3D pharmacophore model-assisted discovery of novel CDC7 inhibitors

Article abstract of DOI:10.1021/ml200029w

A ligand-based 3D pharmacophore model for serine/threonine kinase CDC7 inhibition was created and successfully applied in the discovery of novel 2-(heteroaryl)-6,7-dihydrothieno[3,2-c]pyridin-4(5H)-ones. The pharmacophore model provided a hypothesis for lead generation missed by docking to a homology model. Medicinal chemistry exploration of the series revealed clear structure-activity relationships consistent with the pharmacophore model and pointed to further optimization opportunities.

Full text of DOI:10.1021/ml200029w

LETTER
pubs.acs.org/acsmedchemlett
3D Pharmacophore Model-Assisted Discovery of Novel CDC7
Inhibitors
Mika Lindvall,* Christopher McBride, Maureen McKenna, Thomas G. Gesner, Asha Yabannavar,
Kent Wong, Song Lin, Annette Walter, and Cynthia M. Shafer*
Global Discovery Chemistry/Oncology & Exploratory Chemistry, Novartis Institutes for Biomedical Research, 4560 Horton Street,
Emeryville, California 94608, United States
S Supporting Information
b
ABSTRACT: A ligand-based 3D pharmacophore model for serine/threonine
kinase CDC7 inhibition was created and successfully applied in the discovery of
novel 2-(heteroaryl)-6,7-dihydrothieno[3,2-c]pyridin-4(5H)-ones. The phar-
macophore model provided a hypothesis for lead generation missed by docking
to a homology model. Medicinal chemistry exploration of the series revealed
clear structureÀactivity relationships consistent with the pharmacophore
model and pointed to further optimization opportunities.
KEYWORDS: CDC7, kinase, pharmacophore model, homology model,
lead generation, structureÀactivity relationship
he serine/threonine kinase CDC7 plays an essential role in
the initiation of DNA replication in eukaryotic cells.1 After
CDC7 potency (2 and 3) versus CDK2 potency (Figure 2).22
Consequently, the final five-feature pharmacophore model con-
sisted of four common features (C) and one SAR-based feature (S).
Of equal importance, the superimposition of 1 and 2 suggested that
certain hydrogen-bonding features were not critical for potency as
they were absent (A) in one of the two molecules. The pyrrole
hydrogen bond donor in 1 was determined to be the least critical
due to overlap with a methyl in 2. Thus, the availability of rigid
molecules with comparable shapes, informative distribution of
functional groups, and access to SAR facilitated the creation of a
pharmacophore model for CDC7 activity.
A homology model for CDC7 was also built, using CK2
as the template.23,24 Although 1À3 could be docked to the model
and the likely hinge binding groups in 1À3 could be readily
identified,21 the remaining portions of the molecules were poorly
aligned, and the ligand overlay in Figure 2 was not identified from
poses with optimal interactions to the protein. In addition, no
obvious hydrophobic pocket was found around the allyl group in
2 in any of the poses. The hypothesis formed from the pharma-
cophore model in Figure 2 suggests where to place a hydrophobic
group (F5), and such chemistry guidance was not available from
docking to the homology model. Consequently, ligand-based
models such as a pharmacophore model can sometimes provide
clearer hypotheses than those obtained from homology models.
In particular, key hypotheses may appear less likely or may be
missed altogether due to challenges in docking to models with
T
assembly of the prereplication complex to the replication origin,
the CDC7 kinase phosphorylates MCM (minichromosome
maintenance) proteins and allows for recruitment of CDC45
and DNA polymerase, thereby initiating DNA replication.2
CDC7 requires association with one of its cofactors, ASK (also
known as DBF4) or ASKL1 (also known as Drf1), for kinase
activation.3À6
Recently, CDC7 has emerged as an attractive target for cancer
therapy.7À9 Depletion of CDC7 using siRNA oligonucleotides
results in induction of apoptosis in cancer cell lines, while normal
dermal fibroblast cells are spared.10 Furthermore, CDC7-mediated
phosphorylation sites on MCM2, MCM4, and MCM6 in tumor
cells have been identified, but the functional relevance of those
sites remains to be determined.11À14
Our group was pursuing additional lead generation techniques
for the development of a CDC7 inhibitor.15 In the absence of a
CDC7 crystal structure, a 3D pharmacophore hypothesis was
developed to guide the chemistry efforts. The model was created
from two CDC7 compounds reported in the literature, 116,17
and 218 (Figure 1), via flexible superimposition of their 3D
models,19,20 and identification of the three-dimensional (3D)
features that they shared. Several different overlays of 1 and 2 were
considered,21 with the preferred pharmacophore model (Figure 2)
consisting of four shared features, two acceptor projected points
F1 and F4, and two aromatic rings F2 and F3. While such features
are relatively generic for kinases, an additional hydrophobic feature
F5 was appended to the model based on structureÀactivity
relationship (SAR) where the allyl group in 2 selectively increases
Received: February 2, 2011
Accepted: June 15, 2011
Published: August 02, 2011
r
2011 American Chemical Society
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evaluated by 3D pharmacophore searching using our CDC7
pharmacophore hypothesis (Figure 2). One of the hits was 4,
derived from commercially available advanced intermediate 22
(Table 1).
Compound 4 has an excellent match to the pharmacophore
model (Figure 3). It matches all five features, including the
hydrophobe F5 specifically added for CDC7, and superimposi-
tion with 2 shows a very good overlap between the allyl group in
2 and the spiro-cyclohexyl group in 4. Superimposition of 1 and 4
where thiophene sulfur and pyrrole NH overlap shows that if a
donor feature from pyrrole NH was included in the pharmaco-
phore model, 4 would not have been identified.
Compound 4 proved to be quite potent against CDC7/DBF4
in vitro (Table 1). Thus, using 22, analogues of 4 could be
prepared, allowing rapid expansion of the SAR and evaluation of
the limits of the pharmacophore model.27
Figure 1. CDC7 inhibitors for pharmacophore model generation.
In a retrospective analysis of the CDC7 assay data, the SAR is
in good agreement with the assumption that the heterocycle
group maps to the hinge region. As expected of pharmacophore
models, the agreement with the model and the data is not
quantitative. Compound pair 4À5 shows the importance of
the hydrogen-bonding direction, and the drop in potency is
expected. Similarly, the lack of an acceptor in 8 causes an
expected drop in potency. Examples 6, 7, 10, and 11 are typical
of kinase hinge SAR, where a key acceptor with the right
hydrogen-bonding directionality is retained and an additional
donor for the hinge is tolerated, with or without potency increase.
The lack of potency in 9 and 12, on the other hand, is likely to be
caused by steric clashes with the hinge of CDC7. The steric clash
hypothesis in 9 is consistent with the pharmacophore model, as
the methyl amide donor of 9 overlaps with the acceptor projected
point F1 in the pharmacophore model where a protein donor
atom from the hinge of CDC7 is expected. In compound 11, the
pyrazole NH is assumed to make a hydrogen-bonding contact
with a carbonyl of the hinge, so methylation of the NH yielding
12 is expected to result in a steric clash.28 Such steric clashes can
be incorporated as iterative, SAR-based refinements into the
pharmacophore model via addition of excluded volumes.
Because the pharmacophore model indicated that hydropho-
bic groups would be favored at C6 and C7, only analogues with a
spiro-cyclohexyl appended to the pyridone ring had been
synthesized up to this point. Removal of the cyclohexyl group
was desired to test one of the key hypotheses formed from the
pharmacophore model. Compound 14, which lacks the hydro-
phobic spiro-cyclohexyl group, has at least 30-fold lower potency
against CDC7 than 4, suggesting that the spiro-cyclohexyl
moiety has the same potency-enhancing properties as the allyl
group in 2.
Upon determining that the spiro-cyclohexyl moiety was impor-
tant for biochemical activity, the role of the pyridone nitrogen on in
vitro potency was then evaluated. Compounds (15À17) were
synthesized where the lactam nitrogen was replaced with a CH2. In
the cases of the pyridyl and aminopyridyl hinge binders (15 and
16), a loss of 4À8-fold in potency was observed as compared to
4 and 6, while compound 17 remained equipotent to 11.
In retrospect, it is educational to review how the advanced
intermediate 22 and the resulting initial hit 4 could have been
identified for CDC7 with other computational approaches.29
While a rigorous retrospective study would require appropriate
molecule databases including decoy molecules to study enrich-
ment, recovery, and practical caveats, 2D and 3D similarity
comparisons can hint at how well 22 and our novel series might
Figure 2. 3D pharmacophore model for CDC7 inhibition.
Figure 3. Compound 4 mapped onto the pharmacophore model.
low homology between the template and the protein of interest.
For CDC7, the SAR-driven features and direct chemistry guid-
ance made the pharmacophore model preferable for lead
generation efforts.
The next step was to test the pharmacophore model and apply
it in the design of novel CDC7 inhibitors. As sufficiently large 3D
databases of internal and vendor compounds were not available
at the time when this work was carried out, the typical step of
pharmacophore validation by 3D database searching and hit rate
analysis was not performed. Instead, 2D databases were searched
with a SMARTS query, and the results were refined in 3D
applying the pharmacophore model. Initially, molecules were
sought that would closely match all of the core features and
would explore different hydrophobes.25 As no suitable hits were
found, a new 2D query was used that could map onto features
F3ÀF5 but had a chemical handle, allowing introduction of
moieties that would map onto features F1ÀF2.26 The query hits
were converted to a small virtual library by transforming the
chemical handle to a 4-pyridyl moiety. The virtual library was
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Table 1. SAR of the Thienopyridinone Series
have scored at the computational idea evaluation stage. Com-
pounds 1 and 14 differ only by a single NH to S substitution, with
14 being 15 times less potent than 1. The 2D Tanimoto similarity
between 1 and 14, which have the same number of heavy atoms,
was surprisingly low, ranging from 0.39 to 0.51 using Daylight
fingerprints or ECFPs or FCFPs from SciTegic. Consequently,
traditional 2D approaches would likely have failed to highlight 22 as
is or even after virtual conversion into products such as 4 and 14,
when compared to 1 or other literature examples related to 1.30
In sharp contrast, field-based 3D similarity using the commercial
Cresset package31 between 1 and 14 is very high, 0.91. Moderate to
high 3D similarities of 0.6À0.8 are observed between the starting
points 1À3 and 4. Even the intermediate 22 itself is 0.65À0.69
similar to 1À3 and up to 0.82 for other reported examples related
to 1.30 Similarities to 22 are unexpectedly high, as the lack of an
aromatic ring corresponding to F2 was assumed to significantly
reduce similarity in a whole-molecule similarity calculation.32
Consequently, even whole-molecule 3D similarity searching would
probably have highlighted desirable intermediates such as 22.
Finally, 3D pharmacophore searching can be tuned to specifically
look for relevant advanced intermediates. For example, 22 is a hit
with an rmsd of 0.3 with a query derived from the pharmacophore
in Figure 2 where F1 is deleted and F2 is changed to a volume
feature for Suzuki chemistry compatible atoms.33,34 In summary,
3D approaches can successfully circumvent the failure of traditional
2D fingerprint similarity in the discovery of the intermediate 22 and
the corresponding initial hit 4.
chemistry guidance for lead generation than docking to a low-
resolution homology model. The pharmacophore model was
successfully applied in the discovery of a novel thienopyridone
series made in a single step from commercially available material.
Additional analogues were designed and synthesized to create a
series of inhibitors with clear SAR where simple modifications
yielded at least 2 orders of magnitude changes in potency.
Furthermore, the SAR was consistent with the pharmacophore
model and identified ways for refining the model.
’ ASSOCIATED CONTENT
S
Supporting Information. Experimental details for the
b
synthesis and characterization of select compounds, procedures
for the biochemical and cellular assay, and additional information
on the computational methods utilized. This material is available
free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION
Corresponding Authors
*Tel: 510-923-8086. E-mail: cynthia.shafer@novartis.com (C.M.S.).
Tel: 510-923-3312. E-mail: mika.lindvall@novartis.com (M.L.).
’ ACKNOWLEDGMENT
We thank Dazhi Tang for his mass spectroscopy support,
and Dachuan Lei providing the A549 cells required for stability
studies.
In conclusion, a ligand-based 3D pharmacophore model was
developed from known CDC7 inhibitors and provided clearer
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