3-Arylcoumarins as highly potent and selective monoamine oxidase B inhibitors: Which chemical features matter?

Article abstract of DOI:10.1016/j.bioorg.2020.103964

Monoamine oxidase B inhibitory activity is closely regulated by the interaction of the small molecules with the enzyme. It is therefore desirable to use theoretical approaches to design rational methods to develop new molecules to modulate specific interactions with the protein. Here, we report such methods, and we illustrate their successful implementation by studying six synthetized 3-arylcoumarins (71–76) based on them. Monoamine oxidase B inhibition is essential to maintain the balance of dopamine, and one of its major functions is to combat dopamine degradation, a phenomenon linked to Parkinson's disease. In this work, we study small-molecule inhibitors based on the 3-arylcoumarin scaffold and their monoamine oxidase B selective inhibition. We show that 3D-QSAR models, in particular CoMFA and CoMSIA, and molecular docking approaches, enhance the probability to find new interesting inhibitors, avoiding very costly and time-consuming synthesis and biological evaluations.

Full text of DOI:10.1016/j.bioorg.2020.103964

BioorganicChemistry101(2020)103964
Contents lists available at ScienceDirect
Bioorganic Chemistry
journal homepage: www.elsevier.com/locate/bioorg
3-Arylcoumarins as highly potent and selective monoamine oxidase B
inhibitors: Which chemical features matter?
⁎
Marco Melladoa, Jaime Mellab, , César Gonzálezc, Dolores Viñad,e, Eugenio Uriartef,g,
⁎
Maria J. Matosf,h,
a Facultad de Ciencias, Instituto de Química, Pontificia Universidad Católica de Valparaíso, Av. Universidad #330, Curauma, Valparaíso, Chile
b Instituto de Química y Bioquímica, Facultad de Ciencias, Universidad de Valparaíso, Av. Gran Bretaña 1111, Valparaíso, Chile
c Departamento de Química, Universidad Técnico Federico Santa María, Av. España 1680, Valparaíso, Chile
d Chronic Diseases Pharmacology Group, Center for Research in Molecular Medicine and Chronic Diseases (CIMUS), Universidade de Santiago de Compostela, 15782
Santiago de Compostela, Spain
e Departamento de Farmacología, Farmacia y Tecnología Farmacéutica, Universidade de Santiago de Compostela, 15782 Santiago de Compostela, Spain
f Departamento de Química Orgánica, Facultade de Farmacia, Universidade Santiago de Compostela, 15782 Santiago de Compostela, Spain
g Instituto de Ciencias Químicas Aplicadas, Universidad Autónoma de Chile, 7500912 Santiago, Chile
h CIQUP/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
A R T I C L E I N F O
A B S T R A C T
Keywords:
Monoamine oxidase B inhibitory activity is closely regulated by the interaction of the small molecules with the
enzyme. It is therefore desirable to use theoretical approaches to design rational methods to develop new mo-
lecules to modulate specific interactions with the protein. Here, we report such methods, and we illustrate their
successful implementation by studying six synthetized 3-arylcoumarins (71–76) based on them. Monoamine
oxidase B inhibition is essential to maintain the balance of dopamine, and one of its major functions is to combat
dopamine degradation, a phenomenon linked to Parkinson’s disease. In this work, we study small-molecule
inhibitors based on the 3-arylcoumarin scaffold and their monoamine oxidase B selective inhibition. We show
that 3D-QSAR models, in particular CoMFA and CoMSIA, and molecular docking approaches, enhance the
probability to find new interesting inhibitors, avoiding very costly and time-consuming synthesis and biological
evaluations.
3-Arylcoumarins
Monoamine oxidase B inhibitors
3D-QSAR models
Molecular docking
Drug design
1. Introduction
modifying therapy has not been developed. Therefore, it is urgent to
find new chemical entities to control the evolution of the disease [7].
Parkinson’s disease (PD) is the second most common neurodegen-
erative disease, affecting worldwide ~ 41 people per 100,000 among
those under 50, and rising to > 1900 people per 100,000 among those
over 80 years [1]. Although PD’s pathogenesis is not fully understood, it
is known to start with the loss of dopaminergic neurons of the substance
nigra pars compacta [1]. There is also evidence that several genetic and
environmental factors may be involved, causing oxidative stress, in-
flammatory processes, mitochondrial injury, abnormal α-synuclein de-
position and cell apoptosis. At the moment, the treatment of this disease
is only symptomatic. Some therapeutic options are monoamine oxidase
B (MAO-B) inhibitors [2], acetylcholinesterase (AChE) inhibitors [3],
catechol-O-methyltransferase (COMT) inhibitors [4], dopamine ago-
nists or amantadine [5], which is used in early stages or to relieve
dyskinesia caused by levodopa-carbidopa [6]. To date, a disease-
The coumarin framework is a privileged scaffold in Medicinal
Chemistry [8,9] Several groups have been studying synthetic pathways
to obtain molecules containing this skeleton, as well as their biological
properties against different targets. Because of its chemical simplicity,
and special aromaticity/planarity, the coumarin is been used as core for
more complex chemical structures for multifactorial diseases, as Par-
kinson’s disease [10–12]. Coumarins, specially substituted at three, six
or seven positions attracted special interest for their inhibition of en-
zymes involved on neurodegenerative diseases [13–19].
For more than ten years, our research group has been focused on the
synthesis and biological evaluation of differently substituted coumarins
as potential MAO-B inhibitors [20–23]. Our research has been sup-
ported by several theoretical tools (mainly docking studies) that al-
lowed us accelerating the process to obtain potent and selective MAO-B
⁎ Corresponding authors at: Departamento de Química Orgánica, Facultade de Farmacia, Universidade Santiago de Compostela, 15782 Santiago de Compostela,
Spain (M. Matos).
E-mail addresses: jaime.mella@uv.cl (J. Mella), mariajoao.correiapinto@usc.es, maria.matos@fc.up.pt (M.J. Matos).
https://doi.org/10.1016/j.bioorg.2020.103964
Received 26 March 2020; Received in revised form 2 May 2020; Accepted 20 May 2020
Availableonline26May2020
0045-2068/©2020ElsevierInc.Allrightsreserved.

M. Mellado, et al.
BioorganicChemistry101(2020)103964
Fig. 1. Structural characteristics of the molecules included in the database. (A) 3-arylcoumarins and (B) 3-arylbenzo[f]coumarin.
inhibitors [24]. Different structural features have been considered and
explored. From all the studied families, 3-arylcoumarins proved to be
the most promising molecules, with inhibitory activities in the micro,
nano and even picomolar ranges. In parallel, other targets related to
neurodegenerative diseases have also been considered and studied, in
order to evaluate the multitarget profile of this interesting scaffold.
In the current work, 3D quantitative structure–activity relationship
models (3D-QSAR) models, in particular Comparative Molecular Field
Analysis (CoMFA) and Comparative Molecular Similarity Indices
Analysis (CoMSIA), and molecular docking calculations, were used to
systematize the structural key points to design potent and selective
MAO-B inhibitors, taking as starting point an internal database of se-
venty compounds previously synthetized and studied as MAO inhibitors
by our research group.
value, low number of components, and balance between field con-
tributions, was the CoMSIA-SHE model. From now on, the best models
–CoMFA-SE and CoMSIA-SEH– will be mentioned as CoMFA and
CoMSIA models, to simplify.
In order to validate the predictive capacity of both models, a series
of parameters were determined (Table S2). The value of r2 is a measure
of the external predictive capability of the model. A value of r2 > 0.6 is
considered as acceptable. High r2 values indicate that the model has a
good ability to predict the activity of molecules out of the training set.
As can be observed in Table S2, the models presented good r2 values
(0.947 and 0.896 for CoMFA and CoMSIA, respectively). This in-
formation is aligned with the predictions for the test set (Fig. 2). Ac-
cording to Golbraikh and Tropsha, high values of q2 and r2 (conditions 1
and 2, Table S2) are necessary but not sufficient conditions for the
model validation [25]. For a QSAR model to have a reliable predictive
capability, the line of experimental versus predicted activities should be
as close as possible to the line y = x. This is observed with the fulfill-
ment of conditions (3a or 3b), (4a or 4b) and (5a or 5b), listed in Table
2. Results and discussion
2
2.1. 3D-QSAR: CoMFA and CoMSIA
S2. Condition 7, known as rm metrics, is a quantitative measure to
determine the agreement between the observed and predicted activities
for the test set. CoMFA and CoMSIA models reported here fulfilled all
the conditions for internal and external validation. In addition, the
calculation of several external validation descriptors (conditions 7 to
2.1.1. Statistical results
A database of seventy compounds, generally illustrated in Fig. 1,
and detailed in the Supporting Information, was constructed taking into
account previous results from our research group on the synthesis and
evaluation against MAO enzymes. In particular, we focused our atten-
tion on MAO-B selective inhibitors, as most of our compounds present a
very high selectivity index towards this isoform.
12) was performed [26,27,28]. In all cases, our models passed the va-
2
lidation tests. The only value that is slightly below the standard is QF3
,
for CoMSIA (Table S2).
For both models, there is a good distribution along the ideal line
y = x. The external validation of the models evaluates how close this
line is to the ideal line. Furthermore, the Y-randomization test (Table
S3) was applied to assess the robustness of both models [29]. The de-
pendent variable (biological activity) was randomly shuffled and a new
QSAR model was developed using the original independent variable
Using this structural information, 3D-QSAR CoMFA and CoMSIA
models were performed. Before analyzing the contour maps, statistical
results were analyzed, and the new models were validated. Table S1
shows the statistical results of all CoMFA and CoMSIA models generated
from all possible field combinations (steric, electrostatic, hydrophobic
or hydrophilic, hydrogen bond donor and hydrogen bond acceptor). For
each model, the value of the cross-validation coefficient (q2) was de-
termined. This parameter should be higher than 0.5 for a model to be
considered valid. The value of q2 indicates how good the model is for
predicting the biological activity of the molecules that are the training
set of the model. In the case of CoMFA –the model that considered both
the steric and electrostatic contributions (CoMFA-SE)– it gave the best
q2value (0.924). In addition, the model presented a low value in the
number of components (N = 3) and the high value for the regression
2
matrix. If after multiple randomizations the new values of q2 and rncv
are negative or below the limit of acceptability (q2
<
0.5,
2
rncv < 0.6), it is corroborated that the results obtained in the for-
mulation of the final models are not by chance. In our case, the new
2
QSAR models (after several repetitions) have low q2 and rncv values
2
(Table S3). Only in randomizations 2 and 3, high values for rncv were
obtained. However, the values of q2 were always negative.
The chemical structures of the dataset, values of experimental ac-
tivity, predicted activity and residual values for the CoMFA and
CoMSIA models, are shown in Tables S4 and S5. Analyses were per-
formed on seventy molecules presenting high structural diversity on the
3-arylcoumarin scaffold (Table S4). Fifty-one compounds were used as
a training set (73%) and nineteen as a test set (27%). For both models,
the compounds used as training and test sets were the same. The
CoMFA model presented a total of thirty-four compounds with negative
deviations in the predicted activity values and thirty-six compounds
with positive deviations. The largest deviations for biological activity
were observed for the 3-(2′-bromophenyl)-6-methylcoumarin (10), 6-
bromo-8-hydroxy- 3-(4′-hydroxyphenyl)coumarin (25) and 3-
2
coefficient (rncv = 0.987).
On the other hand, most CoMSIA models presented high values for
q2. The models CoMSIA-E, D, A, ED, EA, EDA, and DA have a q2 value
lower than 0.5. From the rest, only one model has q2 value higher than
0.9 (CoMSIA-S). However, this model has a high number of components
(N = 5). The models with the best q2 values were CoMSIA-S, SEH, SH,
SD and SA. The CoMSIA-DA model showed the lowest value for q2
(-0.068), so models that consider the H-bond donor and acceptor con-
tributions were discarded. On the other hand, CoMSIA-S, SH, SD and
SDA models presented a very high number of components (N ≥ 10), so
they were also discarded. Therefore, the best model in terms of high q2
2

M. Mellado, et al.
BioorganicChemistry101(2020)103964
Fig. 2. Plots of experimental versus predicted pIC50 values for the training and test set compounds. On the right, in black, predictions for the training set. On the left,
in red, predictions for the test set. For the test set, in each case, the regression line for r2 is shown in dark black. (For interpretation of the references to color in this
figure legend, the reader is referred to the web version of this article.)
Fig. 3. Graphs of residual (experimental pIC50
-
predicted pIC50 distribution versus biological
)
activity for CoMFA and CoMSIA models. In each
case, the compounds that experienced a devia-
tion greater than one logarithmic unit in the
predicted value of activity by the equation are
indicated (outlier compounds; residual > 1.0).
Color code: black spheres, training set; red
squares, test set. (For interpretation of the re-
ferences to color in this figure legend, the reader
is referred to the web version of this article.)
phenylcoumarin (46) (negative deviations). These compounds pre-
sented residual values, higher than one logarithmic unit (Fig. 3).
Therefore, they correspond to outliers. In the case of the CoMSIA
models, thirty-three compounds presented negative deviations in the
predicted values of activity, while thirty-seven compounds presented
positive deviations. In the CoMSIA model, the outlier compounds are
the 8-bromo-6-methyl-3-(3′,4′,5′-trimethoxyphenyl)coumarin (19), 6-
bromo-8-hydroxy- 3-(4′-hydroxyphenyl)coumarin (25) and 3-phe-
nylcoumarin (46) (negative deviations, higher than
1 log unit).
Therefore, in terms of error, both models are similar. On the other hand,
the graphs of the distribution of residuals versus biological activity
(Fig. 3) showed an adequate balance in terms of distribution of
3

M. Mellado, et al.
BioorganicChemistry101(2020)103964
residuals across the entire range of biological activity. The largest de-
viations in both cases were observed in low activity ranges
scaffold (Fig. 4A and 5A for compound 7, and Fig. 4B and 5B for
compound 49). Therefore, bulky substitutions at meta and para posi-
tions (Fig. 1) are favorable in this region, as observed by the green
polyhedrons in Fig. 4A and 5A, for compound 7. This information is
corroborated by the trend showed by several 3-arylcoumarins pre-
senting high values of MAO-B inhibitory activity (i.e. MAO-B pIC50
values higher than 8.0 for compounds 3, 7, 9, 11, 14, 15, 17, 18, 24,
26, 28, 29, 31, 35, 39, 40, 45, 48, 53, 54, 57–59 and 61, Table S5).
The versatility of these substituents, as observed in Table S4, can be
high. Methyl, methoxy, hydroxyl or amine groups, as well as bromine
atoms, are allowed in both positions. Combinations of substituents in
both meta and para positions, as 3-(3′-bromo-4′-methoxyphenyl)-6-
methylcoumarin (7), are an excellent option to optimize the potential of
these molecules (MAO-B pIC50 of 9.133). In the same range of MAO-B
inhibitory activity, 3-(3′-bromophenyl)-6-methylcoumarin (14) and 3-
(4′-bromophenyl)-6-methylcoumarin (15) proved that mono-substitu-
tions are also a great choice (MAO-B pIC50 of 9.873 and 9.409, re-
spectively). In both cases, a halogen atom at meta or para positions
seems to be recommendable. For compounds that do not present sub-
stituents other than hydrogen atoms in these positions, the activity
decreases. This can be seen, for example, for the 8-ethoxy-3-phe-
nylcoumarin (20) and 3-phenylcoumarin (46), with MAO-B pIC50 va-
lues lower than 6.0 (Table S5).
(pIC50
< 6.0). The greatest dispersion of residuals occurred for
CoMSIA.
2.2. Applicability domain
The applicability domain is a theoretical region in chemical space
encompassing both the model descriptors and modeled response. It
allows estimating the uncertainty in the prediction of a compound
based on how similar it is to the training compounds employed in the
model. In this work, we used the method developed by Roy et al. for
determining the applicability domain, which is based on the basic
theory of the standardization approach [30]. For both CoMFA and
CoMSIA, all the compounds (training and test set) were within the
applicability domain.
2.3. Contour maps analysis
QSAR studies allow systematizing the information on the struc-
ture–activity relationship (SAR) of the series of computations in order
to design new molecules. QSAR studies can predict the activity of the
proposed molecules. On the other hand, the error involved in per-
forming a qualitative SAR analysis is minimized. Unlike a 2D-QSAR, the
results of a 3D-QSAR can be represented as contour maps around the
studied molecules. The analysis of the different colored polyhedrons
around a molecule (i.e. the most active compound in the series) allows
understanding the main characteristics that are favorable or unfavor-
able for the affinity or biological activity. This is the main goal of the
current theoretical approaches.
From the steric CoMSIA map, it is concluded that the presence of a
bulky substitution at position 6 of the coumarin scaffold can lead to
decrease the MAO-B inhibitory activity (yellow polyhedron in Fig. 5A
and B, for compounds 7 and 49, respectively). For example, the 6-
methoxy-3-(p-tolyl)coumarin (35) presented a MAO-B pIC50 of 8.824.
However, by changing the methoxy group by a larger side chain, as the
6-(2-oxopropoxy)-3-(p-tolyl)coumarin (43), the MAO-B pIC50 decreased
to 5.258. Likewise, by changing the same substituent for an even
bulkier one such as O-cyclopentyl, the MAO-B pIC50 decreased to 4.840
(compound 49). A similar trend can be observed for the naphthylcou-
marin derivatives. Compounds 60–70 (with this extra fused ring) versus
i.e. compounds 2–4, 6–9 (6-methylcoumarins), presented on average 1
logarithmic unit less of MAO-B inhibitory activity (Table S5).
2.4. Steric contour maps
The steric contour maps from the CoMFA (Fig. 4A and B) and
CoMSIA (Fig. 5A and B) analysis are consistent with each other. The
best MAO-B inhibitor obtained from the training set [3-(3′-bromo-4′-
methoxyphenyl)-6-methylcoumarin, 7] and the least good MAO-B in-
hibitor obtained from the training set [6-(cyclopentyloxy)-3-(p-tolyl)
coumarin, 49] where selected for this study. Green polyhedrons can be
observed around the aromatic ring at position 3 of the coumarin
2.5. Electrostatic contour maps
The presence of a red polyhedron around the meta and para
Fig. 4. CoMFA steric (A and B) and elec-
trostatic (C and D) contour maps around the
most active compound obtained from the
training set (7, at the left) and the less active
compound obtained from the training set
(49, at the right). Color code: green, bulky
groups are favorable for activity; yellow,
small groups are favorable for activity; red,
negative charge is favorable for activity;
blue, positive charge is favorable for ac-
tivity. (For interpretation of the references
to color in this figure legend, the reader is
referred to the web version of this article.)
4

M. Mellado, et al.
BioorganicChemistry101(2020)103964
Fig. 5. CoMSIA steric (A and B), electro-
static (C and D) and hydrophobic (E and F)
contour maps around the most active com-
pound obtained from the training set (7, at
the left) and the less active compound ob-
tained from the training set (49, at the
right). Color code: green, bulky groups are
favorable for activity; yellow, small groups
are favorable for activity; red, negative
charge is favorable for activity; blue, posi-
tive charge is favorable for activity. For the
hydrophobic contour map: yellow, hydro-
phobic groups are favorable for the activity;
grey, hydrophilic groups are favorable for
the activity. (For interpretation of the re-
ferences to color in this figure legend, the
reader is referred to the web version of this
article.)
positions of the aromatic ring attached at position 3 of the coumarin
scaffold means that electron-rich groups such as halogens, hydroxyl, O-
alkyl and nitro are favorable for the biological activity (i.e. compounds
3, 7, 9, 11, 14, 15, 24, 26, 28, 31, 39, 40, 45 and 57–59, Tables S4 and
S5).
substitute (i.e. hydroxyl or amine groups). Indeed, when comparing the
activities of the compounds presenting a bromine, hydroxyl and amine
substituent in the mentioned position, the order of MAO-B inhibitory
activity is OH > NH2 > Br (i.e. 3-aryl-6-methylcoumarins with 3-
ortho-substitutions 13, 52 and 10, respectively).
The CoMSIA electrostatic contour maps show that an electropositive
surface at position 6 of the coumarin scaffold increases activity (blue
polyhedron in Fig. 5C and D, for compounds 7 and 49, respectively).
This is seen for the 3-(4′-methoxyphenyl)-6-nitrocoumarin (59), which
presents a nitro group attached to this position (MAO-B pIC50 of 8.523).
The positive nitrogen from the nitro group is responsible for the blue
polyhedron. When comparing this compound with other derivatives
that present substituents that do not have electropositive character-
istics, the activity decreases. For example, the 3-(4′-methoxyphenyl)-6-
methylcoumarin (2) presents a methyl substituent at position 6 and its
MAO-B pIC50 is 7.886 (Table S5). The 3-(4′-methoxyphenyl)coumarin
(56) presents a hydrogen substituent at position 6 and its MAO-B pIC50
is 5.788 (Table S5).
Additionally, Fig. 5E and F shows the presence of a yellow poly-
hedron around the 3-aryl substituent. This indicates that hydrophobic
substitutes at these positions increase MAO-B inhibitory activity. For
example, this is evident for the 3-(3′-bromo-4′-methoxyphenyl)-6-me-
thylcoumarin (7), 3-(3′-bromophenyl)- 6-methylcoumarin (14), 3-(4′-
bromo-3′-methoxyphenyl)-6-methylcoumarin
(31),
3-(3′-bromo-
phenyl)-6-methoxycoumarin
(39),
3-(3′-bromophenyl)-6-hydro-
xycoumarin (40) and 2-(3-(3′-bromophenyl)coumarin-6-yl)oxy)acetyl
chloride (48) which present substitutions with the previously men-
tioned characteristics and present MAO-B pIC50 values higher than 8.0
(Table S5).
2.7. Molecular docking
Finally, the CoMFA analysis shows a red polyhedron on the oxygen
of the carbonyl group (Fig. 4C and D, for both compounds 7 and 49,
respectively). This means that this functional group is essential for the
MAO-B inhibitory activity, which is evidenced by the activity displayed
by all the coumarins presented in this study (Table S5).
The docking analysis was carried out using the Autodock Vina
program and the human MAO-B crystalline structure (PDB ID: 1GOS)
[31]. These calculations showed that the 3-(3′-bromophenyl)-6-me-
thylcoumarin (14, the most active compound from the dataset) presents
affinity energy of −7.1 kcal/mol, while the energy for R-(-)-deprenyl
was −6.6 kcal/mol. These results are consistent with the reported in-
hibitory activity for both compounds (MAO-B pIC50 = 9.873 and 7.708,
respectively). The visualization of the results was performed with
PyMOL and Ligplot+ (Fig. 6A-C).
2.6. Hydrophobic contour map
CoMSIA hydrophobic contour maps (Fig. 5E and F, for compounds 7
and 49, respectively) show a grey polyhedron near the ortho position of
the aromatic ring at position 3 of the coumarin scaffold, indicating that
MAO-B inhibitory activity increases with the presence of a hydrophilic
Fig. 6A shows the general 3D visualization of MAO-B with the best
pose of the 3-(3′-bromophenyl)-6-methylcoumarin (14) within the
5

M. Mellado, et al.
BioorganicChemistry101(2020)103964
Fig. 6. Molecular Docking results. (A) 3D overview of 3-(3′-bromophenyl)-6-methylcoumarin (14) within the active site of MAO-B. (B) MAO-B inhibition site and key
amino acid residues around compound 14. (C) Details of polar and van der Waals interactions established between the MAO-B active site and compound 14.
active site, highlighting FAD600, Tyr435, Tyr398 and Cys172. Fig. 6B
shows an expansion of the active site of MAO-B, where the residue of
Cys172 is close to the oxygen atoms of the coumarin scaffold, favoring
the formation of a hydrogen bond. This interaction is crucial for the
MAO-B inhibition. Fig. 6C shows that the aromatic ring of the 3-ar-
ylcoumarin is oriented towards the Gln206, Tyr435, Tyr60 and FAD
residues of the MAO-B active site. Comparing this with the 3D-QSAR
(Figs. 4 and 5), it is confirmed that the presence of electron-rich sub-
stituents at meta and para positions of the aromatic ring of the 3-ar-
ylcoumarins increases the MAO-B inhibitory activity. This may be due
to the formation of polar and hydrogen-bond interactions with Tyr60,
FAD600 and Tyr435 residues. The last one being one of the responsible
for ligand polarization and subsequent reduction of FAD [32].
The residues of Ile199 and Tyr60 are located near position 4 of the
coumarin core (Fig. 6C). Compared to the CoMSIA analysis (Fig. 5), the
hydrophobic contour map indicates that a substituent of hydrophilic
characteristics would increase the MAO-B inhibitory activity. This is
consistent with a potential hydrogen bond or dipole interaction with
Tyr60 (i.e. in the case of a hydroxyl group).
fragment of the coumarin core (bulky and hydrophilic substituents in
ortho position, electron-rich substituents in para position, and lipo-
philic, electron-rich and bulky substituents in meta position) would
provide a more active compound than if only one or two substitutions
were considered.
2.9. Synthesis and biological evaluation of new compounds
To validate the models experimentally, after analyzing the theore-
tical results, and to obtain new derivatives based on the information
from these models, we carried out the synthesis of 6 new derivatives
(Scheme 1 and Table 1, compounds 71–76) [33]. The compounds were
synthesized based on information from the maps, which suggested the
presence of lipophilic, bulky and electron-rich groups in the phenyl ring
at position 3. Ethoxy, methoxy, methyl and hydroxyl groups and bro-
mine atoms were selected as the best substituents. They were attached
strategically to the best positions predicted by the models. The ex-
perimental and theoretical results obtained by CoMFA and CoMSIA
models are presented in Table 1.
Ile199, Phe168 and Tyr326 residues are located near position 6 of
the coumarin scaffold, which show van der Waals interactions with
compound 14 (Fig. 6C). According to 3D-QSAR analysis, a hydrophilic
substitute (i.e. a hydroxyl group) increases the MAO-B inhibitory ac-
tivity. This is consistent with the polar interactions that may be formed
with Tyr326. In contrast, an electron-rich substitute generates a decay
on the MAO-B inhibitory activity, which could be related to the high
electron density of the Tyr326 ring.
The synthesis of the new compounds was carried out via a tradi-
tional Perkin reaction of different ortho-hydroxybenzaldehydes with the
corresponding arylacetic acids, using N,N’-dicyclohexylcarbodiimide
(DCC) as dehydrating agent, in the presence of dimethylsulfoxide
(DMSO), at 110 °C, for 24 h. This reaction afforded compounds 72–74,
76 and the precursors of compounds 71 and 75 (named 9 and 75′,
respectively). Compound 71 was obtained by acidic hydrolysis of the
respective methoxy derivative 9, using hydriodic acid 57% in the pre-
sence of acetic acid and acetic anhydride, in reflux, for 3 h. Finally,
compound 75 was prepared by brominating the precursor 75′, in the
presence of N-bromosuccinimide (NBS), using azobisisobutyronitrile
(AIBN) as catalysator, in the presence of carbon tetrachloride (CCl4), in
reflux, for 18 h.
Finally, Leu171 and Tyr326 residues are located close to the posi-
tion 8 of the coumarin nucleus. Comparing with the results obtained by
the CoMFA and CoMSIA electrostatic contour maps analyses (Fig. 4C
and D, and 5C and D), an electron-rich substituent decreases the MAO-B
inhibitory activity. This is consistent with the presence of the Tyr326
residue, which presents a high electron density.
The potential effects of the compounds 71–76 on hMAO-B activity
were investigated by measuring the production of hydrogen peroxide
(H2O2) from p-tyramine, using an Amplex® Red MAO assay kit and
MAO isoform prepared from insect cells (BTI-TN-5B1-4) infected with
recombinant baculovirus containing cDNA inserts for hMAO-B. The
inhibition of hMAO activity was evaluated following a general proce-
dure previously described [20]. Experimental results are expressed as
IC50 values of hMAO-B inhibitory activity effects of the tested com-
pounds and are shown in Table 1. All of the compounds in this study
resulted to be hMAO-B inhibitors in the low micro or low nanomolar
ranges.
2.8. Summary of the principal results based on the theoretical models
Important structural information has been extracted from the the-
oretical studies and the new compounds were synthetized based on
that. Fig. 7 summarizes of the main structure–activity relationships
obtained analyzing the different approaches used in the current study.
Based on the results obtained with both CoMFA and CoMSIA
models, and Docking calculations, as more of these requirements are
concomitantly fulfilled, a more active compound may be obtained. The
simultaneous completion of the three characteristics for the aryl
The best compound within the synthetized series –3-(3′,5′-
6

M. Mellado, et al.
BioorganicChemistry101(2020)103964
Fig. 7. Summary of the main structure–activity relationships found in this study.
dimethoxyphenyl)-8-methylcoumarin (72)– presents a MAO-B pIC50 of
8.187, corroborating the structural information obtained by the CoMFA
and CoMSIA studies. 3-(5′-Bromo-2′,4′-dimethoxyphenyl)-6-methyl-
coumarin (75) and 8-methoxy-3-(m-tolyl)coumarin (76) display activ-
ities in the same range. One is a 6-methyl derivative, and the other one
an 8-methoxy derivative. As mentioned above, both positions are in-
teresting in order to optimize the best lead compound. Also, both me-
thyl and methoxy groups (electron donating groups) prove to be good
substituents to increase the potential of the molecules. Regarding the
substitution pattern on the aromatic ring in the position 3 of the cou-
marin scaffold, compound 75 presents a methoxy group at position
ortho, a position potentially undesirable for the MAO-B inhibitory ac-
tivity. However, the methoxy group is consider a bulky and is mildly
hydrophilic group, therefore may be positive for the activity. In addi-
tion, the presence of another methoxy in para position and an extra
bromine atom in meta, seems to balance the risk–benefit of having a
substituent in ortho. From this short series, it can also be extrapolated
that increasing the side chain at position 8 (i.e. 8-ethoxy derivatives 73
and 74), the activity tends to decrease (MAO-B pIC50 of 4.971 and
5.607, respectively).
Table 1
Experimental MAO-B inhibition values (IC50 and pIC50) and predicted activity
(pIC50 CoMFA and pIC50 CoMSIA) for compounds 71–76.
Compound
IC50 MAO-B
pIC50 MAO-B
pIC50 CoMFA
pIC50 CoMSIA
71
72
73
74
75
76
4.7 μM
6.5 nM
10.7 μM
2.5 μM
132 nM
81 nM
5.327
8.187
4.971
5.607
6.879
7.092
6.067
8.125
5.654
5.605
6.480
7.413
5.987
8.284
5.852
5.589
7.254
7.339
concentration. These results are closely aligned with the previous re-
sults reported by our group.
Finally, based on the SAR results, there is an adequate balance be-
tween the predictions made by the CoMFA and CoMSIA models and the
experimental results (Fig. 8). As observed, there is an adequate dis-
tribution of values along the y = x line.
3. Conclusion
To validate the potential of the new compounds as selective MAO-B
inhibitors, and corroborate the theoretical results previously obtained
using the CoMFA and CoMSIA models, their hMAO-A inhibitory activ-
ities were also evaluated. From the series, only compound 71 proved to
inhibit hMAO-A isoenzyme (IC50 = 6.4 μM). All the other synthetized
molecules proved to be inactive at 100 μM, the highest tested
In this study, we constructed two robust 3D-QSAR models –CoMFA
and CoMSIA– based on a series of compounds previously reported by
our research group. The molecules present a 3-arylcoumarin scaffold,
known for their potent and selective MAO-B inhibitory activity. The
best models obtained were validated internal and externally. The ob-
tained values of q2 were 0.924 and 0.863 for CoMFA and CoMSIA,
Scheme 1. Reagents and conditions: (a) DCC (1.56 equiv), DMSO, 110 °C, 24 h; (b) HI, AcOH, Ac2O, reflux, 3 h; (c) NBS (1.2 equiv), AIBN (cat.), CCl4, reflux, 18 h.
7

M. Mellado, et al.
BioorganicChemistry101(2020)103964
automated service utilizing electron impact (EI) ionization. Elemental
analyses were performed using a Perkin-Elmer 240B microanalyser and
were within (0.4% of calculated values in all cases. The purity of
compounds was assessed by HPLC and was found to be higher than
99%.
4.2. Synthetic methodologies
A solution of ortho-hydroxybenzaldehyde (1.0 mmol), the corre-
sponding arylacetic acid (1.25 mmol) and DCC (1.56 mmol), in DMSO
(2.0 mL), was heated at 110 °C in an oil bath for 24 h. Ice (20 g). AcOH
(3.0 mL) was added and the mixture was stirred at room temperature
for 2 h, and then extracted with Et2O (3 Å~ 25 mL). The combined
organic layer was washed with 5% aqueous NaHCO3 solution (50 mL)
and H2O (20 mL) and dried (Na2SO4). The solvent was evaporated
under vacuum and the residue was purified by flash chromatography
(hexane–EtOAc, 9:1) to give the coumarins 9, 72–74, 75′ and 76.
3-(3′,4′-Dimethoxyphenyl)-6-methylcoumarin (9). Yield: 71%; Mp
149–150 °C. 1H NMR (CDCl3) δ: 2.40 (s, 3H, CH3), 3.92 (s, 3H, OCH3),
3.94 (s, 3H, OCH3), 6.92 (d, 1H, H-5′, J = 8.3), 7.21–7.29 (m, 5H, H-5,
H-7, H-8, H-2′, H-6′), 7.72 (s, 1H, H-4). 13C NMR (CDCl3) δ: 20.7, 55.8,
55.9, 99.3, 110.8, 111.6, 115.9, 119.4, 121.1, 127.4, 127.5, 132.0,
134.0, 138.7, 148.5, 149.5, 151.3, 160.8. MS m/z (%): 297 (28), 296
(M+, 100). Anal. Elem. Calc. for C18H16O4: C, 72.96; H, 5.44. Found: C,
72.90; H, 5.49.
Fig. 8. Values of experimental versus predicted MAO-B inhibitory activity for
the synthesized compounds (71–76). Color code: black spheres, CoMFA pre-
dictions; red squares, CoMSIA predictions. (For interpretation of the references
to color in this figure legend, the reader is referred to the web version of this
article.)
respectively. The r2 values for the set test were 0.863 and 0.896 for
CoMFA and CoMSIA, respectively. Therefore, it can be concluded that
both models are reliable and present good external predictive cap-
ability. To validate the models experimentally, six new molecules have
been synthesized. The most powerful compound [3-(3′,5′-dimethox-
yphenyl)-8-methylcoumarin] presented an IC50 = 6.5 nM. The pre-
dictions for the synthesized compounds followed an adequate dis-
tribution along the y = x line. Docking calculations have also been
taken into account in the current study. Some important structural
features that may conditioning the biological activity can be high-
lighted analyzing all the theoretical data together. Regarding the aro-
matic ring at position 3 of the coumarin scaffold, bulky and hydrophilic
substituents in ortho position tend to increase the MAO-B inhibition.
Also, electron-rich substituents in para position favor the activity.
Finally, lipophilic, electron-rich and bulky substituents in meta position
increase the inhibitory activity as well. Substitution patterns at position
6 of the coumarin scaffold were also analyzed. Bulky and electron-rich
substituents may contribute to decrease the MAO-B inhibitory activity.
Finally, at position 8, the presence of electron-rich substituents de-
creases the MAO-B inhibitory activity. The combination of substituents
at both the coumarin core and the aromatic ring at position 3 may be
the key for the successful design of new molecules. Based on all this
information, it can be concluded that the developed 3D-QSAR models
constitute a robust tool for the design of new MAO-B inhibitors pre-
senting the 3-arylcoumarin scaffold.
3-(3′,5′-Dimethoxyphenyl)-8-methylcoumarin (72). Yield 58%. Mp
104–105 °C. 1H NMR (CDCl3) δ: 2.51 (s, 3H, CH3), 3.85 (s, 6H,
2xOCH3), 6.52 (t, 1H, H-4′, J = 2.3), 6.87 (d, 2H, H-2′, H-6′, J = 2.3),
7.22 (d, 1H, H-7, J = 7.8), 7.32–7.40 (m, 2H, H-5, H-6), 7.81 (s, 1H, H-
4). 13C NMR (CDCl3) δ: 15.4, 55.4, 55.5, 100.8, 106.7, 106.7, 119.2,
124.0, 125.6, 125.9, 127.7, 132.8, 136.7, 140.4, 140.6, 151.8, 160.6.
MS m/z (%): 297 (37), 296 (M+, 100). Anal. Elem. Calc. for C18H16O4:
C, 72.96; H, 5.44. Found: C, 73.04; H, 5.51.
8-Ethoxy-3-(3′,4′,5′-trimethoxyphenyl)coumarin (73). Yield 40%. Mp
142–143 °C. 1H NMR (CDCl3) δ: 1.58 (t, 3H, CH3, J = 7.0), 3.94 (s, 3H,
OCH3), 3.96 (s, 3H, OCH3), 3.99 (s, 3H, OCH3), 4.25 (q, 2H, CH2,
J = 7.0), 7.0 (s, 2H, H-2′, H-6′), 7.14–7.20 (m, 1H, H-6), 7.24–7.27 (m,
1H, H-7), 7.30–7.32 (m, 1H, H-5), 7.83 (s, 1H, H-4). 13C NMR (CDCl3)
δ: 14.8, 56.2, 56.3, 65.0, 105.9, 114.5, 119.2, 120.3, 124.4, 128.2,
130.2, 139.7, 133.8, 146.3, 153.0. MS m/z (%): 357 (23), 356 (M+
,
100). Anal. Elem. Calc. for C20H20O6: C, 67.41; H, 5.66. Found: C,
67.42; H, 5.68.
8-Ethoxy-3-(3′,4′-dimethoxyphenyl)coumarin (74). Yield 41%. Mp
138–139 °C. 1H NMR (CDCl3) δ: 1.53 (t, 3H, CH3, J = 7.0), 3.90 (s, 3H,
OCH3), 3.95 (s, 3H, OCH3), 4.21 (q, 2H, CH2, J = 7.0), 6.94 (d, 1H, H-
7, J = 8.2), 7.02–7.09 (m, 2H, H-2′, H-6′), 7.20 (d, 1H, H-5′, J = 7.9),
7.28–7.33 (m, 2H, H-5, H-6), 7.77 (s, 1H, H-4). 13C NMR (CDCl3) δ:
14.5, 55.7, 64.6, 110.7, 111.5, 114.0, 118.9, 120.2, 121.0, 124.0,
127.1, 128.0, 138.7, 142.8, 146.0, 148.4, 149.4, 160.1. MS m/z (%):
327 (55), 326 (M+, 100). Anal. Elem. Calc. for C19H18O5: C, 69.93; H,
5.56. Found: C, 69.91; H, 5.53.
4. Experimental section
4.1. Chemistry: Synthesis of the new compounds
4.1.1. General information
All reagents were purchased from Sigma-Aldrich and used without
further purification. All solvents were commercially available grade. All
reactions were carried out under argon atmosphere, unless otherwise
mentioned. Reaction mixtures were purified by flash column chroma-
tography using Silica Gel high purity grade (Merck grade 9385 pore size
60 Å, 230–400 mesh particle size). Reaction mixtures were analyzed by
analytical thin-layer chromatography (TLC) using plates precoated with
silica gel (Merck 60 F254, 0.25 mm). Visualization was accomplished
with UV light (254 nm) or potassium permanganate (KMnO4). 1H NMR
and 13C NMR spectra were recorded on a Bruker AMX spectrometer at
250 and 75.47 MHz in the stated solvents (CDCl3 or DMSO‑d6) using
tetramethylsilane (TMS) as an internal standard. Chemical shifts were
reported in parts per million (ppm) on the δ scale from an internal
standard (NMR descriptions: s, singlet; d, doublet; dd, double-doublet;
t, triplet; q, quadruplet; m, multiplet). Mass spectroscopy was per-
formed using a Hewlett-Packard 5988A spectrometer. This system is an
3-(2′,4′-Dimethoxyphenyl)-6-methylcoumarin (75′). Yield 55%. Mp
154–155 °C. 1H NMR (CDCl3) δ: 2.40 (s, 3H, CH3), 3.81 (s, 3H, OCH3),
3.83 (s, 3H, OCH3), 6.55–6.57 (m, 2H, H-3′, H-5′), 7.25–7.30 (m, 4H, H-
5, H-6′, H-7, H-8), 7.67 (s, 1H, H-4). 13C NMR (CDCl3) δ: 21.0, 55.7,
56.0, 99.4, 104.8, 116.4, 117.1, 119.6, 126.1, 127.7, 131.7, 132.2,
134.1, 141.6, 151.9, 158.6, 161.2, 161.6. MS m/z (%): 298 (8), 297
(43), 296 (M+, 100). Anal. Elem. Calc. for C18H16O4: C, 72.96; H, 5.44.
Found: C, 73.10; H, 5.50.
8-Methoxy-3-(m-tolyl)coumarin (76). Yield 64%. Mp 151–152 °C. 1
H
NMR (CDCl3) δ: 2.43 (s, 3H, CH3), 4.00 (s, 3H, OCH3), 7.07–7.15 (m,
2H, H-4′, H-6′), 7.20–7.25 (m, 2H, H-7, H-5′), 7.28 (s, 1H, H-2′), 7.35 (t,
1H, H-6, J = 7.9), 7.50 (dd, 1H, H-5, J = 7.9, 1.9), 7.79 (s, 1H, H-4).
13C NMR (CDCl3) δ: 21.7, 56.5, 113.4, 119.5, 120.6, 124.5, 125.9,
128.6, 128.9, 129.4, 129.8, 134.9, 138.3, 140.1, 143.3, 147.2, 160.2.
MS m/z (%): 266 (M+, 100). Anal. Elem. Calc. for C17H14O3: C, 76.68;
8

M. Mellado, et al.
BioorganicChemistry101(2020)103964
H, 5.30. Found: C, 76.70; H, 5.27.
Acknowledgements
A solution of substituted 3-(3′,4′-dimethoxyphenyl)-6-methylcou-
marin (9, 0.50 mmol) in acetic acid (5 mL) and acetic anhydride (5 mL),
at 0 °C, was prepared. Hydriodic acid 57% (10 mL) was added drop-
wise. The mixture was stirred, under reflux temperature, for 3 h. The
solvent was evaporated under vacuum, and the dry residue was purified
by CH3CN crystallization to give the coumarin 71.
This project was partially supported by the University of Porto
(Portugal), University of Santiago de Compostela (Spain), Centro
Singular de Investigación de Galicia, Spain (acreditación 2016-19,
ED431G/05), Xunta de Galicia, Spain (Galician Plan of Research,
Innovation and Growth 2011–2015, Plan I2C, ED481B 2014/086–0 and
ED481B 2018/007) and Fundação para a Ciência e Tecnologia, Portugal
(FCT, CEECIND/02423/2018). M. M. thanks to Comisión Nacional de
Investigación Científica y Tecnológica (CONICYT, Chile, Fondecyt
Postdoctoral Grant 3180408). The authors would like to thank
Professor Lourdes Santana for her scientific support. Authors would like
to thank the use of RIAIDT-USC analytical facilities.
3-(3′,4′-Dihydroxyphenyl)-6-methylcoumarin (71). Yield: 92%. Mp
199–200 °C. 1H NMR (DMSO‑d6) δ: 2.43 (s, 3H, CH3), 6.43 (s, 1H, H-2′),
7.01 (d, 1H, H-5′, J = 7.1), 7.14 (d, 1H, H-6′, J = 7.1), 7.28–7.32 (m,
2H, H-7, H-8), 7.57 (d, 1H, H-5, J = 2.2), 7.83 (s, 1H, H-4), 10.40 (s,
2H, OH). 13C NMR (DMSO‑d6) δ: 20.7, 100.1, 110.7, 111.6, 115.7,
119.4, 120.1, 127.3, 127.4, 132.1, 133.7, 138.8, 146.5, 146.8, 151.3,
161.6. MS m/z (%): 269 (13), 268 (M+, 100). Anal. Calc. for C16H12O4:
C, 71.64; H, 4.51. Found: C, 71.60; H, 4.49.
Appendix A. Supplementary data
A
solution of 3-(2,4-dimethoxyphenyl)-6-methylcoumarin (75′,
1.0 mmol), NBS (1.2 mmol) and AIBN (cat.) in CCl4 (5.0 mL) was stirred
under reflux for 18 h. The resulting solution was filtered to remove
succinimide. The solvent was evaporated under vacuum, and purified
by flash chromatography (hexane–EtOAc, 95:5) to give the coumarin
75.
Supplementary data to this article can be found online at https://
doi.org/10.1016/j.bioorg.2020.103964.
References
3-(5′-Bromo-2′,4′-dimethoxyphenyl)-6-methylcoumarin (75). Yield
78%. Mp 208–209 °C. 1H NMR (CDCl3) δ: 2.43 (s, 3H, CH3), 3.86 (s, 3H,
OCH3), 3.96 (s, 3H, OCH3), 6.58 (s, 1H, H-3′), 7.21–7.32 (s, 3H, H-5, H-
7, H-8), 7.54 (s, 1H, H-6′), 7.66 (s, 1H, H-4). 13C NMR (CDCl3) δ: 20.8,
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