3D-QSAR-assisted design, synthesis, and evaluation of novobiocin analogues

Article abstract of DOI:10.1021/ml300275g

Hsp90 is an attractive therapeutic target for the treatment of cancer. Extensive structural modifications to novobiocin, the first Hsp90 C-terminal inhibitor discovered, have produced a library of novobiocin analogues and revealed some structure-activity relationships. On the basis of the most potent novobiocin analogues generated from prior studies, a three-dimensional quantitative structure-activity (3D QSAR) model was built. In addition, a new set of novobiocin analogues containing various structural features supported by the 3D QSAR model were synthesized and evaluated against two breast cancer cell lines. Several new inhibitors produced antiproliferative activity at midnanomolar concentrations, which results through Hsp90 inhibition.

Full text of DOI:10.1021/ml300275g

Letter
pubs.acs.org/acsmedchemlett
3D-QSAR-Assisted Design, Synthesis, and Evaluation of Novobiocin
Analogues
Huiping Zhao,† Elisabetta Moroni,‡ Bin Yan,† Giorgio Colombo,‡ and Brian S. J. Blagg*,†
†Department of Medicinal Chemistry, The University of Kansas, 1251 Wescoe Hall Drive, Malott 4070, Lawrence, Kansas
66045-7563, United States
‡Istituto di Chimica del Riconoscimento Molecolare, CNR, Via Mario Bianco 9, 20131 Milano, Italy
S
* Supporting Information
ABSTRACT: Hsp90 is an attractive therapeutic target for the treatment of cancer. Extensive
structural modifications to novobiocin, the first Hsp90 C-terminal inhibitor discovered, have
produced a library of novobiocin analogues and revealed some structure−activity
relationships. On the basis of the most potent novobiocin analogues generated from prior
studies, a three-dimensional quantitative structure−activity (3D QSAR) model was built. In
addition, a new set of novobiocin analogues containing various structural features supported
by the 3D QSAR model were synthesized and evaluated against two breast cancer cell lines.
Several new inhibitors produced antiproliferative activity at midnanomolar concentrations,
which results through Hsp90 inhibition.
KEYWORDS: heat shock protein 90, Hsp90 inhibitors, novobiocin, 3D QSAR, breast cancer
he 90 kDa heat shock proteins (Hsp90) are responsible
Tfor the conformational maturation of numerous signaling
proteins,1,2 many of which are directly associated with the six
hallmarks of cancer.3 As a consequence of Hsp90 inhibition, a
simultaneous attack occurs upon multiple signaling pathways
that are essential for the proliferation of transformed cells. The
clinical investigation of 17-AAG and 15 other Hsp90 N-
terminal inhibitors has demonstrated Hsp90 as a promising
cancer therapeutic target.4 Hsp90 C-terminal inhibitors may
provide additional advantage for the pursuit of Hsp90
inhibitors, since no prosurvival heat shock response is observed
at concentrations required to induce client protein degrada-
tion.5
Novobiocin, a natural product isolated from Streptomyces
Figure 1. Structure−activity relationships generated from previous
investigations.
strains, was the first inhibitor of the Hsp90 C-terminal binding
pocket identified, albeit with low efficacy.6 Structural
modifications to the benzamide side chain and coumarin core
have elucidated preliminary structure−activity relationships and
identified analogues that exhibit increased inhibitory activities
(Figure 1).7−10 Investigation of the noviose appendage
demonstrated that the stereochemically complex and rate-
limiting synthon can be replaced with simplified mimics such as
alkyl amines, while simultaneously maintaining or increasing
solubility.11−13
In an effort to further understand the determinants of
novobiocin binding to Hsp90 and to generate a set of rational,
quantitative rules for the development of new derivatives that
go beyond empirical observations, we set out to build a three-
dimensional quantitative structure−activity model (3D QSAR)
of the most potent novobiocin analogues identified in prior
studies (Figure 2). Although this training set is of small size and
may represent a limit within the model, the applicability of the
model to the chemical space of known compounds is to gain
insights into the relative importance of distinct functional
groups. Moreover, the activities of the compounds used in
these studies represent a wide range of values that have been
determined via the same experimental protocol.
Received: September 7, 2012
Accepted: November 23, 2012
© XXXX American Chemical Society
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LOO scheme was q2-LOO = 0.93, while using the 5RG scheme
was q2-RG = 0.91 (Table 1).
Table 1. LOO and 5RG Cross-Validation of the PLS 3D
QSAR Regression Model
IC50
compd
exp (μM)
pred (LOO, μM)
pred (RG, μM)
4
1
13.9
10.9
7.5
10.1
11.2
7.8
3.9
1.5
2.3
1.1
0.4
0.2
1.6
1.6
9.4
11.1
5.9
4.0
1.3
2.2
0.8
0.5
0.1
1.9
1.6
2
3
2.9
6
1.34
0.76
0.31
0.31
0.27
0.21
0.17
5
7
10
9
11
8
Figure 2. Strucures of novobiocin analogues used to build the 3D
QSAR model and their experimental IC50 against SKBr3 cells.
The model that summarizes contributions of the original
variables can be interpreted with the aid of PLS coefficients.
The positive variables represent features found in the most
active compounds, while negative ones represent features
correlated with diminished activity. The PLS coefficients are
plotted in Figure 3, and the most influential variables are
indicated alongside the variable number.
3D QSARs were generated based upon cellular antiprolifer-
ative activities against SKBr3 cells and the 3D properties of
novobiocin analogues enlisting GRIND descriptors.14 These
relationships were calculated, analyzed, and interpreted using
the program PENTACLE (version 1.0.6).14−16 Because no
cocrystal structure with Hsp90-C-terminal inhibitors exists, the
bioactive conformation of these molecules remains unknown.
The Monte Carlo-based conformational search was used to
identify the most probable conformation. GRIND descriptors,
including the O probe (carbonyl oxygen as a hydrogen bond
acceptor), the N1 probe (amide nitrogen as a hydrogen bond
donor), the TIP17 probe (the shape between the ligand and the
protein), and the DRY probe (hydrophobic interactions), were
used to calculate molecular interaction fields (MIFs),18 which
provide an array of interaction energy values between a
molecule of known structure and a probe group calculated at
each node of the grid surrounding the molecule of interest.
Descriptors obtained from this analysis can be graphically
represented in diagrams called “correlogram plots”, wherein the
products from these interaction energies are plotted versus the
distance between them. Four autocorrelograms, including
DRY−DRY, O−O, N1−N1, and TIP−TIP belonging to the
same MIF, and six cross-correlograms (DRY−O, DRY−N1,
DRY−TIP, O−N1, O−TIP, and N1−TIP) from different MIFs
were obtained using interaction energy values. The GRIND
descriptors obtained from this analysis were used to obtain a
multivariable model, and the partial least-squares regression
(PLS) method was used to correlate descriptors with activity.19
The quality of the model was evaluated by the predictive
correlation coefficient (q2), obtained by the leave-one-out
(LOO) scheme, or by a five-random groups (5RG) procedure.
Because not all of the calculated interactions correlated with
activity, the fractional factorial designs (FFD)19 variable
selection procedure implemented in PENTACLE was applied
to exclude variables that increased the standard deviation of
prediction errors. Optimal predictive abilities for the 3D QSAR
model were obtained with a model dimensionality of four latent
variables. The analysis produced 10 correlograms of 68
variables each. The prediction correlation coefficient using the
Not all variables are able to efficiently separate active from
nonactive molecules. An active molecule may feature an
unfavorable functionality that is overcome by that of another
group that correlates with favorable activity. To provide
effective suggestions for lead optimization, only the variables
that have PLS coefficients of high absolute value and those that
clearly distinguish active and less active compounds are
considered, which arise from the O−O, O−N1, and DRY−
DRY correlograms (interpreted in Table S1 in the Supporting
Information). Such variables, and DRY−DRY in particular,
were selected since hydrogen-bonding and hydrophobic/
aromatic interactions represent the most common intermo-
lecular forces that determine host/guest recognition in drugs.
The DRY autocorrelogram (DRY−DRY-43) indicated that
hydrophobic interactions correlated poorly with biological
activity, which may result from the fact that all of these
molecules bear similar hydrophobic groups. However, it is
worth noting that the most relevant variable resulting from this
correlogram suggests that the hydrophobic character of the
piperidine ring and the first aromatic ring on the amide/
carbamide substituent correlate positively with activity. The
most important variable on the O−N1 cross-correlogram
(ON1−529) exhibits a higher value for three of the most
potent compounds (5−7). This variable describes the
interaction between the NH of the protonated piperidine ring
and the oxygen atom in 4′-position with the N1 probe,
indicating that these interactions are important for activity. The
importance of the NH group on the piperidine ring is further
supported by variable OO-120, which produced a higher value
for compounds 5 and 7. Moreover, this variable suggested that
a hydrogen bond donor on the amide side chain can further
enhance inhibitory activity. In the same correlogram, variable
OO-102 produced a high value for the most potent
compounds, all of which bear the piperidine ring (5−11).
This variable suggested a positive effect on inhibitory activity
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Figure 3. PLS coefficients plot of the GRIND variables used in the model. Different correlograms are separated by dotted lines, and the pair probes
are defined at the bottom. The most relevant variables are indicated with the variable number.
given by the amino group on the piperidine ring and the amide
nitrogen.
On the basis of these computationally derived SARs,
compound 5 was chosen as a lead molecule for the
development of a new series of derivatives that contain various
structural features supported by the 3D QSAR model. As noted
by this model, the benzamide side chain plays a beneficial role
toward inhibitory activity. Thus, the initial investigation
commenced with modification of the benzamide side chain.
Analogues containing substituted benzamide side chains were
assembled in a modular fashion (see Scheme S1 in the
Supporting Information) and utilized a Mitsunobu coupling
reaction between 1-methyl-4-hydroxypeperidine 14 and
coumarin phenol 15,13 followed by coupling of amine 12
with various aryl acid chlorides, which were prepared from 13
and provide direct access to a number of functionally distinct
analogues.
Preparation of these analogues is described in Scheme 1.
Methylation of the benzamide 4′-phenol occurred upon
treatment of 5 with sodium hydride followed by iodomethane
to give methylether 16 in good yield. Similar etherifications
occurred by Mitsunobu reactions between 5 and n-butanol or
prop-2-yn-1-ol to afford 17 or 18, respectively. Hydrogenation
of 5 gave the saturated prenyl group found in 19, while
exposure to acid resulted in cyclization to give ether 20. Amide
coupling between aniline 12 and carboxylic acid chlorides
prepared from 13a−e afforded compounds 21a−e, which upon
solvolysis provided 22a, 22b, and 22e, respectively.
In contrast, variables with a high negative impact on
inhibitory activity were identified, in particular, variables OO-
80 and N1N1-141, which correlate with the presence of
multiple hydrogen bond donor and acceptor atoms on the
sugar moiety of compounds 1−4. These data suggested that
hydrophobic groups produce favorable activities. Accordingly,
molecules with lower IC50 values (5 and 7−11) bear the (N-
methyl) piperidine as a replacement for noviose.
The model can be summarized as follows: (i) The side chain
of the coumarin core should be an amide, while the noviose
sugar should be replaced by an amine; (ii) the amide side chain
should exhibit hydrophobic character; (iii) the oxygen atom in
the 4′-position is important for binding; and (iv) the presence
of a hydrogen bond donor on this side chain can bind more
tightly. Figure 4 illustrates the most relevant variables that
produce a direct impact on activities for the most active
compounds.
The antiproliferative activity manifested by these compounds
was determined using two breast cancer cell lines, SKBr3 (ER−,
Her2 overexpressing) and MCF-7 (ER+). As shown in Table 2,
alkylation of the 4′-phenol (16−18) resulted in decreased
antiproliferative activity against the SKBr3 cell line but
maintained similar activity against MCF-7 cells, suggesting
that the 4′-phenol may be binding Hsp90 as an H-bond
acceptor. Saturation of the double bond (19) produced no
effect on inhibitory activity, while intramolecular cyclization
(20), the removal of two terminal methyl groups on the prenyl
chain (21a and 22a), and removal of the prenyl group (21b and
22b) resulted in decreased antiproliferative activities. Thus, the
prenyl side chain appears important and may establish
hydrophobic interactions with the protein binding site.
Consistent with the 3D QSAR studies, removal of the 4′-
Figure 4. Graphical representation of the four most relevant GRIND
variables with direct impact on activity for the most active compounds.
MIFs are shown in colored spheres (DRY probe, yellow; O probe, red;
and N1 probe, blue). The most important variables in each
correlogram correspond to the pairs of grid nodes DRY−DRY, O−
O, and O−N1 linked in this MIFs representation.
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phenol with an alkyl tertiary amine was explored to determine
whether this modification could establish favorable interactions
with the binding pocket. Three amines [2-(dimethylamino)-
ethanol, 3-(dimethylamino)propan-1-ol, and 1-methylpiperidin-
4-ol] were attached to the 4′-phenol benzamide side chain of 5
via Mitsunobu etherification to afford compounds 23−25
(Scheme 2). Prior studies demonstrated that replacement of
Scheme 1. Structural Modifications to the Benzamide Side
Chain
a
Scheme 2. Synthesis of Novobiocin Analogues with an
a
Amine Tether on the Benzamide Side Chain
a
Reagents and conditions: (a) 2-(Dimethylamino)ethanol, Ph3P,
DIAD, THF. (b) 3-(Dimethylamino)propan-1-ol, Ph3P, DIAD,
THF. (c) 1-Methylpiperidin-4-ol, PPh3, DIAD, THF. (d) Compound
12, pyridine, THF. (e) Et3N, MeOH.
a
Reagents and conditions: (a) MeI, NaH, DMF. (b) n-BuOH, Ph3P,
DIAD, THF. (c) CH2OHCCH, Ph3P, DIAD, THF. (d) Pd/C, H2,
THF. (e) HCl/Dioxane. (f) SOCl2, THF. (g) Compound 12,
pyridine, DCM. (h) Et3N, MeOH.
the flexible prenyl side chain with an aromatic ring leads to
improved inhibitory activity.9 Therefore, installation of two
additional functionalities was pursued by the use of biaryl
analogues, 33a and 33b. Accordingly, the phenol was placed on
both phenyl rings of the biaryl system, and 33b was expected to
align with the spatial requirements identified in the 3D QSAR
model, while 33a was not. The syntheses of these two
compounds were achieved by coupling of 12 with acyl chloride
31 (see Scheme S2 in the Supporting Information) and
subsequent basic solvolysis.
Table 2. Experimental and Predicted Antiproliferative
Activity of Analogues Derived from 2
SKBr3 (μM)
predicted (μM)
MCF-7 (μM)
a
16
1.41 0.20
0.99
1.63
0.73
0.58
1.74
2.00
1.42
1.83
2.90
0.97
1.15
0.96
0.33
1.58 0.09
1.44 0.05
1.12 0.16
1.25 0.39
5.01 0.01
2.00 0.18
1.62 0.04
2.10 0.08
11.7 1.0
17
1.40 0.07
1.40 0.11
0.60 0.01
3.46 0.02
2.26 0.12
1.67 0.20
1.68 0.12
11.5 2.0
18
Evaluation of these benzamide analogues against breast
cancer cell lines generated the data in Table 3. These data
19
20
21a
21b
21c
21d
21e
22a
22b
22e
Table 3. Experimental and Predicted Antiproliferative
Activity of Novobiocin Analogues Containing a Modified
Benzamide Side Chain
3.50 0.01
2.43 0.04
0.84 0.18
2.30 0.57
1.61 0.16
2.17 0.11
1.43 0.08
1.48 0.14
SKBr3 (μM)
predicted (μM)
MCF-7 (μM)
a
23
0.33 0.00
0.33
1.75
1.16
0.88
0.25
0.63
0.50
0.72 0.08
0.46 0.05
0.47 0.081
0.91 0.00
0.36 0.06
0.81 0.14
0.38 0.08
24
0.25 0.02
0.21 0.00
0.92 0.06
0.29 0.01
0.86 0.16
0.31 0.04
25
a
Values represent means
standard deviations for at least two
32a
32b
33a
33b
separate experiments performed in triplicate.
phenol (21c) resulted in a 2-fold lower activity, while removal
of both the phenol and the prenyl group (21d) resulted in
significantly decreased activity, confirming that the 4′-phenol is
important for binding. Likewise, replacement of the prenyl
group with electron-withdrawing substituents (21e and 22e vs
21b and 22b) resulted in diminished activity against the SKBr3
cell line, suggesting the potential for interactions between the
4′-phenol and the surrounding environment.
a
Values represent means
standard deviations for at least two
separate experiments performed in triplicate.
demonstrated that tethering the phenol to alkyl amines (23−
25) leads to increased antiproliferative activity, supporting the
formation of stronger interactions with the binding site. For
biaryl analogues (32 and 33), replacement of the prenyl group
with a substituted phenyl ring maintained inhibitory activity
(32a and 33a) as previously suggested; however, switching the
Because the 3D QSAR model suggests the coexistence of a
hydrogen bond donor and a hydrogen bond acceptor on the
benzamide side chain can increase activity, alkylation of the 4′-
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phenol from the first to the second benzene ring increased
activity almost 3-fold (32b and 33b). This latter result suggests
that placement of a hydrogen bond donor at the second ring is
more favorable.
to exhibit promising antiproliferative activities between 200 and
400 nM against two breast cancer cell lines. Western blot
analysis confirmed that the antiproliferative activity manifested
by 24 resulted from Hsp90 inhibition. Overall, these results
support the potential to use quantitative structure-based
rational studies to investigate the chemical space of molecules
targeting the Hsp90 C-terminus. The designed compounds
produced antiproliferative activity and blocked Hsp90 chaper-
oning activity. Together, these features may make this model an
attractive starting point for the rational development of new
Hsp90 inhibitors.
Predictions for the new compounds (Tables 2 and 3 and
Figure S1 in the Supporting Information) show that the QSAR
model is able to predict most of the activities within two σ,
yielding a correlation coefficient r2 of 0.68. Except for 24 and
25, the activities of derivatives with IC50 values lower than 1.5
μM are well predicted, and the best performance was obtained
for compounds that manifest an IC50 < 1 μM. The activities for
less active compounds are underestimated. This result is a
reflection of the properties used for training, which utilized
compounds that manifest activities of less than 1 μM. A
potential source of error may be related to the dependence of
the model on the 3D structures of the compounds used to
calculate the interaction energy maps. The minimum energy
solution conformation used for the calculations can clearly
influence the prediction capabilities in this model, in particular,
if the molecules used to build the model are similar as in this
case, as small differences in the position of functional groups
could yield different activities. The activities of compounds 24
and 25, for example, are overestimated because their
conformations are highly similar to the conformation of
compound 1, and the position of the amino group in the
amide side chains is not optimally described by the model.
However, despite these limitations, the model shows a
nontrivial agreement between the predicted and the exper-
imental activities.
ASSOCIATED CONTENT
* Supporting Information
■
S
Experimental procedures for the synthesis and characterization
of new compounds (1H and 13C NMR, HRMS). This material
is available free of charge via the Internet at http://pubs.acs.org.
AUTHOR INFORMATION
Corresponding Author
*Tel: 785-864-2288. Fax: 785-864-5326. E-mail: bblagg@ku.
■
edu.
Funding
We gratefully acknowledge support of this project by the NIH/
NCI (CA120458) and the DOD Prostate Cancer Research
Program (QH815179). G.C. gratefully acknowledges AIRC
(Associazione Italiana Ricerca sul Cancro) for support through
the Grant IG.11775 and the Flagship “INTEROMICS” project
(PB.P05) funded by MIUR and CNR, and the CARIPLO
Foundation project 2011.1800.
To confirm that the antiproliferative activities observed by
these modified novobiocin analogues resulted from Hsp90
inhibition, Western blot analyses of cell lysates following
administration of 24 was performed. Figure 5 shows that in
Notes
The authors declare no competing financial interest.
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