Design, synthesis, and in silico multitarget pharmacological simulations of acid bioisosteres with a validated in vivo antihyperglycemic effect

Article abstract of DOI:10.3390/molecules26040799

Substituted phenylacetic (1–3), phenylpropanoic (4–6), and benzylidenethiazolidine-2,4-dione (7–9) derivatives were designed according to a multitarget unified pharmacophore pattern that has shown robust antidiabetic activity. This bioactivity is due to the simultaneous polypharmacological stimulation of receptors PPARα, PPARγ, and GPR40 and the enzyme inhibition of aldose reductase (AR) and protein tyrosine phosphatase 1B (PTP-1B). The nine compounds share the same four pharmacophore elements: An acid moiety, an aromatic ring, a bulky hydrophobic group, and a flexible linker between the latter two elements. Addition and substitution reactions were performed to obtain molecules at moderated yields. In silico pharmacological consensus analysis (PHACA) was conducted to determine their possible modes of action, protein affinities, toxicological activities, and drug-like properties. The results were combined with in vivo assays to evaluate the ability of these compounds to decrease glucose levels in diabetic mice at a 100 mg/kg single dose. Compounds 6 (a phenylpropanoic acid derivative) and 9 (a benzylidenethiazolidine-2,4-dione derivative) ameliorated the hyperglycemic peak in a statically significant manner in a mouse model of type 2 diabetes. Finally, molecular dynamics simulations were executed on the top performing compounds to shed light on their mechanism of action. The simulations showed the flexible nature of the binding pocket of AR, and showed that both compounds remained bound during the simulation time, although not sharing the same binding mode. In conclusion, we designed nine acid bioisosteres with robust in vivo antihyperglycemic activity that were predicted to have favorable pharmacokinetic and toxicological profiles. Together, these findings provide evidence that supports the molecular design we employed, where the unified pharmacophores possess a strong antidiabetic action due to their multitarget activation.

Full text of DOI:10.3390/molecules26040799

molecules
Article
Design, Synthesis, and In Silico Multitarget Pharmacological
Simulations of Acid Bioisosteres with a Validated In Vivo
Antihyperglycemic Effect
Elix Alberto Domínguez-Mendoza 1, Yelzyn Galván-Ciprés 1, Josué Martínez-Miranda 1,
Cristian Miranda-González 1, Blanca Colín-Lozano 1, Emanuel Hernández-Núñez 2
,
Gloria I. Hernández-Bolio 2 , Oscar Palomino-Hernández 3,4 and Gabriel Navarrete-Vazquez 1,
*
1
Facultad de Farmacia, Universidad Autónoma del Estado de Morelos, Cuernavaca, Morelos 62209, Mexico;
ElixRose@outlook.com (E.A.D.-M.); yelzyn.galvanc@uaem.edu.mx (Y.G.-C.);
josue.martinezm@uaem.edu.mx (J.M.-M.); cristian.mirandag@uaem.edu.mx (C.M.-G.);
clbi_ff@uaem.mx (B.C.-L.)
2
3
Cátedra CONACyT, Departamento de Recursos del Mar, Centro de Investigación y de Estudios Avanzados,
IPN, Unidad Mérida, Yucatan 97310, Mexico; emanuel.hernandez@cinvestav.mx (E.H.-N.);
hboliog@gmail.com (G.I.H.-B.)
Computational Biomedicine (IAS-5/INM-9), Forschungszentrum Juelich, 52425 Julich, Germany;
o.palomino@fz-juelich.de
ꢀꢁꢂꢀꢃꢄꢅꢆꢇ
ꢀꢁꢂꢃꢄꢅꢆ
4
Department of Chemistry, Rheinisch-Westfälische Technische Hochschule Aachen, 52425 Aachen, Germany
Correspondence: gabriel_navarrete@uaem.mx; Tel.: +52-777-329-7089 (ext. 2322)
*
Citation: Domínguez-Mendoza, E.A.;
Galván-Ciprés, Y.; Martínez-Miranda,
J.; Miranda-González, C.;
Abstract: Substituted phenylacetic (1–3), phenylpropanoic (4–6), and benzylidenethiazolidine-2,
4-dione (7–9) derivatives were designed according to a multitarget unified pharmacophore pattern
that has shown robust antidiabetic activity. This bioactivity is due to the simultaneous polypharma-
Colín-Lozano, B.; Hernández-Núñez,
E.; Hernández-Bolio, G.I.;
Palomino-Hernández, O.;
cological stimulation of receptors PPARα, PPARγ, and GPR40 and the enzyme inhibition of aldose
Navarrete-Vazquez, G. Design,
Synthesis, and In Silico Multitarget
Pharmacological Simulations of Acid
Bioisosteres with a Validated In Vivo
Antihyperglycemic Effect. Molecules
2021, 26, 799. https://doi.org/
10.3390/molecules26040799
reductase (AR) and protein tyrosine phosphatase 1B (PTP-1B). The nine compounds share the same
four pharmacophore elements: an acid moiety, an aromatic ring, a bulky hydrophobic group, and a
flexible linker between the latter two elements. Addition and substitution reactions were performed
to obtain molecules at moderated yields. In silico pharmacological consensus analysis (PHACA) was
conducted to determine their possible modes of action, protein affinities, toxicological activities, and
drug-like properties. The results were combined with in vivo assays to evaluate the ability of these
compounds to decrease glucose levels in diabetic mice at a 100 mg/kg single dose. Compounds
6
(a phenylpropanoic acid derivative) and (a benzylidenethiazolidine-2,4-dione derivative) amelio-
9
Academic Editors: Rosanna Maccari
and Rosaria Ottana
rated the hyperglycemic peak in a statically significant manner in a mouse model of type 2 diabetes.
Finally, molecular dynamics simulations were executed on the top performing compounds to shed
light on their mechanism of action. The simulations showed the flexible nature of the binding pocket
of AR, and showed that both compounds remained bound during the simulation time, although
not sharing the same binding mode. In conclusion, we designed nine acid bioisosteres with robust
in vivo antihyperglycemic activity that were predicted to have favorable pharmacokinetic and tox-
icological profiles. Together, these findings provide evidence that supports the molecular design
we employed, where the unified pharmacophores possess a strong antidiabetic action due to their
multitarget activation.
Received: 5 January 2021
Accepted: 2 February 2021
Published: 4 February 2021
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Keywords: multitarget ligands; drug design; diabetes; molecular dynamics
Copyright:
© 2021 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
1. Introduction
Hyperglycemia and insulin resistance are hallmarks of diabetes; the prevalence of
diabetes is almost 425 million people globally, but this number would be twice that if it con-
sidered the people who are currently undiagnosed [
1]. Inactivity, a hypercaloric diet, and
lack of exercise have aggravated the diabetes epidemic, and this disease has a large impact
Molecules 2021, 26, 799. https://doi.org/10.3390/molecules26040799
https://www.mdpi.com/journal/molecules

Molecules 2021, 26, 799
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on the quality of life of the people who are diagnosed with it [2]. The current pharma-
cotherapy to treat diabetes consists of a wide roster of drugs that have a variety of modes of
action [ ]. Secretagogues, insulin sensitizers, glucose-uptake improvers, glucose-reuptake
blockers, and glucose absorption inhibitors are the main drug families that are currently
sold as pharmacological therapies [ ]. However, the use of these drugs has been limited due
3
4
to their side effects and the fact that they have not been able to control blood glucose in the
diabetic population. The failure of these drugs to maintain blood glucose at healthy levels
has led to research on new drugs with novel modes of action. GPR-40 (G-protein-coupled
receptor 40), PTP-1B (protein tyrosine phosphatase-1B), and aldose reductase (AR) are the
targets that have received increased attention in recent years [5]. GPR-40 agonists have
a secretagogue effect to increase glucose-dependent insulin secretion. PTP-1B belongs
to a family of proteins that catalyze the hydrolysis of phosphorylated tyrosine residues
and can therefore modulate insulin signaling, which decreases insulin resistance [
reduces glucose to sorbitol, but excessive levels of this molecule enhance osmotic pressure
and cellular stress [ ], which can lead to diabetic complications such as nephropathies,
neuropathies, and cardiomyopathies. AR inhibitors have been proposed as a means to ame-
6]. AR
7
liorate these diabetic complications [
territories outside of India and China [
5
,
7
8
]. Currently, none have received market approval in
]. From our medchem lab, a unified pharmacophore
has been proposed (Figure 1) and has shown robust multitarget antidiabetic action over
PPAR , PPAR , GPR40, AR, and PTP1B, five proteins implicated in diabetes [ 10]. This
α
γ
5,9,
multitarget unified pharmacophore is integrated by an acid moiety, an aromatic ring, a
flexible linker group, and a bulky hydrophobic group [11]. Previous work by our research
group and others has shown that molecules with these pharmacophore characteristics can
interact with high affinity with several proteins of interest for the treatment of diabetes. In
particular, they display a simultaneous multitarget stimulation of receptors PPAR
α, PPARγ,
and GPR40 and the enzyme inhibition of AR and PTP1B [ 13]. Thus, the preparation
5,9–
of nine acid bioisosteres (chemical substituents with similar physicochemical properties
that produce broadly similar biological properties related to another chemical entity), the
in vivo antihyperglycemic activity of these compounds in streptozotocin-induced diabetic
mice, and the molecular docking and dynamics prediction of this mode of binding over
PTP-1B and AR enzymes are reported in this work. Altogether, this study supports the
potential of these nine acid bioisosteres (derived from phenylacetic and phenylpropanoic
acids as well as benzylidenethiazolidine-2,4-dione) to control blood glucose through a
“multitarget activity” approach for the experimental treatment of diabetes.
Figure 1. Unified antidiabetic pharmacophore proposed with multitarget activity on PPARα, PPARγ,
GPR40, aldose reductase (AR), and PTP-1B: (A) acid group, (B) flexible linker, (C) bulky hydrophobic
group, and a central phenyl core. A pKa comparison between acid bioisosteric groups used to design
these molecules is also shown.

Molecules 2021, 26, 799
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2. Results and Discussion
2.1. Drug Design
Figure 1 shows the unified multitarget pharmacophore designed in our lab, which
has shown robust antidiabetic activity in previous research [ 13]. We suggest that the
four colored moieties are needed to exert the aforementioned multitarget effect on PPAR
PPAR , GPR40, AR, and PTP-1B [ 10 13]. These groups participate in the interaction
5,9–
α
,
γ
5, ,
between the molecules and the binding pocket of the protein. The acid head group
was considered due to its phosphoric acid-like characteristics; the linker with the free
rotation allows an adequate arrangement in the binding pocket of the protein, and the
bulky hydrophobic group increases the contact surface area to form a larger number of
interactions within the pocket [11].
2.2. Chemistry
The synthesis of the compounds was conducted according to Figure 2. We used three
different reagents to start the synthesis: ferulic acid, (4-hydroxyphenyl)acetic acid (10), and
isovanillin (12). In the case of the ferulic acid, we decided to perform a reduction of the dou-
ble bond using catalytic hydrogenation to improve the free rotation of the final compound
and to decrease the reactivity and the intrinsic toxicity of the α,β—unsaturated carbonyl,
obtaining Compound 11 (Figure 2, Step I). After, we performed a bimolecular nucleophilic
substitution to join the region with the bulky hydrophobic group of arylmethylhalides
with phenolic acids or aldehyde (Figure 2, Step II). Lastly, to generate another acid bioisos-
teric group, a Knoevenagel condensation was performed between isovanillin (12) and
thiazolidine-2,4-dione using a Dean–Stark apparatus to remove the water formed in the
reaction (Figure 2, Step III).
2.3. In Silico Analysis
2.3.1. Structural Analysis of the Targets
Molecular dynamics simulations are used to understand biomolecular structure and
dynamics [14–16]. In particular, for protein–ligand interactions, they have shed light
on multiple venues, ranging from the structure–activity relationships of multiple com-
pounds [17] to the kinetics and thermodynamics of binding [18].
MD simulations of the enzymes AR and PTP-1B were performed in the holo state
(see Materials and Methods for details). Figure 3 shows the binding pockets of the se-
lected molecular targets during the MD simulation, with snapshots every 50 ns. For AR,
the simulation showed high flexibility for the loops composed of Residues 112–136 and
213–228. Given the closeness to the binding site, this suggests that these loops are adaptable
to larger ligands. This is further supported by the presence of aromatic residues in said
loops, which could support binding via hydrophobic interactions. The binding pocket
shows two anchors for negative charges through one side with the backbone of Leu300
and the other with the side chains of Tyr48, His110, and Lys77. Additional π-stacking
interactions are provided by Trp111, in line with a previous work [19]. In the case of PTP-1B,
the receptor shows a more rigid topology, with less aromatic/hydrophobic residues, and a
smaller binding pocket. This pocket consists of an anchor for negative charges composed
of Ser216, Ala217, Gly218, Ile219, Gly220, and Arg221 and additional interactions through
π-stacking with Phe182.
2.3.2. Pharmacodynamics Predictions
Molecular docking was performed with the enzymes PTP-1B and AR. In order to
incorporate the flexibility of the receptor into the analysis, an ensemble docking approach
was performed using the six most representative structures of the MD trajectories. All the
compounds tested were shown to interact with both receptors, although with putative
different affinity, shown both in terms of docking score and ligand efficiency. We considered
the bioisosteric groups to be deprotonated at pH 7.4.

Molecules 2021, 26, 799
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The interactions that we found between the synthesized compounds and the two
proteins are summarized in Table S1 (Supplemental Information). In the case of AR,
Compounds
those established with Tyr48, Leu300, and Trp111. The latter contact has been reported
as one of the main binding residues in previous AR inhibitors [ ]. Moreover, the com-
1 and 4–9 showed interactions similar to the cocrystallized ligand, such as
5
pounds studied here form more interactions with the aromatic residues of the loop regions,
such as Trp20, Phe122, and Trp219. For PTP-1B, most compounds interact similarly with
the residues involved in the catalytic triad constituted by Arg221, Asp181, and Cys215
residues [12,13]. These residues are key for substrate binding, improving complex stability,
and performing the nucleophilic attack [20]. All the reported compounds interact with the
aforementioned negative charge anchor (Residues 216–221) through their negative moiety,
and leave the biphenyl group solvent-exposed. In general, the majority of compounds
presented satisfactory binding scores and interactions, which made them amenable for
further studies.
O
O
I
O
O
OH
OH
HO
HO
Ferulic acid
11
R
OH
R
OH
II
n
n
Ar
X
O
O
HO
Ar
O
16: Ar = 1-Naphthyl
17: Ar = 4ʹ-Biphenyl-2-carbonitrile
18: Ar = 3-Phenylbenzyl
10: R = H, n = 1
11: R = OCH3, n = 2
1: Ar = 1-Naphthyl,R = H, n = 1
2: Ar = 4ʹ-Biphenyl-2-carbonitrile, R = H, n = 1
3: Ar = 3-Phenylbenzyl, R = H, n = 1
4: Ar = 1-Naphthyl, R = OCH3, n = 2
5: Ar = 4ʹ-Biphenyl-2-carbonitrile, R = OCH3, n = 2
6: Ar = 3-Phenylbenzyl, R = OCH3, n = 2
O
NH
O
O
O
S
III
O
16 - 18
H
H
HO
Ar
O
II
Ar
O
O
O
O
S
NH
13: Ar = 1-Naphthyl
14: Ar = 4ʹ-Biphenyl-2-carbonitrile
15: Ar = 3-Phenylbenzyl
7: Ar = 1-Naphthyl
O
8: Ar = 4ʹ-Biphenyl-2-carbonitrile
9: Ar = 3-Phenylbenzyl
X
N
X
Ar
Ar =
X = Cl, Br
X
X
16
17
18
Figure 2. Synthetic route to achieve the desired compounds: (
Benzoic acid (0.3 eq.), piperidine (0.3 eq.), toluene, Dean–Stark apparatus, reflux.
I )
) H2, Pd0/C, EtOH, R.T.; (II) K2CO3, CH3CN, reflux; (III

Molecules 2021, 26, 799
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Figure 3. Snapshots of the Molecular Dynamics (MD) simulations of AR (left) and PTP-1B (right) with their respective
cocrystallized ligands. The protein structure is shown in the white transparent illustration, ligands in CPK representation,
and aromatic residues close to the binding pocket in licorice representation (red: tryptophan; orange: tyrosine; pink:
phenylalanine). The superimposed snapshots illustrate the plasticity of the loops (and its aromatic residues) close to the
binding pocket of AR.
2.3.3. Pharmacokinetics and Toxicological Properties
With the purpose of anticipating potential off-targets, adverse effects and toxicity are
associated with Compounds 1–9, a virtual prediction of their safety profiles was calculated
using the web servers AdmetSAR [21], SwissADME [22], and ACD/ToxSuite software,
v. 2.95 (Tables S2 and S3). We obtained the values of the different properties using in
silico tools to determine whether these molecules were appropriate for animal model
testing. From all the tested molecules (Table S2), the thiazolidine-2,4-diones 7–9 showed the
best intestinal absorption values. However, their toxicological profiles indicate that these
molecules could be hERG blockers and thus show certain degrees of cardiotoxicity, in line
with other examples in the literature on the toxicity of the thiazolidine-2,4-dione moiety
[ –25]. Despite all that, research on this structural motif continues due to the beneficial
23
effects of these drugs, including preservation of the pancreas’s ability to produce suffi-
cient levels of insulin and improve lipid metabolism by increasing levels of HDL [26 27].
,
Moreover, rosiglitazone, which is a thiazolidine-2,4-dione previously withdrawn from
the market, returned to it last year [28]. In the calculation of acute toxicity (Table S3),
Compounds 1–9 demonstrated similar or even higher predicted LD50 values than the
antidiabetic drugs pioglitazone (PIO) and glibenclamide (GLI), regardless of the admin-
istration route in mice and rats: oral (p.o.) or intraperitoneal (i.p.). Therefore, they were
predicted to be less toxic than the common antidiabetic drugs that were used as a refer-
ence in this work (Table S3). The predicted inhibition of the three main cytochrome P450
isoforms for Compounds 1–9 were similar to those of pioglitazone and glibenclamide at
relevant clinical concentrations (less than 10 µM), indicating low probabilities of drug–drug
interactions. Overall, the pharmacodynamics and pharmacokinetic predictions suggested
that the synthesized compounds possessed acceptable properties for further in vivo studies.
2.4. Antidiabetic Assay
After the synthesis and in silico analysis, we conducted the in vivo assay. We used
a streptozotocin (STZ)-nicotinamide (NA)-induced diabetic mouse model to determine
which compounds had an antidiabetic activity (Table 1). Compounds 2, 6, 8, and 9 exerted
a clearly antihyperglycemic effect since they prevented the hyperglycemic peak at the first
hour in a statistically significant manner in contrast to the control group.

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Table 1. Percentage of variation of blood glucose concentration during the in vivo acute antidiabetic
assay performed with Compounds 1–9.
Time Course and % of Variation of Glycemia
Compound
1 h
3 h
5 h
7 h
1
2
3
4
5
6
7
8
9
44.4
−0.2 *
16.3
14.7
24.7
7.1 *
20.6
35.9
−22.8 *
−8.4
30.5
−24.5
−13.7
−2.7
8.2
−21.6
−17.3
−10.4
−16.1
−30.0
−8.8
7.9
−21.1 *
−18.5 *
19.7
−14.1
−18.5
−1.6
−3.1 *
−5.9
−8.1
−31.7
−30.9
−11.5
−64.6 *
3.2 *
−10.7 *
−23.4
Control
Glibenclamide
52.9
−12.8 *
15.3
0.2
−33.9 *
−42.6 *
* Statistically significant versus control group, two-way ANOVA, comparison by Dunnett’s test (n = 6,
mean ± SEM, p < 0.05).
Phenol and acid natural products isolated from marine sponge and plants have been
described in the literature to decrease blood glucose by the inhibition of PTP-1B [29
focused on the design and synthesis of these acid molecules with antihyperglycemic activity.
Figure 4 shows that Compounds and prevented the increase in glucose concentration
,30]. We
6
9
since the first hour post-administration, which was maintained throughout the rest of the
experiment, even though their effects are not as strong as the positive control. Compounds
4
,
5
, and
beyond maintaining the blood glucose in a range that was close to baseline values; this
effect is known to be one of the major regulators of energy in vivo 31]. In contrast, the
positive control glibenclamide reduced the blood glucose levels in a significant manner after
7 h of the assay ( 64.6%), unlike Compounds and 30.0% and 30.9%, respectively).
7 only prevented the increase in glycemia and did not have additional effects
[
−
6
9
(
−
−
These findings are positive because, unlike the secretagogue drugs, these compounds will
not result in hypoglycemia.
Figure 4. Effect of a single dose of
6 and 9 over the blood glucose in STZ-NA-induced diabetic
mice. * Statistically significant difference versus control group by multiple comparison Dunnett’s test.
Glibenclamide was administered at 20 mg/kg. Compounds were administered at 100 mg/kg (n = 6,
mean ± SEM, p < 0.05).
Interestingly, the two compounds with the best activity (
group (3-phenylbenzyl motif). Compound contains a propanoic acid that is attached
to the central aromatic ring, whereas Compound possesses a double bond at position
6 and 9) share the same bulky
6
9

Molecules 2021, 26, 799
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five of the thiazolidine-2,4-dione ring, which results in a conformationally stable molecule
because the double bond is restricted in its rotation [32]. This is consistent with previous
reports [5], where phenylpropanoic acids with bulky and lipophilic groups showed an
antidiabetic effect but were mediated by GPR-40 and PPAR activation. In contrast, when
an electron-withdrawing substituent on the bulky group such as cyano was present in
Compounds 2, 5, and 8, the in vivo biological activity was reduced. On the other hand,
cutting the chain from three carbon atoms (phenylpropionic) to two (phenylacetic) in the
acidic region caused a decrease in antidiabetic activity for Compounds 1–3.
2.5. Pharmacological Consensus Analysis
We performed an in silico pharmacological consensus analysis (PHACA). The data
are reported in Table 2 using a traffic light system. PHACA combines the results of the
previous pharmacodynamics and pharmacokinetic predictions, toxicity predictions, and
additional experimental data. The rationale for a pharmacological consensus analysis
is that, when more predicted parameters agree that a compound is active and has low
toxicity and an adequate pharmacokinetic profile, the selection of a compound with suitable
pharmacological behavior for synthesis is more reliable. Therefore, a compound that has a
high score from a collection of multiple predictions is more likely to present an acceptable
behavior in a biological assay than a compound that has a high score from only a single
prediction. As shown in Table 2, the predictions of computational hits were in agreement
with the ones obtained in the in vivo assay as experimental hits. The five compounds
that showed activity in the in vivo assay in general are shown in green, which means
highly satisfactory results in the PHACA. Moreover, the compound that was inactive
in vivo, due to its unsatisfactory drug-like properties, is shown in red. Taken together,
compounds that show good PHACA results have a greater chance of being bioactive.
We can also disregard molecules with poor predicted results. The findings showed that
almost 50% of the compounds that were designed and synthesized were bioactive and
showed good pharmacokinetic and pharmacodynamics properties alongside an acceptable
toxicological profile.
2.6. Molecular Dynamics Studies of Compounds 6 and 9
The previous results suggested two important points for bioactivity: (1) there are
circa three atoms between the first aromatic ring and the acid functionality and (2) a
phenyl electron-withdrawing substituent appears to decrease the activity. Thus, the most
promising compounds (6 and 9) were analyzed through 300 ns of MD simulations, in
order to analyze key features of the binding events. Relevant plots to the stability of
simulation, such as protein and ligand RMSD are shown in Figure S2 (supplemental
information), which suggested that the simulations were sufficiently converged to extract
molecular insights. The flexibility of the targets was considered via the root-mean-square
fluctuation profiles of the residues of AR and PTP-1B when bound to the crystallographic
ligand and when bound to Compounds
profile appears to be similar for the three compounds, with the exception of Residues
176–189, where the simulation with Compound shows an increase in the fluctuations of
6 and 9 (Figure S3). With respect to PTP-1B, the
6
those residues. Regarding AR, it is interesting to observe that there is a marked change
in the region around Residues 218–230, which shows a decrease in the fluctuations on
those residues when comparing simulations with Compounds
structure. Taken together, these results suggest that the binding event with Compound
involves a decrease in the flexibility of the target. The interaction profile during the
6 and 9 to the crystal
9
simulation for the two compounds was extracted and normalized over the last 200 ns to
account for equilibration of the system (Figure 5). Regarding PTP-1B, the profile seems
symmetrical for both compounds, interacting more frequently with Phe182 and Arg221,
in a similar fashion to the crystallized inhibitor. However, there are some changes in the
nature of the interactions: for Compound
6, there are several contacts that possess an
important component of water mediation, and these contacts are not present in Compound

Molecules 2021, 26, 799
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9. This suggests that Compound
6
is not optimally filling the whole binding cavity, thus
allowing water molecules to mediate several key interactions for binding. Other subtle
differences involve the change in the interactions with Phe182 for Compound (more
hydrogen-bond) and Compound (hydrogen-bond/hydrophobic). Thus, this suggests
that Compounds and have common, yet nonidentical binding determinants in PTP-1B.
For AR, although Compounds and show similar and stable interactions when compared
6
9
6
9
6
9
to the cocrystallized ligand, such as Trp111 and Leu300, other contacts (such as Thr113,
Cys298, Ser302, and Cys303) suggest also that the binding modes of the two compounds
are not the same.
Table 2. Color code pharmacological consensus analysis (PHACA) for the evaluated compounds.
Property/Compound
1
2
3
4
5
6
7
8
9
Physicochemical properties
Log P
Predicted solubility
Lipinski Ro5
Pharmacodynamics properties (Molecular docking)
PTP-1B
AR
Pharmacokinetic properties
Absorption
PGp substrate
CYP1A2 inhibitor
CYP2D6 inhibitor
CYP3A4 Inhibitor
Toxicity prediction
Mutagenicity
Carcinogenicity
hERG blockage
Experimental antihyperglycemic data for analogue compounds [5,17]
In vivo assay
No
No
No
No
Active Active No
Active Active
Overall Score
Predicted analysis
result
Current experimental
result
: green = very satisfactory; : yellow = satisfactory; : red = unsatisfactory.
In order to obtain further insights in the binding mode of the compounds, visual
analysis and a clustering of the trajectories were performed, and the most representative
cluster for each simulation is shown in Figure 6. For PTP-1B, both Compounds
6 and 9
present a similar profile. Both compounds position the core aromatic ring for interaction
with Phe182 and Tyr46, while the negative moiety interacts with Phe182, Arg221, and
Asn266. There is no stabilization of the distal biphenyl moiety, except for the occasional
interactions presented with Phe182, which forces this moiety to be mainly solvent-exposed.
In the case of AR, Compound 6 locates the core aromatic ring for interaction with Trp111,
with hydrogen-bond interactions with Arg296 and Tyr309, while the distal biphenyl moiety
interacts evenly with Phe121 and Phe122. Frequent rotational movements were observed
for the distal phenyl ring. In comparison, Compound
9 does not get as deep inside the
binding pocket, positioning the thiazolidinedione moiety over Trp111. A rearrangement
of Arg296 allows the hydrogen-bond interaction to be fulfilled for this compound, with

Molecules 2021, 26, 799
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further aid from Cys303. As the compound is not buried that deeply, the aromatic distal
moieties interact with the farther tryptophan residues from the flexible loops and fix them
closer to the binding pocket. Hence, Trp20 and Trp219 interact with the compounds, the
nature of these contacts being mostly derived from π-stacking/hydrophobic interactions.
In this binding mode, frequent rotational movements were also observed for the distal
phenyl ring, equivalent due to its inherent symmetry.
Interactions profile in PTP-1B
2
1.5
1
0.5
0
0
0.5
1
1.5
2
TYR46
ASN111
PRO180
PHE182
CYS215
ALA217
ARG221
GLN266
Interactions profile in AR
1.5
1
0.5
0
0
0.5
1
1.5
TRP20
TRP79
TRP111
PHE121
TRP219
CYS298
SER302
TYR309
Figure 5. Protein–ligand interactions profile of Compounds
6 (left) and 9 (right) with PTP-1B and AR during the last 200 ns
of simulation. The color of the bar represents the type of interaction (orange: hydrophobic; green: hydrogen-bond; blue:
water-bridge). The fact that some residues can form more than one type of interaction allows the value to be larger than 1.
The binding poses observed in MD also offer a plausible mechanism for the lack of
activity of the other compounds. In the case of Compounds 1–3, the shorter phenylacetic
moiety would displace more internally the compound in AR, thus moving away the
interactions with Phe121 and Phe122 and therefore decreasing the stability of the compound
inside the binding pocket. In the case of compounds with the biphenyl-2-carbonitrile distal
moiety, the breaking of symmetry regarding the rotation of the last aromatic ring would
impair the interactions with Phe122 and Phe121 of the phenylacetic and phenylpropanoic
heads, or the interactions with Trp219 and Trp20 of the thiazolidinedione heads. Although
a more complete quantification of the binding events would require extensive sampling, the
simulations suggest plausible binding modes and explain all of the experimental biological
data. The simulations also offer support to the pharmacophore model, corroborating the
ligand-based features with the structure-based features of PTP-1B and AR. In particular,
Trp111 for AR and Phe182 for PTP-1B offer support for the first aromatic group, while
Arg296/Cys303/Tyr309 for AR and Phe182/Arg221/Asn266 for PTP-1B act as the anchors
for the negative charges, and Phe121/Phe122/Trp20/Trp219 finally stabilizes the distal
moieties via hydrophobic interactions.

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6
9

6
Figure 6. Most populated cluster for the simulations between Compounds
6 and 9 with PTP-1B (top) and AR (bottom)
during the 300 ns of simulation. Hydrogen-bond interactions are indicated with dashed lines.
3. Materials and Methods
3.1. Chemistry
All of the reagents that we used are commercially available and were purchased from
1
Sigma Aldrich® (Saint Louis, MO, USA) or Merck® (Darmstadt, Germany). H and 13
C
nuclear magnetic resonance spectra were obtained on a Variant Oxford Instrument (Palo
Alto, CA, USA, 600 MHz and 150 MHz, respectively). DMSO-d6 was used as a deuterated
solvent. Molecular masses were obtained with a JMS-700 spectrometer (JEOL, Tokyo,
Japan) with an impact electronic method (70 eV). Melting points were obtained using
an EZ-Melt MPA120 automated apparatus from Stanford Research Systems (Sunnyvale,
CA, USA). Thin layer chromatography (TLC) was conducted on silica gel plates (Merck,
Darmstadt, Germany).
3.1.1. Procedure for the Synthesis of 3-(4-hydroxy-3-methoxyphenyl)propanoic acid (11)
Ferulic acid (1 g) was submitted to catalytic hydrogenation to reduce the double
bond using a Shaker hydrogenation apparatus, Pd0/C (10%, 0.1 g), under a hydrogen
atmosphere at 60 psi for 2 h, with EtOH as the solvent. The crude product was filtered in
vacuo, and the filtrate was then immediately used without any further purification.

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3.1.2. General Procedure for the Synthesis of Compounds 1–6
The compounds were synthesized using 2-(4-hydroxyphenyl)acetic acid (10) and
3-(4-hydroxy-3-methoxyphenyl)propanoic acid (11) as precursors. A mixture of 1.1 eq
of the above compounds and 2.2 eq of K2CO3 was added to a round-bottom flask and
were mechanically stirred using acetonitrile as the solvent. After 1 h, the respective
arylmethylbromide or chloride (1 eq) was added to the mixture and was set in reflux
to generate the respective substitution product under a nitrogen atmosphere. Once the
reaction was completed, cold water and 10% aqueous HCl were added until a pH of 4.0
was reached, and a solid product was obtained. The crude product was filtered in vacuo.
Recrystallization from the appropriate solvent was performed to afford colorless crystals
of each compound.
[4-(1-naphthylmethoxy)phenyl] acetic acid (1)
The mixture of (4-hydroxyphenyl)acetic acid (10), K2CO3, and 1-(chloromethyl)
◦
naphthalene (16) was stirred and heated at 80 C for 9 h. Recrystallized from EtOH,
◦
yield = 70%; mp = 114.8–116.3 C; 1H-NMR (600 MHz, DMSO-d6)
δ: 3.49 (s, 2H, CH2),
5.52 (s, 2H, OCH2), 7.04 (d, 2H, Jo = 8.64, H-3, H-5), 7.19 (d, 2H, Jo = 8.64, H-2, H-6), 7.51
(t, 1H, Jo = 8.1, H-30), 7.54–7.59 (m, 2H, H-6, H-7), 7.67 (d, 1H, H-20, Jo = 6.84), 7.93 (d, 1H,
Jo = 8.22), 7.98 (d, 1H, Jo = 8.58), 8.09 (d, 1H, Jo = 8.04); 13C-NMR (150 MHz, DMSO-d6)
δ
: 40.49 (CH2), 68.23 (OCH2), 115.060(C-30, C-50), 124.31 (C-8”), 125.8 (C-3”),0126.39 (C-6”),
0
126.8 (C-4”), 127.03 (C-7”). 127.8 (C-1 ), 128.9 (C-2”), 129.06 (C-5”), 130.85 (C-2 , C-6 ), 131.55
(C-4”a), 133.71 (C-8”a), 133.02 (C-1”), 157.62 (C-40), 173.4 (C-1) ppm. EIMS: m/z = 292.3
[M+], 157.9 [M-135], 141.0 [M-151].
{4-[(20-cyanobiphenyl-4-yl)methoxy]phenyl}acetic acid (2)
The mixture of (4-hydroxyphenyl)acetic acid (10), K2CO3, and 40-bromomethyl-2-
biphenylcarbonitrile (17) was stirred and heated at 80 ◦C for 15 h. Recrystallization from
EtOH was performed to afford colorless crystals of 2. Yield = 60%; mp = 164.9–166.8
◦C; 1H-NMR (600 MHz, DMSO-d6)
δ: 3.48 (s, 2H, CH2), 5.17 (s, 2H, OCH2), 6.99 (d, 2H,
Jo = 8.58, H-3, H-5), 7.19 (d, 2H, Jo = 8.58, H-4, H-6), 7.59 (m, 5H, H-20, H-60, H-30, H-50,
H-4”), 7.64 (d, 1H, Jo = 7.68, H-6”), 7.79 (t, 1H, Jo = 7.68, H-5”), 7.95 (d, 1H, Jo = 7.68,
H-3”); 13C-NMR (150 MHz, DMSO-d6)
δ: 39.68 (CH2), 68.74 (OCH2), 110.59 (C-2”’), 114.97
(C-30, C-50), 118.99 (C-4), 125.8 (C-3”), 126.39 (C-6”), 127.82 (C-6”’). 128.32 (C-3”, C-5”),
128.68 (C-4”’), 129.24 (C-2”, C-6”), 130.56 (C-10), 130.86 (C-20, C-60), 133.98 (C-3”’), 134.24
(C-5”’), 137.68 (C-4”), 138.27 (C-1”), 144.65 (C-1”’), 157.48 (C-40). 173.40 (C-1) ppm. EIMS:
m/z = 343.3 [M+], 326 [M-17], 193.0 [M-151].
[4-(biphenyl-3-ylmethoxy)phenyl] acetic acid (3)
The mixture of (4-hydroxyphenyl)acetic (10) acid, K2CO3, and 3-phenylbenzyl bro-
◦
mide (18) was stirred and heated at 80 C for 7 h. Recrystallization from EtOH was
performed to afford crystals of
3
. Yield = 75%; mp = 141.9–143.8 ◦C. 1H-NMR (600 MHz,
DMSO-d6) : 3.48 (s, 2H, CH2), 5.16 (s, 2H, OCH2), 6.98 (d, 2H, Jo = 8.58, H-3, H-5), 7.18 (d,
δ
0
0
2H, Jo = 8.58, H-2, H-6), 7.37 (t, 1H, Jo = 7.56, H-5 ), 7.44 (d, 1H, Jo = 7.56, H-4 ), 7.49–7.46 (m,
3H, H-3”, H-4”, H-5”), 7.62 (d, 1H, Jo = 7.56, H-60), 7.67 (d, 2H, Jo = 7.62, H-2”, H-6”), 7.72
(s, 1H, H-20); 13C-NMR (150 MHz, DMSO-d6)
δ: 39.68 (CH2), 68.74 (OCH2), 110.59 (C-2”’),
114.24 (C-30, C-50), 125.58 (C-2”), 125.76 (C-6”), 126.29 (C-4”’), 126.35 (C-2”’, C-6”’), 126.87
(C-5”), 127.17 (C-4”), 128.58 (C-3”’, C-5”’), 128.70 (C-10). 130.02 (C-20, C-60), 137.54 (C-3”),
139.57 (C-1”), 139.95 (C-1”’), 172.55 (C-1). EIMS: m/z = 318.3 [M+], 241 [M-77], 165 [M-153].
3-[3-methoxy-4-(1-naphthylmethoxy)phenyl]propanoic acid (4)
The mixture of 11, K2CO3, and 1-(chloromethyl)naphthalene (16) was stirred and
heated for 8 h. Recrystallization from EtOH was performed to afford
4
. Yield = 64%;
◦
mp = 135.6–136.5 C; 1H-NMR (600 MHz, DMSO-d6)
δ: 2.52 (t, 2H, CH2), 2.77 (t, 2H, CH2),
3.72 (s, 3H, OCH3), 5.46 (s, 2H, OCH2), 6.74 (dd, 10H, H-6, Jm = 1.2, Jo = 8.4 Hz), 6.88 (s, 1H,
0
H-2), 7.08 (d, 1H, H-5, Jo = 8.4 Hz), 7.50 (t, 1H, H-3 , Jm = 1.4, Jo = 7.8 Hz), 7.56 (m, 2H, H-6 ,

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H-70), 7.64 (d, 1H, H-20, 7.2 Hz), 7.93 (d, 1H, H-50, Jo = 8.4 Hz), 7.95 (d, 1H, H-40, Jo = 9 Hz),
8.11 (d, 1H, H-80, Jo = 7.8 Hz), 12.13 (s, COOH). 13C-NMR (150 MHz, DMSO-d6)
δ: 30.03
(C-8, CH2), 35.5 (C-7, CH2), 55.4 (O-CH3), 68.6 (O-CH2), 112.57 (C-2), 114 (C-5), 119.9 (C-6),
0
0
0
0
0
0
124 (C-8 ), 125.9 (C-3 ), 126.3 (C-6 ), 126.6 (C-7 ), 128.2 (C-5 ), 128.5 (C-4 ), 131.1 (C-8a), 131.2
0
(C-1), 133.3 (C-1 , C-4a), 146.1(C-4), 149 (C-3), 173.8 (COOH). EIMS: m/z = 336.3 [M+], 156.0
[M-207], 127.0.
3-{4-[(20-cyanobiphenyl-4-yl)methoxy]-3-methoxyphenyl}propanoic acid (5)
The mixture of 11 and 40-bromomethyl-2-biphenylcarbonitrile (17) was stirred and
◦
heated at 80 C for 2 h. Recrystallization from EtOH was performed to afford white crystals
◦
of
5
. Yield = 60%; mp = 133.7–134.9 C; 1H-NMR (600 MHz, DMSO-d6)
δ
: 2.51 (t, 2H, CH2),
2.76 (t, 2H, H-7, CH2), 3.77 (s, 3H, OCH3), 5.12 (s, 2H, OCH2), 6.72 (dd, 1H, H-6, Jm = 1.8,
Jo = 9.6 Hz), 6.80 (s, 1H, H-2), 6.96 (d, 1H, H-5, Jo = 7.8 HZ), 7.59 (m, 5H, H-20, H-30, H-50,
H-60, H-4”), 7.64 (d, 1H, H-6”, Jo = 7.8 Hz), 7.95 (d, 1H, H-3”, Jo = 7.2 Hz). 13C-NMR
(150 MHz, DMSO-d6) δ: 29.5 (C-7, CH2), 35.1 (C-8, CH2), 55.1 (C-10, OCH3), 69.2 (C-11
OCH2), 109.7 (C-2”), 112.2 (C-2), 117.3 (C-5, C-6”), 118.2 (C-12), 119.5 (C-6), 127.5 (C-20,
C-6”), 127.8 (C-40), 128.3 (C-30, C-50), 129.7 (C-4”), 133.4 (C-1, C-3”), 133.6 (C-5”), 137.7
(C-10), 143.8 (C-4), 145.7 (C-1”), 148.6 (C-3), 173.4 (C-9). EIMS: m/z = 387.4 [M+].
3-[4-(biphenyl-3-ylmethoxy)-3-methoxyphenyl]propanoic acid (6)
The mixture of 11 and 3-phenylbenzyl bromide (18) was stirred and heated at 80 ◦C
for 12 h. Recrysta◦llization from EtOH was performed to afford crystals of
6. Yield = 84%;
δ: 2.50 (t, 2H, CH2), 2.75 (t, 2H, CH2),
mp = 119.5–120.0 C. 1H NMR (600 MHz, DMSO-d6)
3.75 (s, 3H, OCH3), 5.11 (s, 2H, OCH2), 6.70 (dd, 1H, H-6, Jm = 1.8, Jo = 6.3 Hz), 6.87 (s,
1H, H-2), 6.95 (d, 1H, H-5, Jo = 7.8 Hz), 7.37 (t, 1H, H-50, Jo = 7.2, Jo = 7.8 Hz), 7.42 (d, 1H,
H-60, Jo = 7.4 Hz), 7.47 (t, 3H, H-3”, H-4”, H-5”, Jo = 7.2, Jo = 7.8 Hz), 7.61 (d, 1H, H-40,
Jo = 7.8 Hz), 7.65 (d, 2H, H-2”, H-6”, Jo = 7.2 Hz) 12.09 (s, COOH). 13C-NMR (150 MHz,
DMSO-d6) δ: 29.6 (C-8, CH2), 35.1 (C-7, CH2), 55.2 (OCH3), 69.7 (OCH2), 112.2(C-2), 113.5
0
0
0
(C-5), 119.6 (C-6), 126.3 (C-2 , C-4 ), 127.2 (C-6 ), 128.6 (C-3”, C-5”), 133.58 (C-1), 137.2 (C-2”,
C-6”), 139.5 (C-1”, C-4”), 139.8 (C-10,C-30), 145.7 (C-4), 148.69 (C-3), 173.4 (COOH). EIMS:
m/z = (% int. rel): 362.4 [M+].
3.1.3. General Procedure for the Synthesis of Compounds 13–15
The compounds were synthesized using isovanillin (12) as precursor. A mixture of
1.1 eq of 12 and 2.2 eq of K2CO3 was added to a round-bottom flask and was mechanically
stirred using acetonitrile as a solvent. After 1 h, the respective arylmethylbromide or
chloride (1 eq) was added to the mixture and was set in reflux under a nitrogen atmosphere
to form the respective substitution product. The crude product was filtered in vacuo. Cold
water was added to wash the crude product and was then filtered, thus obtaining a solid.
Recrystallization from the appropriate solvent was performed to afford colorless crystals
of each compound.
4-methoxy-3-(1-naphthylmethoxy)benzaldehyde (13)
The mixture of isovanillin and 1-(chloromethyl)naphthalene (16) was stirred and
heated at 80 ◦C for 2 h. Recrystallization from EtOH was performed to afford colorless
◦
1
crystals of 13. Yield = 81%; mp = 114.8–116.3 C; H-NMR (600 MHz, DMSO-d6) δ:
3.92 (s, 3H, OCH3), 5.60 (s, 2H, OCH2), 07.01 (d, 1H, H-5, Jo = 8.3 Hz), 7.45 (t, 1H, H-30,
Jo = 7.2 Hz, Jo = 7.2 Hz), 7.49 (dd, 1H, H-7 , Jm = 1.8 Hz, Jm = 1.8 Hz, Jo = 8.2 Hz), 7.51–7.53
(m, 1H, H-60, Jm = 1.2 Hz, Jo = 7.44 Hz), 7.55 (ddd, 1H, H-6, Jm = 1.5 Hz, Jm = 1.3 Hz,
Jm = 1.3 Hz, Jo = 8.2 Hz, Jo = 6.8 Hz), 7.59 (d, 1H, H-2, Jm = 1.7 Hz), 7.62 (d, 1H, H-20,
Jo = 6.9 Hz), 7.84 (d, 1H, H-50, Jo = 8.2 Hz), 7.88 (d, 1H, H-80, Jo = 7.7 Hz), 8.08 (d, 1H, H-40,
Jo = 8.4 Hz), 9.84 (s, 1H, CHO). EIMS: m/z = 292.1 [M+].

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40−[(5-formyl-2-methoxyphenoxy)methyl]biphenyl-2-carbonitrile (14)
0
The mixture of isovainillin and 4 -bromomethyl-2-biphenylcarbonitrile (17) was stirred
◦
and heated at 80 C for 4 h. Recrystallization from EtOH was performed to afford colorless
crystals of 14. Yield = 82%; mp = 157.4–158.3 ◦C; 1H-NMR (600 MHz, DMSO-d6)
δ: 3.97 (s,
3H, OCH3), 5.24 (s, 2H, OCH2), 7.01 (d, 1H, H-5, Jo = 8.6 Hz), 7.43 (m, 1H, H-6, Jo = 7.7 Hz,
Jo = 7.7 Hz), 7.49 (m, 3H, H-2, H-3”,0 H-4”, Jm = 1.6 Hz, Jm = 1.6 Hz, Jm = 1.6 Hz,
0
0
0
Jo = 8.3 Hz), 7.58 (m, 4H, H-2 , H-3 , H-5 , H-6 , Jo = 8.8 Hz, Jo = 9 Hz), 7.63 (ddd, 1H, H-5”,
Jm = 1 Hz, Jm = 1 Hz, Jm = 1 Hz, Jo = 7.7 Hz, Jo = 7.6 Hz), 7.76 (d, 1H, H-6”, Jo = 7.7 Hz),
9.84 (s, 1H, CHO). EIMS: m/z = 343.2 [M+].
3-(biphenyl-3-ylmethoxy)-4-methoxybenzaldehyde (15)
The mixture of isovanillin and 3-phenylbenzyl bromide (18) was stirred and heated at
80 ◦C for 2 h. Recrystallization from EtOH was performed to afford colorless crystals of
15. Yield = 66%; mp = 82.1–83.9 ◦C. 1H-NMR (600 MHz, DMSO-d6)
δ: 3.96 (s, 3H, OCH3),
5.25 (s, 2H, OCH2), 6.99 (d, 1H, H-5, Jo = 8.2 Hz), 7.35 (t, 1H, H-50, Jo = 7.4 Hz, Jo = 7.4 Hz),
7.42–7.45 (m, 4H, H-2”, H-3”, H-5”, H-6”), 7.46–7.48 (m, 1H, H-6, Jm = 1.8 Hz, Jm = 1.9 Hz,
0
0
Jo = 8.2 Hz), 7.49 (d, 1H, H-2, Jm = 1.7 Hz), 7.53–7.55 (m, 1H, H-4”), 7.59 (dd, 2H, H-4 , H-6 ,
0
Jm = 1 Hz, Jm = 1.2 Hz, Jo = 8.2 Hz), 7.68 (s, 1H, H-2 ), 9.82 (s, 1H, CHO). EIMS: m/z = 318.1
[M+].
3.1.4. General Procedure for the Synthesis of Compounds 7–9
Compounds 13–15 (1 eq) were collocated in a round-bottom flask, and 1.1 eq of
thiazolidine-2,4-dione was added. The mixture was stirred using toluene as the solvent
and was then heated to reflux, after which 0.3 eq of piperidine and 0.3 eq of benzoic acid
were added and coupled with a Dean–Stark system. The mixture was maintained in reflux
for 4 h. The crude reaction was filtered in vacuo and was washed with water to afford the
respective Compounds 7–9.
(5 Z)-5-[4-methoxy-3-(1-naphthylmethoxy)benzylidene]-1,3-thiazolidine-2,4-dione (7)
General procedure was performed obtaining a yellow solid of
7
. Yield = 69%;
◦
1
m.p. = 233.0–234.9 C; H-NMR (600 MHz, DMSO-d6)
δ: 3.81 (s, 3H, OCH3), 5.59 (s,
2H, OCH2), 7.15–7.16 (d, 1H, H-5, Jo = 8.5 Hz), 7.21 (d, 1H, H-20, Jo = 8.5 Hz), 7.43
(d, 1H, H-2, Jm = 1.7 Hz), 7.52 (t, 1H, H-30, Jo = 8 Hz, Jo = 7.2 Hz), 7.56 (dd, 1H, H-70,
Jm = 1 Hz, Jm = 1.4 Hz, Jo = 7.1 Hz, Jo = 7.6 Hz), 7.59 (dd, 1H, H-6, Jm = 1.8 Hz, Jm = 1.1 Hz,
0
Jm = 1.3 Hz, Jo = 8.2 Hz, Jo = 6.8 Hz), 7.67 (d, 1H, H-2 , Jo = 6.9 Hz), 7.75 (s, 1H, H-8,C=CH),
7.94 (d, 1H, H-50, Jo = 8.22), 7.98 (dd, 1H, H-80, Jm = 1.02, Jo = 7.95), 8.12 (d, 1H, H-40,
Jo = 8.2); 13C-NMR (150 MHz, DMSO-d6)
δ
: 55.74 (OCH3), 68.55 (OCH2), 112.44 (C-5),
0
0
0
115.53 (C-2), 120.59 (C-9), 123.88 (C-8 ), 124.08 (C-3 ), 125.36 (C-6), 125.7 (C-8), 125.98 (C-6 ),
126.43 (C-1), 126.67 (C-40), 128.44 (C-70), 128.74 (C-20), 131.11 (C-40a), 132.03 (C-50), 132.19
(C-10), 133.26 (C-80a), 147.89 (C-3), 151.24 (C-4), 167.36 (C-11), 167.93 (C-10). EIMS: m/z:
391.2 [M+].
40-({5-[(Z)-(2,4-dioxo-1,3-thiazolidin-5-ylidene)methyl]-2-methoxyphenoxy}methyl)
biphenyl-2-carbonitrile (8)
General procedure was performed obtaining a yellow solid of
8
. Yield = 63%;
◦
1
m.p. = 256.7–257.2 C; H-NMR (600 MHz, DMSO-d6)
δ: 3.83 (s, 3H, OCH3), 5.21 (s,
2H, OCH2), 7.12–7.14 (d, 1H, H-5, Jo = 8.4 Hz), 7.17–7.19 (m, 1H, H-6, Jm = 1.8 Hz,
Jm = 1.8 Hz, Jo = 8.5 Hz), 7.26–7.27 (d, 1H, H-2, Jm = 1.8 Hz), 7.55–7.57 (ddd, 1H, H-
4”, Jm = 1 Hz, Jm = 1 Hz, Jm = 1 Hz, Jo = 7.7 Hz, Jo = 7.6 Hz), 7.58–7.61 (m, 4H, H-20,
H-30, H-50, H-60, Jo = 8.4 Hz, Jo = 7.6 Hz), 7.71–7.72 (d, 1H, H-6”, Jo = 7.3 Hz), 7.65 (s, 1H,
H-8, C=CH), 7.75–7.78 (ddd, 1H, H-5”, Jm = 1.2 Hz, Jm = 1.1 Hz, Jm = 1.1 Hz, Jo = 7.7 Hz,
Jo = 7.7 Hz), 7.92–7.93 (dd, 1H, H-3”, Jm = 1 Hz, Jo = 7.4 Hz). 13C-NMR (150 MHz, DMSO-
d6)
δ
: 56.22 (OCH3), 690.98 (OCH2), 110.61 (C-2”), 112.85 (C-5), 115.31 (C-2), 124.49 (C-6),
0
0
0
126.38 (C-8), 128.54 (C-2 , C-6 ), 128.69 (C-6”), 129.27 (C-5 , C-3 ), 130.56 (C-4”), 131.43 (C-1),

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133.96 (C-3”), 134.27 (C-5”), 137.78 (C-1”), 137.88 (C-40), 144.63 (C-10), 148.25 (C-3), 151.38
(C-4), 169.22 (C-11), 169.45 (C-10). EIMS, m/z: 442.2 [M+].
(5 Z)-5-[3-(biphenyl-3-ylmethoxy)-4-methoxybenzylidene]-1,3-thiazolidine-2,4-dione (9)
General procedure was performed obtaining a yellow solid of
9
. Yield = 55%;
◦
1
m.p. = 202.1–206.2 C; H-NMR (600 MHz, DMSO-d6)
δ: 3.85 (s, 3H, OCH3), 5.23 (s,
2H, OCH2), 7.14–7.15 (d, 1H, H-5, Jo = 8.5 Hz), 7.20–7.21 (m, 1H, H-6, Jm = 1.5 Hz,
Jm = 1.6 Hz, Jo = 8.5 Hz), 7.27–7.28 (d, 1H, H-2, Jm = 1.9 Hz), 7.36–7.39 (m, 1H, H-50,
Jo = 7.4 Hz, Jo = 7.4 Hz), 7.45–7.50 (m, 4H, H-2”, H-3”, H-5”, H-6”), 7.62–7.63 (d, 1H, H-4”,
Jo = 7.6 Hz), 7.66–7.67 (m, 2H, H-40, H-60, Jm = 1.1 Hz, Jo = 7.8 Hz), 7.71 (s, 1H, H-8), 7.76
(s, 1H, H-20); 13C-NMR (150 MHz, DMSO-d6)
δ
: 55.77 (OCH3), 69.96 (OCH2), 112.41 (C-5),
0
0
114.87 (C-2), 120.69 (C-9), 124.41 (C-4 ), 125.57 (C-6), 126.14 (C-8), 126.29 (C-2 ), 126.69 (C-2”,
C-6”), 126.79 (C-1), 127.54 (C-50), 128.93 (C-3”, C-5”), 129.11 (C-4”), 131.97 (C-60), 137.41
(C-10), 139.88 (C-1”), 140.35 (C-30), 147.81 (C-3), 151.19 (C-4), 167.40 (C-11), 167.92 (C-10).
EIMS, m/z: 417.4 [M+].
3.2. Animals
ICR male mice weighing 25
±
5 g were housed in animal cages with 12 h periods
of light and 12 h periods of dark. Animals were allowed to acclimatize to the laboratory
conditions. The animals were maintained in a 25 ◦C environment, and water and food
were provided ad libitum. All animal experiments were conducted according to protocols
that were approved by the Mexican government NOM-065-ZOO-1999 and NOM-033-ZOO-
2014 [33,34].
3.2.1. Induction of Diabetes
Induction of diabetes was performed according to previous works [35,36]. Animals
were fasted for 8 h. Nicotinamide (NA) was dissolved in sterile water and administered at
a dose of 20 mg/kg i.p. Fifteen minutes later, streptozotocin (STZ) was administered at
a dose of 100 mg/kg i.p. (citrate buffer 0.05 M, pH = 4.5). The mice that presented with
blood glucose levels over 150 mg/dL were used for the in vivo assay.
3.2.2. Acute Antidiabetic Assay
We performed this assay according to a previous methodology that was used in
our lab [5,37]. Briefly, the animals were fasted for 8 h, after which the groups (n = 6)
were administered a 100 mg/kg dose of the different treatments, in addition to one ve-
hicle group (10% Tween 80) and one positive control group (20 mg/kg glibenclamide).
Blood glucose levels were measured at 0, 1, 3, 5, and 7 h by a commercial glucometer
(Accu-Check Performa).
3.3. Crystal Structures
The crystallographic data of the proteins PTP-1B and AR were downloaded from PDB
(4Y14 [38] and 4XZH [39], respectively). Both of these enzymes were cocrystallized along
their specific inhibitors (i.e., bound to compounds CPT157633 and JF0048, respectively).
The structures were manually curated using Maestro 12.1 with the Protein Preparation
Wizard from the Schrodinger suite 2019-4 (Schrödinger LLC, 2019, New York, NY, USA).
After removal of unnecessary molecular entities in each structure such as ions, cosolvents,
and water molecules, the hydrogen-bond network and rotameric conformations were
optimized, and a restrained minimization was performed.
3.4. Molecular Dynamics Simulations
MD studies of the protein–ligand complexes were performed using Desmond (version
2019-4, Schrödinger, New York, NY, USA) with the OPLS3e forcefield [40]. The complexes
were prepared with the System Builder Utility in a 10 Å buffered rhombic dodecahe-
dron box, using the transferable intermolecular potential with a 3-point model for water

Molecules 2021, 26, 799
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(TIP3P). The complexes were neutralized, and NaCl was added in a 0.15 M concentra-
tion. Complexes were minimized using the steep-descent (SD) algorithm followed by
the Broyden–Fletcher–Gold–Farb–Shanno (LBFGS) method in three stages. I−n1the first
−2
stage, water heavy atoms were restrained with a force constant of 1000 kcal mol
Å
for
5000 steps (1000 SD, 4000 LBFGS) with a convergence criterion of 50 kcal mol−1 Å−2; for
the second stage, backbones were constrained with a 10 kcal mol−1 Å−2 force constant
using a convergence criterion of 10 kcal mol−1 Å−2 for 2000 steps (1000 SD, 1000 LBFGS);
for the third stage, the systems were minimized with no restraints for 1000 steps (750 SD,
250 LBFGS) with a convergence criterion of 1 kcal mol−1 Å−2. Equilibration was carried
out in several steps. At first, Brownian dynamics for 250 ps were determined with the
Berendsen thermostat. Simulation on the NVT ensemble was then performed, slowly
heating from 10 to 300 K over 3000 ps. At this stage, constraints were enforced on solute
heavy atoms, using a constant of 50 kcal mol−1. Finally, equilibration on NPT ensemble
used the Berendsen thermostat and Langevin barostat for additional 250 ps was done.
With the system ready for production, 300 ns of simulation were performed, under an
NPT ensemble at 1 bar and 300 K using the Martyna–Tuckerman–Klein (MTK) barostat and
the Nosé–Hoover thermostat. Electrostatic forces were calculated with the smooth PME
method using a 9 Å cut-off, while constraints were enforced with the M-SHAKE algorithm.
Integration was done every 2 fs, with a recording interval of 500 ps. The quality analysis of
the simulation and other trajectory analyses were carried out with the tools implemented
in the Maestro 12.1 (Schrödinger, New York, NY, USA) and with VMD [41].
3.5. Ensemble Docking
The simulations of the two receptors in the holo state (i.e., drug-bound) were clustered
based on the backbone and sidechain RMSD, and the first six clusters, covering ~60% of the
sampled conformational space, were recovered. The obtained conformations were stripped
from waters and ions and subjected to a restrained minimization of the side chains. With
the nine compounds, and the corresponding cocrystallized inhibitor, docking procedures
were performed on the six protein conformations. All docking procedures were performed
with Glide 8.4 with the XP methodology [42] and the OPLS3e forcefield [40]. A grid was
created around the binding pocket in a box of 25 Å of length per side, in order to allow the
ligand to explore diverse binding poses around the pocket.
After the docking calculations were performed for the six protein conformations, the
results for each individual compound were pooled and clustered following the ligand heavy
atoms. For data fusion in ensemble docking, multiple approaches have been proposed [43].
For our study, a simple data fusion approach was followed: from the clustering of the
docked poses, the pose within the most populated cluster was selected. For assigning the
docking score, the lowest value within the most populated cluster was chosen, in order to
reflect the best achieved score.
From the total number of poses, the percentage of poses that were included in the top
cluster was also included in the report table. Validation of the docking protocol used was
carried out by redocking the respective cocrystallized ligands. It was observed that the
cocrystallized inhibitor docked adequately in the six conformers (not more than 2.0 Å of
RMSD) and that the clustering of all poses also generated a most-populated cluster with
the correct crystallographic pose within the 2.0 Å of tolerance.
3.6. Pharmacological Consensus Analysis
An a priori pharmacological consensus analysis (PHACA) was performed with the
results that were obtained from several computational tools. The pharmacodynamics
properties were calculated from the output score that was obtained with Glide XP. The
following pharmacokinetic properties were acquired using AdmetSAR and SwissADME
software: consensus logP, water solubility class, human intestinal absorption, blood–brain
permeability, P-glycoprotein substrate, bioavailability score, and inhibition of CYP3A4. Tox-
icological profiles (Ames, hERG channel blockage, and carcinogenesis) were obtained from

Molecules 2021, 26, 799
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the web server AdmetSAR and ACD/Tox Suite version 2.95 [22
,23
,44]. To visualize the
pharmacological consensus analysis, we used a color code indicating chances that the com-
pound has drug-like properties, as follows: green (very satisfactory), yellow (satisfactory),
and red (unsatisfactory). We considered three properties for each of the points. Depending
on the number of properties they satisfied, we classified them with the respective color
mentioned above. These colors were assigned in reference to other work [45].
3.7. Statistical Analysis
The data were analyzed by two-way ANOVA followed by Dunnett’s post hoc test.
The results are expressed as the means
p values were < 0.05.
±
S.E.M. Statically significance was assumed when
4. Conclusions
In this work, we reported the design, synthesis, and in vivo evaluation of nine com-
pounds that were developed for the experimental treatment of diabetes, and we aimed to
complement a unified antidiabetic pharmacophore with multitarget action. An a priori
analysis of the compounds employing a pharmacological consensus analysis (PHACA)
showed highly satisfactory results for five compounds, and their in vivo evaluation con-
firmed their biological activity. The molecules controlled the blood glucose levels without
exacerbating the effect, in contrast to glibenclamide, which had a robust hypoglycemic
effect. Moreover, molecular dynamics analysis shed light on the molecular recognition
process and provided an explanation for the experimental results obtained. Together, these
findings provide a rationale for the molecular design we employed in our lab, as we found
the unified pharmacophore to possess a strong antidiabetic activity. Moreover, the use of
an acid bioisostere surrogate, such as thiazolidine-2,4-dione or carboxylic acid, resulted in
an interaction similar to those presented by the original cocrystallized inhibitors. Indeed,
Compounds
6 and 9 are interesting bioactive compounds for the treatment of diabetes due
to their antihyperglycemic effect. Further studies are needed to identify other possible
targets in the body [46], in order to fully characterize its polypharmacological profile.
Supplementary Materials: Supplementary materials can be found. Figure S1: Interactions profile for
the co-crystallized compounds during the 300 ns simulation for PTP-1B (left) and AR (right), Figure
S2: Protein and ligand RMSD profiles for compounds
6 and 9 during the 300 ns simulations. The
large fluctuations on the ligand RMSD are due to the flipping of the distal biphenyl moiety, which
in PTP-1B is solvent-exposed, and in AR is stabilized by two contiguous residues, Figure S3: Root
mean square fluctuations of the carbon alpha for the two targets during the simulations with the
crystal inhibitor and compounds
6 and 9, Table S1: Ligand interactions in PTP-1B and AR. Score
values under 6.0 kcal/mol were considered good values. Residues also involved in the interaction
−
with the co-crystallized inhibitor are marked in red. The percentage of conformations in the largest
cluster is reported in cluster size, while the most negative value is reported for the score, Table S2:
Calculated pharmacokinetic and genotoxic properties for compounds 1–9, Table S3: Acute toxicity
profile predicted for compounds 1–9, pioglitazone and glibenclamide.
Author Contributions: Y.G.-C., J.M.-M. and C.M.-G. performed the chemical synthesis of all com-
pounds, acquired the antidiabetic in vivo data, and analyzed the chemical and biological results.
B.C.-L. interpreted the data for SAR analysis, contributed with reagents and analysis tools, drafted
some parts of the manuscript, and made a critical revision. O.P.-H. performed and analyzed the
molecular docking and dynamics of protein complexes. E.H.-N. and G.I.H.-B. performed and in-
terpreted the spectroscopic analysis using nuclear magnetic resonance. E.A.D.-M. carried out and
explained the antidiabetic assays and drafted some parts of the manuscript. G.N.-V. developed the
concept, designed the compounds, acquired funding, and prepared and wrote the manuscript. All
authors have read and agreed to the published version of the manuscript.
Funding: This research and the APC were funded by the Consejo Nacional de Ciencia y Tecnología
(CONACyT), grant No. 253814 (Ciencia Básica 2015) and grant No. 252881 (PEI 2018), given to G.
Navarrete-Vazquez.

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Institutional Review Board Statement: The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Institutional Review Board of Universidad Autónoma
Metropolitana (protocol code 1857; date of approval: 31 January 2019).
Informed Consent Statement: Not applicable.
Data Availability Statement: No other data were reported.
Acknowledgments: We appreciate the support of “Facultad de Farmacia, Universidad Autónoma
del Estado de Morelos” for providing research supplies for this study. E. A. Dominguez-Mendoza is
a CONACyT and PRODEP-SEP postdoctoral fellow (511-6/18-6769). Y. Galván-Ciprés, J. Martínez-
Miranda, and C. Miranda-González received support from CONACyT to complete their Master’s
degrees in Pharmacy studies. We thank “Laboratorio Nacional de Nano y Biomateriales” for the
1H and 13 C NMR services. O. Palomino-Hernandez has been supported by the European Union’s
Horizon 2020 research and innovation programme under Grant Agreement No 765048. This article is
dedicated to Rafael Castillo-Bocanegra for his 77th birthday and for his pioneer contributions to the
Mexican medicinal chemistry.
Conflicts of Interest: The authors declare that there are no competing interests.
Sample Availability: Samples of the compounds are available from the authors.
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