Pyrazole CCK1 receptor antagonists. Part 2: SAR studies by solid-phase library synthesis and determination of Free-Wilson additivity

Article abstract of DOI:10.1016/j.bmcl.2005.09.041

High-throughput screening revealed compound 1 as a potent antagonist of the CCK₁ receptor. Here, we disclose the synthesis of combinatorial libraries by solid-phase synthesis on Kenner 'safety catch' resin. Additive QSAR models were used to determine a lack of consistent additive SAR within the matrix.

Full text of DOI:10.1016/j.bmcl.2005.09.041

Bioorganic & Medicinal Chemistry Letters 16 (2006) 77–80
Pyrazole CCK1 receptor antagonists. Part 2: SAR studies by
solid-phase library synthesis and determination
of Free–Wilson additivity
Clark Sehon, Kelly McClure, Michael Hack, Magda Morton, Laurent Gomez, Lina Li,
Terrance D. Barrett, Nigel Shankley and J. Guy Breitenbucher*
Johnson & Johnson Pharmaceutical Research and Development L.L.C., 3210 Merryfield Row, San Diego, CA 92121, USA
Received 1 August 2005; revised 9 September 2005; accepted 13 September 2005
Available online 19 October 2005
Abstract—High-throughput screening revealed compound 1 as a potent antagonist of the CCK1 receptor. Here, we disclose the
synthesis of combinatorial libraries by solid-phase synthesis on Kenner ꢀsafety catchꢁ resin. Additive QSAR models were used to
determine a lack of consistent additive SAR within the matrix.
Ó 2005 Elsevier Ltd. All rights reserved.
Cholecystokinin (CCK) is a 33 amino acid peptide hor-
mone that is released in response to food intake and reg-
ulates gall bladder contraction, pancreatic enzyme
secretion, gastric acid secretion, gastric emptying, and
duodenal and colonic motility.1 The biological actions
of CCK are mediated through two G-protein coupled
receptors, CCK1 and CCK2. CCKꢁs actions on gall-
bladder contraction, pancreatic enzyme secretion, and
duodenal motility, and gastric emptying rate appear to
be mediated through agonism of the CCK1 receptor.
As a result, a number of CCK1 antagonists have been
evaluated in the clinic for pancreatic disorders, IBS,
and bilary colic. Promising clinical results from a Phase
II trial of constipation dependent IBS with the peptide
derived CCK1 antagonist dexloxiglumide encouraged
our pursuit of a differentiated non-peptide derived
antagonist of CCK1.2
MeO
A
B
N
N
C
O
Me
OH
1
Figure 1. HTS lead compound.
C-ring, was described. To modify the A- and B-rings
simultaneously while maintaining a constant C-ring,
an alternative route in which the variable elements of
the matrix are installed late in the synthesis was desir-
able. In this paper, we describe a solid-phase library
strategy, which allows for this late stage simultaneous
modification of the A- and B-rings within this series.
In the preceding paper, a novel series of pyrazole-based
CCK1 receptor antagonists was described.3 As reported,
compound 1 was identified through high-throughput
screening as a potent antagonist of the CCK1 receptor
(see Fig. 1). In that work, a solution-phase library syn-
thesis, which allowed for access to derivatives of 1 where
the A- or B-ring was varied simultaneously with the
A solid-phase synthesis of diaryl-pyrazoles, similar to
compound 1, was previously described by Chapman
and co-workers.4 The 3-methylphenyl group at position
C in place of the 1-naphthyl ring was chosen because of
its high CCK1 affinity (see, accompanying paper).3
Synthesis of the solid-phase libraries began with the
large-scale preparation of the keto-acid 4 (Scheme 1).
Allylation of the phenylacetic acid ester 2, followed by
Wacker oxidation and hydrolysis, provided 4 in good
yield on a 5–10 gram scale. Initially, 4 was coupled to
Kenner ꢀsafety-catchꢁ resin. Treatment with excess base
and various aryl esters provided diketones of type 6 on
Keywords: CCK1; Cholecystokinin; Combinatorial; Additivity;
QSAR.
*
Corresponding author. Tel.: +1 858 784 3036; e-mail: jbreiten@
prdus.jnj.com
0960-894X/$ - see front matter Ó 2005 Elsevier Ltd. All rights reserved.
doi:10.1016/j.bmcl.2005.09.041

78
C. Sehon et al. / Bioorg. Med. Chem. Lett. 16 (2006) 77–80
O
O
O
Me
Me
O
a
b, c
O
OH
Me
O
90%
65%
Me
Me
2
3
4
Scheme 1. Reagents and conditions: (a) NaH, allyl bromide, DMF, 0 °C, 1 h; (b) PdCl2 (cat), CuCl, O2, DMF/H2O, 12 h; (c) LiOH, THF/MeOH/
H2O, 24 h.
solid support (Scheme 2). Addition of hydrazines and
cleavage from resin provided the desired 1,5-pyrazoles
along with the undesired 1,3-pyrazoles.
prior to attachment to solid support (Scheme 3). Cou-
pling of the resulting acid 11 onto aminomethyl poly-
styrene afforded high loading levels of 12 as judged
by sulfur elemental analysis. Procedures similar to
those described above resulted in the desired 1,5-pyraz-
oles 13 in 20–35% overall yield (see Scheme 4). There
were no differences in pyrazole regioselectivity using
the two procedures. This recovery was sufficient to ob-
tain material for biological testing (5–10 mg product
from 200 mg resin).
The regioselectivity of ring formation was variable and
dependent upon the nature of the hydrazine and dike-
tone coupling partners, but in all cases favored the
1,5-regioisomer. Fortunately the regioisomers were
readily separated by chromatography. Problems with
this initial procedure included incomplete loading of
the keto-acid 4, and some cleavage of the diketone from
the Kenner linker upon treatment with hydrazines. For
these reasons, and the regiochemical issues, low yields of
the desired 1,5-pyrazoles were obtained by this route.
The first library was composed of the elements shown in
Figure 2. The binding data (pKI) are shown in graphical
form in Figure 3. Analysis of the best fit additive data
was performed as described in Eq. 1 of the accompany-
ing paper.3 These data were then plotted against the
measured pKIꢁs and graphed (Fig. 4).
Alternatively the coupling of keto-acid 4 to sulfon-
amide 10 could be effectively accomplished in solution
O
O O
S
O
O O
S
Ar2
b
O
O
a
N
N
S
H
H
O
NH2
O
O
Me
Me
Kenner
sulfonamide resin
6
5
O
O O
S
d, e
5-10% overall
N
Ar1
N
N
c
H
Ar2
Me
7
Ar1
Ar1
Ar2
N
N
N
N
Ar2
CO2H
CO2H
+
9
8
Me
Me
Scheme 2. Reagents and conditions: (a) 4, DIC, DMAP (cat), DIPEA, THF/DCM, 12 h; (b) Ar2CO2Me, NaHMDS, DMA, 85 °C, 3 h;
(c) Ar1NHNH2 ÆHCl, DIPEA, 50 °C, 12 h; (d) TMSCHN2, THF, 1 h; (e) Bu4NOH, dioxane, 60 °C, 12 h.
Me
O
O
Me
a, b
O
HO
O
H
N
NH2
86%
S
S
O
O
O
O
O
10
11
Scheme 3. Reagents and conditions: (a) 4, PyBrop, DIPEA, DMAP (cat), DCM, 12 h; (b) LiOH, THF/MeOH/H2O, 50 °C, 3 h.

C. Sehon et al. / Bioorg. Med. Chem. Lett. 16 (2006) 77–80
79
Me
O
b, c, d, e
20-35%
a
NH2
N
H
O
H
N
S
Aminomethyl-
polystyrene
O
O
O
12
Ar1
Ar1
N
N
N
N
Ar2
Ar2
+
CO2H
CO2H
Me
Me
14
13
Scheme 4. Reagents and conditions: (a) 11, DIC, HOBt, THF, 12 h; (b) Ar2CO2Me, NaHMDS, DMA, 85 °C, 3 h; (c) Ar1NHNH2 ÆHCl, DIPEA,
50 °C, 12 h; (d) TMSCHN2, THF, 1 h; (e) Bu4NOH, dioxane, 60 °C, 12 h.
Ar1
Ar2
8.5
8.0
7.5
7.0
6.5
6.0
N
N
O
OH
Me
Cl
Cl
N
6.0 6.5 7.0 7.5 8.0 8.5
pKi
Cl
S
O
O
Me
Figure 4. Correlation between actual pKI and predicted pKI for the
best fit to additive model.
Ar1 =
D
E
A
B
C
F
The correlation of best fit additive data to actual data
was poor, r2 value being 0.71 and the RMS error being
0.34. By comparison, the experimental variability had an
average standard error of 0.17 log units in triplicate
measurements. Thus, there is clearly non-additive SAR
present in this library.
Cl
Cl
O
Cl
O
O
Ph
Ar2 =
D
A
B
C
E
Figure 2. Input variables for library 1.
In the previous paper, one of the libraries we exam-
ined also showed non-additive effects. In that case,
the non-additive behavior was limited to a single ser-
ies, and removal of that series from the calculation
afforded an additive result. In this case, the non-addi-
tive behavior is not systematic and is found distribut-
ed throughout the matrix. This can be seen more
clearly when the actual activity is subtracted from
the predicted activity showing the deviation from the
additive model for each individual member of the
matrix (Fig. 5).
8.5
8
7.5
7
pKI
Because the deviations are not systematic, there is little
specific justification that can be offered for the non-addi-
tive behavior observed. However, it is likely that this
outcome is a result of the close proximity of the two
variables in the matrix. This makes it more likely that
electronic and steric differences in one substituent will
have an influence on the ground-state or conformational
dynamics of the other substituent. Additional explana-
tions may lie in alternative binding modes for the differ-
ent compounds to the receptor.5
6.5
6
A
B
C
Ar2
F
D
E
D
C
E
B
A
Ar1
Figure 3. CCK1 binding data for library 1.

80
C. Sehon et al. / Bioorg. Med. Chem. Lett. 16 (2006) 77–80
puts for QSAR regression analysis. These descriptors of-
fer the advantage of requiring less computational time
to generate than whole molecule descriptors. However,
Cammareta6 has eloquently outlined the dangers of
using such descriptors when a system is not additive.
Thus, any linear regression analysis that uses exclusively
fragment-based descriptors cannot by definition be any
more than anecdotally relevant if the system in question
is found to be non-additive. In fact, for libraries such as
those we consider here, it can be shown that the additive
model described in the previous paper represents an
upper limit to the accuracy obtainable through frag-
ment-based QSAR. To adequately describe non-additive
effects in this case, for instance, descriptors that simulta-
neously treat both rings A and B are required.
0.6
0.4
0.2
0
∆ pKI
-0.2
-0.4
-0.6
-0.8
A
B
C
Ar2
D
F
E
E
D
Ar1
C
B
A
Another advantage of determining the inherent additiv-
ity in a system is a more practical one. It is often neces-
sary for the medicinal chemist to optimize multiple
properties in a molecular series simultaneously (i.e.,
activity and bioavailability). In these instances, knowl-
edge about a systemꢁs additivity would serve to provide
confidence that when one portion of a series is altered to
optimize a second property, the SAR trends for the pri-
mary target will not be altered. Conversely if a system
has a low degree of additivity then it might be advanta-
geous to adopt a strategy of combinatorial analoging to
avoid missing key parts of the SAR.
Figure 5. Difference between experimental pKI and that predicted by
the best least-squares fit to the additive model for each compound in
the matrix.
Ar1
N
N
O
Ar2
8.3
OH
7.8
Me
7.3
pKI
6.8
6.3
5.8
Acknowledgments
We thank Dale Boger for helpful discussions on the syn-
thesis of compound 4, and Jiejun Wu and Heather
McAllister for analytical support.
Me
O
MeSO
2
N
O
Ar1
Ar2
Me N
2
Figure 6. CCK1 binding affinity for library 2.
Supplementary data
An additional library was made which more clearly illus-
trates the extent of the non-additive relationships in this
series of CCK1 receptor antagonists (Fig. 6). In this
case, when Ar2 is naphthyl or methylenedioxyphenyl
the SAR resulting from changes in Ar1 is roughly flat.
However, when Ar2 is dimethylaminophenyl the SAR
is quite pronounced, resulting in compounds having
>300-fold differences in binding affinity. These results
demonstrate the potential magnitude of non-additive
SAR. It is clear from these results that non-additive ef-
fects can have a significant impact on medicinal chemis-
try programs that are managed with the expectation of
additive behavior, and illustrates the usefulness of gener-
ating a combinatorial matrix of compounds to verify
additive behavior before embarking on linear analoging.
Supplementary data associated with this article can be
found in the online version at doi:10.1016/
j.bmcl.2005.09.041.
References and notes
1. For recent comprehensive reviews of CCK pharmacology
and medicinal chemistry see (a) Herranz, R. Med. Res. Rev.
2003, 23, 559; (b) Tullio, P.; Delarge, J.; Pirotte, B. Exp.
Opin. Investig. Drugs 2000, 9, 129.
2. Varga, G. Curr. Opin. Invest. Drugs 2002, 3, 621.
3. McClure, K.; Huang, L.; Sehon, C.; Hack, M.; Morton,
M.; Barrett, T.; Shankley, N.; Breitenbucher, J. G. Bioorg.
Med. Chem. Lett. 2005, accompanying paper.
4. Shen, D.-M.; Shu, M.; Chapman, K. T. Org. Lett. 2000, 2,
2789.
This case also demonstrates the importance of exercising
caution when using certain QSAR models to predict bio-
logical activity. For instance, fragment-based descrip-
tors such as TPSA, C log P, MW, H-bond donors, and
acceptors, etc., have become increasingly common as in-
5. Plucinska, K.; Kataoka, T.; Yodo, M.; Cody, W. L.; He, J.
X.; Humblot, C.; Lu, G. H.; Lunney, E.; Major, T. C.;
Panek, R. T.; Schelkum, P.; Skeean, R.; Marshall, G. R.
J. Med. Chem. 1993, 36, 1902.
6. Cammareta, A. J. Med. Chem. 1972, 15, 573.