Giovanni Ribaudo1, Marco Bortoli2,3, Colby E Witt4, Brenna Parke5, Sergio Mena5, Erika Oselladore1, Giuseppe Zagotto6, Parastoo Hashemi5,4, Laura Orian2. 1. Dipartimento di Medicina Molecolare e Traslazionale, Università degli Studi di Brescia, Viale Europa 11, 25123 Brescia, Italy. 2. Dipartimento di Scienze Chimiche, Università degli Studi di Padova Via Marzolo 1, 35131 Padova, Italy. 3. Institut de Química Computacional i Catàlisi and Departament de Química, Universitat de Girona, c/Maria Aurèlia Capmany 69, 17003 Girona, Catalonia, Spain. 4. Department of Chemistry and Biochemistry, University of South Carolina, Columbia South Carolina 29201, United States. 5. Department of Bioengineering, Imperial College London, London SW7 2AZ, U.K. 6. Dipartimento di Scienze del Farmaco, Università degli Studi di Padova, Via Marzolo 5, 35131 Padova, Italy.
Abstract
While the neurochemistry that underpins the behavioral phenotypes of depression is the subject of many studies, oxidative stress caused by the inflammation comorbid with depression has not adequately been addressed. In this study, we described novel antidepressant-antioxidant agents consisting of selenium-modified fluoxetine derivatives to simultaneously target serotonin reuptake (antidepressant action) and oxidative stress. Excitingly, we show that one of these agents (1-F) carries the ability to inhibit serotonin reuptake in vivo in mice. We therefore present a frontier dual strategy that paves the way for the future of antidepressant therapies.
While the neurochemistry that underpins the behavioral phenotypes of depression is the subject of many studies, oxidative stress caused by the inflammation comorbid with depression has not adequately been addressed. In this study, we described novel antidepressant-antioxidant agents consisting of selenium-modified fluoxetine derivatives to simultaneously target serotonin reuptake (antidepressant action) and oxidative stress. Excitingly, we show that one of these agents (1-F) carries the ability to inhibit serotonin reuptake in vivo in mice. We therefore present a frontier dual strategy that paves the way for the future of antidepressant therapies.
The most common therapy
for pharmacological treatment of depression
are the selective serotonin reuptake inhibitors (SSRIs), which aim
to increase serotonin levels in the brain since deficiency of this
neuromodulator is thought to underlie depressive-like symptoms in
patients. There is now a growing body of evidence that inflammation
plays an important role in treatment-resistant depression.[1−4] Inflammation is a blanket term to describe a state of immune activation
where immune cells prompt a biochemical cascade (e.g., cytokines) to identify and remove the source of this condition.[5] An important but largely damaging aspect of inflammation
is oxidative stress, which is hallmarked by the generation of reactive
oxygen species (ROS), nitric oxide (NO), and unbalanced catalase activity.[1,6,7] There are conflicting reports
of how SSRIs mediate oxidative stress. It has been reported that venlafaxine
(a selective serotonin norepinephrine reuptake inhibitor) and fluoxetine
(an SSRI) have direct antioxidant properties.[8−10] Other reports
show that fluoxetine can cause hepatotoxicity, and the oxidative stress
associated with this toxicity likely offsets any direct antioxidant
properties.[11] A clinical study showed that
oxidative stress increased in patients successfully treated with antidepressants,
including SSRIs.[1] Given that the damaging
effects of oxidative stress can be easily curtailed via antioxidant therapies that scavenge ROS, there may be important
therapeutic potential in combining SSRI and antioxidant therapy.[12]The Orian group has recently developed
a novel antidepressant–antioxidant
agent in a selenium-modified fluoxetine molecule.[13] Such druglike chimeric compound is designed with the aim
of combining the SSRI effect of fluoxetine with the enhanced antioxidant,
ROS-scavenging activity granted by selenium. The substitution of the
oxygen atom with the more electropositive chalcogen contributes to
scavenging activity, but this activity is strictly related to molecular
topology, as recently reported for phenothiazines in which the replacement
of sulfur by its heavier siblings does not improve antioxidant potential per se.[14] In this work, we describe
the synthesis, characterization, and evaluation of two novel selenofluoxetine
derivatives and importantly show that the compound named 1-F maintains the ability to inhibit in vivo serotonin
reuptake in mice. We therefore present an exciting new strategy to
target both serotonin and oxidative stress as a next-generation antidepressant.
Results
and Discussion
Bioinspired Modification of Fluoxetine to
Create Se Derivatives
Antioxidant potential has been reported
for different classes of
psychotropic drugs,[12] and fluoxetine itself
was previously demonstrated to possess multimechanism antioxidant
effects. More precisely, fluoxetine and its metabolites have been
described as ROS scavengers and stimulants of the enzymatic and nonenzymatic
components of the endogenous antioxidant defense system.[9,10] Potentially offsetting these effects is the hepatotoxicity of fluoxetine
that itself creates oxidative stress.[11] We now report novel Se-based derivatives in this paper that are
endowed with additional antioxidant potential, a feature that is especially
characteristic of organic selenides in the CNS.[15] Serotonin itself is a strong ROS scavenger; thus, we rationalize
that the combined antioxidant approach of the Se-based compounds and
their effects on increasing extracellular serotonin may work in synergy
to strengthen overall antioxidant capability. In fact, computational
and in vitro experimental evidence demonstrated the
antioxidant and radical scavenging properties of serotonin, which
play a primary role. Moreover, the influence of fluoxetine metabolites
cannot be ruled out.[6,9,10]The chemical structures of fluoxetine, selenofluoxetine, and its
synthesized derivatives investigated in this work are reported in Figure .
Figure 1
Chemical structure of
fluoxetine, selenofluoxetine, and its derivatives
studied in this work.
Chemical structure of
fluoxetine, selenofluoxetine, and its derivatives
studied in this work.The preparation of selenofluoxetine
derivatives was performed following
a convergent synthetic scheme, which was designed based on the approach
that we reported previously.[13] Nevertheless,
the compounds considered in the current study belong to the class
of selenofluoxetines that we presented in a previous paper but show
a p-CH3 group and a p-F group, which were not described previously. 1-CH and 1-F bear two substituents endowed
with openly different electronic and steric features, which allowed
us to further explore the role of the modification of the phenyl ring
of selenofluoxetine.[13] Thus, the compounds
were selected as a representative from this class to carry our investigation
to a further level. Both p-CH3 and p-F modifications are tolerated modifications according
to fluoxetine structure–activity relationship (SAR) studies.[16] Moreover, both compounds, in analogy to fluoxetine,
are predicted by SwissADME software to cross the blood–brain
barrier.[17] To the extent of synthesizing
these derivatives, some modifications were adopted to introduce the p-CH3 and the p-F substituents
in the final compounds. The overall multistep synthetic scheme for
the preparation of 1-CH and 1-F is resumed in Figure . Briefly, acetophenone was initially subjected to
a Mannich reaction giving compound 3, which was reduced to provide 4 and then chlorinated
to obtain the intermediate 5.[18−20] On the other
hand, opportune diselenides 8 and 9 were
synthesized by preparing the Grignard reagent from
the corresponding iododerivatives 6 and 7, which were then reacted with elemental selenium.[21] The final step of this convergent synthetic scheme consisted
in the reaction of intermediate 5 with the selenide nucleophile
prepared in situ by reduction of 8 or 9, providing 1-CH and 1-F as the hydrochloride salts. The products were fully characterized
by 1H-NMR, 13C-NMR, and ESI-MS (see the Supporting Information for spectra).
Figure 2
Synthetic scheme
for the preparation of selenofluoxetine derivatives:
(a) dimethylamine HCl/HCOH/EtOH, 98%; (b) NaBH4/KOH/MeOH,
95%; (c) SOCl2, 96%; (d) Mg/Et2O, Se, 58% for 8, 48% for 9; (e) NaBH4/KOH/EtOH,
49% for 1-CH, 47% for 1-F.
Synthetic scheme
for the preparation of selenofluoxetine derivatives:
(a) dimethylamine HCl/HCOH/EtOH, 98%; (b) NaBH4/KOH/MeOH,
95%; (c) SOCl2, 96%; (d) Mg/Et2O, Se, 58% for 8, 48% for 9; (e) NaBH4/KOH/EtOH,
49% for 1-CH, 47% for 1-F.In the studied selenofluoxetine
derivatives, the oxygen which was
originally present in the fluoxetine molecule was replaced by a selenium
atom. The choice of the chalcogen is based on the well-known antioxidant
role selenium plays in biology, as a constituent of the catalytic
residue of enzymes like glutathione peroxidase (GPx)[22] and thioredoxin reductase (TrxR).[23] Importantly, the synthesis of organoselenides as GPx mimics is still
considered an active research task and is worldwide pursued by an
active community.[24−31]Compared to fluoxetine, 1-CH3 and 1-F bear an N,N-dimethyl
group that was introduced to simplify the synthetic procedures. This
modification was previously demonstrated to be tolerated in SAR studies
on ligands targeting SERT.[32] Overall, selenofluoxetine
derivatives show a close structural similarity with fluoxetine, and
thus similar pharmacokinetic and pharmacodynamic properties were expected.
Moreover, in analogy with the original compound that inspired this
study, the new derivatives were synthesized as hydrochloride salts.
In fact, as precisely described by Wong et al. in their review, fluoxetine
hydrochloride was originally identified as the ideal form to overcome
the limitations of the former oxalate salt in terms of water solubility
during the drug development process.[33] Additionally,
recent SAR studies based on computational and mechanistic evidence
showed that substitutions in the para position of
fluoxetine with halogen atoms support the activity of such molecules
on SERT.[34] On the other hand, derivatives
bearing alkyl groups in the para position have been
investigated as antibacterial agents.[35] Thus, based on the evidence from previous findings, 1-F was carried to the further level of investigation.
In Silico Modeling
of ROS-Scavenging Antioxidant Capacity of
Derivatives
Radical scavenging toward three radicals (HO•, HOO•, and CH2=CHOO•) was investigated in silico considering
hydrogen atom transfer processes (HAT) for 1-F. In silico modeling of the HAT process involves the calculation
of the Gibbs free energy of reaction of the abstraction of a H radical
by the selected free radical from each suitable site of the ROS scavenger,
which results in the formation of the radical form of the selenofluoxetine
derivative and the quenched free radical (H2O, H2O2, or CH2=CHOOH). In this work, we
focused on the nonaromatic C atoms of the selenofluoxetine derivatives
as the most suitable sites for HAT. Results are in agreement with
those found for selenofluoxetine[13] and
show how sites C7 and C17 are the most favorable for gas-phase HAT
reactions (Figure ). Gibbs free reaction energies are computed to be negative for processes
involving HO•, whereas in the case of the other
radicals, they are found to be positive (Figure a).
Figure 3
ΔG°HAT (in kcal mol–1) for 1-F in the gas
phase (a), in benzene
(b), and in water (c). Level of theory (SMD)-M06-2X/6-311+G(d,p)//M06-2X/6-31G(d,p).
ΔG°HAT (in kcal mol–1) for 1-F in the gas
phase (a), in benzene
(b), and in water (c). Level of theory (SMD)-M06-2X/6-311+G(d,p)//M06-2X/6-31G(d,p).In the condensed phase, the polarity of the solvent
has a common
effect on all of the sites, making the reactions progressively more
favorable in benzene and water (Figure b,c). However, C7 and C17 remain the sites with the
lowest (negative) ΔG°HAT, and
the trends found in the gas phase are maintained.This behavior
can be qualitatively rationalized by inspecting how
the unpaired electron density is distributed on the radical molecule.
For example, taking C17 and C14 as a comparison, it is clear how the
spin density is more delocalized on the former where it involves the
N atom close to C17 compared to C14 where the contribution of adjacent
atoms is marginal (Figure ).
Figure 4
Spin density of two radicals of 1-F (isosurface value
0.005 a.u.). Level of theory M06-2X/6-311+G(d,p)//M06-2X/6-31G(d,p).
Spin density of two radicals of 1-F (isosurface value
0.005 a.u.). Level of theory M06-2X/6-311+G(d,p)//M06-2X/6-31G(d,p).To gain a deeper insight into the kinetics of the
HAT processes,
the reaction with HO• was furtherly investigated
to obtain the activation energies in the gas phase and in solution.
Results show that the computed activation energies are very low and
that the sites for which HATs are most exergonic, namely, C7 and C17,
are also the two that display the lowest reaction barriers, confirming
once again the agreement with the Bell–Evans–Polanyi
principle, which states that the reaction with the lowest activation
barriers is also the most thermodynamically favored, and supporting
the idea of a simplification of the analysis of HAT processes proposed
in previous works (Table ).[6,7,36,37]
Table 1
Activation Free Energies (kcal mol–1) for HAT Reactions Involving 1-F and HO. Level of Theory (SMD)-M06-2X/6-311+G(d,p)//M06-2X/6-31G(d)
site
gas phase[a]
benzene[a]
water[a]
C7
2.3
3.6
3.3
C14
8.4
10.0
9.6
C15
5.6
6.5
5.0
C17
5.5
5.9
2.5
1-F inhibits In Vivo Serotonin Reuptake in
Mice
A deficit
in extracellular brain serotonin has long been thought to underlie
the pathology of depression, as such SSRIs are the frontline clinical
treatment for the disorder. SSRIs are small organic molecules, structurally
resembling serotonin, that inhibit the serotonin transporter (SERT),
a protein tasked with clearing the monoamine after synaptic release.
However, the therapeutic mechanism of action of SSRIs remains unknown
since the effects of these drugs on in vivo serotonin
chemistry in the brain are hard to assess. Over the past decade, Hashemi
and colleagues have developed a niche tool for monitoring in vivo serotonin in rodent models using FSCV.[38−42] Briefly, FSCV involves implantation of a CFM directly into the brain
of an anesthetized mouse and applying an electrochemical waveform;
direct electrochemical redox of the molecules enables identification
and quantification of serotonin in real time, with subsecond time
resolution.[39,43] This method was used in a series
of studies to evaluate the effects of escitalopram, a common SSRI,
on electrically evoked serotonin in the CA2 region of the hippocampus
in mice. Escitalopram rapidly and profoundly increased release amplitude
and decreased reuptake rate of serotonin.[38,41]In this work, we asked whether the novel selenofluoxetine
derivatives were capable of inhibiting in vivo serotonin
reuptake in a similar manner to the previous escitalopram studies.
A control serotonin signal was isolated in five mice in the CA2 region
of the hippocampus in a mixed-sex cohort (Figure A: blue). After four control files were collected, 1-F was administered via i.p. injection at
50 mg kg–1 in saline (readily soluble). Saline alone
does not affect ambient serotonin dynamics.[44] After 30 min, the signal was taken and averaged between the mice
(Figure A orange),
this time was chosen as it was the point of maximal response and is
in line with the half-life of other common SSRIs in mice.[45] Serotonin release amplitude increased from 22.73
± 4.07 to 25.84 ± 3.41 nM (N.S.), and the t1/2 of reuptake was found to significantly increase from
1.93 ± 0.30 to 3.38 ± 0.41 s (p < 0.05).
A further set of experiments, in a separate mouse cohort, was performed
in the same way with the analogous model fluoxetine at 20 mg kg–1 (Figure B: orange). We found a significant amplitude increase from
35.75 ± 10.16 to 56.05 ± 13.40 nM and a significant t1/2 increase from 1.90 ± 0.46 to 4.01 ±
0.70 s (p < 0.05).
Figure 5
In vivo stimulated serotonin release in the CA2
region of the hippocampus. (A) Representative color plots for this
experimental paradigm, before and 30 min into drug analysis, are found
above the representative IT curves. The averaged control evoked serotonin
in mixed-sex cohort can be found in blue (n = 5)
with SEM calculated and outlined in the lighter shade of blue. The
averaged drug files (1-F, 50 mg kg–1) at 30 min for serotonin in mixed-sex cohort is plotted in orange
(n = 5) with SEM calculated and outlined in the lighter
shade of orange. Stimulation is indicated by the gray bar. (B) Representative
color plots for this experimental paradigm, before and 30 min into
drug analysis, are found above the representative IT curves. The averaged
control evoked serotonin in mixed-sex cohort can be found in blue
(n = 5) with SEM calculated and outlined in the lighter
shade of blue. The averaged drug files (Fluoxetine, 20 mg kg–1) at 30 min for serotonin in mixed-sex cohort is plotted in orange
(n = 5) with SEM calculated and outlined in the lighter
shade of orange. Stimulation is indicated via the gray bar. Statistical
analysis can be found for each data set in Table 2.
In vivo stimulated serotonin release in the CA2
region of the hippocampus. (A) Representative color plots for this
experimental paradigm, before and 30 min into drug analysis, are found
above the representative IT curves. The averaged control evoked serotonin
in mixed-sex cohort can be found in blue (n = 5)
with SEM calculated and outlined in the lighter shade of blue. The
averaged drug files (1-F, 50 mg kg–1) at 30 min for serotonin in mixed-sex cohort is plotted in orange
(n = 5) with SEM calculated and outlined in the lighter
shade of orange. Stimulation is indicated by the gray bar. (B) Representative
color plots for this experimental paradigm, before and 30 min into
drug analysis, are found above the representative IT curves. The averaged
control evoked serotonin in mixed-sex cohort can be found in blue
(n = 5) with SEM calculated and outlined in the lighter
shade of blue. The averaged drug files (Fluoxetine, 20 mg kg–1) at 30 min for serotonin in mixed-sex cohort is plotted in orange
(n = 5) with SEM calculated and outlined in the lighter
shade of orange. Stimulation is indicated via the gray bar. Statistical
analysis can be found for each data set in Table 2.Fluoxetine has a more pronounced effect on the serotonin
signal
than 1-F, despite the lower dose. We believe this is
a consequence of two effects. First, the electronegativity conferred
by the oxygen group in fluoxetine plays an important role in the way
the SSRI binds to the SERTs.[42] Second,
the Se–C bond could be more easily hydrolyzed than the O–C
bond because the valence electrons in the Se–C bond are more
shielded from the nucleus given the Se atom.[39] Nevertheless, preliminary HPLC analysis carried out at different
time points on the studied compounds highlighted that the molecules
are stable in aqueous solution for a time that is longer than that
considered in the in vivo experiments (HPLC chromatograms
are reported in the Supporting Information). On the other hand, this new agent does have the ability to inhibit
serotonin reuptake, and therefore this opens a path for an additional
approach against depression. Further evidence to support the effects
on serotonin reuptake of this newly developed compound is our Michaelis–Menten
analysis. For both fluoxetine and the new analogue, we see that Vmax (maximum rate of reuptake) decreases upon
the administration of the agent. For fluoxetine, Vmax trends from 17.69 to 15.21 nM/s, and for 1-F, it trends from 15.60 to 13.46 nM/s. Thus, our new compounds show
promising chemical efficacy for slowing serotonin reuptake (Table ).
Table 2
Features of In Vivo Experimental Curves:
Mean ± SEM (n = 5 animals) of the Maximum Evoked
Serotonin Amplitude (Ampmax) and Clearance Rate (t1/2)a
maximum release (nM)
t1/2 of clearance (s)
Vmax (nM/s)
control
pre-fluoxetine
35.75 ± 10.16
1.90 ± 0.46
17.69
30 min post-fluoxetine
56.05 ± 13.40*
4.01 ± 0.70*
15.21
control pre-1-F
22.73 ± 4.07
1.93 ± 0.30
15.60
30 min post-1-F
25.84 ± 3.41
3.38 ± 0.41*
13.46
Each parameter was tested for significant
difference between control and drug treatment (paired samples t-test). Significance (*) was defined as p < 0.05. Michaelis–Menten reuptake kinetics model for serotonin
was fitted to the signal. Km was set to
5 nM, while Vmax was optimized to fit
the average experimental trace.
Each parameter was tested for significant
difference between control and drug treatment (paired samples t-test). Significance (*) was defined as p < 0.05. Michaelis–Menten reuptake kinetics model for serotonin
was fitted to the signal. Km was set to
5 nM, while Vmax was optimized to fit
the average experimental trace.
Conclusions
Depression and inflammation are highly comorbid
and, consequently,
there is potential for oxidative stress in depression patients. Besides,
there is conflicting information on whether SSRIs have oxidant properties.
Therefore, in this work, we described novel agents to simultaneously
target serotonin uptake and oxidative stress. We synthesized and characterized
these new selenofluoxetine derivatives and importantly showed that 1-F carries the ability to inhibit serotonin reuptake. Thus,
we presented an exciting dual strategy that paves the way for future
antidepressant therapies.
Materials and Methods
Chemistry
Commercially
available chemicals were purchased
from Sigma-Aldrich and used without any further purification. NMR
experiments were performed on a Bruker Avance III 400 spectrometer
(frequencies: 400.13 and 100.62 MHz for 1H and 13C, respectively) (Bruker, Billerica, MA) equipped with a multinuclear
inverse z-field gradient probe head (5 mm). For data processing, TopSpin
4.0.8 software was used, and the spectra were calibrated using solvent
signal (1H-NMR, δH = 7.26 ppm for CDCl3, δH = 2.50 ppm for DMSO-d6, δH = 4.79 ppm for D2O; 13C-NMR, δC = 77.16 ppm for CDCl3, δC = 39.52 ppm for DMSO). Mass spectra were recorded
by direct infusion electrospray (ESI) on an LCQ Fleet ion trap mass
spectrometer (Thermo Fisher Scientific, Waltham, MA) and on a Xevo
G2 QTof high-resolution mass spectrometer (HRMS; Waters, Milford,
MA). 1H-NMR, 13C-NMR, and ESI-MS spectra are
reported in the Supporting Information.
The purity profile was assayed by HPLC using a Pro-Star system (Palo
Alto, CA) equipped with a 1706 UV–VIS detector (Bio-Rad, Hercules,
CA) and a C-18 column (5 μm, 4.6 × 150 mm)
(Agilent, Santa Clara, CA). An appropriate gradient of 0.1% formic
acid (A) and acetonitrile (B) was used as mobile phase with an overall
flow rate of 1 mL min–1. The general
method for the analyses is reported in the following: 0 min
(60% A–40% B), 5 min (90% A–10% B), 6 min
(90% A–10% B), 8 min (60% A–40% B), and 32 min
(90% A–10% B). Analyses were performed at 254 nm.
Synthesis
of N,N-Dimethyl-3-oxo-3-phenylpropan-1-aminium
chloride (3)
Dimethylamine hydrochloride (2.03
g, 24.9 mmol, 1.5 equiv) and paraformaldehyde (0.65 g, 21.6 mmol,
1.3 equiv) were introduced in a 50 mL round-bottom flask and dissolved
in 2.5 mL of ethanol. Acetophenone (2.00 g, 16.6 mmol, 1 equiv) was
added to the solution together with 40 μL of concentrated HCl.
The reaction mixture was stirred at reflux and monitored by TLC (DCM/MeOH/TEA
97:2.5:0.5). After 2 h, the solution was cooled to room temperature.
A solid precipitate of N,N-dimethyl-3-oxo-3-phenylpropan-1-aminium
chloride salt formed. The solid was filtered using a Buchner funnel
and washed with cold acetone (3 × 10 mL) and hexane (1 ×
10 mL). Yield 3.48 g (98%); white solid; 1H-NMR (400 MHz,
DMSO): δH (ppm) 10.57 (br, 1H, NH), 8.02 (d, 2H, J = 7.2 Hz, Ph-H), 7.69 (t, 1H, J = 7.4 Hz, Ph-H), 7.57 (t, 2H, J = 7.6 Hz, Ph-H), 3.63 (t, 2H, J = 7.2 Hz, C(O)CH2), 3.40 (t, 2H, J = 7.2 Hz, CH2N), 2.80 (s, 6H, N(CH3)2); 13C-NMR (101 MHz, DMSO): δC (ppm) 196.8 (s, C(O)), 135.9 (s, Ph-C), 133.7 (s, 2C, Ph-C), 128.8 (s, Ph-C), 128.0 (s, 2C, Ph-C), 51.8 (s, CH2N), 42.2 (s, 2C, N(CH3)2), 33.1 (s, C(O)CH2); (ESI+) m/z calcd for C11H16NO+ [M + H]+: 178.1232; found:
178.1308.
Synthesis of 3-Hydroxy-N,N-dimethyl-3-phenylpropan-1-amine (4)
Compound 3 (1.80 g, 8.39 mmol, 1 equiv) was
dissolved in 5 mL of distilled
water, and 1.2 mL of 8 M KOH was added to the mixture. A white solid
formed, and the mixture was extracted with DCM (4 × 20 mL). The
organic phases were combined, and the solvent was evaporated under
reduced pressure, yielding the free base of compound 3. The oily residue was dissolved in 10 mL of methanol, and a couple
of drops of 8 M KOH were added. The solution was cooled to 0 °C,
and NaBH4 (0.47 g, 12.6 mmol, 3 equiv) was added to the
solution. After the dissolution of all of the reactants, the ice bath
was removed and the mixture was stirred at room temperature for 1.5
h. Concentrated HCl was added dropwise until acid pH, and the solution
was then basified with KOH 8 M. Methanol was evaporated under reduced
pressure, and the resulting solid was dissolved in 100 mL of DCM and
washed with alkaline water (4 × 10 mL). The organic phase was
dried with anhydrous MgSO4, filtered, and evaporated under
reduced pressure. Yield 1.43 g (95%); colorless oil; 1H-NMR
(400 MHz, CDCl3): δH (ppm) 7.41–7.31
(m, 4H, Ph-H), 7.28–7.22 (m, 1H, Ph-H), 4.91 (dd, J = 7.1, 4.7 Hz, 1H, CH(OH)), 2.66–2.58 (m, 1H, CH(OH)CHAHB), 2.48–2.42 (m, 1H, CH(OH)CHAHB), 2.28 (s, 6H, N(CH3)2), δH 1.86– 1.79
(m, 2H, CH2N); 13C-NMR (101
MHz, CDCl3): δC (ppm) 145.1 (s, Ph-C), 128.1 (s, 2C, Ph-C), 126.8 (s, Ph-C), 125.5 (s, 2C, Ph-C), 75.3 (s, CH(OH)), 58.1 (s, CH2N), 45.2
(s, 2C, N(CH3)2), 34.7 (s,
CH(OH)CH2); (ESI+) m/z calcd for C11H18NO+ [M
+ H]+: 180.14, found: 180.11.
Synthesis of 3-Chloro-N,N-dimethyl-3-phenylpropan-1-aminium
chloride (5)
Compound 4 (1.50 g,
8.37 mmol) was dissolved in a small amount of diethyl ether, and 5
mL of 2 M HCl in ether was added to obtain the corresponding hydrochloride
salt. The solvent was evaporated under reduced pressure and 10 mL
of thionyl chloride was subsequently added to the round-bottom flask.
The resulting solution was stirred under reflux, and the reaction
was monitored by TLC (DCM/MeOH/TEA 97:2.5:0.5). After 2 h, the solvent
was evaporated under reduced pressure obtaining the compound as a
hydrochloride salt. Yield 1.88 g (96%), white solid; 1H-NMR
(400 MHz, DMSO): δH (ppm) 10.90 (br, 1H, NH), 7.52–7.49 (m, 2H, Ph-H), 7.45–7.35
(m, 3H, Ph-H), 5.31 (dd, J = 9.1,
5.2 Hz, 1H, CH(Cl)), 3.23–3.18 (m, 1H, CH(Cl)CHAHB), 3.10–3.04 (m, 1H, CH(Cl)CHAHB), 2.76 (s, 6H, N(CH3)2), 2.63–2.44 (m, 2H, CH2N); 13C-NMR (101 MHz, DMSO): δC (ppm) 141.0 (s, Ph-C), 129.3 (s, 2C, Ph-C), 129.2 (s, 2C, Ph-C), 127.5 (s, Ph-C), 61.1 (s, CH(Cl)), 42.7 (s, CH2N), 42.4 (s, 2C, N(CH3)2), 33.6 (s, CH(Cl)CH2); (ESI+) m/z calcd for C11H17ClN+ [M + H]+: 198.10, found:
198.10.
Synthesis of 1,2-Di-p-tolyldiselane (8)
Under nitrogen atmosphere, magnesium chips (78 mg, 3.21 mmol, 1 equiv)
were introduced in a 50 mL three-neck round-bottom flask. A solution
of p-iodotoluene (700 mg, 3.21 mmol 1 equiv) in 10
mL of dry ether was added dropwise at gentle reflux, and the mixture
was stirred for another 30 min. Afterward, selenium powder (254 mg,
3.21 mmol, 1 equiv) was added under gentle reflux and the reaction
mixture was stirred for another 30 min. The reaction was then poured
into a mixture of cracked ice and concentrated HCl. The cold mixture
was extracted with ether (3 × 20 mL). The combined organic layers
were dried using MgSO4 and filtered. The solvent was removed
under reduced pressure giving the product. Yield 316 mg (58%); orange
oil; 1H-NMR (400 MHz, CDCl3): δH (ppm) 7.56 (d, J = 8.09 Hz, 4H, Ph-H), 7.12 (d, J = 8.09 Hz, 4H, Ph-H), 2.39 (s, 6H, Ph-CH3); 13C-NMR (101 MHz, CDCl3): δC (ppm) 138.0
(s, Ph-C), 132.4 (s, Ph-C), 130.0
(s, Ph-C), 126.9 (s, Ph-C), 21.2
(s, Ph-CH3).
Synthesis of 1,2-Bis(4-fluorophenyl)diselane
(9)
Under nitrogen atmosphere, magnesium chips
(97 mg, 4 mmol,
1 equiv) were introduced in a 50 mL three-neck round-bottom flask.
A solution of 1-fluoro-4-iodobenzene (700 mg, 4 mmol 1 equiv) in 10
mL of dry ether was added dropwise at gentle reflux, and the mixture
was stirred for another 30 min. Afterward, selenium powder (316 mg,
4 mmol, 1 equiv) was added maintaining gentle refluxing and the reaction
mixture was stirred for another 30 min. The reaction was then poured
in a mixture of cracked ice and concentrated HCl. The cold mixture
was extracted with ether (3 × 20 mL). The combined organic layers
were dried using MgSO4 and filtered. The solvent was removed
under reduced pressure giving the product. Yield 331 mg (48%); 1H-NMR (400 MHz, DMSO): δH (ppm) 7.68–7.63
(m, 4H, Ph-H), 7.24–7.18 (m, 4H, Ph-H).
Synthesis of N,N-Dimethyl-3-phenyl-3-(p-tolylselanyl)propan-1-amine
(1-CH)
Compound 8 (316 mg, 0.93 mmol, 1 equiv) was introduced in a 50 mL round-bottom
flask and dissolved in ethanol (10 mL). KOH (157 mg, 2.79 mmol, 3
equiv) was added, and the solution was cooled in an ice bath, followed
by the addition of NaBH4 (211 mg, 5.58 mmol, 6 equiv).
After 1 h, once the mixture changed its color, compound 5 (218 mg, 0.93 mmol, 1 equiv) was added to the solution. The reaction
was stirred at room temperature for 3 h. To quench the unreacted NaBH4, concentrated HCl was added to the mixture until acid pH.
Afterward, 8 M KOH was added until basic pH. Ethanol was evaporated
under reduced pressure, and the resulting solid was dissolved in DCM
(30 mL). The solution was washed with alkaline water (3 × 15
mL), dried over magnesium sulfate, and filtered. The solvent was evaporated
under reduced pressure, and the crude product was purified by column
chromatography (silica gel, DCM:MeOH:TEA 92:7.5:0.5). Yield: 152 mg
(49%); yellow solid; HPLC: 96% (percentage area, 254 nm); 1H-NMR (400 MHz, CDCl3): δH (ppm) 7.30–7.18
(m, 7H, Ph-H), 7.03 (d, J = 7.9
Hz, 2H, Ph-H), 4.30–4.27 (m, 1H, CH(Se)), 2.33 (s, 3H Ph-CH3),
2.32–2.20 (m, 4H, CH-CH2-CH2-N), 2.18 (s, 6H, N(CH3)2); 13C-NMR (101 MHz, CDCl3): δC (ppm) 142.6 (s, Ph-C), 138.0
(s, Ph-C), 136.0 (s, Ph-C), 129.7
(s, Ph-C), 128.4 (s, Ph-C), 127.8
(s, Ph-C), 126.9 (s, Ph-C), 125.7
(s, Ph-C), 58.1 (s, CH(Se)), 46.2
(s, CH2N), 45.6 (s, N(CH3))2, 33.9 (s, CH(Se)CH2), 21.3 (Ph-CH3); (ESI+) m/z calcd for C18H24NSe+ [M + H]+: 334.1068, found 334.1112.
Synthesis of 3-((4-Fluorophenyl)selanyl)-N,N-dimethyl-3-phenylpropan-1-amine (1-F)
Compound 9 (331 mg, 0.95 mmol, 1 equiv) was introduced
in a 50 mL round-bottom flask and dissolved in ethanol (10 mL). KOH
(160 mg, 2.85 mmol, 3 equiv) was added and the solution was cooled
in an ice bath, followed by the addition of NaBH4 (216
mg, 5.70 mmol, 6 equiv). After 1 h, once the mixture changed its color,
compound 5 (223 mg, 0.95 mmol, 1 equiv) was added to
the solution. The reaction was stirred at room temperature for 3 h.
To quench the unreacted NaBH4, concentrated HCl was added
to the mixture until acid pH. Afterward, 8 M KOH was added until basic
pH. Ethanol was evaporated under reduced pressure, and the resulting
residue was dissolved in DCM (30 mL). The solution was washed with
alkaline water (3 × 15 mL), dried over magnesium sulfate, and
filtered. The solvent was evaporated under reduced pressure, and the
product was purified by column chromatography (silica gel, DCM/MeOH/TEA
92:7.5:0.5). Yield: 151 mg (47%); yellow solid; HPLC: 96% (percentage
area, 254 nm); 1H-NMR (400 MHz, CDCl3): δH (ppm) 7.32–7.28 (m, 2H, Ph-H), 7.24–7.15
(m, 3H, Ph-H), 7.12–7.10 (m, 2H, Ph-H), 6.89–6.84 (m, 2H, Ph-H), 4.27–4.23
(m, 1H, CH(Se)), 2.37–2.13 (m, 4H, Se-CH2CH2), 2.19 (s,
6H, N(CH3)2); 13C NMR (101 MHz, CDCl3) δC (ppm) 164.3
(Ph-C), 138.3 (Ph-C), 138.2 (Ph-C), 128.5 (Ph-C), 127.8 (Ph-C), 127.1 (Ph-C), 116.1 (Ph-C),
115.9 (Ph-C), 57.9 (s, CH(Se)),
46.6 (s, CH2N), 45.3 (s, N(CH3)2), 33.4 (s, CH(Se)CH2);
(ESI+) calcd for C17H22FNSe+ [M +
H]+: 338.0818, found 338.0065.
Computational
Methods
For all of the hydrogen atom
transfer (HAT) reactions, geometry optimizations of the reactants
and products were performed in the gas phase without any constraint,
using the M06-2X functional[46] combined
with the 6-31G(d) basis set, as implemented in Gaussian 16.[47] Spin contamination was checked for the doublet
ground state species to assess the reliability of the wavefunction.
Frequency calculations at the M06-2X/6-31G(d) level of theory were
run to confirm the nature of the stationary points and to obtain the
thermodynamic corrections at 1 atm and 298 K. This procedure ascertained
that only positive frequencies were present in minimum-energy structures
and a single negative frequency with the correct vibrational mode
in transition state structures. To obtain more accurate energy values,
single-point energy calculations were performed at M06-2X/6-311+G(d,p)
in the gas phase, and subsequently, in benzene and water, at the same
level of theory, using the continuum solvation model based on density
(SMD).[48] This level of theory is referred
to in the text as (SMD)-M06-2X/6-311+G(d,p)//M06-2X/6-31G(d,p). Benzene
and water represent an apolar and a polar environment, respectively,[49] and were therefore chosen to adequately investigate
the scavenging activity in different environments.
Electrode Fabrication
and Animal Work
Carbon Fiber
Microelectrodes (CFMs) were made individually by aspirating a single
carbon fiber (Goodfellow Corporation, PA) into a 0.6 mm × 0.4
mm glass capillary (A-M Systems, Inc., Sequim, WA). The capillary
was then pulled by a vertical puller (Narishige, Tokyo, Japan) to
create a seal. The carbon fiber was then trimmed to 150 ± 5 μm
for serotonin electrodes. Liquion (LQ-1105, 5% by weight Nafion) (New
Castle, DE) was electrodeposited onto the surface of the carbon fiber
by dipping and applying a constant potential of +1.0 V for 30 s. The
electrode was then dried at 70 °C for 10 min and used after 24
h. The CFM was placed in the CA2 region of the hippocampus of C57BL/6J
mice models (Jackson Laboratory, Bar Harbor, ME). These mice were
injected with a 25% urethane solution based on a calculation that
is dependent on their weight (7 μL/g) in their intraperitoneal
cavity (i.p.). Following anesthetic administration,
the mouse was placed into a stereotaxic system (David Kopf Instruments,
Tujunga, CA), where body temperature was maintained via a heating pad (Braintree Scientific, Braintree, MA). Three holes
were drilled into the skull of the mouse based off coordinates from
the mouse brain atlas. The working electrode was placed in the CA2
region of the hippocampus (CA2: −2.91, +3.35, −2.50);
the stimulating electrode (insulated stainless steel, diameter 0.2
mm, untwisted, Plastics One, Roanoke, VA) was placed in the medial
forebrain bundle (MFB: −1.58, +1.00, −4.80); and the
pseudo-Ag|AgCl reference electrode (made by plating Cl- onto the surface of an Ag wire) was placed in the opposite hemisphere
of the brain as the working and stimulating electrodes. Stimulation
was accomplished via linear constant current stimulus isolator (NL800A
Neurolog, Medical Systems Corp, Great Neck, NY) with the following
parameters: 60 Hz, 360 μA each, 2 ms in width, and 2 s in length.
Animal use followed NIH guidelines and complied with the University
of South Carolina Institutional Animal Care and Use Committee under
an approved protocol.
FSCV Acquisition and Analysis
FSCV
was performed using
a Dagan Potentiostat (Dagan Corporation, Minneapolis, MN), National
Instruments multifunction device USB-6341 (National Instruments, Austin,
TX), WCCV 4.0 software (Knowmad Technologies LLC, Tucson, AZ), and
a Pine Research headstage (Pine Research Instrumentation, Durham,
NC). The data were filtered (zero phase, Butterworth, 2 kHz low-pass)
and smoothed via applications built into the WCCV software. The “Jackson”
waveform was applied to elicit the redox properties of serotonin (5-HT:
+0.2 V to +1.0 V to −0.1 V to +0.2 V, 1000 V s–1). For FSCV data collection, this was applied at 10 Hz, and cyclic
voltammograms were used to confirm that serotonin was elicited as
well as current vs time traces were used to visualize the release
and reuptake of serotonin. Data presented include four evoked stim
files (control) with 10 min between each event averaged and standard
error of the mean displayed for error analysis and 30 min after drug
administration (fluoxetine or selenofluoxetine) with error again displayed
as the standard error of the mean. These files were converted to concentration
via a previously determined calibration factor (49.5 ± 10.2 nA/μM).
Statistical significance was determined via a two-tailed t-test (p < 0.05). All parametric and kinetic
analyses were performed using The Analysis Kid.[50] Maximum amplitude of evoked serotonin release and clearance
rate of the reuptake curve (t1/2) were
measured for each individual FSCV acquisition. The maximum amplitude
is automatically determined using a custom-designed peak-finding algorithm.
An exponential decay curve was fitted to the reuptake of the experimental
traces to estimate the half-life of the neurotransmitter release.
A previously described Michaelis–Menten model with two reuptake
processes[38] was fitted to the average FSCV
trace of each treatment group. For each average trace, Km was kept with a constant value of 5 nM, while Vmax was optimized to fit the signal using the
root-mean-square error between the model and the experimental trace
as the cost function.We utilized previously collected sample
data to determine statistical significance between two serotonin signals.[42] Based on Charan and Kantharia,[51] a power analysis was carried out with these data. The following
formula was used to calculate the sample size between two groupsFrom
these sample data, the pooled standard
deviation was 1.06, with the effect size being 1.96, which resulted
in a Cohen’s d of 1.85.[52] Using a 95% confidence interval, Zα was 1.96 and Zβ was
0.842, assuming 80% power. The power analysis resulted in n = 5.25, which was rounded down to five.
Authors: Jakub Staroń; Wojciech Pietruś; Ryszard Bugno; Rafał Kurczab; Grzegorz Satała; Dawid Warszycki; Tomasz Lenda; Anna Wantuch; Adam S Hogendorf; Agata Hogendorf; Beata Duszyńska; Andrzej J Bojarski Journal: Eur J Med Chem Date: 2021-05-14 Impact factor: 6.514
Authors: Nicole I Wenzel; Natascha Chavain; Yulin Wang; Wolfgang Friebolin; Louis Maes; Bruno Pradines; Michael Lanzer; Vanessa Yardley; Reto Brun; Christel Herold-Mende; Christophe Biot; Katalin Tóth; Elisabeth Davioud-Charvet Journal: J Med Chem Date: 2010-04-22 Impact factor: 7.446
Authors: Rachel A Saylor; Melinda Hersey; Alyssa West; Anna Marie Buchanan; Shane N Berger; H Frederik Nijhout; Michael C Reed; Janet Best; Parastoo Hashemi Journal: Front Neurosci Date: 2019-04-23 Impact factor: 4.677