3D-QSAR design of novel antiepileptic sulfamides

Article abstract of DOI:10.1016/j.bmc.2006.06.010

A three-dimensional quantitative structure-activity relationship method, the comparative molecular field analysis (CoMFA), was applied to design new anticonvulsant symmetric sulfamides. The training set (27 structures) was comprised by traditional and new-generation anticonvulsant (AC) ligands that exhibit a potent activity in MES test. Physicochemical determinants of binding, such as steric and electrostatic properties, were mapped onto the molecular structures of the set, in order to interpret graphically the CoMFA results in terms of field contribution maps. The 3D-QSAR models demonstrate a good ability to predict the activity of the designed compounds (r² = 0.967, q² = 0.756).

Full text of DOI:10.1016/j.bmc.2006.06.010

Bioorganic & Medicinal Chemistry 15 (2007) 1556–1567
3D-QSAR design of novel antiepileptic sulfamides
Luciana Gavernet,a M. Josefina Dominguez Cabrera,a Luis E. Bruno-Blancha,*
and Guillermina L. Estiu´b,*
aMedicinal Chemistry—Department of Biological Sciences, National University of La Plata, 47 and 115,
La Plata B1900BJW, Argentina
bDepartment of Chemistry and Biochemistry, University of Notre Dame, 251 Nieuwland Science Hall Notre Dame,
IN 46556-5670, USA
Received 5 April 2006; revised 5 June 2006; accepted 6 June 2006
Available online 8 December 2006
Abstract—A three-dimensional quantitative structure–activity relationship method, the comparative molecular field analysis
(CoMFA), was applied to design new anticonvulsant symmetric sulfamides. The training set (27 structures) was comprised by
traditional and new-generation anticonvulsant (AC) ligands that exhibit a potent activity in MES test. Physicochemical determi-
nants of binding, such as steric and electrostatic properties, were mapped onto the molecular structures of the set, in order to inter-
pret graphically the CoMFA results in terms of field contribution maps. The 3D-QSAR models demonstrate a good ability to
predict the activity of the designed compounds (r2 = 0.967, q2 = 0.756).
ꢀ 2006 Elsevier Ltd. All rights reserved.
1. Introduction
In recent years, several compounds such as felbamate
(FLB), fosphenytoin sodium, gabapentin, lamotrigine,
levetiracetam, oxcarbazepine (OCZ), tiagabine, topira-
mate (TOP), and zonisamide (ZON) have been
approved as AC drugs.11–13 Other structures, like carab-
ersat, CGX-1007 (Conantokin-G), pregabaline, retiga-
bine, safinamide, SPD421 (DP-VPA), remacemide,
rufinamide, stiripentol, valrocemide, harkoseride, and
talampanel, have been or are currently in the stage of
human testing.11–13
Two million new cases of epilepsy occur in the world
every year, affecting 1–2% of the world’s population.1
Antiepileptic drugs with better safety, less toxicity, and
higher efficacy in difficult-to-control patients are urgent-
ly needed, as the available ones are symptomatically
effective in only 60–70% of patients.1 Regardless of the
optimal use of the available antiepileptic drugs, many
patients with epilepsy fail to experience seizure control,
while others do so only at the expense of significant toxic
side effects.2
Among the new drugs, some have been derived from
optimization of pre-existing compounds (structure-
based design). Others have been developed following a
specific objective of modifying a certain stage of the
mechanism (mechanism-based design) or another target,
like levetiracetam with their new binding site, the synap-
tic vesicle protein.11–13 The design of new anticonvulsant
drugs is often hampered by the fact that the mode of ac-
tion of the drugs involves more than one specific binding
site and is not always linked with one specific binding
site. Therefore, drug identification is partially conducted
via in vivo screening tests using whole animal systems or
via in vitro testing. Moreover, due to the diversity of
mechanisms through which the AC action is exerted, dif-
ferent seizure models have been developed to evaluate
the anticonvulsant activity during the research process.
Among them, the maximal electroshock (MES) test
and the subcutaneous pentylenetetrazole (PTZ) test are
The anticonvulsant (AC) drugs have been categorized
into a number of distinct classes on the basis of the
molecular mechanisms through which they function.3–6
Some of the mechanisms of AC action include: potenti-
ation of GABA-ergic transmission,7 blockade of volt-
age-gated dependent sodium channels (VGSC),8
attenuation of excitatory neurotransmission,8–10 and/or
modulation of voltage-sensitive calcium channels.3
Keywords: Anticonvulsant drugs; Sulfamides; CoMFA; Drug design.
*
Corresponding authors. Tel.: +54 0221 4235333; fax: +54 0221
4223409 (L.E.B.-B.); tel./fax: +1 574 631 4007 (G.L.E.); e-mail
addresses: lbb@biol.unlp.edu.ar; estiu@nd.edu
0968-0896/$ - see front matter ꢀ 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.bmc.2006.06.010

L. Gavernet et al. / Bioorg. Med. Chem. 15 (2007) 1556–1567
1557
the most widely used to characterize the anticonvulsant
activity.14 These models respond well to neuronal VGSC
inhibitors and GABA-ergic compounds, respectively. In
particular, those drugs that are active against the MES
test, but not against the PTZ test, show a pharmacolog-
ical profile similar to phenytoin (PHE), and are called
phenytoin-like compounds.2,15 The picrotoxin test
(PIC), in which the chemically induced seizure is pro-
duced through the non-competitive inhibition of the
GABA receptor,7 is also extensively used, and has been
applied in this investigation.
ular fields at particular regions of the space. From their
correlation with the biological activities we have de-
signed a new class of AC structures that belong to the
family of symmetric sulfamides. The design was sup-
ported by a non-classical bioisosteric replacement of
the groups that contribute to the definition of the polar
end. As a next step, a new CoMFA was applied to an
extended set, built by the addition of the designed sulfa-
mides to the original one.
Sulfamides have traditionally played a role in medicinal
chemistry. They have been studied for their properties as
HIV protease inhibitors,20,21 agonists of the 5-HTID
receptor,22 active components in epinephrine ana-
logues23, and non-hydrolyzable components of pep-
tide-mimetics.24 They have also been analyzed as
carbonic anhydrase inhibitors,25–29 since almost all of
the most potent inhibitors of carbonic anhydrase con-
tain a sulfamide-related function, sulfonamide, as the
anchoring group to the catalytic Zn.30,31 Several AC
drugs of clinical use, like TOP, also bear a sulfamide-re-
lated functionality. Their anticonvulsant action is prob-
ably due to CO2 retention secondary to inhibition of the
red cell and brain enzymes29,32 but other mechanisms of
action, such as blockade of sodium channels, were
hypothesized/proved for some of them.33
The anti-MES profile, which is mainly used to target an
inhibition of the neuronal voltage-dependent Na chan-
nels, has been widely studied, and some pharmacophoric
models have been developed.16,17 The one most recently
proposed is based on the comparison of 13 dissimilar
structures, and combines the existence of a polar moiety
(atoms 1–3, Fig. 1) and a hydrophobic chain (atoms 5–7,
Fig. 1) in a well-defined spatial conformation.18
Other pharmacophoric models have been derived from
2D-QSAR analysis, and also revealed the importance
of electronic and lipophilic regions.19 Among them,
the one developed by Unverferth et al.16 shows three
groups of different characteristics that should be present,
at well-defined distances, in order for the structures to
be active. These different groups involve an aryl ring,
an electron donor atom, and a second donor atom
which, close to the NH group, defines a hydrogen bond
acceptor/donor unit. Several other model pharmaco-
phores have been developed from the comparison of
structures that belong to a given family.17
This article describes the design of novel anticonvulsant-
sulfamide-based ligands that would very likely keep the
same anti-MES profile as the ligands of the training set
from which they have been designed. The AC sulfamides
have been designed from the results of a CoMFA of 27
AC drugs, which is also described here.
Nevertheless, only 3D-QSAR is capable of identifying
the spatial characteristics shared by the active molecules.
On the basis of this consideration, and as an additional
step toward the design of AC drugs, we have applied
3D-QSAR CoMFA studies, using a set of compounds
larger than the one previously used,18 with the aim of
identifying a more reliable pharmacophore for the
anti-MES activity.18
The presentation of the results is organized in three
sections:
1. The comparative field analysis of the AC drugs that
present anti-MES activity.
2. The design, synthesis, and biological evaluation of
novel ligands, based on the CoMFA results.
3. The results of an extended CoMFA in which the
designed structures were added to the original set.
Attending the importance of the molecular alignment
for the success of the CoMFA-derived models, we have
based the molecular alignment on the electrostatic and
structural properties of the previously derived 3D phar-
macophore shown in Figure 1.18
The biological assays demonstrate the ability of the
3D-QSAR models to predict the activity of the designed
compounds and the high quality of the derived model
(r2 = 0.967, q2 = 0.756).
The structural variations of the compounds of the train-
ing set are reflected in variations of the CoMFA molec-
2. Methods
2.1. Data set and alignment
The molecular modeling and comparative molecular
field evaluations were performed using SYBYL version
6.6 running on a Silicon Graphics Octane workstation.34
The 27 compounds selected for this study (Fig. 2) repre-
sent models for 13 structurally different classes of com-
pounds with similar action in the experimental
biological model, MES test. Classical drugs, like PHE,
carbamazepine (CZ), and valpromide (VPD), are
Figure 1. Structure of the pharmacophore previously proposed.18

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L. Gavernet et al. / Bioorg. Med. Chem. 15 (2007) 1556–1567
3O
2
3
HN
5
1
O
NH2
4
1
O
NH2
H2N
O 2
S
O1
3
O 1
O 2
4
H
H23N
H2N
6
3
3 N
3
O1
5
4
N
2
O
O
O1
2
2
1 O
NH2
5
7
2
6
5
N
O
5
4
6
4
11
HN
7
7
HN
6
4
N
4
NH
6
5
7
5
6
O
O
6
7
7
N
7
FLB
VIN
PHE
RMC
ZON
CZ
F
RET
NHMe
3NH2
NHCp
NH2
O
S
O
O
S O
1O 2S
4 O
O
O
S
O
1 O
N3H2
1
O
O
2
7
1
O
O
7
O
O
7
O
6
6
5
7
6
7
O
6
5
O
6
O
4 N
5
5
NH
3
NH
3
2
O
2
O
5
5
O
6
5
O 4
O
4
4
6
O
O 4
O
7
2S
O
O 4
O
1
O
2S
O
2S
O
NH
O
7
NH
O
1
O
O
1
O3
O
O
O
O
OCZ
O3
O3
TOP2Me
PB
PRM
TOP2CP
TOP2
TOP
1
O
O
3
N
3
O
3
NH
1
O
O
3
HN
1
O
1
O
1
O
2
3
3
3
2
4
2
4
2
4
1
O
NH
2
H2NO2S
1
O
2
H2N
H2N
2
4
HN
5
6
4
6
5
7
6
6
6
4
6
7
6
5
7
4
7
7
5
5
5
5
7
7
CHVPD
SUVPD
VRL
PRVP
3
VPD
IPVPD
METS
1
1
O
O
3
HN
1
3
3
1
O
5
1
O
2
O1
5
2
4
N
2
4
5
S
7
N
2
4
HN
HN
O
HO2C
7
S
6
2
t
2 S N H2
3
4
S
N H2
3
H2
6
BuOCOHN
4
4
6
6
O
6
6
5
5
N
N
7
7
N
N
7
5
O
7
DMVPD
ETVPD
CPVPD
BUVPD
HATS
BHATS
Figure 2. Structures of the AC drugs that are active in the MES test and used to define the training set. Atoms are numbered for the centers that
define the pharmacophoric pattern. Other compounds included in the set and not mentioned before are: vipocentine (VIN), 2,3-O-isopropylidene-4,5-
O-sulfonyl-b fructopyranose sulfamate (TOP2), N-methyl 2,3-O-isopropylidene-4,5-O-sulfonyl-b fructopyranose sulfamate (TOP2Me), N-
cyclopropyl 2,3-O-isopropylidene-4,5-O-sulfonyl-b fructopyranose sulfamate (TOP2CP), valprocemide (VRL), N-butylvalpromide (BUVPD), N-
cyclohexylvalpromide (CHVPD), N,N-dimethylvalpromide (DMVPD), N-ethylvalpromide (EtVPD), N-isopropylvalpromide (IPVPD), 4-(valpro-
ilamide) benzoic acid (CPVPD), N-(sulfonamidephenyl)valpromide (SUVPD), propyl valproate (PRVP), methsuximide (METS), phenobarbital
(PB), pyrimidone (PRM), remacemide (RMC) and 5-amino-1,3,4-thiadiazole-2-sulfonamide (HATS), 5-tert-butoxycarbonylamino-1,3,4-thiadiazole-
2-sulfonamide (B-HATS), and retigabine (RET).
included in the training set, together with new genera-
tion ones like FLB, TOP, and zonisamide (ZON). All
of them have shown to block the VGSC as one of their
related mechanisms of AC action.1,13,35–37 N-Substitut-
ed valpromides and one valproate were also included,
with substituents chosen so that size and polarity were
varied. The synthesis and biological activity of these
compounds have been already reported.18,38 They have
been designed on the basis of the consideration of the
AC activity of valpromide, which supercedes valproic
acid, and attending the change of the activity triggered
by N-substitution. These compounds present a pharma-
cological profile similar to PHE.
tics of the bioactive conformation. However, there are
also flexible structures, bearing rotatable bonds in por-
tions that do not overlap with the pharmacophore. In
these cases, several conformations might be considered
as the active one. The search for the characteristics asso-
ciated with the binding of flexible molecules to receptors
is a challenging task because many low-energy confor-
mations are accessible and may coexist in equilibrium.
The goal is to identify the common geometric arrange-
ments of the moieties that are determinants of receptor
recognition or activation, including all the low-energy
conformations of each molecule in the analysis.
Therefore, the rotatable bonds that are not part of the
pharmacophore were allowed to rotate, and the set of
active conformations has been defined as the one which
renders the highest value of q2 in the leave-one-out
(LOO) cross-validation method included in PLS-CoM-
FA calculations (see PLS analysis).
A CoMFA study requires the coordinates of the mole-
cules to be aligned according to reasonable bioactive con-
formations. To this end, we have used the conformation
previously determined for the pharmacophore associated
with Na-channel blocking AC drugs (Fig. 1). The atoms
that define the template are numbered 1–7 in Figure 2.
As a first step, which precedes the superposition analy-
sis, each of the 27 compounds was fully optimized by
means of Density Functional Calculations (G9839
B3LYP/6-31G**). The optimized structures have been
In the superposition with the template, the rigid struc-
tures of the training set determine the main characteris-

L. Gavernet et al. / Bioorg. Med. Chem. 15 (2007) 1556–1567
1559
further fitted to the pharmacophore structural con-
3. Results and discussion
strains using the atom fit method provided by the Sybyl
software.34 As a measure of the ability of the molecules
to adopt the active conformation, the RMS values have
been obtained for the superposition performed by Sybyl.
In the RMS fitting the molecules were aligned by mini-
mizing the RMS distance between corresponding atoms
belonging, respectively, to the fitting molecule and to the
template structure.
3.1. The Comparative Field analysis of the AC drugs
3.1.1. 3D pharmacophore development. We have used a
ligand-based design strategy, oriented to identify and
characterize the minimal molecular requirements for
AC activity, using a training set of 27 known potent
anticonvulsants that are known to be active in MES test
(see Fig. 2).
2.2. CoMFA 3D-QSAR model
The compounds of the set have been used to develop a
3D-QSAR CoMFA model, which represents the three-
dimensional arrangements of specific functional groups
essential for activity. To this end 3D-QSAR models were
constructed in the way previously explained (see data set
and alignment). The molecules aligned in their active
conformations are shown in Figure 3.
The aligned molecules were originally placed in a three-
˚
sp3 carbon probe atom with a charge of +1.0 and a
˚
VdW radius of 1.52 A was used to calculate the CoMFA
steric and electrostatic field descriptors. The distant
dependent dielectric constant was used to treat the elec-
trostatic terms. A default cutoff of 30 kcal/mol was used
to truncate the steric field and the electrostatic field ener-
gies. CoMFA standard scaling was used.
dimensional grid of 2 A in the x, y, and z directions. A
The analysis provided statistically reliable models of
good internal predictive power. The statistical results
are summarized in Table 1.
The regression analysis has been performed by means of
partial least-squares (PLS) estimation. We have initially
used the leave-one-out cross-validation method in order
to reduce the possibility of obtaining chance correlation
and to find out the optimal number of components. In
the leave-one-out technique one compound of the set
was removed at a time and its activity was calculated
using the QSAR model based on the remaining com-
pounds. In this way, the internal predictive ability of
the model was tested and quantified by means of the
cross-validated correlation coefficient q2. The optimal
number of components was determined by the highest
q2 value and the minimum standard error of estimate
SEE in the cross-validated correlation. A 2 kcal/mol
energy column filter was performed to improve the sig-
nal-to-noise ratio. The steric and electrostatic field col-
umns were then used according to the CoMFA STD
default option, and the final QSAR model was calculated
with PLS without cross-validation, using the optimal
number of components previously determined. The
quality of the model was determined by means of the
conventional correlation coefficient (r2).
The regression coefficient (r2) and the cross-validated r2
(q2) of this QSAR model based on atom fit alignment
are 0.960 and 0.748, respectively, with low standard er-
ror (0.107). Based on these values, we consider that
the CoMFA model presents a good quality and exhibits
good predictive capability. In order to further test the
reliability of the CoMFA, we have repeated the fit
including valproic acid (VPA) in the set, using the
ED50 value (ꢀlogED50: ꢀ3.3), previously determined
by us.18 In this way we have broadened the range of
activities considered in the analysis. The inclusion of
VPA does not deviate significantly the values of the sta-
tistical descriptors (q2 = 0.708; r2 = 0.897, Sf = 0.672,
Ef = 0.328) relative to those obtained without this com-
pound. As VPA is known to act through several differ-
ent mechanisms, we have chosen to analyze the results
of the previous CoMFA (Table 1). Nevertheless, the sta-
bility of the q2 value after the inclusion of VPA reinforc-
es the significance of our results.
Table 2 lists the measured and predicted activities (col-
umns 2 and 5, respectively), as well as the RMS values
calculated with the compounds of the training set. The
values of residuals estimated from the comparison be-
tween predicted and experimental values (column 6)
are also shown. These values are lower than 0.25 and
only in eight cases (30%) are larger than 0.1.
2.3. Chemistry
The substituted sulfamides have been synthesized
according to previously reported procedures24,40–44
(Scheme 1 compounds 2a–e), by condensation of an ex-
cess of amine with sulfuryl chloride (Scheme 1). We have
found a non-favorable effect of pyridine in the reac-
tion,45,46 and it was not used as a reactant.
3.1.2. Visualization of the QSAR results. The results
obtained were graphically interpreted in terms of field
O
O
CH2Cl2
N2 - 00C
+ N R1R2.HCl
SO2Cl2 + R1R2NH(excees)
1a-e
S
N R1R2
R2R1N
2a-e
2: a, R1 = nBu, R2= H; b, R1 = Cyclohexyl, R2= H; c, R1 =Benzyl R2= H; d, R1 = Cyclopropyl, R2= H; e, R1R2= Morpholine
Scheme 1. Synthetic route for sulfamides.

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L. Gavernet et al. / Bioorg. Med. Chem. 15 (2007) 1556–1567
Figure 3. Molecules aligned in the conformation used in CoMFA. The atoms used for superimposition are shown in green.
Table 1. Statistical results using CoMFA
gions of the space are mainly related to portions of the
molecules that are not included in the pharmacophore.
Statistical results
ONCa
q2b
4
From the CoMFA correlation of the aligned structures
with their activities, and from the inspection of the
‘pharmacophore side chains’ a new requirement for
the AC activity has been derived on the basis of the fol-
lowing considerations.
0.748
0.960
0.107
0.672
0.328
0.079
r2c
SEEd
Sfe
Eff
Resg
a ONC, optimum number of components.
b q2, cross-validated r2.
c r2, non-cross-validated r2.
d SEE, standard error estimate.
e SF, steric field.
f Ef, electrostatic field.
g Res, average of residuals.
• In the steric contour (Fig. 4a), two regions have been
identified (in green), where the addition of bulky
groups may increase activity. Special attention has
been given to the green region close to the polar
end of the pharmacophore (a-1). This region is close
to atom 3 in Figure 1. Thence, an increase in the
activity can be attained through the addition of bulky
substituents to the polar moiety of the pharmaco-
phore. The characteristics of the group are partially
dictated by the presence of a yellow region, which
exerts a negative effect on the activity. The optimum
orientation of the substituent, which minimizes inter-
actions with the yellow region, is associated with a
value of s4 close to 0ꢁ (Fig. 4). Among the com-
pounds of the set, the highest activity is attained by
molecules like PB, PHE, and PRM, which have a
bulky substituent in the orientation required. These
compounds have a value of s4 close to 0, which is
fixed by a rigid portion. The green region a-2 may
be considered an extension of the non-polar portion
in the pharmacophore, attending the way it is posi-
tioned relative to the non-polar end. In this way,
whereas the size of the non-polar part shown in
Figure 1 is the minimum one required for activity, a
larger size may increase the AC potency.
contribution maps, which are shown in Figure 4, with
CZ as the reference molecule.
The contour maps have permitted an understanding of
the steric and electrostatic requirements for ligand bind-
ing. Physicochemical determinants of binding, such as
steric and electrostatic properties, have different contribu-
tions to the CoMFA model. As reported, the steric contri-
bution (Sf, Table 1) is more than two times larger than the
electrostatic one (Ef Table 1). This is indicative of a larger
influence of the steric effect in modulating the AC activity.
The alignment method used in this study was based on
the fitting of the atoms that define the pharmacophoric
pattern. After their superposition, they have almost
the same coordinates in the CoMFA 3D space grid. Be-
sides, their electronic characteristics are similar, since
the pharmacophore derivation has been also based on
the comparison of the charges on the corresponding
atoms.18 Because of this, the contributions of the phar-
macophore atoms to CoMFA descriptors are similar,
and the structural variations in the training set that give
rise to variations in the molecular fields at particular re-
• The yellow contours identify regions where the addi-
tion of bulky groups may decrease activity (region a-
3). They are located near the polar end of the phar-
macophore, opposite to the green region, as it was
previously discussed. The analysis of the green and
yellow regions provides an explanation to the low
activity of the METS molecule when compared to

L. Gavernet et al. / Bioorg. Med. Chem. 15 (2007) 1556–1567
Table 2. Biological data measured and predicted
1561
a
Compound
ED50
RMS fit
ꢀlogED50
CoMFA
Extended CoMFA
ꢀlogED50 predicted Residuals
ꢀlogED50 predicted
Residuals
CZ
PHE
37
38
0.000
0.104
0.213
0.118
0.094
0.499
0.706
0.142
0.200
0.308
0.846
0.846
0.846
0.276
0.062
0.222
0.091
0.089
0.088
0.088
0.088
0.088
0.088
0.087
0.088
0.493
0.493
0.296
0.601
0.293
ꢀ1.57
ꢀ1.58
ꢀ2.32
ꢀ2.47
ꢀ1.49
ꢀ1.96
ꢀ1.89
ꢀ1.90
ꢀ1.72
ꢀ1.97
ꢀ1.35
ꢀ1.31
ꢀ1.30
ꢀ2.05
ꢀ2.58
ꢀ2.55
ꢀ2.88
ꢀ1.98
ꢀ1.72
ꢀ1.79
ꢀ2.58
ꢀ2.69
ꢀ3.09
ꢀ2.30
ꢀ2.54
ꢀ1.95
ꢀ1.87
ꢀ2.47
ꢀ2.18
ꢀ2.38
ꢀ1.64
ꢀ1.72
ꢀ2.22
ꢀ2.47
ꢀ1.53
ꢀ1.89
ꢀ1.91
ꢀ1.73
ꢀ1.83
ꢀ1.89
ꢀ1.36
ꢀ1.33
ꢀ1.38
ꢀ1.98
ꢀ2.33
ꢀ2.49
ꢀ2.93
ꢀ2.07
ꢀ1.64
ꢀ1.95
ꢀ2.69
ꢀ2.74
ꢀ3.09
ꢀ2.17
ꢀ2.65
ꢀ1.94
ꢀ1.83
—
0.07
0.14
ꢀ1.64
ꢀ1.74
ꢀ2.29
ꢀ2.50
ꢀ1.49
ꢀ1.84
ꢀ1.96
ꢀ1.77
ꢀ1.78
ꢀ1.89
ꢀ1.35
ꢀ1.33
ꢀ1.36
ꢀ1.98
ꢀ2.37
ꢀ2.57
ꢀ2.92
ꢀ2.04
ꢀ1.72
ꢀ1.90
ꢀ2.67
ꢀ2.69
ꢀ3.09
ꢀ2.18
ꢀ2.65
ꢀ1.88
ꢀ1.81
ꢀ2.40
ꢀ2.29
ꢀ2.30
0.07
0.16
FLB
210
292
ꢀ0.10
0.00
0.04
ꢀ0.03
ꢀ0.03
0.00
RMC
RET
ZON
30.67
92
77
ꢀ0.07
0.02
0.12
VIN
OCZ
ꢀ0.07
ꢀ0.13
0.06
79
ꢀ0.17
0.11
PRM
PB
52.3
93.9
22.3
20.4
19.8
ꢀ0.08
0.01
0.02
ꢀ0.08
ꢀ0.00
ꢀ0.02
0.06
TOP2ME
TOP2CP
TOP2
TOP
0.08
112
ꢀ0.07
ꢀ0.25
ꢀ0.06
0.05
ꢀ0.07
ꢀ0.21
ꢀ0.02
0.04
METS
VPD
VRL
377.5
353
759.5
96
PRVP
SUVPD
CHVPD
IPVPD
BUVPD
CPVPD
ETVPD
DMVPD
HATS
B-HATS
DBS
0.09
0.06
53
61
ꢀ0.08
0.16
0.11
ꢀ0.00
0.11
0.09
384
487
1225
200
347
90
0.05
0.00
0.00
0.00
ꢀ0.13
0.11
ꢀ0.12
0.11
0.07
ꢀ0.01
ꢀ0.04
—
74
0.06
0.07
295
251
238
DCHS
DBZS
—
—
—
—
0.11
0.08
Residuals and RMS fit values are also given.
a Experimental activity ED50: median effective dose (ED50, lmol/kg) measured in the MES test (from the literature).2,37,38,47–52
Figure 4. Electrostatic and Steric Fields of the CoMFA contour map with CZ molecule as reference. The pharmacophoric pattern is shown in green
for CZ. (a) Green contours indicate the regions where the addition of bulky groups may increase activity. Yellow contours indicate the regions where
the addition of bulky groups may decrease activity. (b) Blue contours indicate regions where positive groups may increase the activity. Red contours
indicate regions where negative groups may increase activity. The most important regions are identified by numbers.
PB, PHE, and PRM. Whereas PB, PHE, and PRM
bear substituents in the green region that define a rig-
id structure and a value of s4 close to 0, the N-methyl
substituent of METS is located in the yellow zone.
This observation can be used to justify its lower
activity.

1562
L. Gavernet et al. / Bioorg. Med. Chem. 15 (2007) 1556–1567
• Several other compounds bear bulky moieties near
the polar end. Nevertheless, in most of the cases s4
is not fixed but defined by a rotatable bond. For these
cases, the influence of the green and yellow regions on
the AC was examined in detail through the evaluation
of the cross-validated correlation coefficient (q2). A
good correlation was obtained for values of s4 close
to 0ꢁ in the most active compounds (ED50 lower than
200 mmol/kg), and close to 180ꢁ in less active ones
(ED50 greater than 200 mmol/kg).
The previous observations led us to conclude that the
differences in the activities are originated in the presence
(or absence) of substituents occupying the green region
a1 (see Fig. 4a). The optimum size and directionality
of this substitution is determined by the positive and
negative influences of the green and yellow regions on
the activity. The directionality of the subtituent can be
defined through the value of the torsion angle s4, for
which an optimum value of zero degree has been de-
rived. The size can be defined averaging the distance
from atom 2 of the pharmacophore (Fig. 1) to the atom
of the substituent that, being part of the green region, is
farther from it. From this average, which extends over
the active compounds, a distance between 4 and 7 A
can be estimated, corresponding to an approximate size
˚
of 3 A for the substituent. From this analysis, we con-
clude that the consideration of an additional require-
ment would certainly help to optimize the AC activity
of a given compound. This requirement is associated
with the presence of a bulky substituent attached to
the polar moiety, oriented in a way that defines values
of s4 close to 0ꢁ (Fig. 5).
Figure 6. Spatial orientation of TOP2 relative to the steric contours
derived from CoMFA.
(like b3 and b4) that indicate regions where negative
groups may increase activity and blue zones where posi-
tive groups may raise the activity. The position of the
blue zones (b1, b2) perfectly matches those of the nega-
tive end of the pharmacophore (atoms 1 and 3, Fig. 1).
Moreover, the presence of a positive charge near these
blue regions (b1, b2), or a negative charge near the red
zones (b3, b4), may point to key features to be consid-
ered in the further design of AC ligands.
˚
3.2. The design, synthesis, and biological evaluation of
novel ligands, based on the CoMFA results
3.2.1. Rational design and bioisosteric substitution. The
results of the CoMFA model were used to design new
active prototypes. The design was mainly based on the
information derived from the analysis of the steric prop-
erties, which seem to more largely determine the AC
activity (Table 1).
The new requirement allows us to explain the fact that
several TOP derivatives have higher activities than
TOP itself. In this subset of compounds, the consider-
ation of the sulfamate/sulfate function as the polar end
renders the highest value of q2 in the leave-one-out
cross-validation method. In this way, the spatial orienta-
tion of the TOP derivatives (Fig. 6) places the methyl
groups of the 2,3-O-isopropylidene-4,5-O-sulfonyl-b
fructopyranose in the green portion (a-1, Fig. 4). In
TOP, the methyl groups are not able to accommodate
in this position.
As the template used for the alignment (Fig. 5) was a
‘through space’ 3D pharmacophore, rather than a
‘through bonds’ 2D pharmacophore, compounds with
entirely different molecular scaffolds may be searched
to comply with it. With the spatial orientation of the
bulky portions defined, bioisoteric functional group
information has been used to design a new polar moiety.
In order to do this, we have analyzed the atoms that de-
fine the polar moiety of the pharmacophore (numbered
1–3 in Fig. 1) and the bioisosteric replacement of a
group by sulfoxide or sulfone functionality.53–55
The electronic contour map shown in Figure 4b does not
allow an easy interpretation. There are several red zones
Following this line, we have designed five N-alkyl disub-
stituted symmetric sulfamides. The structures, (shown in
Fig. 7) were prepared before by other authors.24,40–44
We have synthesized them according to the literature
and evaluated their AC activity in our laboratory for
the first time.
The set of sulfamides comprises two compounds (DBS,
DBZS) that are able to adopt the conformation de-
fined by the new proposed pharmacophore (Fig. 5).
Their non-polar portions can acquire the values
Figure 5. Structure of the new proposed pharmacophore. The addi-
tional requirement described as a bulky region is shown in green and
its preferred orientation defined through s4.

L. Gavernet et al. / Bioorg. Med. Chem. 15 (2007) 1556–1567
1563
1
O
1
O
O
1
O
5
O
2
5
2
S
6
S
O
7
5
S
N
N 4
3H
H
2
N
3N
N 4
N
3 H
7
7
H4
H
H
6
6
DCHS
DBS
DBZS
O
O
O
O
S
S
N
N
N
H
N
H
O
O
DCPS
DMS
Figure 7. Structures of the sulfamides synthesized and evaluated. Atoms are numbered for the centers that define the pharmacophoric pattern in
active sulfamides.
determined for the torsion angles. The other three
structures of the set are rigid in their hydrophobic
portion (DCPS, DMS, and DCHS). Due to the lack
of rotation of its hydrocarbon chain, DCPS is unable
to fit the structural constrains of the pharmacophore
hydrophobic chain (atoms 5–7, Fig. 5) and can be
predicted to be inactive.
(Fig. 2). The same alignment procedure, CoMFA
parameters, and PLS analysis previously described have
been followed. Atoms numbered 1–7 in the Figure 7
were used to fit the sulfamides with the pharmacophore
template. Table 2 shows the RMS values obtained from
the superimposition of the sulfamides with the pharma-
cophoric pattern, together with the biological data used
for CoMFA study.
3.2.2. Pharmacology. The sulfamides were tested for
(AC) activity by their ability to suppress experimentally
induced convulsions in laboratory animals. The tests
used were the MES test, related to electrical induction;
and the PTZ and PIC tests, which generate the seizure
by chemical induction. The RotoRod test was used to
determine the neurotoxic effects. The results obtained
are listed in Table 3. The AC activity was expressed as
ED50, which measures the dose that is effective in 50%
of the tested animals.
The RMS values of Table 2 are in agreement with the
fact that DBS and DBZS present a bulky portion capable
of adopting the pharmacophore conformation (Fig. 8),
and responsible for the steric effect. This portion is ab-
sent in the inactive molecules (DCPS and DMS). The
higher RMS value of DCHS is originated in the rigidity
of the cyclic portion that defines the non-polar end of the
pharmacophore. This rigidity precludes a perfect over-
lapping of the bulky moiety, but allows it to fill the same
region of the space, generating similar steric effect.
MES tests, PTZ tests, and PIC tests were performed fol-
lowing the standard procedures provided by the antiep-
ileptic drug development program developed by the
National Institute of Health.50
For the electrostatic effect, two different descriptions
have been compared, which are based on Gasteiger–
According to our prediction, DBS, DCHS, and DBZS
are active in the MES tests, whereas DMS and DCPS
are inactive at the doses evaluated. This behavior is
interpreted in the next section in light of the CoMFA
results.
Details for the evaluation of anticonvulsant activity and
determination of Median Effective Dose are given in the
experimental section.
3.3. Results of extended CoMFA
Figure 8. Superposition of DCHS, DBS, and DBZS with the
pharmacophore conformation. The pharmacophoric portion is shown
in orange for DCHS and in green for DBS and DBZS.
CoMFA was applied to an extended set, built by the
addition of the active sulfamides to the original set
Table 3. Activity values (ED50, lmol/kg) and time of peak effect (TPE) determined (mice) for the sulfamide derivatives designed in this research work
Compound
ED50 MES test (lmol/kg)
TPE MES test
ED50 PTZ test (lmol/kg)
ED50 PIC test (lmol/kg)
DBS
295 (246–355)
151 (91–253)
238 (153–371)
Inactive
2 h
Inactive
Inactive
Inactive
Inactive
Inactive
Inactive
Not testeda
Inactive
Inactive
Inactive
DCHS
DBZS
DCPS
DMS
30 min
2 h
—
Inactive
—
Compounds are defined as inactive when they are not effective at the doses evaluated (lower than 290 lmol/kg).
a Due to the low solubility of the drug.

1564
L. Gavernet et al. / Bioorg. Med. Chem. 15 (2007) 1556–1567
Table 4. Statistical results using CoMFA for both models
than 0.21 and the number of residuals which are larger
than 0.1 in 8 (27% of the total).
Method
Model 1
Model 2
Charge
ONCa
q2b
Gasteiger–Huckel
3
Merz–Kollman
3
0.681
The contour plot of the CoMFA field for model 1 is
shown in Figure 9. It closely resembles the one obtained
with the original set (Fig. 3).
0.756
0.967
0.093
0.663
0.337
0.068
r2c
0.941
0.121
SEEd
Sfe
Eff
Resg
0.595
0.405
0.085
The electrostatic map in Figure 9b shows two main re-
gions where the positive potential is favorable for AC
activity (colored in blue) and two main regions where
the model favors the negative charge (in red). They show
a good correspondence with the areas obtained in the
original set (Fig. 4b). In terms of steric fields (Fig. 9a),
the similarity is even larger, as both models result in
the same green and yellow regions.
a ONC, optimum number of components.
b q2, cross-validated r2.
c r2, non-cross-validated r2.
d SEE, standard error estimate.
e SF, steric field.
f Ef, electrostatic field.
g Res, average of residuals.
4. Conclusions
Huckel charges (implemented in Sybyl-model 1) and on
a Merz–Kollman approximation (G98-model 2), respec-
tively. The statistical results of the CoMFA are summa-
rized in Table 4, showing no significant difference
between the two models.
A 3D-QSAR model has been developed for a series of
chemically diverse AC drugs, related by a common
pharmacological profile: the drugs are active against
the MES test. Moreover, most of them are inactive
against PTZ test (phenytoin-like compounds). The
model points to a steric effect as higher determinant
of the activity.
Both analyses render statistically reliable models of
good predictive power. Good cross-validated q2, togeth-
er with acceptable r2 values, confirm the dependence be-
tween the molecular descriptors that we have chosen
(electrostatic and steric fields) and the biological activi-
ties of the molecules.
The CoMFA, applied to a training set of 27 diverse
structures, validates the pharmacophore previously pro-
posed, which is associated with polar and lipophilic re-
gions. In addition, it led to the definition of a new
requirement: one or more substituents capable of filling
the green region a1 (Fig. 4). The substituents can be of
the size of the green portion or larger, but have to avoid
interactions with the yellow portion. The required orien-
tation can be defined by a torsion angle (s4, Fig. 5) close
to 0ꢁ.
The statistical parameters obtained from the extended
CoMFA study are comparable with those derived from
the original CoMFA. This result is to be expected, since
the sulfamides included in the new set have been de-
signed on the basis of the results of the first analysis.
The contribution of the steric field was higher than the
electrostatic field in both cases (approximately 60–70%
of the total). The activities measured and estimated from
the use of model 1 are reported in Table 2 (columns 4
and 7, respectively) together with the values of residuals
(column 8), as another demonstration of the good pre-
dictability of this model. The residual values are lower
In order to test the predictive power of the new pharma-
cophore, new active compounds have been designed,
synthesized, and evaluated. The design was largely
based on a bioisosteric substitution of the amide moiety
by a sulfamide functionality. We have chosen this sub-
stitution on the consideration that amide groups define
a
b
b-2
a-3
b-4
a-2
b-1
a-1
b-3
Figure 9. Electrostatic and steric fields of the CoMFA contour maps obtained with the extended set, using the CZ molecule as reference. The
pharmacophore is shown in green. (a) Green contours indicate the regions where the addition of bulky groups may increase activity. Yellow contours
indicate the regions where the addition of bulky groups may decrease activity. (b) Blue contours indicate regions where positive groups may increase
the activity. Red contours indicate regions where negative groups may increase activity.

L. Gavernet et al. / Bioorg. Med. Chem. 15 (2007) 1556–1567
1565
the polar portion in a large number of compounds of the
set. The new compounds perfectly fit in the CoMFA
model. The active sulfamides show the pharmacological
profile characteristic of phenytoin-like compounds.
They are inactive against the PIC and PTZ tests at the
doses evaluated (lower than 290 lmol/kg). Their anti-
MES activities are closer to those of classical and new-
generation AC drugs (see Table 1), with ED50 values
close to those determined for RMC and ETVPD; and
lower than those obtained for METS, VRL, VPD,
IPVPD, BUVPD, CPVPD, and DMVPD. According
to their anti-MES activity, the active sulfamides have
been classified as class 1 anticonvulsants17 (anticonvul-
sant activity at 100 mg/Kg or less), the most potent class
of antiepileptic drugs.
under reduced pressure to leave a white solid (1.68 g).
Crystallization with CH2Cl2 afforded (622 mg, 31%
yield) of sulfamide 2b as a white solid. Mp 154–155 ꢁC
(CH2Cl2) (described 151–153 ꢁC). Rf: 0.38 (SiO2,
1
CH2Cl2). IR (KBr): 3283 (NH), 1337, 1138 (SO2). H
NMR (CDCl3): 4.35 (d, J = 7.6 Hz, 2H: NH), 3.16 (m,
2H: CH), 2.01–1.12 (m, 20H: 2 (CH2)5). 13C NMR
(CDCl3): 52.63 (a-C), 34.01 (b-C), 25.27 (d-C), 24.78
(c-C). Anal. Calcd for C12H24N2O2S: C, 55.8; H, 8.6;
N, 10.8; S, 12.4. Found: C, 55.4; H, 8.9; N, 10.5; S, 12.4.
5.1.3. N,N0-Dibenzylsulfamide (2c). This compound was
prepared according to the general procedure, using a
solution of benzylsulamine (4.7 mL, 43.2 mmol, 6 equiv)
in CH2Cl2 (5 mL) and SO2Cl2 (0.58 ml, 7.2 mmol,
1 equiv) in CH2Cl2 (5 ml). After 22 h of reaction, the
resulting solution was filtered, and the organic layer
was washed with 3% HCl (2·), brine (2·), and dried
(SO4Na2). The solution was filtered, concentrated under
reduced pressure to leave a white solid (804 mg). Crys-
tallization with MeOH afforded (816 mg, 36% yield) of
sulfamide 2c as a white solid. Mp 181–183 ꢁC (MeOH);
(described 180–182 ꢁC.). Rf: 0.36 (SiO2, CH2Cl2). IR
(KBr): 3277 (NH), 3088, 3065, 3033 (CH-Ar), 1321,
1148 (SO2). 1H NMR (DMSO-d6): 7.47 (br s, 2H:
NH), 7.31 (m, 10H: Ar-H), 4.01 (s, 4H: CH2). 13C
NMR (DMSO-d6): 138.36 (C1-Ar), 128.25, 127.72,
127.06 (C2,3,4-Ar), 45.81 (CH2). Anal. Calcd for
C14H16N2O2S: C, 61.3; H, 5.1; N, 10.2; S, 11.7. Found:
C, 60.9; H, 5.5; N, 9.9; S, 11.9.
The active N,N0-dialkylsulfamides define new active pro-
totypes. Compounds bearing this functionality are the
focus of our present research. A rational modification
of these structurally simple structures can lead to new
AC drugs of optimized potency, attained by a balanced
combination of polar and non-polar groups in the active
structure.
5. Experimental
5.1. Chemistry
The sulfamides shown in Scheme 1 were synthesized
according to the literature,24,40–44 in the way previously
described in Section 2. The compounds were identified
and characterized by elemental analyses, 1H, 13C
NMR,and IR spectroscopy. The equipmentsused were
a Carlo Erba EA1108 apparatus, a Bruker AC-200 spec-
trometer with tetramethylsilane as an internal standard,
and a Bruker IFS 66 spectrometer.
5.1.4. N,N0-Dicyclopropilsulfamide (2d). This compound
was prepared according to the general procedure, using
a solution of cyclopropylamine (4.84 mL, 68.4 mmol,
6 equiv) in CH2Cl2 (5 mL) and SO2Cl2 (0.91 ml,
11.4 mmol, 1 equiv) in CH2Cl2 (5 mL). After 30 h of
reaction, CH2Cl2 (20 mL) was added, and the solution
was washed with 3% HCl (2·), brine (2·) and dried
(SO4Na2). The solution was filtered, concentrated under
reduced pressure to leave a white solid (1.7 g). Crystalli-
zation with hexane/CH2Cl2 afforded (965 mg, 48% yield)
of sulfamide 2d as a white solid. Mp 147–149 ꢁC (Hex-
ane/CH2Cl2). Rf: 0.36 (SiO2, CH2Cl2). IR (KBr): 3271
(NH),1316, 1142 (SO2). 1H NMR (CDCl3): 5.01 (s,
2H: NH), 1.98 (m, 2H: a-CH), 0.71 (m, 8H: b-CH2).
13C NMR (CDCl3): 24.15 (a-C), 6.02 (b-C). Anal. Calcd
for C6H12N2O2S: C, 41.4; H, 5.8; N,16.1; S,18.4. Found:
C, 41.1; H, 6.2; N, 15.8; S, 18.2.
5.1.1. N,N0-Dibutylsulfamide (2a). This compound was
prepared according to the general procedure, using n-
butylamine (5.70 mL, 57.6 mmol, 6 equiv) in CH2Cl2
(5 mL) and SO2Cl2 (0.77 ml, 9.6 mmol, 1 equiv) in
CH2Cl2 (5 mL). Purification of the resulting oily color-
less residue through column chromatography (SiO2,
dichloromethane) affords the sulfamide 2a (1.98 g, 75%
yield) as a white solid. Mp 127–127.5 ꢁC (CH2Cl2); (de-
scribed 126.5 ꢁC,); Rf: 0.36 (SiO2, CH2Cl2). IR (KBr):
1
3286 (NH), 1313, 1142 (SO2). H NMR (CDCl3): 4.55
(br t, J = 6.0 Hz, 2H: NH), 3.04 (m, 4H: a-CH2) 1.52
(m, 4H: b-CH2), 1.37 (m, 4H: c-CH2), 0.93 (t,
J = 7.1 Hz, 6H: CH3). 13C NMR (CDCl3): 43.10 (a-C),
31.80 (b-C), 20.11 (c-C), 13.89 (CH3). Anal. Calcd for
C8H20N2O2S: C, 46.6; H, 8.8; N, 13.6; S, 15.5. Found:
C, 46.4; H, 9.1; N, 13.2; S, 15.4.
5.1.5. N,N0-Sulfonil bis tetrahydro-1,4-oxazine (2e). This
compound was prepared according to the general proce-
dure, using a solution of morpholine (4.2 ml, 50.8 mmol,
6 equiv) in CH2Cl2 (5 mL) and SO2Cl2 (0.68 ml,
8.5 mmol, 1 equiv) in CH2Cl2 (5 mL). After 17 h of reac-
tion, the solvent was concentrated under reduced pres-
5.1.2. N,N0-Dicyclohexylsulfamide (2b). This compound
was prepared according to the general procedure, using
a solution of cyclohexylamine (5.25 mL, 46.2 mmol,
6 equiv) in CH2Cl2 (5 mL) and SO2Cl2 (0.62 ml,
7.7 mmol, 1 equiv) in CH2Cl2 (5 mL). After 20 h of reac-
tion, the resulting solution was filtered, and the organic
layer was washed with 5% HCl (2·), brine (2·), and
dried (SO4Na2). The solution was filtered, concentrated
sure to leave
a
crude yellow oil. Column
chromatography (SiO2, CH2Cl2) afforded 1.66 g of a sol-
id. Crystallization with Cl4C afforded (1.47 g, 73% yield)
of sulfamide 5a as a white solid. Mp 141–142.5 ꢁC
(Cl4C). Rf: 0.72 (SiO2, AcOEt). IR (KBr): 1334, 1131
(SO2). 1H NMR (CDCl3): 3.73 (t, J = 4.9 Hz, 8H:
CH2–O), 3.25 (t, J = 4.9 Hz, 8H: CH2N). 13C NMR
(CDCl3): 66.32 (C–O), 46.55 (C–N). Anal. Calcd for

1566
L. Gavernet et al. / Bioorg. Med. Chem. 15 (2007) 1556–1567
10. Rogawski, M. A.; Porter, R. J. Pharmacol. Rev. 1990, 42,
223.
C8H16N2O4S: C, 40.7; H, 6.8; N, 11.9; S, 13.6. Found:
C, 40.9; H, 6.6; N, 12.3; S, 13.3.
11. Bialer, M.; Johannessen, S. I.; Kupferberg, H. J.; Levy, R.
H.; Perucca, E.; Tomson, T. Epilepsy Res. 2004, 61, 1.
12. Bialer, M.; Johannessen, S. I.; Kupferberg, H. J.; Levy, R.
H.; Loiseau, R. H.; Perucca, E. Epilepsy Res. 2002, 51, 31.
13. Bialer, M.; Johannessen, S. I.; Kupferberg, H. J.; Levy, R.
H.; Loiseau, R. H.; Perucca, E. Epilepsy Res. 2001, 43, 11.
14. Loscher, W.; Schmidt, D. Epilepsy Res. 1994, 17, 95.
15. Anger, T.; Madge, D.; Mulla, M.; Riddall, D. J. Med.
Chem. 2001, 44, 115.
5.2. Biological data
The evaluation of the anticonvulsant activity followed
the Anticonvulsant Drug Development (ADD) Program
of the National Institute of Health.50 Adult male and fe-
male albino mice (18–25 g) were used as experimental
animals. Animals of the same age and weight have been
selected, in order to minimize biological variability. The
animals were maintained on a 12-h light/dark cycle and
allowed free access to food and water, except during the
time they were removed from their cages for testing. The
test substance was administered in 30% polyethylene
glycol 400 (PEG) and 10% water. The drugs were
administered intraperitoneally (ip) in mice in a volume
of 0.01 ml/g body weight.
16. Unverferth, K.; Engel, J.; Ho¨fgen, N.; Rostock, A.;
Gunther, R.; Lankau, H.-J.; Menzer, M.; Rolfs, A.;
¨
Liebscher, J.; Muler, B.; Hofmann, H.-J. J. Med. Chem.
¨
1998, 41, 63.
17. Malawska, B.; Kulig, K.; Spiewak, A.; Stables, J. P.
Bioorg. Med. Chem. 2004, 12, 625.
18. Tasso, S. M.; Moon, S. Y.; Bruno-Blanch, L. E.; Estiu´, G.
L. Bioorg. Med. Chem. 2004, 12, 3857.
19. Estrada, E.; Pen˜a, A. Bioorg. Med. Chem. 2000, 8, 2755.
20. Ba¨ckbro, K.; Lowgren, S.; Osterlund, K.; Atepo, J.; Unge,
T.; Hulten, J.; Bonham, N. M.; Schaal, W.; Hallberg, A. J.
Med. Chem. 1997, 40, 898.
5.3. Determination of median effective dose (ED50)
21. Hulten, J.; Bonham, N. M.; Nillroth, U.; Hansson, T.;
Zuccarello, G.; Bouzide, A.; Aqvist, J.; Classon, B.;
Danielson, U. H.; Karlen, A.; Kvarnstrom, I.; Samuels-
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All quantitative studies were conducted at the previously
determined time of peak effect (TPE). The ED50 was
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of Litchfield and Wilcoxon56 was used to compute the
ED50 and 95% confidence intervals.
The AC activity of valproic acid, valproate, valpromide,
and their derivatives was determined in the way previ-
ously reported.18,37 All the other biological data were
taken from the literature2,37,38,47–52 for assays where
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Acknowledgments
L. E. Bruno-Blanch is a member of the Facultad de
Ciencias Exactas, Universidad Nacional de La Plata;
L. Gavernet is a fellowship holder of Consejo Nacional
27. Casini, A.; Winum, J.-Y.; Montero, J.-L.; Scozzafava, A.;
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de Investigaciones Cientıficas y Tecnicas de la Republica
Argentina (CONICET). J. Domınguez is a fellowship
´
´
´
´
holder of Agencia de Promocion Cientıfica y Tecnolog-
ica. This research was supported in part through grants
´
´
´
´
´
from Agencia de Promocion Cientıfica y Tecnologica
(PICT 06-11985/2004), CONICET, and Universidad
Nacional de La Plata, Argentina.
32. Shank, R. P.; Gardocki, J. F.; Streeter, A. J.; Maryanoff,
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