The most commonly used antidepressant drugs are the serotonin transporter inhibitors. Their effects depend strongly on the selectivity for a single monoamine transporter compared to other amine transporters or receptors, and the selectivity is roughly influenced by the spatial protein structure. Here, we provide a computational study on three human monoamine transporters, i.e., DAT, NET, and SERT. Starting from the construction of hDAT and hNET models, whose three-dimensional structure is unknown, and the prediction of the binding pose for 19 known inhibitors, 3D-QSAR models of three human transporters were built. The training set variability, which was high in structure and activity profile, was validated using a set of in-house compounds. Results concern more than one aspect. First of all, hDAT and hNET three-dimensional structures were built, validated, and compared to the hSERT one; second, the computational study highlighted the differences in binding site arrangement statistically correlated to inhibitor selectivity; third, the profiling of new inhibitors pointed out a conservation of the inhibitory activity trend between rabbit and human SERT with a difference of about 1 order of magnitude; fourth, binding and functional studies confirmed 4-(benzyloxy)-4-phenylpiperidine 20a-d and 21a-d as potent SERT inhibitors. In particular, one of the compounds (compound 20b) revealed a higher affinity for SERT than paroxetine in human platelets.
The most commonly used antidepressant drugs are the serotonin transporter inhibitors. Their effects depend strongly on the selectivity for a single monoamine transporter compared to other amine transporters or receptors, and the selectivity is roughly influenced by the spatial protein structure. Here, we provide a computational study on three human monoamine transporters, i.e., DAT, NET, and SERT. Starting from the construction of hDAT and hNET models, whose three-dimensional structure is unknown, and the prediction of the binding pose for 19 known inhibitors, 3D-QSAR models of three human transporters were built. The training set variability, which was high in structure and activity profile, was validated using a set of in-house compounds. Results concern more than one aspect. First of all, hDAT and hNET three-dimensional structures were built, validated, and compared to the hSERT one; second, the computational study highlighted the differences in binding site arrangement statistically correlated to inhibitor selectivity; third, the profiling of new inhibitors pointed out a conservation of the inhibitory activity trend between rabbit and human SERT with a difference of about 1 order of magnitude; fourth, binding and functional studies confirmed 4-(benzyloxy)-4-phenylpiperidine 20a-d and 21a-d as potent SERT inhibitors. In particular, one of the compounds (compound 20b) revealed a higher affinity for SERT than paroxetine in human platelets.
The
current generation of antidepressant drugs, both more effective
and more tolerable than older antidepressants, acts predominantly
by targeting the serotonin transporter (SERT).[1] After a long period of selective serotonin reuptake inhibitors (SSRIs)
predominance,[2] with the introduction of
drugs such as fluoxetine, citalopram, sertraline, and paroxetine,
researchers focused on additional pharmacologic mechanisms.[3] In the early 1990s, the serotonin-norepinephrine
reuptake inhibitors were commercialized;[4] in the first decade of the 21st century, there were incremental
studies on serotonin-norepinephrine-dopamine reuptake inhibitors;[5] and over the past decade, dual action inhibitors
have emerged, which had very different sizes and scaffolds compared
to pure reuptake inhibitors, while retaining good affinities for SERT.[6] Among these are vilazodone, which combines 5-HT1a partial agonism with SERT inhibition,[7] and vortioxetine,[8] which combines
5-HT1a and 5-HT1b partial agonism, 5-HT7 and 5-HT3 antagonism, and SERT inhibition. These
various strategies provided the possibility of targeting residual
symptoms, which were not well treated by SERT inhibition alone, and
also reducing the side effects, such as sexual dysfunction, but at
the same time, they introduced other side effects due to the action
against multiple receptors.In this context, the diversity of
the structures able to inhibit
SERT induced a deep curiosity for the transporter structure and the
binding site location. On the other hand, the search for transporter-selective
ligands requires the knowledge of the SERT structural requirements.
Initially, due to the lack of a crystal structure, many attempts to
construct a homology model based on the LeuT crystal structure were
performed for rationalizing the affinity of so different drugs against
SERT.[9−16] The SERT models published before 2009 were usually constructed using
LeuT as a template in its outward-occluded structure. After the crystallization
of LeuT with a competitive inhibitor in its open-to-out conformation,
the outward-open structures of the transporter were also studied.[13,14] In these outward-open structures, the vestibular, usually denoted
as S2 and recently as allosteric, and the substrate (S1) binding sites,
which were separated in the occluded form through the Tyr176-Phe335
gate, were combined in one cavity. This was considered by several
experts as the putative binding site of SSRIs, which are thought to
stabilize the outward-facing conformations of SERT by preventing closure
of the extracellular gate.[17,18]At the moment,
3D structures of dDAT and hSERT are available,[19] and the structures have led to advancement in
the study of the transporter interactions with their ligands and of
the structural differences among SERT, DAT, and NET. Unfortunately,
dDAT and hDAT share only 55% homology at the amino-acid
level. This degree of structural similarity is not enough to directly
extrapolate hDAT information from dDAT data. In fact, the homology
between dDAT and hDAT is similar to that found for each human transporter
related to the others (hDAT/hNET = 67%, hDAT/hSERT = 50%, hNET/hSERT
= 53%). Another consideration relates to the transporter crystal structure
resolution, which is ≥3 Å for all structures deposited
in the Protein Data Bank (PDB) to date.[19] This value could not guarantee correct folding and accurate side-chain
rotamers. Furthermore, until 2019 hSERT was crystallized in an engineered
form that contained a point mutation in a strategic position, namely
Thr439Ser. This position represents an interaction point for several
inhibitors, including escitalopram and paroxetine, and it is one of
the main binding site differences between hSERT and hNET. As the 3D
structure of NET is also lacking, it is difficult to elucidate the
molecular basis of the transporter inhibition. Our aim is the construction
and validation of human amine transporter models, for rationalizing
the selectivity of known inhibitors and to highlight the structural
differences in protein arrangement which are responsible for their
different activities against hDAT, hNET, and hSERT. This goal is ambitious
considering that a very small change in inhibitor substitutions can
produce a weak difference in the activity against one transporter
and a full order of magnitude variation in another one. Validated
models could also be a good starting point for designing novel compounds
and predicting new inhibitor potency. We therefore constructed the
hDAT and hNET models and optimized the wild-type form of hSERT, using
the successive cryo-EM structure of wild-type hSERT for comparison.[20] In order to investigate the basis of selectivity
for SERT and unveil a strategy for improving the potency of some interesting
in-house piperidine derivatives, which show high affinity for SERT,[21,22] we defined the biological profile of compounds 20a–d and 21a–d (Chart ).
Chart 1
Chemical Structures of the Compounds Studied by Docking
in hDAT,
hNET, and hSERT Models and Used as a Training Set for 3D-QSAR Evaluations (1–19) and in-House Compounds Used as an External Test
Set (20a–d and 21a–d)
Results and Discussion
Construction of the Transporter
Models
Our work aimed
to elucidate the inhibitor selectivity for SERT and therefore was
principally focused on the binding site structure. The investigation
began with a BLAST[23] search of hDAT and
hNET sequences derived from the UNIPROT Web site,[24] using the PDB database as a search set.[19] The BLAST analysis of the sequence homology showed a similar
identity and query coverage using the 3D structure of crystallized
dDAT or hSERT as a template (see Table ). The higher similarity between hNET and hDAT (67%
of identity) was not preserved comparing hNET with dDAT (58% of identity),
so there was no reason to choose just one preferred transporter as
a template to construct hDAT and hNET models. We chose to perform
a multitemplate modeling and use the structural information on both
the crystallized transporters in the construction of our targets.
Table 1
Summary of BLAST Analysis Results
resulting
PDB accessions
query
dDAT (11 structures)
hSERT (3 structures)
hNET
58–59% identity
53% identity
87–89% query coverage
88% query coverage
642–667 total score
603–606 total score
hDAT
55% identity
52% identity
87–88% query
coverage
87% query coverage
619–636 total score
591–594 total score
The alignment of the
human transporters on the dDAT and hSERT sequences
(Figure S1) showed high consensus scores
except for the EL3 region and unaligned N- and C-termini. Only the
last helices, TM9 to TM12, showed some variability in the sequences
with a consequent decrease of the consensus scores. However, such
variability did not interfere with a good alignment of the transporters
on the templates. The three-dimensional models of hNET and hDAT were
generated using the MODELLER program,[25] on the basis of the multialignment reported in Figure S1. MODELLER constructed the unaligned EL2 loop using
the simulated annealing, preserving the strictly conserved disulfide
linkage between two conserved cysteines of EL3: Cys180 and Cys189
of hDAT and Cys176 and Cys185 of hNET.[26] The longest unaligned tract was the N-terminal chain, which is irrelevant
in studying the binding of inhibitors to the transporter.The
hSERT crystal structure (PDB code 5I6X) was just mutated in the four points
engineered: Ala291Ile, Ser439Thr, Ala554Cys, and Ala580Cys. The models
were refined by means of Molecular Mechanics (MM) and Dynamics (MD)
calculations in a fully hydrated phospholipid bilayer environment
and checked with PROCHECK[27] (see the Methods section for details). The Ramachandran plots
of hNET and hSERT models (Figure ) showed four and six residues in disallowed regions,
respectively. In hSERT, Lys84 was in the N-terminal region and Thr323
was at the end of EL3 loop, while Glu392 and Asp393 were localized
in the EL4 loop, and His456 and Ala459 were in IL4. The four disallowed
residues of hNET, Phe133, Lys201, Ile376, and Asp546, were situated
in IL1, EL2, EL4, and EL6, respectively. They were all in the border
regions, exposed to the solvent, far away from the binding sites.
Figure 1
RMSD of
alpha-carbons of the protein from the postheating coordinates.
The dark colored line is the RMSD of the full sequence, while the
gray colored line represents the RMSD of the structured region sequence.
RMSD of
alpha-carbons of the protein from the postheating coordinates.
The dark colored line is the RMSD of the full sequence, while the
gray colored line represents the RMSD of the structured region sequence.The stability of the models was evaluated by calculating
the root-mean-square
deviation (RMSD) of the alpha carbons of the transporters along the
trajectory (see in Figure the postheating trajectory) from the starting model structures,
hSERT, hDAT, and hNET. In all transporter plots, the effects of the
first constraint’s relax after 400 ps and total relax of residues
after 1000 ps were evident. Starting from a very refined structure,
the higher stability of hSERT in comparison with the other transporters
was also evident. In hDAT, the whole system showed only small fluctuations
near 2 Å of RMSD during the simulation; in the range between
5 and 6 ns, the RMSD increment was due to the intracellular loop flexibility,
as confirmed by the “no loop” (gray) plot. During the
dynamic simulation, the hNET graph showed more fluctuations of about
2.5 Å of RMSD in the full sequence plot and of 1.5 Å in
the plot restricted to the structured regions (gray plot). Between
2 and 5 ns of simulation, an instability concerning especially the
folding of the intracellular tract of TM8 was registered. In general,
all systems achieved an equilibrated structure.The final model
of hDAT showed an overall structure deviation of
2.8 Å (1.22 Å considering just the structured regions) with
respect to the dDAT crystal structure (see Figure S2a).In particular, TM5, TM12a, and IL10 presented a
shift of the helices
over the starting template which reached 2 Å of distance in some
points. This is due to the degree of not conserved residues, which
caused in the free dynamics simulation a rearrangement of some structured
regions. As an example, in Figure S3 the
IL5 loop is represented. The alignment target–template in this
region was very poor, and the substitution Pro514 (dDAT) - Arg515
(hDAT) produced a different turn of the backbone. Moreover, the hydrogen
bond between IL10 and IL6, involving residues Tyr337 and Asp509 in
the crystal structure of dDAT, was disrupted in hDAT because of Tyr337
and Asp509 substitution with Phe338 and Gln510, respectively. The
structured tract of IL10 shifted 2 Å.The impact of the
overall different packing in the binding site
is reported in Figure S2b where the main
unconserved residues are labeled. The binding site of hDAT resulted
in being slightly larger than the dDAT one, in particular in the TM10
tract. The different conformation of the unwound region between TM6a
and TM6b is most significant. In fact, the substitution of dDAT Pro323
with hDAT Val324 turns the entire tract of the backbone in such a
way to direct hDAT Phe326 toward Ile484. This arrangement of the backbone
and side chains probably concurs to the shift of TM10 and the enlargement
of the binding site.Predictably, the situation of hSERT, which
started from a 3D structure
analogue to the targeted one, was very different. The overall deviation
between the initial and refined model (see Figure S2c) was 1.8 Å (1.06 Å considering just the structured
regions). In the binding site (Figure S2d), there was a diffuse but slight change of the backbone and side
chain arrangement partially due to the substitutions generated for
the construction of the transporter in the wild-type form (e.g., the
Ser439Thr mutation). An example is the ribbon shift near the piperonylic
moiety of paroxetine.The three final models are reported in Figure a, which shows the
structured regions are
quite superposed with an analogue RMSD of the C alpha of all transporters
of 1.2 Å. Obviously, the largest variability is due to loop and
terminal regions.
Figure 2
a) Superimposition of hDAT (cyan), hNET (gold), and hSERT
(gray)
models and b) sequence alignment of the binding site residues: in
red are highlighted the identical regions, while conservative substitutions
are highlighted in scale from cyan to orange.
a) Superimposition of hDAT (cyan), hNET (gold), and hSERT
(gray)
models and b) sequence alignment of the binding site residues: in
red are highlighted the identical regions, while conservative substitutions
are highlighted in scale from cyan to orange.Small differences in helix windings are detectable in many regions.
In particular, there are many fluctuations in TM10 and TM12 because
of the significant variability of EL6 conformation, which strongly
interacts with TM10, and the presence of a helix turn in TM10. Anyway,
the effects of these fluctuations are not appreciable in the binding
site.The binding site amino acid composition shows about 50%
conservation
and main semiconservative mutations across the transporters with only
three nonconservative mutation points (see Figure b). This degree of modifications inside the
binding site region produces a different shape and volume distribution
of the cavity. In particular, some mutation points strongly influence
the accessibility of specific microdomains. As reported in Figure , substitutions such
as Ala145/Ser149, Tyr151/Phe155, and Ala321/Val324 between hNET and
hDAT or the different conformations of hNET Phe323 and hDAT Phe326
create some supplementary cavities (cyan zones in Figure b) in the hDAT binding site.
Further modifications in hNET/hDAT/hSERT, such as Ala145/Ser149/Ala169,
V148/V152/I172, M424/M427/L443, S420/A423/T439, and Y151/F155/Y175,
produce a diffuse but modest enlargement of the principal cavity.
The different conformations of the unwound region between TM6a and
TM6b were already discussed concerning dDAT and hDAT. In this region,
there is high homology between hSERT and dDAT and high conservation
between hNET and hDAT, which cause a pocket widening in the zone labeled
as S-SERT in Figure c. The principal reason for the different conformation of this turn
in hSERT containing the main chain of Phe341 is the presence of Pro339
substituted by Ala and Val in hNET and hDAT, respectively. Ile481/Ile484/Val501
and Ala477/Ala480/Thr497 mutations further contribute to the creation
of the S-SERT cavity, and the last one is also responsible for a different
polarity of the microdomain.
Figure 3
Effect of the substitutions on the size and
shape of the three
transporter binding sites: a) binding site surface of hNET; b) hDAT
(cyan colored) superposed on hNET; and c) hSERT superposed on both
the other transporters. Nonconserved residues are expressed, together
with some critical conserved residues.
Effect of the substitutions on the size and
shape of the three
transporter binding sites: a) binding site surface of hNET; b) hDAT
(cyan colored) superposed on hNET; and c) hSERT superposed on both
the other transporters. Nonconserved residues are expressed, together
with some critical conserved residues.
Docking of Known Ligands
In order to test the ability
of hSERT, hDAT, and hNET models to predict both potency and selectivity
of monoamine transporter inhibitors, such models were used for the
docking of well-known ligands. The attempt to find in the literature
homogeneous competitive displacement data for these inhibitors was
not easy at all. For many inhibitors, the results of reuptake experiments
were available instead of displacement data. Moreover, the Ki or IC50 values were often determined
on tissues/cells from different species, affecting the collection
of homogeneous data. However, as reported from Han and co-workers,[28] it is possible to consider the test results
performed in rodent and human tissues as comparable since human and
mouse transporters show a similar sensitivity to tested drugs.From the literature we collected the Ki values of 19 compounds, which were chosen in order to explore different
chemical structures and selectivity profiles (compounds 1–19, Chart ) among TCAs (tricyclic antidepressants), SSRI, SNRI
(selective norepinephrine reuptake inhibitors), and SDRI (selective
dopamine reuptake inhibitors).As in our previous study,[29] docking
of compounds 1–19 was performed by
using the GOLD program.[30] In addition,
a double check of docking poses was realized through Flapdock.[31] These two programs use very different methodologies
for the pose prediction: the first one calculates the solvent accessible
surface of each atom in the defined binding site, assigning potential
donor and acceptor fitting points. Each trial ligand docking is generated
through a genetic algorithm by a least-squares fit of mapping points,
and one or more protein side chains can be treated as flexible. The
FLAP program uses fingerprints, derived from the GRID Molecular Interaction
Fields (MIFs).[32] Ligand conformers are
generated and then scored inside the pocket using the GRID MIF similarities
(describing hydrogen-bonding interactions, hydrophobic interactions,
and shape) and additional energy terms. In this work, the same main
parameters were selected for calculations with both programs (see
the Methods section for details). Exceptions
are in regards to peculiar features of each program, as the flexibility
of some selected side chains during the GOLD docking, or the accuracy
of the pocket surface mapping due to the variety of available GRID
probes in Flapdock.Figure reports
the docking predicted through the GOLD program[30] of four crucial compounds, the not selective DRI RTI-55
(10), the SNRI nisoxetine (8), the SSRI
paroxetine (1), and the SDRI vanoxerine (12), in hNET, hDAT, and hSERT.
Figure 4
Docking of some classical transporters inhibitors:
RTI-55 (a,b,c),
nisoxetine (d,e,f), paroxetine (g,h,i), vanoxerine (l,m,n) in hDAT
(cyan colored), hNET (gold colored), and hSERT (gray colored) models.
Docking of some classical transporters inhibitors:
RTI-55 (a,b,c),
nisoxetine (d,e,f), paroxetine (g,h,i), vanoxerine (l,m,n) in hDAT
(cyan colored), hNET (gold colored), and hSERT (gray colored) models.The region occupied by RTI-55 (10),
nisoxetine (8), and paroxetine (1) is the
same; only vanoxerine
(12), due to the larger size of the molecule, explores
the adjacent space engaging interactions with further residues. All
compounds place the protonated amine near the central TM1 flexible
hNET Asp75, hDAT Asp79, and hSERT Asp98 although with a variable distance
between 3 and 6 Å. The strength of this interaction seems to
be correlated to the range of inhibition, although it is not the only
requirement.
GOLD Docking of RTI-55
The RTI-55 pose in hNET and
hDAT is very similar. A small difference in inclination of the phenyl
group is due to the interaction of iodine, which prefers the not conserved
hDAT Ser149 (hNET Ala145) and hNET Ser420 (hDAT Ala423) for halogen
bonding. Furthermore, a rotation of about 60° of the tropane
moiety is detected, due to the hindrance of hNET Tyr151(hDAT Phe155),
whose hydroxyl group causes a shift of the ester group toward Tyr152.
Both these differences produce a distancing of Asp79 in hNET and a
weakening of lipophilic stabilization of the methoxy group due to
Val152, Phe326, and Ala480 in hDAT. In hSERT, the ligand assumes a
different orientation, due especially to the presence of Ile172 (Val
in the other transporters), which precludes the insertion of the iodophenyl
ring in the same cavity of hDAT and hNET. Moreover, the presence of
Tyr95 instead of the hDAT and hNET Phe shifts the tropane moiety toward
Asp98. A rotation of about 30 degrees avoids the clash with Ile172
and directs the methoxyl group in a region accessible just in hSERT
(S-Sert in Figure c), due to the “downward” conformation of Phe341, which
is not equivalent to the ones of hDAT Phe326 and hNET Phe323. The
swinging conformation of this residue was highlighted in the crystallographic
structures of dDAT and hSERT and was stable during the molecular dynamics
simulation of the three transporters. The final structure showed a
similar conformation for this amino acid in hDAT and hNET, analogous
to the dDAT one; the original “downward” conformation
of Phe341 in the hSERT crystal structure was also retained during
the simulation. Some plasticity of this residue was already discussed
for dDAT crystallization,[33] thus emerging
that Phe325 rotates inward to maintain edge-to-face aromatic interactions
with different scaffolded cocrystallized ligands (tropane-based RTI-55
and cocaine, nortriptyline and nisoxetine). In the hSERT crystallized
complexes, Phe341 did not show the same plasticity. Just in the last
structures cocrystallized with sertraline and fluvoxamine this amino
acid assumed an alternative conformation to the “downward”
one of paroxetine and escitalopram complexes, which is different anyway
from the many conformations detected for Phe325 in dDAT.No
information about the pose of tropane-derivatives in hSERT was known
since it was only cocrystallized with citalopram, paroxetine, sertraline,
and fluvoxamine. To test the possible correlation between Phe341 conformation
and the nature of the cocrystallized ligand, we performed a molecular
dynamics simulation on the theoretical hSERT-cocaine complex using
the same procedure and the same cocaine pose described in the Methods section for the construction of the other
transporter models. Starting from some steric engagement between Ile172
and the ligand, the complex evolved toward a stable structure, which
showed a value of RMSD between the starting and ending conformation
of Phe341 and Ile172 and cocaine of 0.4, 0.1, and 0.7 Å, respectively.
The analysis of the molecular dynamic simulation suggests that the
“downward” conformation of Phe341 in the hSERT-cocaine
complex was dependent on the proximity of the unconserved Ile172,
rather than on the nature of the complexed ligand. The slightly different
pose of RTI-55 in hSERT with respect to the other transporters allows
anyway a good interaction with Asp98 and a disposition of the iodophenyl
moiety in the cavity delimited by Tyr95, Ala169, Ile172, and Phe341.
These last two residues are also responsible, together with Val489,
for the lipophilic stabilization of the methoxy group. The role of
the carbonyl is not clear, which is not involved in particular interactions
with the binding site.A similar trend was predicted for RTI-31,
while for RTI-229 and
RTI-113 (see Figure S4) the bulkier substituents
provoke a reversed docking in hNET, pointing the halogen toward Ala477
and disclosing the amine to Asp75. This result is not the only one
suggested by the GOLD program, but it is the best scored. A similar
pose to the ones assumed in hDAT and hSERT was generated with a lower
score, showing a minor stability of the complex, probably due to the
smaller cavity of hNET. In hDAT, the halogenated ring of RTI-113 and
RTI-229 is superposed on the RTI-55 one, the phenoxy and pyrrolidine
rings find a good location between unconserved Phe155 and Phe320,
and the protonated amine lies at 3.4 Å from Asp79. In hSERT,
a small deviation of the ligands due to the hindrance of unconserved
Ile172 and Tyr175, in place of hDAT Phe155, shifts the ligand about
2 Å increasing the distance between Asp98 and the amine to 4.2
Å and reducing the stabilization of phenoxy and pyrrolidine rings.
GOLD Docking of Paroxetine
Paroxetine (1) occupies
the same region in hDAT and hNET and is rotated in hSERT.
However, the piperonylic moiety shows the same pose in all transporters.
On the contrary, as for the RTI-55 docking, the fluorophenyl ring
occupies two different cavities. In hDAT and hNET, the ring fills
the same cavity, which is instead modified by Ile172 presence in hSERT.
In hDAT, the piperonylic moiety interaction with unconserved Ser149
(hNET Ala145) and the Phe155 presence on the other side of paroxetine
instead of a Tyr produce a shift of about 1 Å with respect to
the pose of the same compound in hNET leading to an increment of the
distance from Asp79. In hSERT, as in the 5I6X complex of PDB, this distance is about
3 Å. The insertion of the fluorophenyl ring in the cavity delimited
by Ile172, Phe335, Phe341, and Val489 and the unconserved Thr497 guarantees
a high lipophilic stabilization and a small quite polar surface able
to receive, as in the crystallographic structures, the fluorine of
paroxetine and also the cyano group of escitalopram. The docking of
paroxetine in the wt-hSERT model is superposed to
the crystallographic one, in spite of the small deviation in binding
site arrangement due to Thr439Ser mutation.
GOLD Docking of Nisoxetine
The docking pose of nisoxetine
(8) is also very similar to the one reported for the
same compound in dDAT even though alternative dispositions characterized
by similar scores were calculated by GOLD in all transporters. In
particular, the methoxyphenyl ring could interchange the position
with the unsubstituted one or rotate in such a way to put the methoxy
chain toward the conserved Tyr (hDAT Tyr156, hNET Tyr152, and hSERT
Tyr176). In hDAT and hNET, the nisoxetine disposition is analogous,
but the presence of Ser149 and Phe155 in hDAT instead of Ala145 and
Tyr151 of hNET produces a shift of the ligand, which weakens the interaction
with hDAT Asp79. As already discussed for the 4XNU crystal structure
(nisoxetine-dDAT complex),[34] the pocket
surrounding the methoxy chain in hDAT is quite polar with Ser149 and
Ser429 in place of Ala145 and Ala426 of hNET. These serines are expected
to have less favorable interactions with nisoxetine. In hSERT, the
bulky Ile172 in the middle of the cavity partially clashes with the
biaromatic system of nisoxetine, moving the protonated amine at a
distance of 5 Å from Asp98. Interestingly, in all transporters,
the flexible Phe (hDAT Phe326, hNET Phe323, and hSERT Phe341) almost
assumes the same conformation, which is different from the starting
position in hDAT and hNET and is able to stabilize the methoxy group.
The congeners atomoxetine and esreboxetine have similar poses in hDAT
and hNET with respect to nisoxetine (data not shown). In hDAT, the
longer ethoxy chain of esreboxetine protrudes toward the unconserved
Ser149 and Ser429 without appreciable effects on the protonated amine
position. The polar serine environment is less favorable for the stabilization
of the ethoxy moiety. In hNET, this portion of esreboxetine occupies
the same lipophilic region as nisoxetine, but the rigid morpholine
shifts in such a way to decrement the interaction with Asp75. In hSERT,
the docking calculations predicted a different pose for esreboxetine
with respect to atomoxetine and nisoxetine, giving the reversed pose
as a lower scored pose, which puts the ethoxyphenyl toward Thr497.
Actually, this kind of disposition is frequently predicted in all
transporters with slightly lesser scores.
GOLD Docking of Vanoxerine
Different from compounds
described above, there is no clear information about the possible
pose of vanoxerine (12) in the transporters. It is one
of the “atypical” DAT inhibitors possessing in vivo and in vitro effects, which are
distinct from those of standard DAT inhibitors, such as cocaine. Atypical
DAT inhibitors promote a longer-lasting increase in extracellular
dopamine without any abuse potential.[35]Both size and shape of vanoxerine are very different from
those of the other inhibitors. Some mutational studies on hDAT revealed
that the substitution of Trp84 with a Leu was related to an affinity
decrease for diphenylmethoxy compounds such as vanoxerine, in contrast
to the increment of Kapp for tropane inhibitors.[36]This evidence reinforced the previous
hypothesis that “atypical”
DAT inhibitors, like benztropine (BZT) and its diphenyl ether analogs
(similar to vanoxerine), could stabilize the inward-facing conformation.[37] In contrast, Cys accessibility results and molecular
dynamics simulations suggested that aryltropane analogs can bind DAT
and stabilize its outward-facing conformations like cocaine, yet producing
effects that differ from those of cocaine.[38] In addition, some diphenylmethoxy derivatives also seem to prefer
an outward-open DAT conformation.[39] In
this context, the only experimental evidence is the probable involvement
of Trp84 in vanoxerine binding stabilization. Our docking results
show a binding pose filling the whole cavity and the propylphenyl
chain of vanoxerine which protrudes toward the extracellular side.
In hDAT, the monophenyl cap strongly interacts with Trp84 through
an aromatic stacking strengthened by the presence of unconserved Phe472
and Phe155. This aromatic environment is unique for hDAT due to Phe472
substitution with the aliphatic residues Leu469 and Val489 in hNET
and hSERT, respectively. Furthermore, in hSERT, the Trp103 region
is less accessible for the presence of Glu493, which interacts with
Arg104 occluding the cavity. In hDAT and hNET, the substitution of
Glu493 with the shorter Asp476 and Asp473 opens the binding site toward
the extracellular side making too long the distance toward the Arg
residue. In hDAT, this assessment also produces a very good π–π
interaction of the diphenoxy tail with Phe326 and a distance of 3
Å between the protonated amine and Asp79.
GOLD Docking
of Other Known Inhibitors
The binding
cavity is also preserved for other inhibitors, and the recent crystallization
of fluvoxamine and sertraline in hSERT (pdb codes 6AWP and 6AWO) confirmed the pose
predicted by our docking in the wild-type model. For fluoxetine and
sertraline which have been already crystallized in LeuBAT, the pose
predicted in hSERT is similar to the one in the LeuBAT complex crystal
structure. Generally, all these hSERT selective inhibitors, such as
escitalopram, show small deviations in the position assumed in the
three transporters. Anyway, in hNET, they are affected by the presence
of the shorter Asp473 instead of hSERT Glu493, which changes the shape
of the region near Tyr151. For fluoxetine, fluvoxamine, and escitalopram,
a reversed pose, which directs the fluorine or bulkier trifluoromethyl
group between Tyr151 and Ala477, was also generated obtaining a similar
score in hNET.All small mutations disseminated along the binding
site, which have been until now described, affected the binding of
maprotiline (see Figure S5a). Since this
drug is unable to plastically adapt to the binding site for its high
rigidity, it yields a different orientation of the aminic chain. In
hSERT, the presence of the longer Ile172 side chain prevents the insertion
of the ligand in the usual orientation of the aminic chain toward
Asp98.With regards to indalpine (see Figure S5b), it occupies a half site sharing the position of the piperonylic
group of paroxetine. Only in hSERT the cavity is large enough to direct
the indole nitrogen toward Thr439. In this conformation, the piperidine
well exposes the protonated amine to Asp79. In hDAT, in spite of the
good stabilization of the indole through the unconserved Ser149, the
ligand conformation cannot allow the interaction with Asp79. A similar
orientation is calculated for indalpine in hNET. Zimelidine (Figure S5c), another hSERT selective inhibitor,
occupies almost the same region of RTI-55 (see Figure a–c for comparison) but shows a different
rigidity degree of the aromatic caps due to the geminal substitution
on a double bond. Both in hDAT and hSERT, the propenyl linker directs
the aminic cap 1.5 Å further away from Asp in comparison with
RTI-55. In hNET, analogous to the bulkier RTI-229 and RTI-113, the
halogenated aromatic cap prefers the insertion toward Ala477 showing
a reversed docking mode, which prevents any interaction with Asp75.
FLAP Docking
Flapdock results have a trend very similar
to the GOLD ones. Generally, the interaction with the central Asp
(hNET Asp75, hDAT Asp79, and hSERT Asp98) is retained, and for many
ligands reversed orientations are predicted in the same calculation
with very similar scores. In Figure , a summary of the deviation between GOLD and Flapdock
results, expressed as RMSD, is reported.
Figure 5
Comparison between the
results of docking performed using GOLD
and Flapdock programs. The RMSDs between the poses predicted for compounds 1–19 in the three transporters are reported.
Comparison between the
results of docking performed using GOLD
and Flapdock programs. The RMSDs between the poses predicted for compounds 1–19 in the three transporters are reported.Most of the poses predicted through Flapdock show
an RMSD less
than 2.5 with respect to the GOLD predictions. Higher values are observed
for GBR-12909 and GBR-12935, due to their high degree of freedom,
and for viloxazine and maprotiline in hSERT. For these less bulky
ligands, the docking is probably more conditioned by the Phe335 conformation,
which is fixed in the Flapdock calculation. The only aromatic portion
of these ligands prefers to align with the piperonylic moiety of paroxetine
leaving the second cavity (also shaped by Phe335) empty. The bulkiest
ligands have no alternative possibilities and fill both cavities.
In the GOLD calculation, the flexibility of Phe335 allows a crude
remodeling on-the-fly of the pocket. Similar to the GOLD results for
RTI-113, RTI-229, and zimelidine, Flapdock emphasizes the prediction
in hNET of two reversed poses with very similar scores retaining the
ionic interaction with Asp79.
Docking and Activities
of Compounds 20a–d and 21a–d
Piperidine derivatives 20a–d and 21a–d, which were designed
in our laboratory and partially characterized as SERT inhibitors,[21,22] are reported in this work to complete their profiling and are also
used as the test set for our 3D-QSAR model (see Chart ). They share principal features with paroxetine,
such as the protonated piperidine, the methoxy spacer, and two aromatic
rings at the ends of the molecule. Compounds 20d and 21d also share the presence of the piperonylic moiety with
paroxetine.
Chart 2
In-House Piperidine Derivatives 20a–d and 21a–d Used as an External
Test
Set
However, the position of the
substituents is different, and, in
particular, the geminal substitution on piperidine represents an interesting
feature leading to a slightly constrained conformation. Affinities
of compounds 20b, 20d, 21b,
and 21d toward the serotonin transporter have been already
reported to be in the picomolar range[21,22] overlapping
that of paroxetine. Moreover, they revealed significantly lower affinity
toward the dopamine transporter, thus indicating a higher selectivity
of such compounds. In this paper, we tested the effect of 4-F and
2-F substitutions on their scaffold enlarging the set with analogues 20a, 20c, 21a, and 21c. Furthermore, we completed the affinity profile of these compounds
adding the results of NET binding displacement assays.In Figure , the
poses calculated for compounds 20a–d and 21a–d are reported in comparison with the one of paroxetine
since it is the most similar inhibitor. In all transporters, they
occupy the same region. The piperonylic moiety of compounds 20d and 21d is superposed on the same moiety
of paroxetine in all complexes, but the piperidine ring and especially
the second aromatic moiety are disclosed to the paroxetine in hDAT
and hNET. Instead of the interaction with Phe155 and Tyr156 of hDAT,
or Tyr151 and Tyr152 of hNET, the second aromatic ring (in particular
for the bulky trifluoromethyl derivatives 20b, 21a, and 21b) occupies the region near Asp476
and Asp473 with the worst stabilization due to the high polarity of
the cavity included between the Asp residue and an Arg residue (Arg85
and Ar81). This arrangement is not consistent with the hSERT binding
cavity where the presence of Tyr95 instead of the Phe residue of hDAT
and hNET shifts the piperidine ring toward Asp98 and Tyr176. In this
pose, the R substituent is not inserted in the polar region between
charged amino acids, also because the longer side chain of Glu382
makes a bridge with Arg104 closing the cavity. It occupies the same
area of the paroxetine fluorine, between Thr497 and Val489, in the
region already labeled as S-SERT in Figure c. As if there was a general rule, all compounds
direct the bulkiest end toward Thr439 in hSERT; just the ortho-substituted
phenyl moiety of compounds 20c and 21c prefers
the opposite cavity toward Thr497. Therefore, in hSERT, all compounds
arrange the methoxyaromatic chain toward Thr439 (Figure c), like paroxetine, except
for compounds 20c, 21a, and 21c, which direct
it toward Thr497. The interaction of compounds 20a–d and 21a–d with the central Asp residue is of
the same extent of paroxetine, but the higher rigidity of their scaffold,
in particular in compounds with bulkier substituents, leads to the
worst stabilization of one aromatic end in hDAT and hNET. In hSERT,
the shape and size of the S-SERT region allow a better stabilization
of these compounds, especially if the ligand substituent is sterically
able to fit the cavity near Thr439. The docking poses are in good
correlation with the high affinity of compounds 20b, 20d, 21b, and 21d for hSERT and
with a 10-fold decrease of activity on hDAT with respect to paroxetine.
Figure 6
Docking
of compounds 20a–d and 21a–d and paroxetine (black colored) in
hDAT (a) and hNET (b); c) docking of compounds 20a (light
green), 20b (purple), 20d (forest green), 21b (brown), and 21d (light blue) and paroxetine
(black) in hSERT; and d) docking of compounds 20c (magenta), 21a (pink), and 21c (orange) and paroxetine (black)
in hSERT.
Docking
of compounds 20a–d and 21a–d and paroxetine (black colored) in
hDAT (a) and hNET (b); c) docking of compounds 20a (light
green), 20b (purple), 20d (forest green), 21b (brown), and 21d (light blue) and paroxetine
(black) in hSERT; and d) docking of compounds 20c (magenta), 21a (pink), and 21c (orange) and paroxetine (black)
in hSERT.
3D-QSAR Modeling of Known
Ligands
The best GOLD docking
poses of compounds 1–19 were used
as a training set alignment for constructing a 3D-QSAR model. The
aim was to validate our transporter models through a quantitative
description of known inhibitors activities. Unfortunately, the inhibition
data of known inhibitors against SERT, DAT, and NET are usually related
to different species, and only a small amount of information about
human transporters collected in homologues assays is available. However,
in a semiquantitative manner, it is possible to consider test results
performed in rodent and human tissues as comparable since human and
mouse transporters are similar in their sensitivities to tested drugs.[28] Therefore, the 3D-QSAR could be considered a
good method for model validations.The collected activities
of 19 known compounds were used to perform a Leave One Out (LOO) cross-validated
partial least-squares (PLS) analysis on the GRID MIFs[32] generated through the FLAP program[31] on the relative docking poses. Their activities are shown in Table , in comparison with
the ones predicted through the resulting 3D-QSAR models.
Table 2
Binding Affinity (pKi) of Known Compounds 1–19 for DAT, NET, and SERTm
DAT
pKi
NET
pKi
SERT
pKi
compound
exp
pred
exp
pred
exp
pred
1: paroxetinea,k
6.31
5.86
7.4
7.03
10
8.80
2: fluoxetinea,k
5.42
5.5
6.62
5.59
9.1
7.85
3: escitalopramb,k
5.2
5.78
5
6.45
8.7
7.39
4: femoxetinea,k
5.7
6.52
6.12
6.29
7.96
7.76
5: esreboxetinec,k
5.2
5.2
8.98
8.29
6.18
6.47
6: atomoxetined,k
5.8
6.1
8.30
8.59
7.14
6.88
7: maprotilinea,k
6
5.96
7.95
7.21
5.24
6.62
8: nisoxetinee,l
6.3
6
9.34
8.15
6.8
7.80
9: RTI-31f,k
8.57
8.1
7.4
7.70
7.7
8.18
10: RTI-55f,k
8.41
8.2
7.7
7.33
8.4
7.97
11: fluvoxaminea,k
5.03
6.09
5.89
6.29
8.7
8.97
12: GBR-12909g,l
7.92
7.02
5.9
6.16
6.98
6.96
13: GBR-12935h,k
7.14
7.19
6.2
6.44
5.7
5.88
14: indalpinei,k
6
6.22
6.29
7.10
8.76
8.46
15: sertralinea,k
7.60
6
6.38
6.99
9.52
8.56
16: viloxazinea,k
5
6.19
6.81
7.65
4.76
6.65
17: RTI-229j,k
6.22
6.16
6.8
7.19
18: zimelidinea,k
4.93
6.14
5.03
5.77
6.82
6.66
19: RTI-113j,l
5.75
6.04
6.67
6.86
Reference (39).
Reference (41).
Reference (42).
Reference (43).
Reference (44).
Reference (45).
Reference (46).
Reference (47).
Reference (48).
Reference (49).
Binding affinity for human transporters.
Binding affinity for rat transporter.
For each transporter, the
experimental
and predicted pKi values are reported.
Reference (39).Reference (41).Reference (42).Reference (43).Reference (44).Reference (45).Reference (46).Reference (47).Reference (48).Reference (49).Binding affinity for human transporters.Binding affinity for rat transporter.For each transporter, the
experimental
and predicted pKi values are reported.In the 3D-QSAR models generated
using the docking poses in hSERT,
the first PLS component explained 83% of variance and was only quietly
predictive (Q2 = 0.532), but the second
PLS component improved the fitting (R2 = 0.966) and the predictive ability of the model (Q2 = 0.640). The third, fourth, and fifth PLS components
provided further improvement in fitting (LV3: R2 = 0.994, Q2 = 0.657; LV4: R2 = 0.997, Q2 =
0.657; LV5: R2 = 0.999, Q2 = 0.660), whereas the sixth PLS component provided no
further significant improvement. Thus, the model optimal dimensionality
was given by five components. Also for hNET and hDAT models, the LV5
model showed the best 3D-QSAR capabilities with fitting and predictivity
similar to the hSERT model (see Table ). The experimental/predicted plots reported in Figure for the training
set (compounds 1–19 in blue) show
a similar trend and a good predictivity for the three transporters.
Unfortunately, for hDAT, the biological activity space is not distributed
as for the other transporters. Almost all training set compounds cover
a span of affinity in the 5–7 range of pKi in hDAT. For the best inhibitors RTI-229 and RTI-113, only
inhibition values of dopamine uptake in rats are known. Anyway, the
predictivity range between 7 and 9 can be considered very good except
for sertraline, and the good R2 and Q2 values provided a statistical validation of
our transporter models.
Table 5
Statistical Results
of the 3D-QSAR
Calculation
DAT
NET
SERT
R2 0.999
R2 0.997
R2 0.999
Q2 0.62
Q2 0.68
Q2 0.66
(SDEPext 1.46)
SDEPext 0.60
SDEPext 0.78
Figure 8
Contour maps of the PLS pseudocoefficient plots
obtained with the
N1 (blue), O (red), and DRY (green) probes superposed on hDAT-RTI-55
(a), hNET-nisoxetine (b), and hSERT-paroxetine (c) models. Energy
levels are set at −0.206 (range −0.636–0) for
N1 MIF, −1.17 (range −3.01–0) for DRY MIF, and
−0.603 (range −1.63–0) for O MIF in a), −0.852
(range −2.39–0) for N1 MIF, −0.268 (range −0.807–0)
for DRY MIF, and −0.773 (range −2.22–0) for O
MIF in b), and −0.789 (range −2.05–0) for N1
MIF, −0.196 (range −0.603–0) for DRY MIF, and
−0.552 (range −1.46–0) for O MIF in c). On the
left, plot of the predicted against experimental activity of a training
set (black) and an external test set (red) against hDAT (a), hNET
(b), and hSERT (c).
The best GOLD docking poses of compounds 20a–d and 21a–d were used as an
external test set
for validating our 3D-QSAR models in a semiquantitative way even though
our compounds were tested using rabbit cerebral tissue. Calculations
produced predicted Ki values for all compounds
in the millimolar and nanomolar ranges for hDAT and hSERT, respectively.
Good predictions were obtained for compounds 20b, 20d, 21b, and 21d. Prediction values
are reported in the next sections together with compound experimental
results.
Chemistry
The synthesis of 4-((4-aryl)methyloxy)-4-(4-fluorophenyl)piperidine 20a–d (R = F) and 21a–d (R = CF3) is described in Scheme . 1-Benzyl-4-piperidone (22)
was reacted with the appropriate aryl Grignard reagent in anhydrous
tetrahydrofuran (THF) at reflux temperature to give the 4-piperidinols 23 and 24 in good yields. By reaction of the
sodium salt of 23 or 24 dissolved in anhydrous
THF with the appropriate benzyl bromide in the presence of tetrabutylamoniun
iodide were obtained the ethers 25a–d and 26a–d. The catalytic hydrogenolysis of 25a–d and 26a–d in acidic medium gave the 4-(arylphenyl)-4-[(4-aryl)benzyloxy]piperidines 20a–d and 21a–d as hydrochloride salts.
Scheme 1
Biochemical Studies
Radioligand
Binding Studies
The same experimental procedure
already described for evaluating 20b, 20d, 21b, and 21d compound affinities towards
SERT and DAT[21] was used for 20a, 20c, 21a, and 21c compounds.
Results (Table ) showed
that the R1 substitution with 2-F or 4-F produced a decrement
in the affinity for SERT with respect to the bulkier CF3 in the 4 position or a piperonylic group. In order to complete their
selectivity profile toward all transporters, all compounds were tested
for the ability to displace [3H]nisoxetine binding to NET
in rabbit cortical membrane. Overall our experimental data (Table ) confirmed that 20b, 20d, 21b, and 21d compounds but also the new tested 20a, 20c, 21a, and 21c compounds possess a very high affinity toward SERT. In
fact, all compounds are potent inhibitors of [3H]paroxetine
binding to SERT showing Ki values within
the nanomolar range, while the Ki values
for displacing [3H]-WIN 35,428 binding to DAT are in the
micromolar range (Table ). Furthermore, the binding assays also revealed an affinity ratio
SERT/NET higher than 10,000 for the best inhibitors, 20b, 20d, 21b, and 21d. In particular, 20b and 20d compounds seem to be 10-fold more
potent inhibitors of [3H]paroxetine binding than unlabeled
paroxetine and also show a higher selectivity toward SERT.
Table 3
Competition of 20a–d and 21a–d Compounds of
[3H]-WIN 35,428 Binding to Rabbit Striatal Membranes, [3H]Nisoxetine, and [3H]Paroxetine Binding to Rabbit
Cortical Membranes
compoundb
[3H]-WIN
35,428, Kia (nM)
[3H]nisoxetine, Kia (nM)
[3H]paroxetine, Kia (nM)
20a
>100,000
520
1.68 ± 0.49
20b
>100,000c
10,000
0.027 ± 0.005c
20c
>100,000
400
32.55 ± 12.56
20d
>100,000c
1,740
0.034 ± 0.0098c
21a
>100,000
>10,000
14.13 ± 3.04
21b
>100,000c
>10,000
0.316 ± 0.101c
21c
11,100 ± 4,900
2,070
50.02 ± 18.93
21d
11,200 ± 2,700c
2,450
0.250 ± 0.07c
paroxetined
769e
80 ± 1
0.31 ± 0.018
The Ki values are expressed as the
mean ± SE of three or more independent
experiments.
Prepared and
tested as hydrochloride
salts.
Reference (21).
Reference (29).
The Ki value represents the average of two independent
experiments.
The Ki values are expressed as the
mean ± SE of three or more independent
experiments.Prepared and
tested as hydrochloride
salts.Reference (21).Reference (29).The Ki value represents the average of two independent
experiments.Stimulated
by the 3D-QSAR results, the ability of the most active
compounds (20b, 20d, 21b, and 21d) to inhibit [3H]paroxetine binding to human
platelet membranes was also investigated with the aim of assessing
whether they displayed similar inhibition potencies toward rabbit
and human SERT and verifying whether the underestimation in the 3D-QSAR
predictions could be due to species differences. Indeed, the Kd value of paroxetine for rabbit SERT is 0.056
nM,[29] while the Kd value for hSERT reported in the literature is 0.1 nM.[40] The Ki values of
tested compounds and paroxetine for inhibiting [3H]paroxetine
binding to human platelet membranes are shown in Table . The results show that the
ability of synthesized compounds to inhibit [3H]paroxetine
binding to SERT in human platelet membranes was in the low nanomolar
range with a subnanomolar Ki value (0.08
nM) for 20b. These values are of 1 order of magnitude
higher than the ones measured in rabbit tissue.
Table 4
Inhibition Constants (Ki) of 20a,
20c, 21a, and 21c Compounds and Paroxetine for
Inhibiting [3H]Paroxetine
Binding and the [3H]-5-HT Uptake to SERT in Human Platelets
binding inhibition
[3H]-5-HT uptake inhibition
compound
[3H]paroxetine Kia (nM)
IC50 (nM)b
Kia (nM)c
20b
0.17 ± 0.02
0.13 ± 0.01
0.08
20d
1.71 ± 0.15
2.64 ± 0.3
1.75
21b
5.60 ± 0.52
4.20 ± 0.4
2.77
21d
8.72 ± 0.93
4.90 ± 0.5
3.26
paroxetine
0.09 ± 0.01
0.11 ± 0.01
0.051
The Ki values
are expressed as the mean ± SE of three or more independent
experiments.
[3H]-5-HT (25 nM) and
increasing concentrations (0.01–1000 nM) of compounds or paroxetine
were used.
The Ki values were derived from Km values determined
using a fixed concentration of compounds or paroxetine in saturation
experiments of the [3H]-5-HT uptake as described in the Methods section.
The Ki values
are expressed as the mean ± SE of three or more independent
experiments.[3H]-5-HT (25 nM) and
increasing concentrations (0.01–1000 nM) of compounds or paroxetine
were used.The Ki values were derived from Km values determined
using a fixed concentration of compounds or paroxetine in saturation
experiments of the [3H]-5-HT uptake as described in the Methods section.
Functional Studies
SERT Uptake Experiment
The uptake inhibitory activities
of compounds (20b, 20d, 21b, and 21d) were measured by [3H]5-HT uptake
kinetic experiments on human platelets. The Km value for the [3H]5-HT uptake was determined in
saturating conditions as described in the Methods section. Under these conditions, the [3H]5-HT uptake
showed a Km of 87.59 nM and a Vmax of 132.7 pmol/109 plt/min. The
Michaelis–Menten constant for substrate was determined from
the initial rate measurements at 37 °C by a nonlinear regression
analysis using the GraphPad Prism 5.0 program.In order to study
compound activity, we initially verified whether they caused a 50%
inhibition of the specific [3H]5-HT uptake, and thus the
IC50 (inhibition constant at 50% of control) values could
be determined. The inhibitory activity of compounds and paroxetine
was assessed using 25 nM [3H]5-HT and six different concentrations
of the inhibitors. The inhibition curves are shown in Figure (panel a), while the derived
IC50 values are reported in Table .
Figure 7
a) Inhibition of the [3H]5-HT uptake
in human platelets
by 20b, 20d, 21b, and 21d compounds. Platelets were incubated in duplicate with
[3H]5-HT in the presence and absence of increasing concentrations
of each compound as described in the Methods section. b) The Lineweaver–Burk plot showing competitive
inhibition of the [3H]5-HT uptake by compound 20b and paroxetine.
a) Inhibition of the [3H]5-HT uptake
in human platelets
by 20b, 20d, 21b, and 21d compounds. Platelets were incubated in duplicate with
[3H]5-HT in the presence and absence of increasing concentrations
of each compound as described in the Methods section. b) The Lineweaver–Burk plot showing competitive
inhibition of the [3H]5-HT uptake by compound 20b and paroxetine.Compounds were further
characterized by means of full uptake kinetics,
to verify the type of inhibition. Hence, saturation experiments of
the [3H]5-HT uptake were performed in the presence and
absence of compounds (0.1 nM) or paroxetine (0.1 nM) using six different
[3H]5-HT concentrations (10 to 1,000 nM). Thus, the apparent Km and Vmax values
of the [3H]5-HT uptake in the presence of inhibitors were
determined using the Lineweaver–Burk plot. The Ki values (Table ) of each compound and paroxetine were derived using the apparent Km and the Km value
obtained in the absence of the inhibitor.The competitive behavior
of the most potent inhibitor (20b) is demonstrated by
the fact that different concentrations of the
compound did not modify the Vmax value
of the [3H]5-HT uptake kinetic, while the Km value changed. In Figure (panel b), the competitive behavior of compound 20b and paroxetine for the [3H]5-HT uptake is graphically
shown by the Lineweaver–Burk plot. To mention, the Ki value of such a compound, which is within
the low nanomolar range (Ki, 0.08 nM),
is similar to the one of paroxetine (Ki, 0.05 nM (Table )).
3D-QSAR Prediction of 20a–d and 21a–d Compounds
The best GOLD docking
poses of 20a–d and 21a–d compounds were used as an external test set
for our 3D-QSAR models.
Results in terms of SDEPext are reported in Table , while the predicted activities over the experimental ones are plotted
in Figure . Unfortunately, the experimental results on DAT reported
in Table were not
adequate for an external prediction since the Ki values of these compounds were not in the same applicability
domain of our hDAT 3D-QSAR model.Contour maps of the PLS pseudocoefficient plots
obtained with the
N1 (blue), O (red), and DRY (green) probes superposed on hDAT-RTI-55
(a), hNET-nisoxetine (b), and hSERT-paroxetine (c) models. Energy
levels are set at −0.206 (range −0.636–0) for
N1 MIF, −1.17 (range −3.01–0) for DRY MIF, and
−0.603 (range −1.63–0) for O MIF in a), −0.852
(range −2.39–0) for N1 MIF, −0.268 (range −0.807–0)
for DRY MIF, and −0.773 (range −2.22–0) for O
MIF in b), and −0.789 (range −2.05–0) for N1
MIF, −0.196 (range −0.603–0) for DRY MIF, and
−0.552 (range −1.46–0) for O MIF in c). On the
left, plot of the predicted against experimental activity of a training
set (black) and an external test set (red) against hDAT (a), hNET
(b), and hSERT (c).The SDEPext values are satisfactory for hNET and
hSERT, whereas the SDEPext value is unreliable for
hDAT.In the hSERT model, the external test set covered a wide
range
of activities, so we tried to use this model to study the species
dependence of the inhibition results. A first attempt was the construction
of the hSERT 3D-QSAR model using only compounds tested on human transporter
and subsequent prediction of the rat experimental data as an external
test set. The result in terms of SDEP was 0.6, comparable to the Q2 value relative to our model (Table ) generated using human and
rat data as the training set. This result seems to confirm a similar
sensitivity of human and mouse transporters to tested drugs. On the
contrary, the results produced in rabbit SERT and predicted using
our hSERT model showed an SDEPext value of 0.78.
As shown in Figure S7, this higher value
than the one of Q2 is due to an underestimation
of almost all predicted inhibition values. This result seems to suggest
a different sensitivity of rabbit SERT compared with the human one
toward our compounds. Therefore, the most potent compounds (20a, 20c, 21a, and 21c) were experimentally tested for inhibiting [3H]paroxetine
binding to SERT in human platelet membranes (Table ). The insertion of the new Ki values in our 3D-QSAR model led to a decrease of the
extSDEP to 0.65. At this point, the Ki values predicted by our hSERT model were of the same extent of those
measured using human platelet membranes. Thus, a discrepancy of about
1 order of magnitude of compound activities toward the rabbit- and
human SERT was validated. Nevertheless, the trend of the inhibitory
potencies is conserved between the two species.The MIFs generated
during the 3D-QSAR analysis on the docking poses
could be represented in a graphical mode using the PLS pseudocoefficient
plots.[50] They are very useful to visualize
favorable interactions between the DRY, N1, and O probes and the molecules
studied. Such regions are related to the ligand poses and are independent
from the protein during their generation. The superposition of these
maps with transporter could give information about the regions of
the protein, which enhance the activity and are responsible for the
selectivity. Figure a–c represents the comparison of the PLS pseudocoefficient
contour plots for DRY (green polyhedrons), N1 (blue polyhedrons),
and O (red polyhedrons) probes overlapping the hDAT-RTI-55 (a), hNET-nisoxetine
(b), and hSERT-paroxetine (c) complexes. All maps are visualized at
the same relative energy value. It is immediately clear the importance
of the donor probe in hSERT and the DRY probe in hDAT, but a more
deep analysis of the interactions is required for MIFs interpretation.For all transporters the lipophilic contribution of the not conserved
S149/A145/A169, V152/V148/I172, and A423/S420/T439 residues is decisive
although different for hDAT, hNET, and hSERT. These mutations, already
highlighted in another study,[51] are responsible
for the different stabilizations of one of the inhibitor aromatic
moieties and ring substitution effects on biological activity and
transporter selectivity. In particular, Ala423 in place of a Ser or
Thr residue in hNET and hSERT enhances the favorable lipophilic region
in hDAT (green in Figure b) with respect to the other transporters. In hDAT, a large
blue region, which is favorable for a donor probe, is superposed and
fused with a red one (favorable for an acceptor probe). This map seems
to be due to a moiety capable of being both donor and acceptor. Flexibility
of Tyr156 could be responsible for the blue-red region through its
OH group. In hNET, a quite large blue region related to the N1 probe
is perfectly overlapped to the Phe272 position. This discordance could
be solved by checking in detail the behavior of MIFs at different
energy values (Figure S6) in the Phe323
region. The PLS pseudocoefficient plot of the DRY probe at a higher
energy level (points of medium interaction with a lipophilic probe)
overlaps the N1 probe plot at a lower energy level (points of maximum
interaction with a donor probe). This mixed lipophilic-donor region
corresponded to the ethers of nisoxetine, esreboxetine, and atomoxetine
docking poses, for which FLAP calculated a top stabilizing effect
by an electron rich donor probe and a moderate stabilizing effect
by a lipophilic group. A Phe residue cannot engage hydrogen bonds
but can form oxygen lp−π interactions.Human SERT
showed the bigger blue area (Figure c) formed by four regions as follows: the
first corresponding to Thr439, the second to Tyr95, and the third
to Thr497, which are not conserved residues. The latest blue region
seems to be incoherent with the overlapping protein residues. This
N1 MIF corresponds to Ala169, Ala173, Val343, and Leu443, which are
all lipophilic residues. In this case, FLAP did not calculate any
favorable interaction with the DRY probe in this region, which is
related to halogens of fluvoxamine, sertraline, and fluoxetine. This
is the real “halogen binding pocket” highlighted by
crystallographic structures.In all transporters, limited red
regions related to the O probe
are just calculated near the flexible conserved Asp, whose interaction
with the inhibitor amine is mandatory for the activity.In summary,
FLAP MIFs calculated on the docking pose of inhibitors
after superposition on the protein structure were able to detect some
unconserved residues as key elements for transporter selectivity.
Small mutations spreading to the binding site, such as F76/F72/Y95,
S149/A145/A169, V152/V148/I172, G153/G149/A173, F155/Y151/Y175, A423/S420/T439,
and A480/A477/T497, influence the cavities shaping and polarity and
allow a different stabilization of the inhibitors in the three transporters.
These mutations seem to be statistically correlated to the chemical
features of classical inhibitors, whose docking in hDAT, hNET, and
hSERT models also revealed the importance of the cavity delimited
by Trp84, Arg85, Asp476, Pro387, and Phe472 for the hDAT selectivity.
In particular, the role of the unconserved Asp476 and Phe472 seems
to be essential in stabilizing “atypical” DAT inhibitors.
In hSERT, a pocket widening in the zone labeled as S-SERT near Phe431
(Figure c) which is
influenced by the conserved Ile172 seems to be involved in the stabilization
of selective hSERT inhibitors. The three-dimensional model of the
three human transporters aided to rationalize the activities of 20a–d and 21a–d compounds, which are able to strongly inhibit [3H]paroxetine binding to SERT in rabbit membranes and also show a
quite discriminative power between SERT and the other transporters.
Although the most active compounds showed 1 order of magnitude lower
potency in human platelet membranes than in rabbit cortical membranes,
all of the data point out that the 20b compound possesses
a very interesting profile and our three-dimensional and 3D-QSAR models
can represent promising tools for predicting the inhibitory activity
of new molecules targeting human transporters.
Methods
Analytical grade reagents
and solvent were
purchased from Sigma-Aldrich (St. Louis, MO) and were used as supplied.
Solvents were dried according to standard methods. Melting points
were determined on a Köfler hot-stage apparatus and are uncorrected. 1H NMR spectra were recorded with a Varian Gemini-200 MHz spectrometer
in an ca. 2% solution of CDCl3. Chemical shifts (δ)
are reported in parts per million (ppm) downfield from tetramethylsilane
(TMS) as internal standard. The following abbreviations are used:
singlet (s), broad (br), and multiplet (m). Reactions were monitored
by thin layer chromatography (TLC) on silica gel plates containing
a fluorescent indicator (Merck Silica Gel 60 F254), and spots were
detected under UV light (254 nm). Chromatographic separations were
performed on silica gel columns by flash column chromatography (Kieselgel
40, 0.040–0.063 mm; Merck). Na2SO4 was
always used as the drying agent. Evaporation was carried out “in
vacuo” (rotating evaporator). Elemental analyses were performed
by our analytical laboratory and agreed with the theoretical values
to within ±0.4%.
Synthesis of 1-Benzyl-4-(4-fluorophenyl)piperidin-4-ol
(4) and 1-Benzyl-4-(4-(trifluoromethyl)phenyl)piperidin-4-ol
(5)
These compounds were prepared slightly modifying
the synthetic route previously described.[29] In brief: the opportune 4-(aryl)magnesium bromide prepared in the
usual manner and refluxed under stirring for 30 min was treated dropwise
at room temperature with a THF solution of N-benzyl-4-piperidone
(3), and then the solution was refluxed under stirring
for 15 h. After the usual workup, the oily residue so obtained was
crystallized from hexane to give pure 4 as a pale yellow
solid or purified by column chromatography on silica gel (EtOAc/hexane
4:6) to give pure 5 as a white solid.
General Procedure
for the Synthesis of 1-Benzyl-4-aryloxy-4-arylpiperidine 25a–d and 26a–d
To a stirred solution of the opportune 1-benzyl-4-(4-fluorophenyl)piperidin-4-ol 4 or 1-benzyl-4-(4-(trifluoromethyl)phenyl)piperidin-4-ol 5 (1.76 mmol) in anhydrous THF (10 mL) was added NaH 60% (1.84
mmol) under nitrogen atmosphere. Then to the reaction mixture was
added tetrabutylammonium bromide (0.018 mmol) and, dropwise under
stirring, a solution of the opportune benzyl chloride (0.78 mmol).
The mixture was stirred for 3 days at room temperature and then water
was added, and the solution was extracted with EtOAc. The organic
extracts were evaporated to yield a crude oil which was purified by
column chromatography on silica gel (EtOAc/hexane 4:6) to yield the
compounds 25a–d or 26a–d.
General Procedure for the Synthesis of 4-Aryloxy-4-arylpiperidine
Hydrochlorides 20a–d and 21a–d
To a solution of 25a–d and 26a–d (0.82 mmol) in EtOH anhydrous
(50 mL) was added a solution of EtOH·HCl to pH ≈ 3. The
mixture was shaken under hydrogen at room temperature and atmospheric
pressure for 24 h in the presence of 10% Pd on charcoal (65 mg), then
the catalyst was filtered off, and the solution was evaporated to
yield the crude piperidine hydrochlorides that were crystallized by
Et2O to give 20a–d and 21a–d.
The primary sequences of
the transporters were retrieved from the UNIPROT protein sequence
database (Q01959 and P23975, respectively).[24] A BLAST[23] search of these sequences against PDB[19] sequence entries was performed. The BLAST-derived
scores suggested a close homology between both transporters and the
crystallized Drosophila melanogaster dopamine transporter
(best scored the one complexed with cocaine, PDB code 4XP4)[52] and with the hSERT-paroxetine crystallized complex (PDB
code 5I6X).[53] Therefore, their 3D coordinates were retrieved.
A multiple structure alignment of all the human transporters on Drosophila melanogaster DAT was performed using Praline[54] with a gap open penalty of 15 and a gap extension
penalty of 1. For all the extra or intracellular loops, we used the
crystal structures as a template due to the good quality of alignment.
Only for the long EL2 loop of human transporters, namely for NET,
the template is not so good for a 13 amino acid region, which lacks
in the crystallized DAT. This region is shorter in the SERT structure
where the gap is six residues long. The unaligned area is comprised
between two beta-sheet regions in the extreme external zone at more
than 26 Å from the binding site. Considering the area is unessential
for the goal of this study, we decided to leave it free of template
and assign the conformation of this short sequence through the simulated
annealing.The 3D models of hDAT and hNET were constructed using
the MODELLER program[25] on the basis of
the alignment obtained from Praline. Cocaine was manually included
in the binding sites of hDAT and hNET in the same conformation and
orientation of the template crystal structures. MD simulations for
10 ns were performed for hDAT-cocaine and hNET-cocaine complexes embedded
in DOPC bilayers. The CHARMM-GUI web server[55] was employed in order to obtain a pre-equilibrated membrane. This
was composed of 130 lipids embedded in a 80 × 80 Å2 square membrane, and the ligand-transporter complex was placed into the membrane orienting it along
the z-axis. This structure was solvated with TIP3P
water and 0.15 M KCl extending 15 Å at the top and bottom of
the membrane. The system was rebuilt in Amber14[56] using xLeap in order to generate a topology file, protonation,
angles, and dihedrals. N- and C-termini of the protein model systems
were capped by acetyl and methylamino groups. General Amber force
field (GAFF) parameters were assigned to cocaine, while partial charges
were calculated using the AM1-BCC method as implemented in the Antechamber
suite of AMBER 14. The total number of atoms of each complex was approximately
69,000. The default particle mesh Ewald method (PME) was employed
to calculate long-range electrostatic interactions with an Ewald coefficient
of 0.275 Å. Van der Waals and short-range electrostatic interactions
were smoothly truncated at 10.0 Å. The Langevin thermostat was
utilized to equilibrate the temperature, and the anisotropic Berendsen
barostat was used to control the pressure. Periodic boundary conditions
were applied (80 × 80 × 120) Å.[3] Ten thousand steps of steepest descent and conjugate gradient minimization
were performed, with harmonic restraints of 50 kcal mol Å–2 applied on all solute atoms followed by 10,000 steps
of minimization without restraints. The heating simulation was run
in two phases: at first, a 200 ps simulation kept the system at 100
K in the NVT (constant number of particles, volume, and temperature)
ensemble with protein complexes restrained with a force constant of
50 kcal mol Å–2, while lipids and ions were
initially restrained with a force constant of 50 kcal mol Å–2 and then progressively relaxed (lipids after 80 ps,
ions after 140 ps); then, the temperature was raised during a further
200 ps MD simulation to 300 K in the NPT ensemble with the same restraining
scheme of the first heating. The temperature of 300 K was used in
equilibration MD in order to ensure that the membrane state was well
above the melting point of DOPC. An equilibration of 10 ns was performed
in two stages: in the first 1 ns, the protein complexes were restrained
with a force constant of 10 kcal mol Å–2 initially
(400 ps) on all complex atoms and then only on the C alpha. The second
stage of 9 ns was a NPT simulation without restraints.The stereochemical
quality of the resulting protein structures
was evaluated by inspection of the Psi/Phi Ramachandran plot obtained
from PROCHECK analysis.[27] The MD snapshots
were obtained through the MD/Ensamble Analysis module of Chimera.[57]
Optimization of wt-hSERT in Complex with
Paroxetine
The wt-hSERT structure was obtained through Ala291Ile,
Ser439Thr,
Ala554Cys, and Ala580Cys mutations performed by Maestro in the 5I6X(53) PDB structure. The complex was embedded in a DOPC membrane
using the already described procedure, and the system was subjected
to the same simulation protocol of hNET and hDAT complexes.
Docking
Procedure
Automated docking of the ligands
into models was carried out by means of the GOLD 5.1 program[30] and by Flapdock[31] for a double check of the training set docking poses. The ligands
were built using the Maestro program[58] and
subjected to a Conformational Search (CS) of 1,000 steps in a water
environment using the Macromodel program.[58] The Monte Carlo algorithm was used with the MMFFs force field. The
ligands were then minimized using the Conjugated Gradient method to
a convergence value of 0.05 kcal/Å·mol using the same force
field and parameters as for the CS.The region of interest was
defined in GOLD[30] in such a manner that
it contains all residues within 10 Å from ligands. The “allow
early termination” command was deactivated. All ligands were
submitted to 40 Genetic Algorithm runs using the ChemScore fitness
function, rescoring through the PLP function, and clustering the output
orientations on the basis of a RMSD distance of 1.5 Å. The default
GOLD parameters were used for all variables except for the side chains
rotamers. The EXTRA PARAMETER option was used to allow the free side
chain flexibility of Asp79 and Phe320, Asp75 and Phe317, and Asp98
and Phe335 for hDAT, hNET, and hSERT, respectively. The best docking
pose for each ligand was then used for further studies.The
FLAP database of ligands was generated using the standard GRID
probes H, DRY, N1, and O with a spatial resolution of 0.75 Å.
For each ligand, up to 25 conformers were generated with an RMSD cutoff
of 0.3 Å between two conformers. The H probe describes the shape
of the molecule, whereas the DRY probe detects hydrophobic interactions.
The hydrogen-bond acceptor and hydrogen-bond donor capacities of the
target are described by the amide N1 and carbonyl O probe, respectively.
Transporter proteins were loaded specifying Na+ and Cl– as metals, while RTI-55, nisoxetine, and paroxetine
were imported as reference ligands for hDAT, hNET, and hSERT, respectively.
The region of interest was defined within 10 Å from ligands.
All default parameters were used for docking, and the best five poses
ranked by S-Score were analyzed. The self-docking of RTI-55, nisoxetine,
and paroxetine produced good results ranking the five best poses on
the basis of H*DRY scores (shape combined with hydrophobicity). In
this way, the contribution of small mutations spreading on the pocket
shape and lipophilic stabilization of ligands was particularly taken
into account. This procedure gave a self-docking result calculated
as mean RMSD between predicted and reference poses of 0.8 Å.
So, the best H*DRY scored poses for all ligands were compared with
the GOLD results in terms of RMSD. The graphical analysis of the docking
results was performed by Chimera.[57]
3D-QSAR
Modeling
The docking conformations of known
SERT, DAT, and NET inhibitors were used as transporter-based alignments
to construct a FLAP[31] database for each
transporter. FLAP (Fingerprints for Ligands And Proteins) is able
to compare molecules using fingerprints. The fingerprints are derived
from the GRID Molecular Interaction Fields (MIFs) and/or the GRID
atom types and are characterized as quadruplets of pharmacophoric
features. The MIFs produced by the GRID force field describe the type,
strength, and direction of the interactions owed to a molecule. A
quantitative examination of the MIF contributions to the activity
of a set of aligned structures allows the construction of 3D-QSAR
models. In this context, the GOLD docking conformer of each ligand
was imported in the FLAP database. MIFs were then calculated using
the acceptor (O), donor (N1), hydrophobic (DRY), and shape (H) probes
as implemented in FLAP and using a grid resolution of 0.75 Å.The interaction point energies were defined as independent variables,
while inhibitor activity expressed as pKi was set as the dependent variable. So, the docked data set was used
as the training set to construct 3D-QSAR models analyzing through
PLS the combinations of descriptors, which best explain the activity.
The models were cross-validated using the LOO method and analyzed
in terms of R2 and Q2.The optimal number of latent variables was chosen
for each model,
and the prediction capability of the models toward each inhibitor
was examined. To perform a study on selectivity, the MIF coefficients
of each transporter model were plotted as isocontours comparing in
a 3D view the most relevant MIFs, which represent the regions of a
favorable interaction between an inhibitor substituent and the probes
resulting in an increase of activity with hSERT, hDAT, and hNET binding
site regions.
Radioligand Binding and Functional Studies
Membrane
Preparation for Radioligand Binding Studies
Cerebral tissue
was from adult New Zealand White rabbits (4–5
kg) obtained from a commercial source (Charles River Laboratories,
Inc., Wilmington, MA). Animals were maintained in standard laboratory
conditions and fed in sawdust-lined cages and at a 12-h light/dark
cycle. They were killed by intravenous injection of a lethal dose
of pentobarbital. All procedures conformed to the guidelines of the
International European ethical standards for the care and use of laboratory
animals. All protocols were approved by the Ethical Deontological
Committee for animal experimentation of the University of Pisa.Cortical membranes for NET binding assays were prepared by homogenizing
freshly dissected rabbit cerebral cortex in 30 vols of ice-cold 50
mM Tris-HCl buffer, pH 7.4, containing 120 mM NaCl and 5 mM KCl (T1
buffer). The homogenate was centrifuged at 48,000g for 10 min at 4 °C. The resulting pellet was suspended in T1
buffer, incubated at 37 °C for 10 min to remove endogenous norepinephrine,
and centrifuged at 48,000g for 10 min at 4 °C.
This washing procedure was repeated twice. The resulting pellet was
immediately used in the binding assay or frozen at −80 °C
until the time of the assay.Membranes used in DAT and SERT
binding assays were prepared from
frozen rabbit striatum and frontal cortex as previously
described.[29]
Separation of Human Platelets
and Membrane Preparation
Venous blood (20 mL) was collected
from healthy human subjects and
gently mixed with 1 mL of anticoagulant (0.15 M EDTA). Platelet-rich
plasma was obtained by low-speed centrifugation (200g for 20 min at 22 °C). Platelets were counted automatically
with a flux cytometer (Cell-dyn 3500 system; Abbott, Milano, Italy).
Written consent was obtained from all subjects, and the study was
approved by the local Ethics Committee.For measurement of the
[3H]5-HT uptake, platelets were used immediately; whereas
for [3H]paroxetine binding, platelets were precipitated
by centrifugation at 10,000g for 10 min at 4 °C,
and the pellets were then stored at −80 °C until the assay.For human platelet membrane preparation, platelet pellets were
washed with 10 mL of 50 mM Tris-HCl buffer, pH 7.4, containing 150
mM NaCl and 20 mM EDTA. Pellets were lysed and homogenized in 10 mL
of 5 mM Tris-HCl buffer, pH 7.4, containing 5 mM EDTA and protease
inhibitors (200 μg/mL bacitracin, 160 μg/mL benzamidine,
and 20 μg/mL soybean trypsin inhibitor) using an Ultra-Turrax
homogenizer and centrifuged at 48,000g for 15 min
at 4 °C. The resulting pellets were resuspended in 50 mM Tris-HCl
buffer, pH 7.4, containing 120 mM, NaCl, and 5 mM KCl (assay buffer).
Protein concentration was determined according to the method of Lowry
et al.[59] after solubilization in 0.75 M
NaOH and using bovine serum albumin (BSA) as standard.
[3H]Nisoxetine Binding Assay to
Rabbit Cortical Membranes
For NET binding assays, [3H]nisoxetine binding was performed
essentially as described by Tejani-Butt et al.[60] The cortical membrane pellet was resuspended in 50 mM Tris-HCl
buffer, pH 7.4, containing 300 mM NaCl and 5 mM KCl (T2 buffer). The
binding assay was performed incubating aliquots of membranes (0.2–0.3
mg of protein) in T2 buffer with 1 nM [3H]nisoxetine (specific
activity, 80 Ci mmol–1; PerkinElmer Life Science)
in a final volume of 0.5 mL. Incubation was carried out at 4 °C
for 4 h. Nonspecific binding was defined in the presence of 10 μM
desipramine. Specific binding was obtained by subtracting nonspecific
binding from total binding and approximated to 85–90% of total
binding. The binding reaction was quenched by filtration through Whatman
GF/C glass-fiber filters using a Brandel Harvester. Filters were washed
four times with 5 mL of the ice-cold binding buffer and placed in
vials with 4 mL of a scintillation cocktail. Radioactivity was measured
by means of a β-counter.NET binding parameters (maximal
binding capacity, Bmax, fmol/mg protein;
dissociation constant, Kd, nM) were evaluated
in rabbit cortical membranes by measuring specific binding of [3H]nisoxetine
at increasing concentrations of the radioligand.
[3H]WIN 35,428 Binding Assay to Rabbit Striatal Membranes
DAT binding assays were performed using 2 nM [3H]WIN
35,428 (specificity activity, 84.5 Ci mmol–1; PerkinElmer
Life Science) as previously described by Nencetti et al.[29]
[3H]Paroxetine Binding Assay to
Rabbit Cortical Membranes
SERT binding assays were performed
using 0.1 nM [3H]paroxetine
(specificity activity, 15–20 Ci mmol–1; PerkinElmer
Life Science) as previously described.[29]
[3H]Paroxetine Binding Assay to Human Platelet Membranes
For SERT binding assays, human membranes preparations and [3H]paroxetine (specific activity, 19 Ci mmol–1; PerkinElmer Life Science) were incubated as previously described
by Giannaccini et al.[61] Platelet membrane
pellets were resuspended in assay buffer, and the binding assay was
performed incubating aliquots of membranes (0.05–0.1 mg of
protein) in a final volume of 2 mL of assay buffer. Incubation was
carried out at 22 °C for 1 h. Nonspecific binding was defined
in the presence of 10 μM fluoxetine. Specific binding was obtained
by subtracting nonspecific binding from total binding and approximated
to 85–90% of total binding. The binding reaction was quenched
by filtration through Whatman GF/C glass-fiber filters using a Brandel
Harvester. Filters were washed four times with 5 mL of the ice-cold
binding buffer and placed in vials containing 4 mL of a scintillation
cocktail. Radioactivity was measured by means of a β-counter.SERT binding parameters (maximal binding capacity, Bmax, fmol/mg protein; dissociation constant, Kd, nM) were evaluated in human platelet membranes by measuring
specific binding of [3H]paroxetine at increasing concentrations
of the radioligand.Compounds (stock solutions 1 mM) were routinely
dissolved in ethanol
and then diluted in Tris-HCl assay buffer at the required concentration.
Competition binding assays were performed using at least seven different
compound concentrations, which spanned 3 orders of magnitude and approximately
adjusted for the IC50 value of each compound. The concentration
of tested compounds which produced 50% inhibition of specific [3H]paroxetine binding (IC50 values) was computer-generated
using a nonlinear regression analysis of the GraphPad Prism, Version
5.0, program (GraphPad Prism, Inc., San Diego, CA). The IC50 values were converted to inhibition constants values (Ki) using the Cheng and Prusoff[62] equation, Ki = IC50/([L]/Kd, where [L] is the ligand concentration. The Kd of [3H]paroxetine binding to human
platelet membranes was 0.08 ± 0.02 nM.
Functional
Studies: The [3H]5-HT Uptake to Human
Platelets
The [3H]5-HT uptake was performed in
human platelets as described by Bazzichi et al.[63] Briefly, aliquots of platelets (2 × 106 platelets) were incubated for 10 min at 37 °C with six different
concentrations (15 to 700 nM) of [3H]5-HT (specific activity,
30 Ci mmol–1; PerkinElmer Life Science) in a 0.5
mL final volume of 1.17 mM KH2PO4/25 mM NaHCO3, pH 7.4, buffer containing 118 mM NaCl, 4.7 mM KCl, 1.07
mM MgSO4, 11.6 mM glucose, 0.1% ascorbate, and 100 μM
pargyline. A nonspecific uptake was measured in the presence of 10
μM fluoxetine. A specific uptake was obtained by subtracting
the nonspecific uptake from the total uptake and approximating it
to be 85–90% of the total uptake. The uptake reaction was quenched
by filtration through Whatman GF/C glass-fiber filters using a Brandel
Harvester (see above). Filters were washed four times with 5 mL of
the ice-cold reaction buffer and placed in vials with 4 mL of a scintillation
cocktail. Radioactivity was measured by means of a β-counter
(see above).The maximal uptake rate of SERT (Vmax, pmol/109 cells per minute) and the Michaelis–Menten
constant (Km, nM) were determined in saturating
conditions by increasing [3H]5-HT concentration. The Vmax and Km values
were obtained by direct weighted nonlinear regression analysis of
uptake rates against [3H]5-HT concentrations using the
GraphPad Prism, Version 5.00, program (GraphPad Prism, Inc., San Diego,
CA).Compounds were dissolved in ethanol to obtain 1 mM stock
solutions
and then diluted in a Tris-HCl saline buffer at the required concentrations.
In the assay, ethanol never exceeded 0.5%. Using a saturating concentration
of [3H]5-HT (25 nM) and increasing concentrations (0.01
to 1,000 nM) of the compounds, we initially evaluated the percentage
inhibition of the [3H]5-HT uptake. The IC50 value
of each tested compound was computer-generated using a nonlinear regression
analysis of the GraphPad Prism program (Version 5.00). To obtain compound Ki values, the apparent Km of the [3H]5-HT uptake in the presence of fixed
inhibitor concentrations was determined using the double-reciprocal
Lineweaver–Burk plot, which also allowed for verification of
the type of inhibition.
Authors: H M Berman; J Westbrook; Z Feng; G Gilliland; T N Bhat; H Weissig; I N Shindyalov; P E Bourne Journal: Nucleic Acids Res Date: 2000-01-01 Impact factor: 16.971
Authors: Maarten E A Reith; Bruce E Blough; Weimin C Hong; Kymry T Jones; Kyle C Schmitt; Michael H Baumann; John S Partilla; Richard B Rothman; Jonathan L Katz Journal: Drug Alcohol Depend Date: 2014-12-18 Impact factor: 4.492
Authors: Lucy R Forrest; Yuan-Wei Zhang; Miriam T Jacobs; Joan Gesmonde; Li Xie; Barry H Honig; Gary Rudnick Journal: Proc Natl Acad Sci U S A Date: 2008-07-22 Impact factor: 11.205
Chart 1
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Chart 2
Figure 6
Figure 8
Scheme 1
Figure 7