-weighted charged partial surface areas (WPSA1, WPSA2, WPSA3,
WNS1, WNSA2 and WNSA3), partial positive surface area (PPSA1), partial negative
surface area (PNSA1), total charge weighted positive surface area (PPSA2), total charge
weighted negative surface area (PNSA2) and atomic charge weighted positive surface area

(PPSA3) and iv) shadow Indices as shadow_Xlength (shadX), shadow_Ylength (shadY)
and shadow_Zlength (shadZ) [49-52].
Preliminary analysis showed some selected molecular descriptors for the tested
compounds (Table 7). Depending on a correlation analysis, two correlation matrixes were
constructed for the training set compounds toward PPARγ binding affinity (Table 8), and
insulin-secreting ability of (Table 9).
Table 7: Values of selected parameters used in QSAR studies of synthesized and reported
compounds
Comp. Alog P MR
MPSA CHI2 HBA HBD µ
WPSA1 WPSA2 PPSA1 Shad-Y
5.49
5.20
4.96
4.98
4.71
4.45
2.22
1.93
1.69
2.89
2.60
2.36
3.64
3.35
3.10
2.23
1.70
3.32
2.79
1.98
1.45
1.84
1.31
161.55 250.71 18.83 7.00 3.00 7.70 542.35 2376.87 589.79 11.92
160.32 248.71 18.83 7.00 3.00 7.87 450.36 1978.60 506.42 11.92
154.41 249.71 17.59 7.00 3.00 7.71 500.54 2161.16 557.20 11.85
162.97 249.94 19.00 8.00 3.00 8.95 567.53 2600.09 605.16 13.01
161.73 247.63 19.00 6.00 3.00 9.23 480.24 2205.42 525.19 13.02
155.83 215.94 17.76 8.00 3.00 9.03 530.02 2393.86 574.98 12.97
128.67 215.41 15.78 6.00 3.00 7.14 423.66 1723.30 531.83 8.78
127.43 213.04 15.77 6.00 3.00 7.25 355.86 1451.42 456.45 8.77
121.53 215.41 14.53 6.00 3.00 7.00 393.27 1574.20 504.57 8.55
133.29 215.41 15.97 6.00 3.00 7.11 463.34 1907.87 563.45 9.67
132.05 213.04 15.96 6.00 3.00 7.31 383.49 1583.24 481.59 9.63
126.15 215.41 14.72 6.00 3.00 7.10 428.00 1734.62 532.80 9.59
140.92 215.41 16.60 6.00 3.00 5.24 451.67 1841.65 530.73 9.88
139.68 213.04 16.60 6.00 3.00 6.77 378.62 1547.94 454.30 9.84
133.77 213.04 15.35 6.00 3.00 6.71 419.27 1682.38 501.75 9.73
128.95 235.41 15.78 6.00 3.00 7.72 418.79 1709.40 528.65 9.77
121.81 235.41 14.53 6.00 3.00 7.76 394.72 1585.57 508.35 9.61
136.94 248.44 15.78 6.00 3.00 6.62 414.53 1584.12 520.08 9.74
129.80 248.44 14.53 6.00 3.00 6.65 389.64 1462.66 502.89 9.61
125.90 245.09 15.38 6.00 4.00 6.36 384.02 1581.04 494.22 9.25
118.76 145.09 14.13 6.00 4.00 6.48 347.09 1406.47 460.76 9.27
126.56 145.09 15.26 6.00 4.00 5.05 395.40 1628.70 508.01 9.43
119.42 145.09 14.01 6.00 4.00 3.92 355.48 1441.23 481.84 9.47
19a
19b
19c
19d
19e
19f
25a
25b
25c
25d
25e
25f
25g
25h
25i
28a
28b
28d
28e
29a
29b
30a
30b

Table 8 : Correlation matrix for binding affinities of training set compounds against
PPARγ
Descriptor PEC50 ALogP MR
pKa HBA MV
MPSA CHI0 CHI1 CHI2
µ
WPSA1
PIC50
ALogP
MR
1.000
0.704 1.000
0.656 0.987 1.000
pKa
0.610 0.542 0.494 1.000
HBA
MV
0.447 0.793 0.862 0.371 1.000
0.676 0.967 0.985 0.568 0.871 1.000
MPSA
CHI0
CHI1
CHI2
µ
0.587 0.880 0.903 0.348 0.917 0.870 1.000
0.636 0.961 0.986 0.554 0.889 0.988 0.872 1.000
0.601 0.953 0.985 0.516 0.880 0.983 0.859 0.996 1.000
0.601 0.949 0.977 0.506 0.825 0.969 0.821 0.980 0.991 1.000
0.600 0.578 0.600 0.665 0.686 0.629 0.652 0.657 0.635 0.603 1.000
0.657 0.879 0.904 0.589 0.846 0.949 0.795 0.931 0.923 0.896 0.679 1.000
WPSA1

Table 9: Correlation matrix for insulin-secreting abilities of training set compounds
against SUR
Descriptor PEC50 ALogP MR
HBA MV
CHI1 µ
PPSA1 WPSA1 WPSA2 Shad-Y
PIC50
ALogP
MR
1.000
0.167
0.235
0.392
0.303
0.306
0.389
0.491
0.443
0.475
0.415
1.000
0.987
0.793
0.967
0.953
0.578
0.739
0.879
0.864
0.885
1.000
HBA
0.862 1.000
MV
0.985 0.871 1.000
CHI1
µ
0.985 0.880 0.983 1.000
0.600 0.686 0.629 0.635 1.000
PPSA1
WPSA1
WPSA2
Shad-Y
0.761 0.709 0.830 0.788 0.635 1.000
0.904 0.846 0.949 0.923 0.679 0.957 1.000
0.909 0.902 0.948 0.944 0.680 0.915 0.985
0.927 0.957 0.917 0.925 0.640 0.715 0.864
1.000
0.904
1.000
With regard to the binding affinity against PPARγ, it was observed a high colinearity
(r > 0.5) between different parameters. A high interrelationship (MR, r = 0.99) was
observed between topological parameters (CHI0, CHI1, CHI2) and molar refractivity.
While low interrelationship (r < 0.600) was observed between pKa and topological
parameter, HBA, MV and MPSA. The correlations of different molecular descriptors with
PPARγ binding affinity are presented in Table 10.
The correlation matrix (Table 8) indicated the predominance of lipophilic
parameter as partition coefficient (Alog P), molar refractivity (MR), molecular polar
surface area (MPSA) and Jurs descriptors (PPSA1 and WPSA1) in describing the PPARγ
binding affinity of the training set compounds. The generated QSAR model showed a
correlation between pIC50 and ALogP (Eq. 1).
pIC50 = - 6.961 + 0.108 ALogP
………….( Eq. 1)
n = 18, r = 0.704, r2 = 0.496, r2 (adj) = 0.464, r2 (pred) = 0.373, s = 0.022698

Here and thereafter, n: number of data points; r: correlation coefficient; r2: squared
correlation coefficient value, r2 (adj): r2 adjusted for the number of terms in the model; r2
(pred): predictive squared correlation coefficient value, equivalent to q2 from a leave-1-out
cross validation and s: Least-Squared error.
In order to improve the value of correlation coefficient, we coupled ALogP with
molar refractivity (MR), molecular polar surface area (MPSA), partial positive surface area
(PPSA1) and surface-weighted charged partial surface areas (WPSA1), which resulted in
best QSAR model for PPARγ binding affinity of the training set compounds (Eq. 2). This
QSAR model was represented graphically by scattering plots of the experimental versus
the predicted PPARγ binding affinity values pIC50 for the training set compounds as shown
in Fig. 14.
pIC50 = -9.489 + 8.748 ALogP - 1.691 MR+ 2.033 MPSA+ 2.267 PPSA1 - 2.264 WPSA1
…………(Eq. 2)
n = 18, r = 0.899, r2 = 0.809, r2 (adj) = 0.729, r2 (pred) = 0.619, s = 0.008594
Fig. 14. Predicted versus experimental pIC50 of the training set compounds against
PPARγ according to Eq. 2 (r2 = 0.809).

Table 10: Correlation of PPARγ binding affinities and insulin-secreting abilities of
training set compounds with calculated molecular descriptors
Descriptor pIC50
pEC50
0.167
0.235
-0.022
0.024
0.392
-0.040
0.303
0.184
0.299
0.306
0.282
0.147
-0.232
-0.228
Descriptor pIC50
pEC50
0.297
0.389
0.084
0.491
0.189
ALogP
MR
0.704
0.656
-0.847
0.610
0.447
-0.872
0.676
0.587
0.636
0.601
0.601
0.391
-0.307
-0.320
Wiener
µ
0.580
0.600
0.455
0.640
0.546
MS
PNSA1
PPSA1
WNSA1
WNSA2
WNSA3
WPSA1
WPSA2
WPSA3
Shad-X
Shad-Y
Shad-Z
pKa
HBA
HBD
MV
-0.500 -0.256
-0.374 -0.374
MPSA
CHI0
CHI1
CHI2
IC
0.657
0.566
0.053
0.662
0.544
0.549
0.443
0.475
0.215
0.190
0.415
0.117
JX
JY
On the other hand, from the correlation matrix (Table 8) of the insulin-secreting
ability against different descriptors, it was observed a high co-linearity between pEC50 of
the training set compounds and MR, HBA, CHI1, PPSA1, WPSA2 and Shad-Y,
indicating the predominance of these parameters in describing the insulin-secreting ability
of the training set compounds. The generated QSAR model showed the correlation
between pEC50 and these different descriptors (Eq. 3). The correlations of different
molecular descriptors with insulin-secreting ability are presented in Table 10. The
generated QSAR model was represented graphically by scattering plots of the experimental
versus the predicted insulin-secreting ability values pEC50 for the training set compounds
as shown in Fig. 15.
pEC50 = 1.821 - 5.579 MR - 0.256 HBA - 0.734 CHI-1 - 5.115 PPSA-1 + 3.584 WPSA-2 +
0.487 Shad-Y
…………( Eq. 3)