Setup and validation of a reliable docking protocol for the development of neuroprotective agents by targeting the sigma-1 receptor (S1R)

Article abstract of DOI:10.3390/ijms21207708

Sigma-1 receptor (S1R) is a promising molecular target for the development of novel effective therapies against neurodegenerative diseases. To speed up the discovery of new S1R modulators, herein we report the development of a reliable in silico protocol suitable to predict the affinity of small molecules against S1R. The docking method was validated by comparing the computational calculated K_i values of a test set of new aryl-aminoalkyl-ketone with experimental determined binding affinity. The druggability profile of the new compounds, with particular reference to the ability to cross the blood–brain barrier (BBB) was further predicted in silico. Moreover, the selectivity over Sigma-2 receptor (S2R) and N-methyl-d-aspartate (NMDA) receptor, another protein involved in neurodegeneration, was evaluated. 1-([1,1^′-biphenyl]-4-yl)-4-(piperidin-1-yl)butan-1-one (12) performed as the best compound and was further investigated for acetylcholinesterase (AchE) inhibitor activity and determination of antioxidant activity mediated by aquaporins (AQPs). With a good affinity against both S1R and NMDA receptor, good selectivity over S2R and favorable BBB penetration potential together with its AChE inhibitory activity and its ability to exert antioxidant effects through modulation of AQPs, 12 represents a viable candidate for further development as a neuroprotective agent.

Full text of DOI:10.3390/ijms21207708

International Journal of
Molecular Sciences
Article
Setup and Validation of a Reliable Docking Protocol
for the Development of Neuroprotective Agents by
Targeting the Sigma-1 Receptor (S1R)
Giacomo Rossino 1, Marta Rui 1, Luca Pozzetti 1 , Dirk Schepmann 2, Bernhard Wünsch 2
Daniele Zampieri 3 , Giorgia Pellavio 4 , Umberto Laforenza 4 , Silvia Rinaldi 5,
Giorgio Colombo 6, Laura Morelli 7 , Pasquale Linciano 1 , Daniela Rossi 1 and
,
Simona Collina 1,
*
1
Department of Drug Sciences, Medicinal Chemistry and Pharmaceutical Technology Section,
University of Pavia, V.le Taramelli 12, 27100 Pavia, Italy; giacomo.rossino01@universitadipavia.it (G.R.);
marta.rui01@universitadipavia.it (M.R.); luca.pozzetti01@universitadipavia.it (L.P.);
pasquale.linciano@unipv.it (P.L.); daniela.rossi@unipv.it (D.R.)
Institute of Pharmaceutical and Medicinal Chemistry, University of Münster, Corrensstraße 48,
48149 Münster, Germany; dirk.schepmann@uni-muenster.de (D.S.); wuensch@uni-muenster.de (B.W.)
Department of Chemical and Pharmaceutical Sciences, University of Trieste, 30126 Trieste, Italy;
dzampieri@units.it
Department of Molecular Medicine, Human Physiology Unit, University of Pavia, via Forlanini 6,
27100 Pavia, Italy; giorgia.pellavio01@universitadipavia.it (G.P.); lumberto@unipv.it (U.L.)
SCITEC-CNR, via Mario Bianco 9, 20131 Milano, Italy; Silvia.rinaldi24@gmail.com
Dipartimento di Chimica, Università di Pavia, V.le Taramelli 12, 27100 Pavia, Italy; g.colombo@unipv.it
Department of Medical Biotechnology and Translational Medicine, University of Milan, Via Saldini 50,
20133 Milan, Italy; Laura.Morelli@unimi.it
2
3
4
5
6
7
*
Correspondence: simona.collina@unipv.it
ꢀꢁꢂꢀꢃꢄꢅꢆꢇ
ꢀꢁꢂꢃꢄꢅꢆ
Received: 17 September 2020; Accepted: 14 October 2020; Published: 18 October 2020
Abstract: Sigma-1 receptor (S1R) is a promising molecular target for the development of novel effective
therapies against neurodegenerative diseases. To speed up the discovery of new S1R modulators,
herein we report the development of a reliable in silico protocol suitable to predict the affinity of
small molecules against S1R. The docking method was validated by comparing the computational
calculated Ki values of a test set of new aryl-aminoalkyl-ketone with experimental determined binding
affinity. The druggability profile of the new compounds, with particular reference to the ability
to cross the blood–brain barrier (BBB) was further predicted in silico. Moreover, the selectivity
over Sigma-2 receptor (S2R) and N-methyl-d-aspartate (NMDA) receptor, another protein involved
in neurodegeneration, was evaluated. 1-([1,10-biphenyl]-4-yl)-4-(piperidin-1-yl)butan-1-one (12
)
performed as the best compound and was further investigated for acetylcholinesterase (AchE)
inhibitor activity and determination of antioxidant activity mediated by aquaporins (AQPs). With a
good affinity against both S1R and NMDA receptor, good selectivity over S2R and favorable BBB
penetration potential together with its AChE inhibitory activity and its ability to exert antioxidant
effects through modulation of AQPs, 12 represents a viable candidate for further development as a
neuroprotective agent.
Keywords: Sigma-1 receptor; docking; molecular modelling; neuroprotection; neurodegeneration;
acetylcholinesterase; aquaporins; oxidative stress
Int. J. Mol. Sci. 2020, 21, 7708; doi:10.3390/ijms21207708
www.mdpi.com/journal/ijms

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1. Introduction
Neurodegenerative diseases (which include Alzheimer’s diseases, Parkinson’s disease,
amyotrophic lateral sclerosis and multiple sclerosis, among others) are unanimously considered
one of the most important and difficult therapeutic challenges of our time [1,2]. The lack of effective
therapies is due to the complex multifactorial nature of these pathologies, which involve the alteration
of several molecular pathways, eventually leading to loss and impairment of neurons [1–3].
For several years Sigma-1 receptor (S1R) has attracted the attention of medicinal chemists as
potential target for treating neurodegenerative diseases. The endogenous ligand of S1R has not been
unanimously identified yet and along the years it has been discovered that S1R can be activated
by some endogenous compounds, like neuropeptides, neurosteroids, N,N-dimethyltryptamine,
or by physiological signals like oxidative or endoplasmic reticulum (ER) stress [
transmembrane chaperone protein, present in the ER, nucleus and plasma membranes but particularly
enriched in mitochondria-associated ER membranes (MAMs) [ 10]. Modulation of S1R may have a
4–7]. S1R is a
8–
relevant potential from a therapeutic standpoint. In fact, S1R agonists possess neuroprotective and
neuroplastic activities, since they modulate several molecular cascades, including calcium homeostasis
regulation, glutamate excitotoxicity inhibition and oxidative stress [9,11–13]. The latter is a typical
condition in debilitating pathologies where S1R is involved, like neurodegenerative diseases. In fact,
neurodegenerative diseases, such as Alzheimer’s and Parkinson’s diseases as well as amyotrophic lateral
sclerosis share common features, and these pathological cellular mechanisms develop in different ways,
but all ultimately lead to apoptosis, necrosis, synapse and cell loss [1,3,14]. In our effort to discover
new chemical entities against neurodegeneration, we recently disclosed two compounds as dual
S1R modulators/acetylcholinesterase (AChE) inhibitors, characterized by the aryl-aminoalkyl-ketone
structure reported in Figure 1 [15]. These compounds belong to a series of Multi Target Directed Ligands
(MTDL) designed by combining the pharmacophoric elements of compound RC-33, the well-known
AChE inhibitor Donepezil, and the antioxidant natural product Curcumin [15]. In particular, RC-33 is
an in-house developed S1R agonist showing a nanomolar affinity against S1R and Sigma-2 receptor
(S2R) (KiS1R = 1.8
±
0.1 nM; KiS2R = 45
±
16 nM) , a good selectivity over S2R, µ-, and κ-opioid
receptors, a high metabolic stability in several biological matrices and a suitable pharmacokinetic
profile and Central Nervous System (CNS) distribution for in vivo administration. In addition, RC-33
promotes the neurite differentiation and elongation in rat dorsal root ganglia (DRG) experimental
model [15].
Figure 1. General scaffold of the published aryl-aminoalkyl-ketone series [15] and elements of structural
differentiation suitable for the development of new analogs.
The principal aim of this work was to further investigate the potential of this scaffold, integrating
computational and synthetic approaches to generate new molecules with optimized S1R binding
affinities. The first step entailed developing a reliable docking protocol able to recapitulate the
affinity of a series of known analogues for S1R and shed light on the most relevant pharmacophoric
determinants for the interaction. Afterwards, we designed and synthesized a small library of new
aryl-aminoalkyl-ketone (Figure 1) and evaluated the S1R binding affinity of the new molecules.

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From the correlation obtained upon comparing computational and experimental data, the efficacy of
the proposed model was proved.
To identify compounds with potential for development into anti-neurodegenerative agents, the
druggability, with particular reference to the ability to cross the blood–brain barrier (BBB), and the
selectivity over S2R and N-methyl-d-aspartate receptor (NMDAR), another well-known molecular
target involved in neurodegeneration, was investigated [16–18]. Lastly, we assayed the potential in
restoring the damage induced by oxidative stress of the compound with the best profile. In detail,
we evaluated the ability of our best compound to restore the aquaporin (AQP)-mediated water
permeability in heat-stressed cells. AQPs are integral membrane proteins able to facilitate the diffusion
of water and H2O2 from the producing cells across the plasma membranes to the extracellular
fluid [19,20]. Since a crucial pathological event of neurodegenerative diseases is the oxidative stress,
the possibility to chemically regulate the pore gating of AQPs, may provide a new direction to the
development of new therapeutic treatments for degenerative diseases and in ageing [21,22].
2. Results
2.1. Setup and Validation of a Docking Model to Predict Affinity toward S1R
To develop a model able to correlate the structure of ligands to their ability to bind
S1R, we docked ligands to the crystal structure (PDB ID: 5HK2) of the receptor published by
Schmidt et al. [23]. As a first step towards a reliable protocol, we tested the combination of parameters
that could best reproduce (with high accuracy) the X-ray structure of the complex between S1R and
N-(1-benzylpiperidin-4-yl)-4-iodobenzamide (4-IBP). In this framework, the ligand was removed
and subsequently redocked with the rigid docking procedure described in Material and Methods.
After running the calculations, we evaluated the root mean square deviation (RMSD) of the positions
of the heavy atoms of the ligand between the most favorable calculated pose and the crystallographic
one. Importantly, the RMSD was as low as 0.9 Å, demonstrating that the docking protocol was able to
reproduce the binding mode of crystallographic pose (see Figure 2A). Subsequently, the redocking
procedure was re-examined using an induced fit docking (IFD) approach (see Material and Methods).
This type of docking allows the binding pocket residues to adapt to the presence of the ligand,
mimicking the protein-ligand cross-talk that expectedly takes place upon binding events. In this case,
RMSD between the docked and the crystalized ligand increased to 1.6 Å due to small rearrangements
of atoms and functional groups (the orientation of ketone group). However, it is worth pointing out
that the overall binding mode is conserved, as shown by the superimposition of the two poses (see
Figure 2B).
Figure 2. Difference of 4-IBP poses in ligand docking (A) and induced fit docking (B) from the crystal
pose (shown in green color).

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Once established and validated by reproducing the binding properties of the experimental crystal
structures, the IFD docking protocol was applied to representative compounds ( ) previously
1–7
synthetized and evaluated by our group (Figure 3). In particular, we selected compounds that exhibited
different binding profiles to cover a wide range of Ki values. Besides hit compounds emerged from our
previous study (
1 and 2) [15], we docked compounds 3–5 endowed with binding affinities lower than
10 nM, and compounds 6, 7 which exhibited Ki > 300 nM.
Figure 3. Chemical structures of the previously developed S1R ligands, which have been assessed in
the present work for docking calculation.
Overall, we considered 11 ligands because for compounds which had a pKa next to the physiologic
one (1, 4–6) we considered both states (dissociated and not). At the end of the runs, we compared
results of Gscore based ligand efficiency (LE), a scoring function which describes the binding energy
considering all chemical properties of atoms involved in the interaction [24], with Ki values, under the
hypothesis that the two parameters are correlated. LE normalizes the calculated binding energy as a
function of the number of heavy atoms that make up the ligand. In this context, the Glide Gscore LE
represents a proxy for affinity (see Table 1). For ligands that exist in different states in physiological
conditions, we calculated the average of values.
Table 1. Glide Gscores, calculated Ligand Efficiencies and experimentally determined Ki values of
compounds 1–8 employed for the elaboration of the docking model.
Glide
Gscore
−10.560
−10.467
−8.885
−8.553
−10.295
−8.081
−9.101
−8.745
−11.411
−9.685
−9.048
Average
Gscore
Ligand Efficiency
(LE)
Average
LE
Ki S1R (nM)
± SEM a
27 ± 1.8
Cmpd
1
State
ln(Ki)
diss
undiss
diss
diss
diss
undiss
diss
undiss
diss
undiss
diss
−0.4062
−0.4026
−0.4039
−0.4277
−0.4680
−0.3673
−0.4551
−0.4373
−0.4075
−0.3459
−0.3619
−10.514
−0.4044
3.296
2
3
−8.885
−0.4039
15 ± 1.1
2.708
0.788
−8.553
−0.4277
2.2 ± 0.7
4
5
6
−9.188
−8.923
−0.4176
−0.4462
2.9 ± 0.3
9 ± 0.7
1.065
2.197
−10.548
−9.048
−0.3767
−0.3619
340
478
5.829
6.170
7
a
Compounds with high affinity were tested three times. For compounds with low SR affinity (>250 nM), only one
measure was performed.
Importantly, the use of LE as the affinity parameter returned a good correlation with experimental
affinity values (R2 = 0.77), as reported in Figure 4.

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Figure 4. Correlation between experimentally determined ln(Ki) and predicted ligand efficiency.
The data showed that compounds
ligand efficiency. Conversely, LE for
This model correctly captures the difference between the weak affinity of compounds
correctly placed in the region of the graph corresponding to bad performance) and the rest of the series,
clustering compounds with Ki in the range of tens nM (namely the two hit compounds ) differently
from those with Ki 9 nM. These results suggest that this approach can be proficiently used as a first
1
6–7
–
5
were correctly located in a region that corresponds to a good
correctly predicted their worse performances (LE > −0.3800).
(which are
6–7
1, 2
≤
screening of compound affinities for S1R. To corroborate this hypothesis, we designed a small new
series of analogues with the purpose to calculate their predicted affinity and then to synthesize and
evaluate their experimental Ki through binding assays.
2.2. Design of New Ligands
Among the two molecules previously emerged as the most promising ligands, compound 2 was
chosen as the starting point to prepare a new library of ketones [15]. This choice is strictly related to
a putative easier way to perform the future in vitro assays. The piperidine moiety confers a much
higher solubility in the aqueous medium, thus improving the pharmacokinetic properties and the
drug-likeness of the molecules. In the previous work, the piperidine ring was present only in the series
of ketones with two carbon atoms in the linker (n = 2). For this reason, we decided to expand the library
with compounds characterized also by a single carbon atom (n = 1) or by three carbon atoms (n = 3
)
linker. The aromatic moieties explored include naphth-2-yl, 4-biphenyl and 4-(benzyloxy)phenyl.
In particular, this last group maintains a similar hydrophobicity as the other two moieties, but at the
same time it possesses some new features able to give interesting information. Firstly, the presence of
an oxygen atom, which can act as a H-bond donor, gives us insights about the actual hydrophobicity of
this side of the binding pocket. Furthermore, we reasoned that we could further explore the binding
pocket by elongating the molecule with the addition of another aromatic ring together with an sp3
carbon atom as a more flexible linker. The designed library consists of six different ketones reported in
Figure 5. This compound library still maintains the structural features needed to interact with S1Rs,
namely the basic nitrogen and the hydrophobic moieties [23,25].

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Figure 5. Chemical structures of the new aryl-aminoalkyl-ketone derivatives.
The four major concerns in pharmacokinetics are absorption, distribution, metabolism and
excretion (ADME). These four criteria influence the pharmacological activity of bioactive compounds
and therefore their developability. In silico pharmacokinetic properties of the designed compounds
8
–
13 were predicted using QikProp program to assess a number of significant properties such as
blood–brain barrier (BBB) permeability, polar surface area, human oral absorption percentage and
molecular weight [26 27]. The results are reported in Table 2. Considering the pKa of compounds 13
compound was computed as free base at physiological pH, whereas compound was considered
,
8–
,
8
9
both as dissociated and undissociated species. All the other entries are protonated species. All values
were found to be within the range of most approved drugs, meaning that our designed compounds are
characterized by a good developability and drug-likeness. In particular, the QPlogBB value indicates
the predicted brain/blood partition coefficient. QPlogBB values of compounds
8
–
13 fall between 3.0
−
and 1.2, which is the range of 95% of known drugs. Overall, the designed compounds are suited for
targeting CNS receptors. Moreover, no violations to Lipinski’s rule of five or to Jorgensen’s rule of
three were found [28,29].
Table 2. Relevant descriptors and properties predicted with QikProp.
QPlog
HERG
−6.564
−7.077
−6.893
−7.246
−6.442
−6.98
QPP
Caco
QPlog
BB
0.34
0.198
0.202
0.085
0.242
0.156
−0.023
Oral
Cmpd
MW
PSA
HBD
HBA
QPlogPo/w
QPlogS
Abs b
8 a
9 a
9
10
11
12
13
279.38
309.40
309.40
323.43
281.39
307.43
337.46
30.874
38.211
36.978
38.951
31.865
32.094
39.309
0
0
0
0
0
0
0
4
3.51
3.924
3.899
4.112
3.612
4.268
4.28
−3.414
−3.75
1053.825
1057.244
1049.048
972.122
984.507
971.725
899.612
3
3
3
3
3
3
3
4.75
4.75
4.75
4
4
4.75
−3.604
−4.229
−3.565
−4.372
−4.622
−7.356
1 low
<25 poor
>500 great
Drug-likeness c
130–725 7.0–200
0–6
2–20
−2.0–6.5
−6.5–0.5
<−5
−
3.0–1.2 2 med
3 high
a
b
Compounds computed as free bases. The other entries are protonated species. Predicted qualitative human oral
absorption: 1, 2, or 3 for low, medium, or high. The assessment uses a knowledge-based set of rules, including
checking for suitable values of PercentHumanOralAbsorption, number of metabolites, number of rotatable bonds,
c
logP, solubility and cell permeability. Range or recommended values of each property for comparison with those
of 95% of known drugs.
2.3. Synthesis
To obtain the designed compounds we adopted the same synthetic strategy reported in our previous
work [15]. A divergent synthesis was exploited, as reported in Scheme 1. Key intermediates IV VI
-
were obtained through Weinreb amide formation and subsequent nucleophilic substitution. Finally,
target compounds 8–13 were obtained via Weinreb ketone synthesis, using different aryl bromides.

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Scheme 1. Synthetic route to achieve the target compounds 8–13.
All the designed compounds were obtained in suitable amount and purity for further
biological investigation.
2.4. S1R Binding Profile
Compounds 8–13 were docked on S1R using the same protocol and their ligand efficiencies were
calculated. This allowed us to predict their Ki values exploiting the equation of the correlation line
reported in Figure 4. The obtained results, reported in Table 3, indicate that the new compounds are
expected to exhibit an excellent binding profile, with affinity as low as sub-nanomolar. Even the less
performing compound 13 still displays a predicted Ki < 300 nM.
Table 3. LE, predicted and experimental binding affinities of compounds 8–13 toward S1R binding site.
Predicted
ln(Ki)
Predicted
Ki (nM)
44.92
21.10
15.92
0.57
1.72
298.28
Experimental
Ki S1R (nM) ± SEM
132 ± 55
Experimental
ln(Ki)
cmpd
LE
8
9
10
11
12
13
−0.39271
−0.40309
−0.40696
−0.45271
−0.43748
−0.36672
3.9754
3.3053
3.0555
0.1022
1.0854
5.6531
4.8828
3.8712
3.2189
1.4816
3.4965
4.4659
48 ± 12
25 ± 7
4.4 ± 2.4
33 ± 14
87 ± 28
In particular, we compared the specific poses of best performing ligands, namely 11 and 12
.
We noticed that 11 had better affinity because the protonated piperidine established an additional
H-bond with Glu172 (see Figure 6). The formation of this additional interaction may be reconnected
to the presence of naphthalene, a less cumbersome substituent than biphenyl. Naphthalene, indeed,
allows a better adaptation of the molecule and, therefore, it induces the ligand to populate a more
favorable orientation for the interaction.
Overall, analyses of our models indicated that most of the ligands with protonated piperidine
established an electrostatic interaction with Glu172 and a cation-π interaction with Phe107. This amino
ring is further stabilized by weak interactions with another aromatic residue, Trp89. Moreover,
the aromatic substituent establishes principally stacking interactions with Tyr103 and Tyr206. The pocket
is purely hydrophobic (see Figure 7) and, therefore, the carbon skeleton establishes numerous additional
hydrophobic interactions with the residues that line the pocket. All of these interactions contribute to
stabilize the ligand pose.

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Figure 6. Binding mode of 11 (in turquoise sticks) and 12 (in yellow sticks). The main amino acids of
the protein involved in the binding are represented as black wires. Dashed lines indicate the stackings
π
(in cerulean), the electrostatic interactions (in pink), and H-bonds (in blue). Atoms are colored-coded
(oxygen in red, nitrogen in blue).
Figure 7. Binding mode of 11 in 2D space (
A) and 3D space (B). The residues of the protein involved in
hydrophobic interactions are reported in white sticks.
Based on the docking correlation analysis and the inspection of the poses reported above,
we hypothesize that the aromatic moiety, alkyl linker and the protonated basic substituent are
fundamental pharmacophoric elements to ensure affinity for the target. In this framework, the presence
of a longer alkyl linker (n = 2/n = 3) on the one hand favors interactions with pocket’s hydrophobic
residues, while making the compound more flexible on the other hand. It is tempting to speculate that

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such flexibility would allow the amino substituent of the ligand to dynamically adapt to the binding
pocket, which is in turn expected to undergo conformational adaptation to the presence of the ligand.
Facilitating the exploration of a larger number of interactions, this could favor the efficient selection
of a more stable pose. Moreover, the carbonyl group is not involved in any particular interaction
with the binding pocket residues. This can actually be an advantage in the pursuit of multi-target
ligands: The ketone moiety could be exploited for interaction with other molecular targets involved in
neurodegeneration—such as NMDA receptor—without affecting binding to S1R.
To verify reliability of the developed docking protocol, we performed experimental binding
assays toward S1R. Binding site affinities of compounds 8–13 were measured through competition
experiments, using radioligands. The assay for S1R was performed using homogenized guinea pig
cerebral cortex membranes, in the presence of [3H]-(+)-pentazocine, as a potent and selective S1R
radioligand. Nonspecific binding values were determined using non-radiolabeled (+)-pentazocine
and haloperidol in excess (see experimental section). The results are presented in Table 3.
The experimental affinities were compared with the predicted ones as reported in the graph of
Figure 8. Blue dots represent already published compounds that were used to build the model, and the
blue line is the correlation between LE and ln(Ki) for such compounds. The orange dots correspond to
the new analogues, and the orange line indicates the correlation between predicted and experimental
affinity once all compounds 1–13 are taken into account. Interestingly, the correlation coefficient
indicates a good predictivity of the model (R2 = 0.64). It is worth noting that the orange correlation
line is obtained from a wider and more comprehensive data set.
Figure 8. Correlation between calculated LE and experimental ln(Ki). Blue color indicates the line,
the equation and the R2 value for the correlation between LE and ln(Ki) for the first data set compounds
1
–
7
employed for the model construction. With orange color are indicated the line, the equation and the
R2 value for the correlation between LE and ln(Ki) for the wider data set, including all compounds
1–13
.
Overall, the model was able to place new compounds correctly into the proper region of ligand
efficiency, separating best performing molecules from those displaying weaker receptor affinity.
Moreover, the dataset employed covers a significant range of binding affinities, from low nanomolar to
hundreds nanomolar, suitable for ligands that can be further developed and/or optimized.

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2.5. In Depth Pharmacological Profile
All compounds have also been tested for their affinity towards S2R and NMDAR. Homogenized
rat liver membranes were adopted to evaluate the S2R binding values, employing [3H]-DTG—a
non-selective S2R radioligand—and, non-tritiated (+)-pentazocine to mask the S1R. Compounds with
high affinity were tested twice. For compounds with low affinity, only one measure was performed.
Affinity towards GluN2 subunit of NMDA was determined through competitive binding assays on
membrane extracts of L cells (tk-), stably transfected with a vector containing the genetic information of
GluN1a and GluN2B subunits. [3H]-Ifenprodil was employed as a selective and potent GluN2 inhibitor
radioligand. Compounds with high affinity were tested three times. For compounds with low NMDA
affinity, only one measure was performed. A negligible affinity vs. S2R and NMDAR (Ki S2R > 200 nM
,
Ki NMDAR > 500 nM) was observed with the only exception of compound 11 (Ki S2R = 50 nM) and
compound 12 (Ki NMDAR = 200 nM). Therefore, compound 12, the only characterized by a good
affinity for both S1R and NMDAR and no affinity for S2R was then selected for undergoing further
in vitro investigation, by evaluating the AchE and antioxidant properties.
To test the potential to inhibit AChE, a spectrophotometric procedure was adopted, based on the
well-known Ellman’s method [30]. Firstly, the target compound was tested at a concentration of 50 µM
and a 72% of inhibition was observed. Accordingly, the IC50 value was determined, which resulted in
0.68 ± 0.09 µM.
Prompted by these encouraging results, we decided to draw the antioxidant profile of compound
12, since it is well-known that oxidative stress is connected with insurgence and exacerbation of
neurodegenerative diseases [3,18,31,32]. In particular, the AQP-mediated antioxidant properties of
RC-33 (i.e., 1-[3-(1,10-biphen)-4-yl]butylpiperidine, our in-house developed selective S1R agonist) and
12 were evaluated in HeLa cells, following the procedures reported in our most recent studies [22].
Briefly, osmotic water permeability was measured by a stopped-flow light scattering method and
expressed as percentage of k relative. Compounds RC-33 and 12 were tested at 20 µM whereas
curcumin was used as control of the goodness of the cellular assay, since its capability to affect
the AQPs permeability was assessed in our previous work [22]. The compounds were initially
assessed on non-stressed HeLa cells to evaluate the capability of the compounds to affect the AQPs
permeability in absence of an oxidant stimuli. As reported in Figure 9A, compound 12 did not affect
the water permeability and showed, as expected, a profile comparable to the parent compounds RC-33.
Conversely, curcumin directly affect AQPs resulting in a significative permeability reduction (75% of k
relative). RC-33, 12 and curcumin were further assessed under oxidative stress conditions induced
by heating the cell cultures. As reported in Figure 9B, both RC-33 and 12 were able to prevent the
water permeability reduction (values compared to the control), as instead significatively observed
in cells offended by heating (80% of k relative). Notably, the curcumin was not able to protect the
AQPs’ permeability properties in heat-treated cells. Thus, to probe the entanglement between S1R,
AQP permeability and antioxidant protection, compound 12 was assessed against stressed HeLa cells
in presence of NE-100, a well-known and fully characterized S1R antagonist. NE-100 was able to
significatively quench the antioxidant effect of the S1R ligand 12, with k relative values comparable
to NE-100 tested as single agent (% of k relative of 78% and 80% for compounds NE-100 and the
combination NE-100 + 12, respectively). Thus, these results suggest that compound 12 acts as S1R
agonist, exerting an AQP-mediated antioxidant effects by the engagement of S1R.

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Figure 9. Effect of the compounds 12, RC-33 and curcumin on the water permeability of HeLa cells
in non-stressed condition ( ) and in heat-stress condition ( ). (
) Hela cells were incubated at 21 ◦C
for 3 h with the compounds at the final concentration of 20 M. Vehicle cells are incubated without
compounds (white bars). Bars represent the osmotic water permeability of HeLa cells expressed as
A
B
A
µ
percent of k relative. (B) HeLa cells were exposed to a 150 mOsm osmotic gradient in three different
conditions: (1) Untreated cells (Controls, Ctr); (2) cells treated at 42 ◦C for 3 h (heat-stressed, Heat);
(3) heat-stressed cells pre-treated with the compound 12, RC-33, NE-100, NE-100 + 12 combination,
and curcumin at 20
µ
M final concentration. Values are means
±
SEM of 4–15 single shots (time course
curves) for each of 4–18 different experiments. * p < 0.05 vs. Ctr, 12, RC-33; p < 0.05 vs. vehicle, 12,
RC-33 (ANOVA, followed by Newman–Keuls’s Q test).
‡
3. Discussion
In this work, we developed a docking protocol able to predict the S1R affinity of new
aryl-aminoalkyl-ketone. Firstly, a reliable docking protocol was set up and validated. The co-crystallized
ligand 4-IBP has been removed from the crystal structure and redocked using both ligand docking
and induced fit docking (Figure 2). Results showed that both protocols were able to reproduce the
binding mode of the crystallized ligand. A series of our previously synthesized SR ligands with
aminoalkyl-ketone scaffold (
binding affinities (1 < Ki < 500 nM). Regarding compounds with a pKa close to the physiologic one
(i.e., ligands ), protonated species and free bases have been considered. Gscore based Ligand
1–7) were used as training set. These compounds covered a wide range of
1, 4–6
Efficiency (LE) resulted in the most valuable parameter to predict ligand’s affinity. As a result, a linear
correlation between LE values and the experimental Ki was observed with a R2 = 0.77 (Figure 4).
Importantly, compounds in the same range of affinity were correctly clustered together. On the wave
of the results obtained, the docking protocol was validated by designing and synthesizing a test
set of six structurally related compounds, namely
were calculated. All designed compounds 13 resulted endowed with good BBB permeability, oral
bioavailability. In addition, no violations to Lipinski’s rule of five and Jorgensen’s rule of three supported
their druggability. Compounds 13 have then been docked on S1R using the abovementioned protocol
8–13. Moreover, the in silico ADME parameters
8–
8–
and their LE scores calculated. Based on the equation reported in Figure 4, the Ki values were predicted.
In details, sub-nanomolar Ki was predicted for compound 11, while Ki values in the tens of nanomolar
were predicted for other compounds, with the only exception of compound 13. The S1R binding assay
showed Ki values in accordance with the predicted ones. The satisfying correlation between LE and
the experimental Ki values testified the goodness of our docking protocol to predict the binding affinity
of potential S1R ligands based on aminoketone scaffold. Overall, compounds 8–12 can be considered
promising S1R ligands and were selected for further investigation.
The binding profile has been therefore extended by evaluating the affinity towards S2R and
NMDAR. All compounds showed a negligible affinity vs. S2R and NMDAR with the only exception of
compound 11 (Ki S2R = 50 nM) and compound 12 (Ki NMDAR = 200 nM). Importantly, NMDAR is
strictly related to synaptic plasticity and synapse formation, which are key mechanisms underpinning
learning and memory. Dysregulation of NMDAR activity leads to excitotoxicity and cell death, and is
thus considered a potential mechanism of neurodegeneration [16–18]. Taking into account the relevant
role of both S1R and NMDAR in neurodegenerative diseases, compound 12, was then selected for
undergoing further in vitro investigation, by evaluating the AchE and antioxidant properties. In fact,

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it is well known that dysfunction of neurotransmitter pathways (such as acetylcholine impairment) and
oxidative stress are distinctive features of neurodegenerative conditions [18 31 33 34]. Furthermore,
,
, ,
compounds exerting antioxidant effects are commonly regarded as attractive therapeutic tools for the
treatment of neurodegenerative disorders. In fact, the brain is deficient in antioxidant defense systems
and it requires a high level of oxygen, it is highly vulnerable to oxidative stress. AQPs are a family
of water channels that play a role in scavenging reactive oxygen species, alleviating oxidative injury,
and counteracting neuropathies [35
recognized as druggable molecular targets [37
,
36]. These still poorly explored proteins have only recently been
39]. In our recent investigations, we have identified a
–
number of molecules (both natural and synthetic) that are able to modulate AQPs and thus prevent cell
damage due to oxidative stress [22]. Results obtained showed that compound 12 possesses anti-AChE
activity (IC50 = 0.68
±
0.09 µM) and it is able to prevent water permeability decrease in heat-stressed
HeLa cells. In particular, combination studies with NE-100 (a S1R antagonist) suggested that the S1R
agonist is able to exert an AQP-mediated antioxidant effects.
To sum up, in our ongoing quest for new chemical entities against neurodegeneration, we recently
reported on aryl-aminoalkyl-ketone, as dual S1R modulators/AChE inhibitors endowed with
antioxidant properties [15]. To speed up the identification of other promising neuroprotective
agents, in the present work we developed a reliable computational protocol to predict affinity of novel
compounds toward S1R. The proposed model is simple and efficient and can be proposed for designing
new derivatives. Overall, the docking protocol herein proposed appears to capture the principal
determinants for S1R binding, proving its immediate transferability to the design of new viable leads
whose activity is experimentally supported. Our combined approach thus expands the chemical space
of S1R ligands, in parallel generating new opportunities for testing in multitargeted strategies. Among
the small series of novel compounds developed in the present work, we identified compound 12 as
the most promising, being endowed with good affinity toward both S1 and NMDA receptors, good
selectivity over S2R and favorable BBB penetration potential. Moreover, its AChE inhibitory activity
and its ability to exert antioxidant effects through modulation of AQPs make 12 a viable candidate for
further development as a neuroprotective agent and can be moved to in vivo studies.
4. Materials and Methods
4.1. Laboratory Materials and Equipment
Reagents and solvents for synthesis and deuterated solvents for NMR were purchased from
Sigma Aldrich. Silica gel for flash chromatography (60 Å; 230–400 Mesh) was purchased from
Sigma Aldrich. Solvents were evaporated at reduced pressure with the Heidolph Laborota 4000
Efficient equipment. Analytical thin layer chromatography (TLC) analyses were carried out on silica
gel pre-coated glass-backed plates (TLC Silica Gel 60 F254; Merk) impregnated with a fluorescent
indicator and visualized with the instrument MinUVIS DESAGA® Sarstedt-GRUPPE by ultraviolet
(UV) radiation from UV lamp (λ = 254 and 366 nm) or by stain reagents such as Ninidrine. NMR were
measured at room temperature (15–25 ◦C) on a Bruker Avance 400 MHz and/or 500 MHz spectrometer,
using tetramethylsilane (TMS) as internal standard and a BBI 5 mm probe. All raw FID files were
processed with Top Spin program from Bruker and the spectra analyzed using the MestReNova 6.0.2
program from Mestrelab Research S.L. Chemical shifts are expressed in parts per million (ppm; δ scale).
1H-NMR spectroscopic data are reported as follow: chemical shift in ppm; multiplicity; coupling
constants J (in Hz), and integration intensity.
The multiplicities are abbreviated as s (singlet), d (doublet), t (triplet), q (quartet), m (multiplet)
and brs (broad signal). The chemical shift of all symmetric signals is reported as the center of the
resonance range. 13C-NMR spectroscopic data are reported as follows: chemical shift in ppm.

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4.2. General Experimental Details
All the following reactions were performed with dry glassware, previously flamed with Bunsen
burner, fitted with rubber septum, under an atmosphere of nitrogen and with magnetic stirring.
Liquid reagents, air-/moisture- sensitive and dry solvents were added using plastic syringes with metal
needle, previously conditioned with nitrogen. Solid reagents were transferred opening the rubber
septum under nitrogen flow or solubilizing them in appropriate dry solvents.
Low temperatures were reached with cooling agents, such as ice (0 ◦C), mixture of ice, methanol
(MeOH) and sodium chloride (NaCl) (
placed in a Dewar suitable for the reaction flask.
−
18 ◦C), or mixture of solid carbon dioxide and acetone ( 78 ◦C)
−
Reactions progress and ending were monitored by TLC; in addition, the final products were
1
analyzed with H and 13C Nuclear Magnetic Resonance (NMR). Purity of final compounds was
assessed by UPLC-UV-ESI/MS. Analyses were carried out on a Acuity UPLC Waters LCQ FLEET
system using an ESI source operating in positive ion mode, controlled by ACQUIDITY PDA and
4 MICRO (Waters). Analyses were run on a ACQUITY BEH Phenyl (ABP) (50 2.1 mm, 1.7 mm) or
ACQUITY BEH Shield (ABS) (100 2.1 mm, 1.7 mm) columns, at room temperature, with gradient
elution (solvent A: Water containing 0.1% of formic acid; solvent B: Methanol containing 0.1% of formic
acid; gradient: 10% B in A to 100% B in 3 min, followed by isocratic elution 100% B for 1.5 min, return
to the initial conditions in 0.2 min) at a flow rate of 0.5 mL min−1
.
4.3. Synthetic Procedures
4.3.1. General Procedure for the Preparation of Compounds II–III
To an aqueous solution of potassium carbonate (K2CO3, 2.0 equiv.) was added diethyl ether (Et2O)
and N,O-dimethylhydroxyamine (NODMHA) hydrochloride (1.5 equiv.). The resulting mixture was
cooled at 0 ◦C and then the corresponding acyl chloride (1.0 equiv.) was added dropwise. The reaction
was let to reach room temperature and then it was stirred overnight. The reaction mixture was extracted
with Et2O (2
×
10 mL) and washed with water (10 mL) and brine (20 mL). The organic phase was
dried (anhydrous sodium sulphate Na2SO4), filtered and, after removal of the solvent under reduced
pressure, the pure compounds II–III were obtained.
3-Chloro-N-methoxy-N-methylpropanamide (II)
By following the General Procedure, starting from 3-chloropropanoyl chloride (127 mg, 1.00 mmol,
1.0 equiv.), K2CO3 (276 mg, 2.0 mmol, 2.0 equiv.), N,O-dimethylhydroxyamine hydrochloride (146 mg,
1.5 mmol, 1.5 equiv.), H2O (3 mL) and Et2O (3 mL), the desired product was obtained in 92% (139 mg)
as a pale yellow oil. 1H NMR (500 MHz, CDCl3):
δ 3.80 (t, J = 6.9 Hz, 2H, CH2CH2Cl), 3.70 (s, 3H,
NOCH3), 3.19 (s, 3H, NCH3), 2.91 (t, J = 6.7 Hz, 2H, CH2CH2CO). 13C NMR (125 MHz, CDCl3):
δ
170.8, 61.4, 39.2, 35.0, 32.0.
4-Chloro-N-methoxy-N-methylbutanamide (III)
By following the General Procedure, starting from 4-chlorobutanoyl chloride (141 mg, 1.00 mmol,
1.0 equiv.), K2CO3 (276 mg, 2.0 mmol, 2.0 equiv.), N,O-dimethylhydroxyamine hydrochloride (146 mg,
1.5 mmol, 1.5 equiv.), H2O (3 mL) and Et2O (3 mL), the desired product was obtained in a quantitative
amount (yield >99.9%) (166 mg) as a bright yellow oil. 1H NMR (500 MHz, CDCl3):
δ 3.70 (s, 3H,
NOCH3), 3.63 (t, J = 6.4 Hz, 2H, CH2CH2Cl), 3.18 (s, 3H, NCH3), 2.62 (t, J = 6.7 Hz, 2H, CH2CH2CO),
2.11 (m, J = 6.9 Hz, 2H, CH2CH2CH2). 13C NMR (125 MHz, CDCl3): δ 170.8, 61.4, 39.2, 35.0, 32.0.
4.3.2. General Procedure for the Preparation of Compounds IV–V–VI
To a solution of the appropriate Weinreb amide
(1.0 equiv.) and K2CO3 (1.5 equiv.) were added. In case of compound IV the mixture was stirred for
36 h at room temperature; for compound four days at room temperature were needed; lastly for
I–III (1.0 equiv.) in acetonitrile (ACN), piperidine
V

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compound VI the temperature was raised to 50 ◦C for 8 h, then it was let to reach room temperature
and stirred for five days. After removal of the solvent under reduced pressure, the crude was extracted
with dichloromethane (DCM) (3
case of compound IV, this work-up was sufficient to obtain the pure compound. Conversely, an acid
(pH = 3–4)/base (pH = 8–9) work-up was required for and VI, the combined organic phases were
×
5 mL) and washed with water (5 mL) and brine (10 mL). In the
V
dried over anhydrous Na2SO4, filtered and, evaporated under vacuum to get the desired compounds.
N-methoxy-N-methyl-2-(piperidine-1-yl)-acetamide (IV)
By following the General Procedure, starting from 2-chloro-N-methoxy-N-methylacetamide
(
I
, commercially available) (138 mg, 1.00 mmol, 1.0 equiv.), K2CO3 (207 mg, 1.5 mmol, 1.5 equiv.),
piperidine (175 mg, 176 mL 1.0 mmol, 1.0 equiv.) and ACN (10 mL), the desired product was obtained
in 85% yield (235 mg) as a dark oil. 1H-NMR (500 MHz, CDCl3)
: 3.71 (s, 3H, NOCH3), 3.29 (s, 2H,
δ
CH2N), 3.19 (s, 3H, NCH3), 2.50 (m, 4H, CH2 piperidine), 1.71 (m, 4H, CH2 piperidine), 1.47 (m, 2H,
CH2 piperidine).
N-methoxy-N-methyl-3-(piperidine-1-yl)-propanamide (V)
By following the General Procedure, starting from 3-chloro-N-methoxy-N-methylpropanamide
(152 mg, 1.00 mmol, 1.0 equiv.), K2CO3 (207 mg, 1.5 mmol, 1.5 equiv.), piperidine (85 mg, 99 mL,
1.0 mmol, 1.0 equiv.) and ACN (7 mL), the desired product was obtained in 56% yield (112 mg) as a
transparent oil. 1H NMR (400 MHz, CDCl3):
δ 3.76 (s, 3H, NOCH3), 3.24 (s, 3H, NCH3), 2.76 (s, 4H,
CH2N, CH2CH2N), 2.52 (t, 4H, pip), 1.70–1.56 (m, 6H, pip). 13C NMR (100 MHz, CDCl3):
δ 175.2, 61.3,
54.5, 54.2, 29.7, 25.9, 24.2.
N-methoxy-N-methyl-4-(piperidin-1-yl)-butanamide (VI)
By following the General Procedure, starting from 4-chloro-N-methoxy-N-methylbutanamide
(166 mg, 1.00 mmol, 1.0 equiv.), K2CO3 (207 mg, 1.5 mmol, 1.5 equiv.), piperidine (85 mg, 99 mL,
1.0 mmol, 1.0 equiv.) and ACN (7 mL), the desired product was obtained in 47% yield (101 mg) as a
yellow oil. 1H-NMR (400 MHz, CDCl3)
δ: 3.70 (s, 3H, NOCH3), 3.16 (s, 3H, NCH3), 2.56 (t, J = 2.7 Hz,
2H, CH2N), 2.51 (m, 4H, CH2N piperidine), 2.19 (t, J = 7.4Hz, 2H, COCH2), 1.93 (m, 2H, COCH2CH2),
1.71 (m, 4H, CH2 piperidine), 1.46 (m, 2H, CH2 piperidine).
4.3.3. General Procedure for the Preparation of Compounds 8–13
Under nitrogen atmosphere, tert-butyllithium (2.5 equiv., 1.7 M in pentane) was added dropwise
to a
78 ◦C cooled solution of the appropriate arylbromide (1.5 equiv.) in anhydrous tetrahydrofuran
−
(THF). After 20 min, the solution of the corresponding Weinreb amide in anhydrous THF was added
dropwise. The stirring was continued for 5 additional hours and then quenched with water. The reaction
was extracted with Et2O (3
×
7 mL) and washed with water (5 mL) and brine (10 mL). The organic
phase was dried over anhydrous Na2SO4, filtered and, after removal of the solvent under reduced
pressure, the so-obtained crude mixture was subjected to chromatography (silica gel) to afford pure
compound. Lastly, pure compound was converted into its corresponding hydrochloride, adding an
ethereal solution of hydrogen chloride (HCl, 1.0 equiv., 1 M in Et2O).
1-(2-([1,10-biphenyl]-4-yl)-2-oxoethyl)piperidin-1-ium hydrochloride (8)
By following the General Procedure, starting from 4-bromo-1,10-biphenyl (1.5 equiv.),
N-methoxy-N-methyl-2-(piperidin-1-yl)-acetamide (1.0 equiv.), t-BuLi (1.7 M, 2.5 equiv.) and THF,
the gyesired product was obtained in 35% yield as a yellow solid after chromatography on silica
gel (90:10 ethylacetate/methanol) and converted into the corresponding hydrochloride. 420 mg, pale
yellow solid.
8.15 (d, J = 8.3 Hz, 2H, Ph-PhCO), 7.89 (d, J = 8.3 Hz, 2H, Ph-PhCO), 7.74 (d, J = 7.7 Hz, 2H, Biph),
7.53 (t, J = 7.5 Hz, 2H, Biph), 7.46 (t, J = 7.2 Hz, 1H, Biph), 4.99 (s, 2H, COCH2N), 3.65 (brs, 2H, pip),
R
f: 0.27 (80:20 ethylacetate/methanol). mp: 236–238 ◦C. 1H NMR (400 MHz, MeOD):
δ

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3.16 (t, J = 13.1 Hz, 2H, pip), 2.08 – 1.87 (brs, 5H, pip), 1.69 – 1.55 (brs, 1H, pip). 13C-NMR (100 MHz,
MeOD) δ: 190.12, 147.42, 139.19, 132.31, 128.81, 128.67, 128.41, 127.17, 126.89, 61.19, 54.23, 22.57, 21.21.
UHPLC-ESI-MS: ABP tR = 3.98, 98% pure (λ = 294 nm), m/z = 281.1 [M + H]+.
1-(2-(4-(benzyloxy)phenyl)-2-oxoethyl)piperidin-1-ium hydrochloride (9)
By following the General Procedure, starting from 1-(benzyloxy)-4-bromobenzene (1.5 equiv.),
N-methoxy-N-methyl-2-(piperidin-1-yl)-acetamide (1.0 equiv.), t-BuLi (1.7 M, 2.5 equiv.) and THF,
the desired product was obtained in 29% yield as a yellow solid after chromatography on silica gel
(80:20 ethylacetate/methanol) and converted into the corresponding hydrochloride. 400 mg, pale
yellow solid.
R δ
f: 0.19 (80:20 ethylacetate/methanol). mp: 86–88 ◦C. 1H NMR (400 MHz, MeOD):
8.03 (d, J = 8.8 Hz, 2H, OPhCO), 7.47 (d, J = 7.4 Hz, 2H, PhCH2), 7.41 (t, J = 7.3 Hz, 2H, PhCH2), 7.36
(d, J = 7.2 Hz, 1H, PhCH2), 7.19 (d, J = 8.8 Hz, 2H, OPhCO), 5.24 (s, 2H, PhCH2OPh), 4.87 (s, 2H,
COCH2N), 3.61 (d, J = 12.1 Hz, 2H, pip), 3.11 (brs, 2H, pip), 1.94 (brs, 5H, pip), 1.60 (m, 1H, pip).
13C-NMR (100 MHz, MeOD)
δ: 193.52, 130.63, 128.58, 127.74, 115.10, 70.33, 59.24, 52.20, 23.04, 21.50.
UHPLC-ESI-MS: ABP tR = 4.12, 96% pure (λ = 285 nm), m/z = 311.2 [M + H]+.
1-(3-(4-(benzyloxy)phenyl)-3-oxopropyl)piperidin-1-ium hydrochloride (10)
By following the General Procedure, starting from 1-(benzyloxy)-4-bromobenzene (1.5 equiv.),
N-methoxy-N-methyl-2-(piperidin-1-yl)-propanamide (1.0 equiv.), t-BuLi (1.7 M, 2.5 equiv.) and THF
(5 mL), the desired product was obtained in 31% yield as a yellow solid after chromatography on silica
gel (90:10 DCM/methanol) and converted into the corresponding hydrochloride. 560 mg, white solid.
1H NMR (400 MHz, MeOD):
δ 8.05 (d, J = 8.2 Hz, 2H, OPhCO), 7.47 (d, J = 6.3 Hz, 2H, PhCH2), 7.40 (t,
J = 6.5 Hz, 2H, PhCH2), 7.35 (brs, 1H, PhCH2), 7.14 (d, J = 8.3 Hz, 2H, OPhCO), 5.22 (s, 2H, PhCH2O),
3.62 (brs, 2H, COCH2CH2), 3.55 (brs, 4H, pip), 3.04 (t, J = 12.5 Hz, 2H, COCH2CH2), 1.98 (brs, 2H, pip),
1.82 (brs, 3H, pip), 1.58 (brs, 1H, pip). 13C-NMR (125 MHz, MeOD)
δ: 194.91, 163.38, 136.55, 131.05,
129.00, 128.08, 127.49, 126.54, 113.90, 69.85, 53.31, 52.22, 32.29, 22.95. UHPLC-ESI-MS: ABP tR = 4.03,
98% pure (λ = 263 nm), m/z = 324.4 [M + H]+.
1-(4-(naphthalen-2-yl)-4-oxobutyl)piperidin-1-ium hydrochloride (11)
By following the General Procedure, starting from 2-bromonaphthalene (1.5 equiv.),
N-methoxy-N-methyl-4-(piperidin-1-yl)-butanamide (1.0 equiv.), t-BuLi (1.7 M, 2.5 equiv.) and
THF, the desired product was obtained in 58% yield as a yellow solid after chromatography on silica
gel (60:40 ethylacetate/methanol) and converted into the corresponding hydrochloride. 554 mg, white
solid.
R δ 8.66
f: 0.17 (50:50 ethylacetate/methanol). mp: 213–215 ◦C. 1H NMR (400 MHz, MeOD):
(s, 1H, Napht), 8.10 – 8.05 (m, 2H, Napht), 8.01 – 7.94 (m, 2H, Napht), 7.64 (m, 2H, Napht), 3.65 (d,
J = 12.3 Hz, 2H, pip), 3.40 (t, J = 6.6 Hz, 2H, CH2CH2CH2), 3.28 – 3.20 (m, 2H, CH2CH2CH2), 3.01 (t,
J = 12.4 Hz, 2H, pip), 2.29 – 2.17 (m, 2H, CH2CH2CH2), 2.01 (d, J = 14.9 Hz, 2H, pip), 1.86 (m, 3H,
pip), 1.63 – 1.52 (m, 1H, pip). 13C-NMR (125 MHz, CDCl3)
δ: 199.1, 130.0, 129.7, 128.7, 128.5, 127.8,
126.1, 122.6, 56.7, 53.1, 35.6, 22.4, 22.1, 18.0. UHPLC-ESI-MS: ABP tR = 3.77, 98% pure (
m/z = 282.0 [M + H]+.
λ = 248 nm),
1-(4-([1,10-biphenyl]-4-yl)-4-oxobutyl)piperidin-1-ium hydrochloride (12)
By following the General Procedure, starting from 4-bromo-1,10-biphenyl (1.5 equiv.),
N-methoxy-N-methyl-4-(piperidin-1-yl)-butanamide (1.0 equiv.), t-BuLi (1.7 M, 2.5 equiv.) and
THF, the desired product was obtained in 72% yield as a yellow solid after chromatography on silica
gel (40:60 ethylacetate/methanol) and converted into the corresponding hydrochloride. 671 mg, white
solid.
R δ 8.13 (d,
f: 0.23 (40:60 ethylacetate/methanol). mp: 250–251 ◦C. 1H NMR (400 MHz, MeOD):
J = 8.3 Hz, 2H, Ph-PhCO), 7.81 (d, J = 8.3 Hz, 2H, PhPhCO), 7.71 (d, J = 7.6 Hz, 2H, PhPhCO), 7.51 (t,
J = 7.5 Hz, 2H, PhPhCO), 7.43 (t, J = 7.3 Hz, 1H, PhPhCO), 3.63 (brs, 2H, pip), 3.28 (t, J = 6.6 Hz, 2H,
CH2CH2CH2), 3.25–3.18 (m, 2H, CH2CH2CH2), 3.00 (brs, 2H, pip), 2.25–2.13 (m, 2H, CH2CH2CH2),
1.92 (brs, 5H, pip), 1.59 (brs, 1H, pip).13C-NMR (125 MHz, CDCl3)
δ: 198.8, 145.8, 139.7, 135.7, 130.0,

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128.9, 128.6, 128.3, 127.3, 56.7, 53.2, 35.6, 22.5, 22.2, 18.0. UHPLC-ESI-MS: ABP tR = 4.30, 97% pure
(λ = 285 nm), m/z = 308.1 [M + H]+.
1-(4-(4-(benzyloxy)phenyl)-4-oxobutyl)piperidin-1-ium hydrochloride (13)
By following the General Procedure, starting from 1-(benzyloxy)-4-bromobenzene (1.5 equiv.),
N-methoxy-N-methyl-4-(piperidin-1-yl)-butanamide (1.0 equiv.), t-BuLi (1.7 M, 2.5 equiv.) and THF,
the desired product was obtained in 53% yield as a yellow solid after chromatography on silica gel
(60:40 ethylacetate/methanol) and converted into the corresponding hydrochloride. 798 mg, white
solid.
R δ: 8.02
f: 0.18 (40:60 ethylacetate/methanol). mp: 188–190 ◦C. 1H NMR (400 MHz, MeOD)
(d, J = 8.8 Hz, 2H, OPhCO), 7.47 (d, J = 7.4 Hz, 2H, PhCH2), 7.40 (t, J = 7.3 Hz, 2H, PhCH2), 7.35 (d,
J = 7.1 Hz, 1H, PhCH2), 7.12 (d, J = 8.8 Hz, 2H, OPhCO), 5.21 (s, 2H, PhCH2OPh), 3.60 (brs, 2H, pip),
3.18 (m, 4H, CH2CH2CH2), 2.98 (brs, 2H, pip), 2.21–2.08 (m, 2H, CH2CH2CH2), 2.04–1.72 (brs, 5H,
pip), 1.57 (brs, 1H, pip).13C-NMR (125 MHz, CDCl3)
δ: 198.1, 160.0, 138.1, 137.4, 130.1, 128.1, 127.7,
127.3, 114.4, 69.7, 56.3, 53.1, 31.2, 22.8, 21.5, 18.1. UHPLC-ESI-MS: ABP tR = 4.32, 99% pure (
λ = 273
nm), m/z = 338.0 [M + H]+.
4.4. Molecular Modelling on S1R
Docking analyses and calculations were carried out using the software Maestro, Schrodinger, LLC,
New York, 2019, via the following protocol:
4.4.1. Ligand Preparation
Each ligand is designed with 2D sketcher and then transferred into a 3D structure. Every
ligand is prepared with LigPrep program of the MAESTRO suite from Schrodinger (release 2019-1;
www.schodinger.com) considering a pH between 6 and 8 to mimic the physiological one. The force
field for calculations was OPLS3 [40].
4.4.2. Protein Preparation
As a receptor, we use the cocrystal-structure with PBD code 5HK2. Before starting calculations,
the protein is prepared using the Protein Preparation Wizard program [41] of MAESTRO. Water
molecules and all ligands (detergents etc.) except for 4-IBP are eliminated. The crystallographic
structure is pre-processed adding hydrogens, create disulphide bonds where possible, assign bond
orders, convert all selenomethionines into methionine and add missing/ill-resolved sidechains with
Prime [42]. After completing the preparation of the structure, the protein is minimized using force field
OPLS3 to remove bad contacts, take angle and dihedral values close to ideal, and remove excess strain.
4.4.3. Grid Generation
This step is aimed to construct a grid that defines the shape and chemical properties of the targeted
binding site. In particular, two fundamentals grids are generated: Bounding box and enclosing
box. The bounding box circumscribes the site where ligand is free to move during the docking run.
The enclosing box, on the other hand, is bigger and represents the delimited physical space where
ligand atoms can move, to allow for potentially necessary rearrangements.
Before creating a grid, as an additional check of the capability of the program to correctly recognize
binding sites, the SiteMap [43] program is used in a blind search starting from the protein structure
devoid of ligand to detect the druggable site. SiteMap correctly returned to position of the actual
binding site. Then, the energy grid is calculated using the Grid Generation tool of Maestro, defining
the position of ligand 4-IBP as the centroid with a radius of 15 Å. Default Van der Waals scaling factor
was used, with scaling factor of 1.0 and partial charge cut-off of 0.25.

Int. J. Mol. Sci. 2020, 21, 7708
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4.4.4. Ligand Docking
Ligand docking is carried out using the Glide program [44]. In this step, the ligand is located into
the grid previously created with Grid Generation tool. Control docking experiments are carried out
using the originally co-crystallized ligand 4-IBP in the corresponding crystal structure 5HK2 in order to
validate and optimize docking parameter settings. The best docking pose obtained for 4-IBP correctly
and accurately reproduces its crystal binding mode. Docking is performed employing Standard
Precision (SP) mode, using OPLS3 force field and default Van der Waals scaling factor, with scaling
factor of 0.80 and partial charge cut-off of 0.15. For each docking run 10 poses for ligand are saved.
4.4.5. Induced-Fit Docking
The IFD protocol [45] includes docking of ligands with Glide Docking, Prime Refinement [46
]
and Glide Redocking of the complex and calculation of the binding score. In this case the centroid
considered is the best pose (with the lowest GlideEmodel value) of the ligand which is obtained
in Ligand Docking. Prime Refinement is performed on residues within 5 Å of the ligand pose.
Glide Redocking is performed using standard precision (SP) scoring method of Glide, and the OPLS3
all-atom force field. Ten poses for ligand are saved for each run.
For each best pose, the Ligand Efficiency is calculated. This parameter normalizes the binding
energy by considering the molecular size, which rescales the higher energy binding of ligands with
high molecular weight, forming van der Waals and hydrophobic interaction [47].
4.5. General Protocol for Binding Assays
The test compound solutions were prepared by dissolving 10 µmol (usually 2–4 mg) of test
≈
compound in DMSO (unless otherwise specified), so that a 10 mM stock solution was obtained. To obtain
the required test solutions for the assay, the DMSO stock solution was diluted with the respective assay
buffer. The filtermats were pre-soaked in 0.5% aqueous polyethylenamine solution for 2 h at r.t. before
use. All binding experiments were carried out in duplicate in 96-well multiplates. The concentrations
given are the final concentrations in the assay. Generally, the assays were performed by addition of
50
10−6, 10−7, 10−8, 10−9 and 10−10 M), 50
respective receptor preparation into each well of the multiplate (total volume 200
µ
L of the respective assay buffer, 50
µ
L test compound solution at various concentrations (i.e., 10−5
L of corresponding radioligand solution, and 50 L of the
L). The receptor
,
µ
µ
µ
preparation was always added last. During the incubation, the multiplates were shaken at a speed
of 500–600 rpm at the specified temperature. Unless otherwise noted, the assays were terminated
after 120 min by rapid filtration using the harvester. During the filtration each well was washed five
times with 300 µ
L of water. Subsequently, the filtermats were dried at 95 ◦C. The solid scintillator was
melted on the dried filtermats at 95 ◦C for 5 min. After solidifying the scintillator at r.t., the trapped
radioactivity in the filtermats was measured with the scintillation analyzer. Each position on the
filtermat corresponding to one well of the multiplate was measured for 5 min with the [3H]-counting
protocol. The overall counting efficiency was 20%. The IC50 values were calculated with GraphPad
Prism 3.0 (GraphPad Software, San Diego, CA, USA) by nonlinear regression analysis. The IC50 values
were subsequently transformed into Ki values using the equation of Cheng and Prusoff. The Ki values
are given as mean value ± SEM from three independent experiments.
4.5.1. S1R Binding Assay
The assay was performed with the radioligand [3H](+)-pentazocine (22.0 Ci mmol−1; Perkin-Elmer).
The thawed membrane preparation of guinea pig brain cortex ( 100 µg protein) was incubated with
≈
various concentrations of test compounds, 2 nM [3H](+)-pentazocine, and Tris buffer (50 mM, pH 7.4)
at 37 ◦C. The nonspecific binding was determined with 10 mM unlabeled (+)-pentazocine. The Kd
value of (+)-pentazocine is 2.9 nM.

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4.5.2. S2R Binding Assay
The assay was performed using 150
µ
g of rat liver homogenate, which was incubated with
various concentrations of test compound for 120 min at room temperature, along with 3 nM [3H]-DTG
(Perkin-Elmer, specific activity 58.1 Ci mmol−1) in 50 mM Tris-HCl, pH 8.0, 0.5 mL final volume.
(+)-pentazocine (100 nM) and haloperidol (10 µM) were used to mask S1R and to define nonspecific
binding, respectively.
4.5.3. GluN2 Binding Assay
The competitive binding assay was performed with the radioligand [3H]-ifenprodil (60 Ci mmol−1
BIOTREND, Cologne, Germany). The thawed cell membrane preparation from the transfected L(tk-)
;
cells (about 20 µg protein) was incubated with various concentrations of test compounds, 5 nM
[3H]-ifenprodil, and TRIS/EDTA buffer (5 mM TRIS/1 mM EDTA, pH 7.5) at 37 ◦C. The non-specific
binding was determined with 10 mM unlabeled ifenprodil. The Kd value of ifenprodil is 7.6 nM.
4.6. Inhibition of AChE
AChE inhibitory activity of compound 12 was determined by the modified Ellman’s method.
Briefly, a stock solution of tested compound (5.0 mM) was prepared in DMSO and diluted using 0.1 M
KH2PO4/K2HPO4 buffer (pH 8.0) to afford a final concentration range between 1 and 50 mM. Enzyme
solutions were prepared by dissolving lyophilized powder in double-distilled water. The assay solution
consisted of 845 mL of 0.1 M phosphate buffer KH2PO4/K2HPO4, 25 mL of AChE solution (0.22 U/mL,
E.C. 3.1.1.7, from electric eel) and 10 mL of various concentrations of test compounds, which was
allowed to stand for 5 min at 25 ◦C before 100 mL of 0.01 M DTNB were added. The reaction was
started by addition of 20 mL of the 0.075 M substrate solution (acetylthiocholine iodide) and exactly
2 min after substrate addition, the absorption was measured at 25 ◦C at 412 nm. In enzyme-free assay
systems the non-enzymatic hydrolysis of acetylthiocholine iodide was measured, and the results were
employed as blank. In control experiments, inhibitor-free assay systems were utilized to measure the
full activity. A positive control of Donepezil was used to afford a final concentration range from 10 nM
to 50
µ
M. The percent inhibition was calculated, using the expression: (1
−
Ai/Ac)
×
100, where Ai and
Ac are the absorbances obtained for AChE in the presence and absence of the inhibitors, respectively,
after subtracting the respective background. Each experiment was performed in triplicate, and the
mean
±
standard deviation was calculated. Data from concentration-inhibition experiments of the
inhibitors were calculated by nonlinear regression analysis, using the Excel program.
4.7. Water Permeability Measurements
Osmotic water permeability was measured in HeLa cells suspension by the stopped-flow light
scattering method as previously described [22].
The antioxidant effect of compound 12 on water permeability HeLa cells were divided into
◦
different groups: (1) Controls, cells incubated at room temperature (21 C); (2) heat-stressed cells,
cells subjected to heat-treatment by incubating them in a water thermostatic and shacking bath at
42 ◦C for 3 h; (3) heat-stressed cells pre-treated, cells heat-stressed in the presence of the compound 12
at 20 µM final concentration (dissolved in methanol). Moreover, to test the possible capacity of the
molecules to affect the AQP gating in normal condition, HeLa cells were treated in the presence and in
the absence of the compounds by incubating at 21 ◦C for 3 h.
Author Contributions: Conceptualization, S.C. and G.C.; methodology, G.R., M.R., P.L., G.P., S.R., and L.M.;
software, G.C. and S.R.; validation, G.C., S.R., D.S., B.W., U.L., G.P., P.L., G.R., and D.R.; formal analysis, D.Z.;
investigation, D.S., B.W., G.P., and U.L.; data curation, G.C., U.L., G.R., and P.L.; writing—original draft preparation,
G.R., M.R., and L.P.; writing—review and editing, P.L., D.R., G.C., and S.C.; visualization, P.L.; supervision,
project administration, funding acquisition, S.C. All authors have read and agreed to the published version of
the manuscript.

Int. J. Mol. Sci. 2020, 21, 7708
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Funding: This research received no external funding. The authors gratefully acknowledge the University of Pavia
for the postdoctoral fellowship to GR.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
AChE
ADME
AQP
BBB
Acetylcholinesterase
Absorption, distribution, metabolism and excretion
Aquaporin
Blood–brain barrier
CNS
ER
IFD
Central nervous system
Endoplasmic reticulum
Induced fit docking
LE
Ligand efficiency
MAM
NMDA
NMDAR
RMSD
ROS
Mitochondria-associated ER membrane
N-methyl-d-aspartate
N-methyl-d-aspartate receptor
Root-mean-square deviation
Reactive oxygen species
Sigma-1 receptor
S1R
S2R
Sigma-2 receptor
SEM
Standard error of the mean
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