Theoretical investigations and density functional theory based quantitative structure-activity relationships model for novel cytotoxic platinum(IV) complexes

Article abstract of DOI:10.1021/jm3016427

Octahedral platinum(IV) complexes are promising candidates in the fight against cancer. In order to rationalize the further development of this class of compounds, detailed studies on their mechanisms of action, toxicity, and resistance must be provided and structure-activity relationships must be drawn. Herein, we report on theoretical and QSAR investigations of a series of 53 novel bis-, tris-, and tetrakis(carboxylato)platinum(IV) complexes, synthesized and tested for cytotoxicity in our laboratories. The hybrid DFT functional wb97x was used for optimization of the structure geometry and calculation of the descriptors. Reliable and robust QSAR models with good explanatory and predictive properties were obtained for both the cisplatin sensitive cell line CH1 and the intrinsically cisplatin resistant cell line SW480, with a set of four descriptors.

Full text of DOI:10.1021/jm3016427

Article
pubs.acs.org/jmc
Theoretical Investigations and Density Functional Theory Based
Quantitative Structure−Activity Relationships Model for Novel
Cytotoxic Platinum(IV) Complexes
Hristo P. Varbanov,† Michael A. Jakupec,† Alexander Roller,† Frank Jensen,*,‡ Markus Galanski,*,†
and Bernhard K. Keppler†
†Institute of Inorganic Chemistry, University of Vienna, Wahringer Strasse 42, A-1090 Vienna, Austria
̈
‡Department of Chemistry, University of Aarhus, Langelandgade 140, 8000 Aarhus C, Denmark
S
* Supporting Information
ABSTRACT: Octahedral platinum(IV) complexes are promising candidates in the
fight against cancer. In order to rationalize the further development of this class of
compounds, detailed studies on their mechanisms of action, toxicity, and resistance
must be provided and structure−activity relationships must be drawn. Herein, we report
on theoretical and QSAR investigations of a series of 53 novel bis-, tris-, and tetrakis-
(carboxylato)platinum(IV) complexes, synthesized and tested for cytotoxicity in our
laboratories. The hybrid DFT functional wb97x was used for optimization of the structure
geometry and calculation of the descriptors. Reliable and robust QSAR models with good
explanatory and predictive properties were obtained for both the cisplatin sensitive cell line
CH1 and the intrinsically cisplatin resistant cell line SW480, with a set of four descriptors.
wide variety of Pt(II) compounds was investigated for its anti-
tumor activity in a sarcoma 180 mouse model.5 Results from
variation of carrier ligands, leaving groups, geometry, and charge
and some physicochemical parameters like solubility and kinetics
of hydrolysis affecting the antimalignant properties of cisplatin
analogues were studied. The authors found that the cis geometry
and neutral charge of the complexes, chloride or dicarboxylates as
leaving groups, and primary amines as carrier ligands are crucial
for the biological activity within the series studied. Today, dif-
ferent compound classes are known, violating the classical SAR
set up by Cleare and Hoeschele, as for example complexes with
trans geometry featuring high cytotoxicity.6 Theoretical study
attempts and a quantitative structure−activity relationship (QSAR)
model for the anticancer activity of 26 Pt(II) complexes in vivo in
mice models was reported in 1982.7 Nevertheless, QSAR analysis
results based on in vitro cytotoxicity of Pt(II) compounds in
different cell lines were first published 23 years later.8 Reliable
models with good predictive strength, based on four molecular
descriptors (chosen from 197), were obtained for a series of 16
Pt(II) complexes, including the clinically established drugs cisplatin,
carboplatin, and oxaliplatin. The results confirmed the structure−
activity relationships (SAR) reported by Cleare and Hoechele. Later,
Sarmah and Deka reported QSAR and quantitative structure−
properties relationship (QSPR) models for several platinum com-
plexes, using density functional theory (DFT) and MM derived
descriptors.9 The authors showed that DFT and molecular
INTRODUCTION
■
Platinum complexes are among leading drugs in anticancer che-
motherapy. Since the discovery of the cytotoxic effect of cisplatin
and its Food and Drug Administration (FDA) approval in 1978,
seven other Pt(II) compounds were introduced in clinics world-
wide (carboplatin and oxaliplatin) or in selected countries (nedaplatin,
lobaplatin, heptaplatin, miriplatin, and dicycloplatin).1−3 Approx-
imately 30 more Pt(II) and Pt(IV) complexes have been or are in
clinical trials at different stages.1 Despite the great medical
success of platinum-based cytostatics, there are some major
drawbacks that restrict their usage, mainly severe dose-limiting
side effects, intrinsic or/and acquired resistance, and the uncom-
fortable and cost intensive way of administration (iv infusion).
Thousands of metal compounds have been synthesized and in-
vestigated during the past decades with the aim of breaking these
limitations. Nevertheless, in order to design a metal-based drug with
improved pharmacological profile, details of the mechanism of
action, toxicity, and resistance have to be studied4 and structure−
activity relationships have to be drawn. It is generally accepted that
square planar platinum(II) complexes are acting like prodrugs,
containing two carrier ligands and two leaving groups. The two
leaving groups are exchanged in the cell, forming reactive aqua
species capable of forming DNA adducts responsible for the cyto-
toxic effects of the compounds (Figure 1). Octahedral Pt(IV) com-
plexes also possess antimalignant properties and can act as prodrugs
for Pt(II) agents (reduction in vivo to the corresponding Pt(II)
counterparts).
The first comprehensive SAR study of cytotoxic metal com-
plexes was reported by Cleare and Hoeschele in 1973, where a
Received: November 7, 2012
Published: December 10, 2012
© 2012 American Chemical Society
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Figure 1. Scheme of the mechanism of action of platinum-based cytostatics.
mechanics (MM+) methods could be used successfully in the
prediction of lipophilicity and cytotoxicity of platinum com-
pounds. Furthermore, the usage of solvent models for calculation
of the descriptors gave better results than those obtained in the
gas phase.
complex series; moreover, compounds featuring amide moieties
in the axial ligands are less effective than expected from their log P
values.20,21 The tetracarboxylato complexes have shown in
principle a lower cytotoxic potency and a different redox kinetic
behavior.14 In order to find quantitative explanations of the
phenomena and to rationalize the further development of anti-
malignant Pt(IV) complexes, we enlarged the series by including
three diamminetris(carboxylato)platinum(IV) complexes, pro-
drugs of nedaplatin, and performed a QSAR study based on DFT
calculated and constitutional molecular descriptors. Moreover,
with the help of the calculations, we tried to better understand
the redox behavior of the complexes in the series to explain the
experimental data and to group them in subseries.
Up to now, there is only one report of a QSAR study for Pt(IV)
complexes;22 models based on the cytotoxicity of 23 compounds
in two tumor cell lines were developed, using experimentally
determined (log Po/w and Ep) and theoretical descriptors. Later,
the authors suggested QSPR models able to predict the lipo-
philicity and the redox potential of Pt(IV) complexes, using (a
slightly broadened) series from the QSAR study. The semi-
empirical method PM6 was used for optimization of the structures
and calculation of the descriptors.23 Total and polar surface area,
orbital energies, atomic charges, and dipole moments were found
to be significant descriptors.
As mentioned above, Pt(IV) complexes act as prodrugs of their
Pt(II) counterparts and represent an important part of recent
metal-based anticancer research. Their geometries and phys-
icochemical features (octahedral coordination sphere with a max-
imum of six ligands, kinetic inertness in ligand-exchange reactions,
reduction under hypoxic conditions, etc.) present advantages in fine-
tuning of the pharmacological profile, providing the possibility for
oral administration, targeted therapy, reduced side effects, etc.10
As summarized in Figure 1, there are more parameters (in
comparison with platinum(II) complexes), which should be
taken into account when designing a Pt(IV) based drug. Some
SARs based on a small set of Pt(IV) complexes have been estab-
lished during the past decade.11 It was shown that cytotoxicity of
the compounds is dependent on their redox potential and
lipophilicity and that these parameters have optimal values when
the axial ligands are carboxylates.12 However, it was found re-
cently that redox potential does not always correlate with the rate
of reduction and that the equatorial ligands can also play a crucial
role.13,14 Moreover, reduction of Pt(IV) complexes is not always
accompanied by release of the axial ligands; in some rare cases a
more complicated picture can be observed.15,16
To the best of our knowledge, there is still no QSAR study on
Pt(IV) complexes based on DFT derived descriptors in the
literature. Herein, we report theoretical and QSAR investigations
on 53 novel Pt(IV) complexes (listed in Figure 2), synthesized
and tested in our laboratories, using the hybrid DFT functional
wb97x for optimization of the structure geometry and calculation
of the descriptors. MLR, PCA, and simulated annealing were
employed for the development of the statistical models.
cis-Diam(m)inebis(carboxylato)dichloridoplatinum(IV) and
cis-diam(m)inetetrakis(carboxylato)platinum(IV) complexes
with cytotoxicity ranging from low nanomolar to high micromolar
IC50 values have recently been reported from our group (see
Figure 2).14,17−20 It was found that cytotoxicity in general increases
with increasing lipophilicity of the axial ligands, but this effect is much
more pronounced in the diam(m)inedichloridobis(carboxylato)
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such as esters, amides, or free carboxylic acids. Synthesis and
detailed characterization of compounds 1−20 and 51−53 are
given in refs 17, 18, and 19, and those for 21−47 are given in refs
14 and 20. Nedaplatin derivatives (48−50) were synthesized
using analogous procedures. Their detailed characterization is
based on 1H, 13C, 15N, and 195Pt 1D and 2D NMR measurements.
Interestingly, two different-shaped signals for the NH3 groups
can be observed in the 1H spectra of compounds 48−50: a broad
signal at around 6.1 ppm and a multiplet around 6.6 ppm in which
the 14N−1H and 195Pt−1H couplings can be seen. The existence of
1
two signals in the H and 15N spectra can be explained by the
unsymmetrical surroundings of the NH3 groups, one is in trans
position to carboxylate and the other one to alcoholate.
Cytotoxicity. All complexes (1−53) were tested for in vitro
cytotoxicity in comparison with cisplatin, carboplatin, oxaliplatin,
and nedaplatin in two human tumor cell lines (CH1 ovarian
carcinoma and SW480 colon carcinoma), using the MTT color-
imetric assay. The resulting IC50 values are listed in Table 1. The
cell line CH1 is sensitive to the clinically applied platinum drugs,
while the second one (SW480) is resistant to them with the ex-
ception of oxaliplatin. The set of compounds covers a large range
of cytotoxicity with nanomolar IC50 values up to 174 μM in the
cell line CH1 and from 0.1 μM to negligible activity (>500 μM)
in the cell line SW480. In general, the diam(m)inebis(carboxylato)-
dichlorido complexes (1−26, subset 1) show higher activity in
comparison with the tri- and tetrakis(carboxylato)diam(m)-
ine compounds (27−53, subset 2). With increasing the lipo-
philicity, complexes with higher cytotoxicity than the clinically
applied platinum(II) drugs could be obtained in subset 1, while
this observation is not valid for the compounds in subset 2. In
general for all complexes in the set, cytotoxicity is dependent
on lipophilicity, but this is much more pronounced for the
diam(m)inedicarboxylatodichlorido complexes. When the axial
ligands are compared, terminal ester groups are most favorable
for antiproliferative activity, followed by amide derivatives;
compounds featuring terminal carboxylic or hydroxy groups in
the axial chain showed the lowest cytotoxic potency (see Figure 3).
In subset 2, cytotoxicity of amide and ester derivatives is comparable.
Lack of activity of all compounds from the subset (except for
oxaliplatin analogues 51−53 and partially for nedaplatin analogues
49−50) in the cisplatin-resistant cell line SW480 can be observed
(Table 1).
Crystal Structure. The result of the X-ray diffraction analysis
of 7 is shown in Figure S1 in Supporting Information. The
compound crystallized in the triclinic centrosymmetric space
group P1. The Pt(IV) atom has an octahedral coordination
̅
geometry with one ethylenediamine and two chlorido ligands in
the equatorial plane and two 4-methoxysuccinates coordinated in
axial positions. The bond lengths and angles are well comparable
with the crystal structure of analogous complex 6 previously
published.17 Interestingly, the orientations of the axial ligands in
complex 7 are different from those observed in 6. This is probably
due to dissimilar crystal packing and H-bonding pattern. An
analogous difference in the conformational behavior was observed
in the structures of complexes 1 and 22, whereas the 4-methoxy-
succinate ligands in 22 have a straight orientation20 while in 1 one
succinate is twisted and the other is straight.18
Geometry Optimization. Comparison of geometry param-
eters, obtained after the optimization procedure in vacuum, in a
water model and from the available X-ray data for compounds 1,
6, 7, 22, and 38 is shown in Table S1 (Supporting Information).
A good agreement between experiment and calculation could be
observed.
Figure 2. Schematic formulas of the investigated complexes.
RESULTS AND DISCUSSION
■
Synthesis and Characterization. The entire set of 53
Pt(IV) complexes, which are an object of this study, is presented
in Figure 2. On the basis of the equatorial ligands, the com-
pounds can be divided into six subseries, namely, derivatives of
cisplatin (1−5), its ethylenediamine analogue (6−20), its bis-
(ethylamine) analogue (21−26), carboplatin (27−47), neda-
platin (48−50), and oxaliplatin (51−53). The axial ligands are
represented by dicarboxylato chains, containing different spacers
between the two carbonyl groups and diverse terminal moieties
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Table 1. Cytotoxicity of the Investigated Platinum(IV) Complexes in Comparison with the Clinically Applied Platinum(II) Drugs
in the CH1 and SW480 Human Cancer Cell Lines
a
a
IC50 (μM)
IC50 (μM)
compd
CH1
1
SW480
compd
30
CH1
5
SW480
>500
1
19
136 16
3.8 1.0
183 28
24
2
0.62 0.32
31
8.6 1.7
11
62 26
350 39
181 44
>500
>500
>500
>500
>500
>500
>500
>250
>500
>500
>500
>500
>500
>500
>500
>500
3
28
12
2
4
32
6
4
48
24
95
16
4
4
5
1
33
5
1.9 0.2
34
44
15
28
8
5
2
6
5.5 2.2
35
7
0.68 0.20
0.34 0.11
0.068 0.024
0.018 0.007
36
8
4.1 0.5
0.63 0.20
0.22 0.08
142 23
31 15
37
31 13
114 23
33 13
7.7 1.4
9
38
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
39
24
3
40
2.3 1.1
1.9 0.2
32 19
1.1 0.2
41
89
128 48
23
7
19
9
42
160 10
3.5 0.1
90 21
43 22
43
9
44
49 13
21
8
45
125 35
22 12
46
22
33
21
8
4
6
7.8 1.0
21
5
47
0.17 0.05
0.055 0.006
5.6 1.6
2.9 1.0
0.96 0.4
40 12
1.0 0.3
0.30 0.05
0.11 0.01
0.39 0.07
6.1 0.6
>500
48
49
1.9 0.3
2.1 0.3
55 28
100
6
50
161 33
0.16 0.05
0.061 0.015
0.014 0.002
0.0094 0.0012
0.75 0.10
51
44
14
12
9
3
5
52
19
11
5
2
53
cisplatin
carboplatin
0.16 0.03
1.36 0.40
0.33 0.09
0.14 0.05
3.50 0.29
85 28
b
171
32 10
28
1
oxaliplatin
0.30 0.08
6.3 1.3
>500
nedaplatin
4
>500
a
b
The reported 50% inhibitory concentrations are the means standard deviations obtained from three independent experiments. Data taken from
ref 24.
Analysis of the Calculated Physicochemical Param-
The described circumstances make q(Pt) a good descriptor for
a further QSAR model. In Figure 6, a qualitative MO analysis of
the frontier orbitals, together with their energies for complexes
22 and 38, is shown. The low (negative) values of EHOMO (<−9 eV)
and the good correlating high ionization potentials (between 8 and
10 eV in vacuum and between 7 and 8.5 eV in water) are a logical
consequence of the inability of Pt(IV) complexes to act as
reductants. However, a clear tendency of the change of these param-
eters in the series cannot be observed.
The negative values of ELUMO (Table S3, Figure 6) show that
the compounds can act as oxidants and can be reduced relatively
easily. The nedaplatin derivatives (complexes 48−50) have
higher (close to zero, even slightly positive for complexes 49 and
50b) values of ELUMO, which probably is connected with their
eventual lower oxidative capability. In general, a clear relationship
between the variations of ELUMO with the structures in the series
could not be found. Interestingly, the values of the electron
affinity in gas phase (between 0.6 and 1.8 eV) and their corre-
sponding vertical (between 2 and 3 eV) and adiabatic (around
4 eV) redox potentials in water do not correlate with each other
as would be expected. In principle, the small differences observed
between the values of the adiabatic redox potential in water imply
that the investigated compounds can be reduced with relatively
equal effort. The different tendencies for the vertical and
adiabatic electron affinity in water also show that the acceptance
of an electron in aqueous media is associated with significant
eters. The dipole moments (μ) vary from 3 to 15 D, implying
that all the complexes are quite polar compounds (Table S2).
Nevertheless, no trend in the alteration of this parameter within
the investigated compounds could be found.
The energies of solvation (Es and Es′) differ from 70 to
160 kJ mol−1 for all compounds. In general the complexes exert-
ing carboxylic or amide groups in the axial ligands have higher
solvation energies compared with their ester analogues. The dis-
tribution of the electron density, based on electrostatic potential
(ESP) for compounds of the two subtypes, shows that the most
electropositive regions in the molecule could be found around
the nitrogen donor atoms and the most negative around the
oxygen and chlorine atoms (Figure 4).
The natural population analysis (NPA) charge at Pt (q(Pt))
differentiates well the two major subtypes (in structure deviation
and in cytotoxic activity): bis(carboxylato)dichlorido complexes
(subset 1) versus tris- and tetrakis(carboxylato) complexes
(subset 2). It also shows some minor discrimination in the two
subtypes, dependent mainly on differences in the equatorial
ligands. Expectedly, the deviations of the terminal fragments of
the axial ligands do not affect the charge at the Pt atom. A plot of
the NPA charge at Pt for the 53 investigated complexes (average
values from the calculated conformers of compounds of 2 and 50
are used) is shown in Figure 5.
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Figure 3. Comparative diagram of the cytotoxicity (IC50 values, logarithmic scale) of some Pt(IV) complexes from the series in the CH1 cell line,
depending on their equatorial ligands (y axis) and the terminal moieties of the axial ligands (x axis), and the clinically approved Pt(II) drugs: cisplatin,
carboplatin, oxaliplatin, and nedaplatin.
than 0.3 Å. In principle, the last findings correlate well with the
shape of the LUMO orbitals which, for complexes from subset 2,
are mainly situated around the axial ligands while for those from
subset 1 they could be found in the square-planar sphere around
platinum (Figure 6). Consequently, a different mechanism of
reduction between the complexes of the two main subtypes is
expected.
Figure 4. ESP color mapped electron density for complexes 22 (left)
Reduction Model Studies. In order to gain a deeper insight
and 36 (right).
into the mechanism of reduction of Pt(IV) prodrugs, further
investigations based on two simple model systems, namely,
(OC-6-33)-bis(acetato)diamminedichloridoplatinum(IV)
(M1), representing complexes of subset 1, and (OC-6-33)-
changes in the geometry and the energy of the system. It is
interesting to follow how the geometry of the complexes has
been changed with the acceptance of one electron. From the
bis(acetato)diamminemalonatoplatinum(IV) (M2), represent-
NPA charges it can be concluded that the extra electron mainly
ing complexes of subset 2 (Figure S2, Supporting Information),
resides on the Pt atom (and the coordinated chloride, in the case
were performed. Applying again the most prominent hypothesis
of complexes of subset 1), a Pt(III) radical is formed, and
for the reduction pathway of Pt(IV) complexes featuring axial
expectedly the largest and important geometry alterations will be
carboxylato ligands, which is an outer sphere reduction going via
in the Pt coordination sphere. In Table 2, a comparison of the
a Pt(III) intermediate,15 the structures of M1, M2, and their
bond lengths changes between the neutral and the anion radical
(both optimized in water) for complexes 24 and 28 is shown.
Table 2 shows that addition of an electron to a complex from
subset 2 results in an elongation of Pt−Oax bonds while Pt−N
and Pt−Oeq in the equatorial sphere remain almost unchanged.
These observations are in accordance with the expected reduc-
tion and loss of the axial ligands. Contrary to bis(carboxylato)-
dichlorido complexes from subset 1 (with the exception of some
of the cisplatin analogues, namely, complexes 1, 2c, and 3), the
axial Pt−O bonds have shifted insignificantly but the equatorial
Pt−Cl and trans standing Pt−N have been elongated by more
analogous monoanionic Pt(III) radicals were optimized in a
water model. The same was done for the respective intermediates
of reduction, the pentacoordinate complex after cleavage (ionic
or radical) of one ligand (acetate or chloride). The energy of the
cleaved acetate and chloride in water was also calculated. The
possible ligand dissociation reactions after one-electron reduc-
tion are presented schematically in Figure 7. The dissociation
energies, calculated for possible ionic or radical cleavage of a
ligand (chloride or acetate) from the neutral Pt(IV) complexes or
from their anion radicals, are listed in Table 3.
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Figure 5. NPA charge at the Pt atom (in au), calculated for complexes 1−53. A scheme of the coordination sphere of subsets 1 and 2 is presented on
the right.
Figure 6. Frontier orbitals (with their energies) of complexes 22 (top) and 38 (bottom).
As presented in Table 3, reasonable dissociation energies are
observed only when a ligand is cleaved from a Pt(IV) complex,
which has already accepted an electron (and has been trans-
formed into a Pt(III) radical). From the obtained values, it can be
concluded that cleavage of equatorially bound chloride from
the ionized complex M1 requires less energy than dissociation
of axial acetate. Furthermore, the dissociation of an axial
carboxylato ligand appears to happen more easily in the case of
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Table 2. Bond Length Changes in Complexes 24 and 28 after
the Acceptance of an Electron in Aqueous Medium
Table 3. Energies (in kJ/mol) Required for Dissociation of a
Ligand from Complexes M1 and M2
complex
reaction of reduction
M2-ac
M1-ac
M1-Cl
PtIVL6 → (PtIVL5)+ + L−
305.6
251.3
124.8
303.7
251.6
113.5
246.7
256.9
88.0
Δ bond length (Å)
24
28
PtIVL6 → (PtIIIL5)• + L•
Pt−O1ax
Pt−O2ax
Pt−N1
Pt−N2
Pt−Cl1/Pt−O3eq
Pt−Cl2/Pt−O4eq
0.04
0.03
0.34
0.01
0.04
0.34
0.35
0.37
0.01
0.01
0.04
0.04
(PtIVL6)•− = (PtIIIL6)•− → (PtIIIL5)• + L−
far. It looks like that the rate-limiting factor of reduction is the
transfer of an electron to the Pt(IV) atom, not breaking of the
Pt−ligand bond as a result of the one-electron transfer. In this
context the kinetics of reduction of platinum(IV) complexes are
dependent not only on the compound itself but also on the bio-
reducing agent (ascorbate, glutathione, cysteine, methionine, etc.) and
the surrounding pH.27−29 How these factors influence the behavior of
Pt(IV) complexes will be a matter of further investigation.
QSAR Analysis. Initial Screening of the Descriptors.
From the initial screening of the correlation between properties
and biological response, it was found that the most significant
descriptors for the biological activity (as single parameters) in
both cell lines are the number of H-bond acceptors (Hacc), charge
at the Pt atom (q(Pt)), vertical and adiabatic electron affinities
(Eeas and Eeas′), followed by the number of H-bond donors
(Hdon) (see Table 4).
bis(carboxylato)dichlorido complexes (like M1) in comparison
to tetracarboxylato species (like M2). The last findings correlate
with the experimental data from electrochemical experiments,
where it was found that bis(carboxylato)dichlorido(ethane-1,2-
diammine)platinum(IV) complexes (6−20) have similar but
slightly higher redox potentials (approximately −0.6 V vs NHE)
than the corresponding carboplatin analogues (27−47) (approx-
imately −0.7 V vs NHE).14,21 Nevertheless, the electron affinity (the
energy released after the attachment of an electron to a neutral
complex) is much higher than the obtained dissociation energies:
380.2 kJ/mol for M1 and 392.0 kJ/mol for M2. For this reason,
reduction of both complexes with dissociation of acetate or chloride
is possible, which is in agreement with Gibson’s observation for more
than one product of reduction of diacetatodiam(m)inedichlorido-
platinum(IV) complexes25 as well as with the nonaxial ligand loss
reduction recently reported by Hambley/Gibson13 and Cullinane.26
The thermodynamically comparable redox properties (in
theory and experimentally) for both types of compounds were
different with respect to their kinetic behavior. Compounds of
subset 1 were reduced by ascorbic acid much more quickly than
those from subset 2.14 In order to learn more about the kinetics of
reduction, modeling of the energy change as a function of elonga-
tion of the Pt−acetate bond (in both models) or Pt−Cl (in M1)
was performed. Unfortunately, the attempts to find an energy
barrier and the corresponding transition state are unsuccessful so
Table 4. Significance of the Descriptors (as Single
Parameters), Based on the Properties−Biological Response
a
Correlation
correlation with
the response
CH1 cells
SW480 cells
strong (R > |0.5|) Hacc, q(Pt), Eeas′, Hdon, Eeas Hacc, q(Pt), Eeas
middle (R < |0.5|) COOH, Es′, Es, Eea Hdon, Eeas′
weak (R < |0.3|)
E
HOMO,MW,Ei,H/Lgap,Eis Es′,Es,COOH,MW,EHOMO,-
Eea, ELUMO, Eis, Vm, Ei
very weak
(R < |0.15|)
Vm, α, μ, SASA, ELUMO
α, μ, SASA, H/Lgap
a
for the abbreviations see the experimental section.
Figure 7. Scheme of possible reduction reactions to pentacoordinated Pt complex for M1 (bottom) and M2 (top).
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A strong correlation between MW, Vm, and α can be found.
The charge at Pt, the vertical redox potential (Eeas), and the
number of H-bond acceptors also have a strong correlation with
each other. Expectedly, there is an excellent correlation between
EHOMO and the ionization energy (Ei) as well as among the
vertical and adiabatic solvation energies. A good agreement
between ELUMO and Eea, as well as among the HOMO/LUMO
gap and first ionization energy in vacuum could be also observed
during the descriptor analyses. From a group of descriptors
having strong correlations with each other, only a single one is
expected to contribute to a good QSAR model. With the aim
of using the final QSAR model for screening purposes, it is ad-
vantageous to select the descriptor in each group as one that requires
the least computational effort, e.g., the molecular weight is much
easier to calculate than the molecular polarizability; vertical solvation
energy can be calculated more quickly than adiabatic.
The most promising models based on a single descriptor or a
combination of two, three, four, or five descriptors were chosen
with the help of simulated annealing. It was demonstrated that
the best merit for both cell line models could be derived by com-
bination of four descriptors; utilizing more than five descriptors
decreased the merit. How R2 and Q2 of the models change with
increasing the number of the descriptors is shown in Figure S3
(Supporting Information).
when quantum mechanical calculations are not possible or too
expensive. Combining α or the autocorrelated MW with q(Pt)
and Hdon gave models with moderate explanatory (∼72%) and
predictive (∼69%) properties, which totally failed in the external
validation, partition e where all nedaplatin derivatives (48−50)
are in the predictive set (Tables S5 and S6).
Reliable (R2 ≥ 80%) and predictive (Q2 ≥ 70%) models could
be constructed only by adding a fourth descriptor. The best
results are obtained via combining polarizability with the best
two three-descriptor models, where R2 of 86%, Q2 of 82%, and
AAR < 0.4 could be achieved. Using MW (easy to calculate and
having strong correlation with α) instead does not decrease the
quality of the models. The external validation proved the robust-
ness and the predictive properties and showed that the most
reliable model is built by using MW, Eeas′, Hdon, and Hacc as
variables. Developing a model by adding a fifth descriptor can
slightly increase the R2 and Q2 values only when autocorrelating
descriptors (e.g., α and MW, Eeas and Eeas′) are included, which
results in overfitting and fake higher predictability.
The complete regression equation for the final predictive
model we have chosen is as follows:
pIC50(CH1) = 0.006 MW − 3.920Eeas′ − 0.417Hdon
− 0.363Hacc + 16.186
QSAR Models for the Cell Line CH1. Statistical data for the
best regression models for cytotoxicity in the cell line CH1 are
summarized in Table 5. Additional statistical information for
The plot of experimental and predicted pIC50 values of the model
is shown in Figure 8.
In the PCA method all variables are combined into new
descriptors that are ranked according their ability to describe the
variation in the descriptor data. For the present case, 85% of the
variance could be explained by five components, but a QSAR
model using five components performs no better than the four-
component MLR model (R2 = 0.83). Since the PCA approach
requires the calculation of all descriptors, the MLR approach is
better suited for screening purposes. When PCA was applied on
the four descriptors (MW, Eeas′, Hdon, and Hacc) used for devel-
oping our MLR model, three components, together explaining
88% of the variance, were obtained (their loading plots are shown
in Figure S4, Supporting Information). The score plot, obtained
by combination of the first two of them (PC1 and PC2, ex-
plaining 73% of the variability), grouped well the complexes in
five clusters (Figure 9). The compounds from subsets 1 and 2
were split, depending on the terminal moieties of the axial
ligands, separating the more active esters in one cluster from the
less active amides and free carboxylic acids in another. Com-
pounds 3, 11, and 16, featuring terminal CH2OH and equipped
with the lowest cytotoxicity in subset 1, formed another cluster.
Interestingly, nedaplatin derivatives (48−50) from subset 2
grouped together with the amides and free carboxylic acids from
subset 1, and esters 17 and 18 (having three CH2 groups spacer
between the carbonyls in the axial chains) from subset 1 grouped
together with amides and carboxylic acids from subset 2.
QSAR Models for the Cell Line SW480. Statistical data for
the best regression models for cytotoxicity in the SW480 cell line
are summarized in Table 6. Additional statistical information for
these and other one- to five-variable regressions is presented in
Table S6 (Supporting Information).
Table 5. Statistical Data for the Best Regression Models for
Cytotoxicity in the Cell Line CH1 of the 53 Investigated
Pt(IV) Complexes
no. of
variables
descriptors
R2
Q2(LTOP) rms
1
2
3
4
4
4
4
5
6
Hacc
don, Hacc
0.51
0.70
0.79
0.86
0.85
0.85
0.85
0.87
0.88
0.48
0.67
0.75
0.82
0.81
0.82
0.82
0.84
0.84
0.78
0.62
0.53
0.45
0.47
0.46
0.46
0.43
0.43
H
q(Pt), Eeas′, Hdon
α, q(Pt), Eeas′, Hdon
MW, q(Pt), Eeas′, Hdon
MW, Eeas′, Hdon, Hacc
α, Eeas′, Hdon, Hacc
MW, α, q(Pt), Eeas’, Hdon
MW, α, q(Pt), Eeas′, Es, Hdon
these and other models based on one, two, three, four, or five
descriptors is presented in Table S4 (Supporting Information).
Models derived by using only one descriptor (the good
autocorrelating q(Pt) or Hacc), expectedly have low explanatory
and predictive properties (R2 and Q2 under 50%). Essential
improvement could be achieved by adding Hdon; R2 increased to
nearly 70%, and the predictability was over 65%, respectively.
Further enhancement could be obtained by adding a third
descriptor to the best two-variable models (Hdon and Hacc or
q(Pt) and Hdon). R2 over 75% is achieved by including the
adiabatic redox potential in water (Eeas′). The developed models
also showed high predictive strength (Q2 > 75%) and robustness,
which was proved in the external validation under severe condi-
tions (Table S5, Supporting Information). Interestingly, by inclusion
of the presence/absence of COOH in the combination of the
Hdon and Hacc model, only constitutional (easy to calculate)
molecular descriptors with R2 = 75% and Q2 = 72% could be
obtained (Table S4). The latter also showed good Pred.R2 on the
external validation and can be a good alternative for screening
Using only one descriptor cannot give a model with good
explanatory and predictive properties (R2 and Q2 under 65%).
Including a second (q(Pt) and Hdon) increases R2 up to 73% and
Q2 to 70%. However, in order to reach values over 75%, models
with a combination of three or four descriptors should be used.
Increasing the number of variables to more than four gives models
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Figure 8. Predicted (with the selected four-variable model) vs experimental cytotoxicity in the cell line CH1. The coloring is based on the subtypes
containing the same equatorial ligands.
Table 6. Statistical Data for the Best Regression Models for
Cytotoxicity in the SW480 Cell Line of the 53 Investigated
Pt(IV) Complexes
no. of
variables
descriptors
R2
Q2(LTOP) rms
1
2
3
4
4
4
5
5
6
7
Hacc
0.63
0.73
0.77
0.80
0.80
0.82
0.84
0.82
0.85
0.86
0.60
0.70
0.73
0.75
0.76
0.79
0.80
0.78
0.81
0.80
0.75
0.65
0.61
0.59
0.58
0.54
0.52
0.56
0.51
0.52
q(Pt), Hdon
Es, Hdon, Hacc
Es, Hdon, Hacc, COOH
Ei, Eeas, Eeas′, Hdon
Ei, Eea, Eeas, Hdon
EHOMO, Ei, Eea, Eeas, Hdon
q(Pt), H/Lgap, Ei, Eeas, Hdon
EHOMO, Ei, Eea, Es, Eeas, Hdon
EHOMO, Ei, Eea, Es, Es′, Eeas, Hdon
pIC50(SW480) = −1.094Ei − 2.634Eea + 4.971Eeas
− 0.404Hdon − 1.281
Figure 9. Scoring plot derived from PCA on the four descriptors (MW,
Eeas′, Hdon, and Hacc) used in the proposed model for cytotoxicity in the
CH1 cells: cluster I, esters from subset 1; cluster II, esters from subset 2;
cluster III, amides and free carboxylic acids from subset I and nedaplatin
derivatives (48−50); cluster IV, amides and free carboxylic acids from
subset 2 and complexes 17 and 18 from subset 1; cluster V, compounds
with terminal CH2OH groups in the axial ligands.
(3)
The plot of the predicted vs experimental pIC50 values for the
models is shown in Figure 10. Model 3 gives the best linear fit
(R2 = 0.82 vs R2 =0.80 for models 1 and 2) and can predict better
the activity of the oxaliplatin analogues (51−53), the only com-
pounds from subset 2, showing some activity in the cell line
SW480 (due to the DACH carrier ligand). On the other hand,
model 1 showed higher predictive R2 (near 60%) in the severe
cross-validation partitioning e and in addition is built from easy
to calculate molecular descriptors. It is therefore favorable for
screening of new compounds.
As most of the compounds from subset 2 did not show
cytotoxic activity in the SW480 cell line and IC50 could not be
detected up to 500 μM, in the current study IC50 of 600, 1000,
and 2000 μM were used as input for their cytotoxic activity. By an
increase of these values from 600 to 2000, slightly better models
with increased R2 and Q2 could be observed (Table S8); however,
the AAR values increased too and the results from the external
validation deteriorate slightly. The data, presented in Table 6, are
based on the optimal input IC50 of 1000 μM. The tables in
Supporting Information are based on the study using IC50 = 600 μM
for complexes inactive in SW480 cells.
with marginally higher R2, but this is mainly due to overfitting,
since they contain autocorrelated descriptors (EHOMO or H/Lgap
and Ei). The actual predictability of the best models, featuring
three, four, or five descriptors, using external validation, is sum-
marized in Table S7 (Supporting Information). The lowest
predictive capability of the models could be observed on training
sets c and e where most of the ethylenediamine derivatives or the
oxaliplatin, nedaplatin, and part of the carboplatin analogues are
moved to the predictive set.
The complete regression equations for the final predictive
models we have chosen are the following:
pIC50(SW480) = −0.024Es − 0.353Hdon − 0.534Hacc
(1)
− 0.526 COOH + 2.090
pIC50(SW480) = −0.637Ei + 4.162Eeas − 4.522Eea s′
− 0.384Hdon + 12.060
(2)
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Figure 10. Predicted (with the selected four-variables model) vs experimental cytotoxicity in the SW480 cell line: top, model 1; middle, model 2;
bottom, model 3. The coloring is based on the subtypes containing the same equatorial ligands.
Model 2 showed the smallest difference between the predicted
pIC50 values for the inactive carboplatin analogues (−2.8 0.4)
for input value pIC50 = −3.0 (IC50 = 1000 μM).
complexes 17 and 18 from subset 1 are in the subset 2 amides and
acids cluster. In addition the EtNH2 ester derivatives (22−25)
built a subcluster.
Applying PCA, using the descriptors from the chosen four-
variable models, showed that 88−89% of the variance in the set
can be explained by three components. By plotting of the scores
of PC1 and PC3 (covering 60% of the variance) produced from
model 3 descriptors combination, a nice clustering of the series
could be observed (Figure 11). Similar to the clustering obtained
with the CH1 cells model PCA, the compounds split into subsets
1 and 2 esters and subsets 1 and 2 amide and free carboxylic acids.
Compounds 3, 11, and 16 are again in a separate cluster, and
Conformational Differences. Four different conformers
for compound 2 and two different conformers for compound 50
were generated (Figure S5, Supporting Information), and their
molecular properties were calculated. The descriptors with the
smallest differences were q(Pt), α, EHOMO, H/Lgap, Ei, Eis,
Eeas, and Eeas′, with RSD < 2%. A moderate effect of the con-
formation was observed on Vm and SASA (RSD < 8%). The
dipole moment (μ) and solvation energies (Es and Es′) are more
dependent on the conformation, where RSD rises to 17% in the
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following models were obtained:
pIC50(CH1) = −0.135A(NH3) + 0.025A(en)
+ 0.212A(EtNH2) − 0.049A(DACH)
+ 0.259L(Cl) − 0.280L(CBDA)
+ 0.082L(glyc) − 0.049L(ox)
− 0.010X(succ) − 0.104X(Glu)
+ 0.107X(MeGlu) + 0.053X(DiMeGlu)
− 0.277R(COOH) + 0.053R(COOMe)
+ 0.167R(COOEt) + 0.185R(COOPr)
+ 0.184R(COOiPr) + 0.209R(COOBut)
− 0.104R(CONHProp)
− 0.014R(CONHcp)
+ 0.022R(CONHch)
+ 0.016R(CONHBz)
− 0.232R(CONH(CH2)2OH)
− 0.021R(CONH(CH2)2OMe)
Figure 11. Score plot derived from PCA using four descriptors (Ei, Eea,
Eeas, and Hdon), applied for modeling the cytotoxicity in SW480 cells
(model 3): cluster I, compounds with a terminal free CH2OH group in
the axial ligands; cluster II, amides and free carboxylic acids from subset
1; cluster III, amides and free carboxylic acids from subset 2 and 17 and
18 from subset 1; cluster IV, esters from subset 1 (without the EtNH2
derivatives); cluster V, esters from subset 1/the EtNH2 derivatives;
cluster VI, esters from subset 2.
for which R2 = 0.90, Q2 = 0.76, and AAR = 0.28.
case of μ. The autocorrelating ELUMO and Eea showed great
dependency on the conformation, which excludes them from
the list of descriptors able to produce reliable models. The in-
fluence of the conformations of complex 2 on the predicted
cytotoxicity from the best chosen four-descriptor models is
summarized in Table 7.
In comparison to model 1, models 2 and 3 gave better results
with respect to cytotoxicity in SW480 cells. However, the de-
pendency on conformers was higher (high RSD values). This
circumstance is expected, since the conformation has a significant
impact on descriptor Eea. The vertical and adiabatic redox potentials
in water have small conformational dependence but also close values
in the series, which pronounce the effect of the conformation to the
predicted cytotoxicity.
Free−Wilson QSAR Model. In order to judge the
contribution of different substituents to the biological activity
of the compounds, Free−Wilson QSAR models for cytotoxi-
city of the complexes in the CH1 and SW480 cell lines were
developed. The models are based on the concept that each
substituent makes an additive and constant contribution to the
biological activity regardless of substituent variation in the rest of
the molecule.30 Each compound was presented as a binary string
with a length of 24 substituents (the different equatorial ligands,
spacers between the two carbonyls, and terminal functional
groups in the axial ligands). A term is equal to 1 when a substituent is
present at a particular position and 0 when it is absent. The
contribution of each substituent was calculated using MLR, and the
pIC50(SW480) = −0.140A(NH3) − 0.034A(en)
+ 0.223A(EtNH2) + 0.061A(DACH)
+ 0.341L(Cl) − 0.351L(CBDA)
− 0.056L(glyc) + 0.061L(ox)
− 0.053X(succ) − 0.038X(Glu)
+ 0.094X(MeGlu) + 0.048X(DiMeGlu)
− 0.195R(COOH) − 0.007R(COOMe)
+ 0.146R(COOEt) + 0.177R(COOPr)
+ 0.085R(COOiPr)
+ 0.206R(COOBut)
− 0.066R(CONHProp)
− 0.036R(CONHcp)
+ 0.012R(CONHch)
+ 0.012R(CONHBz)
− 0.199R(CONH(CH2)2OH)
+ 0.012R(CONH(CH2)2OMe)
Table 7. Average IC50 Values for Conformers of Complex 2, Derived from the Best Four-Variable QSAR Models, for CH1 and
a
SW480 Cells in Comparison to Experimental Data
cell line
CH1
RSD, %
SW480 model 1
RSD, %
SW480 model 2
RSD, %
SW480 model 3
RSD, %
exptl
0.62 0.32
0.59 0.46
0.60 0.50
0.91 0.77
b
52
80
83
85
3.8 1.0
26
45
52
55
3.8 1.0
8.3 8.6
8.6 9.3
42.3 10.6
26
104
108
25
3.8 1.0
4.6 4.8
5.0 5.5
3.7 3.6
26
104
110
97
linear fit
10.9 4.9
12.6 6.4
35.2 19.5
cross-validat predictions (LOOP)
b
ext validation
a
Results are presented as the mean sd. Values from partitioning, where all the conformers are in the training set.
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for which R2 = 0.91, Q2 = 0.80, AAR = 0.26.
Intensities of reported IR bands are defined as follows: br = broad, s =
strong, m = medium, and w = weak. Electrospray ionization mass
spectrometry was carried out with a Bruker Esquire 3000 instrument
using MeOH/H2O as solvent. Elemental analyses were performed using
a Perkin-Elmer 2400 CHN elemental analyzer at the Microanalytical
Laboratory of the University of Vienna, Austria, and are within 0.4% of
the calculated values, confirming their ≥95% purity (see Table S9,
Supporting Information).
Synthesis and characterization of complexes 1−47 and 51−53 are
described in refs 14, 17, and 20. The synthetic procedure for compounds
48−50, their precursor nedaplatin, and its dihydroxidoplatinum(IV)
analogue is reported herein. Synthesis and characterization of com-
pound 48 was reported recently.22
In the above equations, A is the carrier ligand, L the leaving
groups, X the spacer between the two carbonyl groups in the axial
ligands, and R the terminal functional group of the axial chains.
Explanation of results and predictability of the models with the
given set of substituents is good. The highest positive effect
on the cytotoxicity in both cell lines have A = EtNH2, L = Cl,
R= COOEt, COOPr and COOBut. The lowest cytotoxic effect
is due to A = NH3, L = CBDA, R = COOH, and CONH-
(CH2)2OH. The spacers between the carbonyl groups in the axial
chains have a lower impact on the cytotoxicity in the series. In the
model for SW480 cells, A = DACH and L = ox have a slightly
positive effect on the cytotoxicity, contrary to the model for CH1
cells. In contrast, R = COOiPr has a much higher positive effect
on the pIC50 values in the cell line CH1 than in SW480 cells.
(SP-4-3)-Diammineglycolatoplatinum(II) (Nedaplatin). Neda-
platin was prepared starting from K2PtCl4 via cis-Pt(NH3)2I2. An
amount of 1.206 g (2.4972 mmol) of the latter was suspended in 36 mL
of triply distilled water, and 870 mg (4.7564 mmol) of silver glycolate
were added. The suspension was left stirring overnight in the dark, and
then the obtained silver iodide was filtered through a sintered glass
funnel with a filter paper disk (MN GF-3). The clear solution was stirred
at room temperature in the dark for 4 h, and then Amberlite·HCl
(conditioned with NaOH to its OH form) was added slowly in small
portions to the solution of the complex while stirring until pH (9−10)
was achieved. The mixture was left stirring overnight in the dark. Then
Amberlite and traces of reduced Pt(0) were filtered off through a
sintered glass funnel with a filter paper disk (MN GF-3). The volume of
the filtrate was reduced and cooled in the fridge. The obtained pre-
cipitate was filtered off, washed with acetone, and dried in a vacuum
desiccator over P2O5 to yield 498 mg of a white to pale yellow solid. Yield:
CONCLUSIONS
■
Reliable, robust, and predictive four-variable models for the in
vitro cytotoxicity of bis-, tris-, and tetrakis(carboxylato)platinum-
(IV) complexes in cisplatin sensitive CH1 cells and intrinsically
cisplatin resistant SW480 cells were developed. The QSAR
model of choice (R2 = 85%, Q2 = 82%) for CH1 cells was built
using the combination of MW, Hdon, Hacc and Eeas′. For the
SW480 cell line, models consisting of Es, Hdon, Hacc, and COOH
(R2 = 80%, Q2 = 75%) and Ei, Eeas, Eeas′, and Hdon (R2 = 80%,
Q2 = 76%) were proposed. The autocorrelating descriptors
q(Pt), Hacc, and Eeas distinguished well the two main subtypes of
compounds, namely, bis(carboxylato)dichlorido (subset 1) from
tris- and tetrakis(carboxylato) (subset 2) complexes and showed
some minor discrimination within the subsets, depending on dif-
ferent equatorial ligands. Hacc predicted a slightly higher activity
for nedaplatin analogues compared to the other compounds in
subset 2 (the case of CH1 cells), while q(Pt) and Eeas dis-
criminated oxaliplatin analogues as more active (the case of
SW480 cells). The constitutional descriptor Hdon discriminated
the main functionalities on the axial ligands: amides and free
carboxylic acids from esters. Therefore, the latter is crucial for
building a good model. MW as a descriptor indicates the increase
of lipophilicity (respectively, cytotoxicity) in the series with
increasing the size of the axial chains or the size of equatorial
amines. Eeas′ and Es, redox behavior and solubility correspond to
important physicochemical parameters of Pt(IV) complexes and
expectedly show significance for the prediction of the biological
response. The results of the study represent a step toward a better
understanding of the biological behavior of Pt(IV) carboxylato
complexes and their further rational development.
498 mg (69%). 1H NMR: δ = 4.02 (s (with Pt satellites) H-1) ppm. 13
C
NMR: δ = 194.8 (C-2), 68.1 (C-1) ppm. 195Pt NMR: δ = −47 ppm. IR
(ATR): ν = 3201 br, 2995 br (νN−H); 2889 br; 1613 s, 1578 s (νCO); 1443
w; 1337 s, 1319 m, 1060 w cm−1. Anal. (C2H8N2O3Pt) C, H, N.
(OC-6-44)-Diammineglycolatodihydroxidoplatinum(IV). Ne-
daplatin (1.0995 g, 3.6267 mmol) was suspended in 22 mL of triply
distilled water, and then an amount of 11 mL of 30% H2O2 was added.
The mixture was stirred for 3 h at 30 °C (in the dark). The volume of the
clear yellow solution obtained was reduced on a rotavapor, cooled in the
fridge, and then precipitated with a sufficient amount of cold acetone.
The precipitation was finalized with the help of ultrasonic waves and
then the final product was filtered off, washed with acetone, and dried in
vacuo to obtain a white to pale yellow solid. Yield: 1.3150 g (94%). 1H
NMR: δ = 4.30 (s + d, 3JPt,H = 20.8 Hz, H-1) ppm. 195Pt NMR: δ = 3222
ppm. IR (ATR): ν = 3462 br (νPtO‑H); 3229 br, 3045 br (νN−H); 2791 w;
1653 s, 1588 m (νCO); 1346 s, 1308 s; 1060 w cm−1.
(OC-6-42)-Diamminebis(3-carboxypropanoato)-
glycolatoplatinum(IV) (48). Succinic anhydride (950 mg, 9.4934 mmol)
and 800 mg (2.0607 mmol) of (OC-6-44)-diammineglycolatodihydro-
xidoplatinum(IV) were suspended in dry DMF (26 mL), and the
reaction mixture was stirred at 60 °C for 8 h. During this time, the solid
material dissolved to form a pale yellow solution. DMF was then
removed under reduced pressure. The residue was suspended in acetone
with the help of ultrasonic waves, filtered off, and washed with acetone.
The pale yellow solid obtained was then dried in vacuo. Yield: 1.0865 g
(98%). 1H NMR: δ = 12.36 (bs, 2H, COOH), 6.47 (m, 3H, NH3), 6.05
(bs, 3H, NH3), 4.08 (bs, 2H, H-1), 2.56 (t, 3JH,H = 6.5 Hz, 4H, H-4 or
H-5), 2.49 (t, 3JH,H = 6.5 Hz, 4H, H-4 or H-5) ppm. 13C NMR: δ = 187.0
(C-2), 180.1 (C-3 or C-6), 174.0 (C-3 or C-6), 70.8 (C-1), 30.6 (C-4 or
C-5), 29.8 (C-4 or C-5) ppm. 15N NMR: δ = −58.6, −50.7 ppm. 195Pt
NMR: δ = 3460 ppm. IR (ATR): ν = 3201 br, 3109 br (νN−H); 2934 br;
1715 m, 1654 s, 1620 s, 1574 s (νCO); 1405 w; 1340 s, 1307 s, 1242 m,
1198 m, 1162 m, 1049 w cm−1. Anal. (C10H18N2O11Pt) C, H, N.
(OC-6-42)-Diammineglycolatobis((4-propyloxy)-4-
oxobutanoato)platinum(IV) (49). CDI (253 mg, 1.5566 mmol) in
dry DMF (9 mL) was added to a solution of 48 (408 mg, 0.7593 mmol)
in dry DMF (10 mL), and the mixture was heated to 60 °C. After 10 min
of being stirred, the solution was cooled to room temperature and CO2
was removed by flushing with argon. Sodium propanolate in n-propanol
(9 mL) (a piece of Na (15 mg), dissolved in 10 mL of n-propanol) was
added to the solution and stirred for 30 h at room temperature. Then
EXPERIMENTAL SECTION
■
All reagents and solvents were obtained from commercial suppliers and
were used without further purification. Water was purified through
reverse osmosis, followed by double distillation. For column chro-
matography, silica gel 60 (Fluka) was used. 1H, 13C, 15N, 195Pt, and two-
dimensional 1H−1H COSY, 1H−13C and 1H−15N HSQC, and 1H−13C
HMBC NMR spectra were recorded with a Bruker Avance III 500 MHz
NMR spectrometer at 500.32 MHz (1H), 125.81 MHz (13C), 107.55
MHz (195Pt), and 50.70 MHz (15N) in DMF-d7 or D2O (in the case of
nedaplatin and its dihydroxido Pt(IV) analogue) at ambient temper-
1
ature. The splitting of proton resonances in the H NMR spectra are
defined as follows: s = singlet, bs = broad singlet, d = doublet, t = triplet,
and m = multiplet. 15N chemical shifts were referenced relative to
external NH4Cl, whereas 195Pt chemical shifts were referenced relative
to external K2[PtCl4] (see Figure 2, compounds 48−50, including NMR
numbering scheme). IR spectra were recorded with a Bruker Vertex
70 FT-IR spectrometer (4000−400 cm−1) by using an ATR unit.
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propanol and DMF were removed under reduced pressure to form a
yellow oil. The crude product was purified by column chromatography
(EtOAc/MeOH, 4:1), then isolated from an EtOAc suspension, and
dried in vacuo to yield a white to pale yellow powder. Yield: 95 mg
(20%). 1H NMR: δ = 6.57 (m, 3H, NH3), 6.12 (bs, 3H, NH3), 4.06 (bs,
2H, H-1), 4.01 (t, 3JH,H = 6.5 Hz, 4H, H-7), 2.57 (m, 4H, H-4), 2.53 (m,
(MTT = 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2H-tetrazolium bro-
mide, purchased from Fluka). Cells were harvested from culture flasks
by trypsinization and seeded in 100 μL aliquots in complete medium
into 96-well microculture plates (Iwaki/Asahi Technoglass) in the
following densities to ensure exponential growth of untreated controls
throughout the experiment: 1.5 × 103 (CH1) and 2.5 × 103 (SW480)
viable cells per well. Cells were allowed to settle and resume exponential
growth in drug-free complete culture medium for 24 h, followed by the
addition of dilutions of the test compounds in 100 μL/well of the same
medium. After continuous exposure for 96 h, the medium was replaced
by a 100 μL/well RPMI 1640 medium (supplemented with 10% heat-
inactivated fetal bovine serum and 4 mM L-glutamine) plus 20 μL/well
solution of MTT in phosphate-buffered saline (5 mg/mL) (all purchased
from Sigma-Aldrich). After incubation for 4 h, medium/MTT mixtures
were removed, and the formazan product formed by viable cells was
dissolved in DMSO (150 μL/well). Optical densities at 550 nm were mea-
sured with a microplate reader (Tecan Spectra Classic), using a reference
wavelength of 690 nm to correct for unspecific absorption. The quantity of
viable cells was expressed as the percentage of untreated controls, and 50%
inhibitory concentrations (IC50) were calculated from concentration−effect
curves by interpolation. Evaluation is based on the mean from three in-
dependent experiments, each comprising triplicates per concentration level.
Theoretical Calculations. All calculations were performed with the
Gaussian 09 software package.34 The starting structures for
optimizations (complexes 1, 6, 7, 22, 38) were taken from the available
X-ray data;14,17,18,20 the crystal structure of complex 7 is reported herein.
The other compounds were modeled by modification of the latter. A
complete conformational search is not feasible for systems with as many
degrees of freedom as the present. Furthermore, there are very few force
fields capable of handling Pt complexes. In order to see the influence of
different conformations to the calculated parameters and to the QSAR
models, four different conformers of compound 2 and two of compound
50 were modeled and their geometry was optimized (Figure S5,
Supporting Information). In principle, the possible conformational
uncertainties are in the chains of the axial ligands because the surround-
ings around the platinum atoms were taken from the crystallographic
data. For compounds 19, 20, 42, 43, and 44, which have two chiral
centers in the axial carbon chains, the meso RS forms were taken for the
calculations. These compounds were tested for cytotoxicity as a mixture
of RR/SS/RS = 1:1:2 stereoisomers. In the particular case, stereo-
chemistry will not affect essentially the activity because the chiral centers
are in the middle of the carbon chains of the axial ligands, which are
supposed to be lost after the activation of the complexes via reduction in
vivo. In contrast, in the case of oxaliplatin, featuring DACH (1,2-
diaminocyclohexane), the R,R configuration of the ligand should be
respected (compounds 51−53).
The DFT long-range corrected hybrid wb97x functional was used for
all calculations35 in connection with the Def2-SVP basis set36 with
effective core potential37 for optimizing the geometries and calculation
of the molecular descriptors. For the calculations of polarizability and dipole
moment the basis set were augmented by a set of diffuse functions.
Geometry optimizations were performed in the gas phase and in a
water solvent model by using the IEFPCM38 method. Solvent accessible
surface area (SASA) was extracted after single point energy calculation
of the gas optimized structures in water environment with the ipcm39
method, where the cavity is defined by a self-consistent isodensity
contour in a water solvent model. Atomic charges were calculated using
the NPA approach.40
The molar volume and the HOMO and LUMO energies were taken
from the gas phase optimized geometries. The energies of solvation were
calculated by extracting the energies in water environment (using the
iefpcm method) with (adiabatic Es′) or without (vertical Es) optimization
from the total energies in gas phase. For estimation of the ionization
potential and electron affinity, the energies of the corresponding anion and
cation radicals were calculated in the gas phase and in solvent, with (only for
the anion radicals) and without geometry optimization.
4H, H-5), 1.62 (m, 4H, H-8), 0.91 (t, 3JH,H = 7.2 Hz, 6H, H-9) ppm. 13
C
NMR: δ = 186.9 (C-2), 179.8 (C-3), 172.6 (C-6), 70.8 (C-1), 65.6
(C-7), 30.5 (3JPt,C = 41.0 Hz, C-4), 29.9 (C-5), 21.9 (C-8), 9.9 (C-9)
ppm. 15N NMR: δ = −58.3, −49.7 ppm. 195Pt NMR: δ = 3464 ppm. IR
(ATR): ν = 3301 br, 3212 br, 3048 br (νN−H); 2971 br; 1729 m, 1707 s,
1621 s, 1585 m (νCO); 1483 w; 1435 m, 1345 m, 1316 s, 1249 w,
1180 s, 1166 m, 1105 m, 984 w cm−1. ESI MS (positive): m/z 643.9
[M + Na+]+. ESI MS (negative): m/z 620.8 [M − H+]−, 656.7 [M + Cl−]−.
Anal. (C16H30N2O11Pt·0.5H2O) C, H, N.
(OC-6-42)-Diamminebis((4-cyclopentylamino)-4-
oxobutanoato)glycolatoplatinum(IV) (50). CDI (176 mg, 1.0835
mmol) in dry DMF (7 mL) was added to a solution of 48 (284 mg,
0.5285 mmol) in dry DMF (9 mL), and the mixture was heated to 60 °C.
After 10 min of being stirred, the solution was cooled to room tem-
perature and CO2 was removed by flushing with argon. Cyclopentyl-
amine (115 μL, 1.1628 mmol) in 4 mL of dry DMF was added to the
solution and stirred for 24 h at room temperature (the solution changed
to a yellow suspension). DMF was removed under reduced pressure to
form a pale brown solid. The crude product was purified by column
chromatography, using EtOAc/MeOH = 2:1, and subsequently isolated
from an EtOAc suspension, washed with EtOAc and Et2O, and dried in
vacuo to yield an almost white solid. Yield: 116 mg (33%). 1H NMR: δ =
7.78 (d, 3JH,H = 6.6 Hz, 2H, CONH), 6.49 (m, 3H, NH3), 6.06 (bs, 3H,
NH3), 4.09 (m, 2H, H-7), 4.06 (s, 2H, H-1), 2.50 (t, 3JH,H = 7.4 Hz, 4H,
H-4 or H-5), 2.36 (t, 3JH,H = 7.3 Hz, 4H, H-4 or H-5), 1.83 (m, 4H, H-8),
1.66 (m, 4H, H-9), 1.52 (m, 4H, H-9), 1.44 (m, 4H, H-8) ppm. 13C
NMR: δ = 187.0 (C-2), 180.7 (C-3), 171.2 (C-6), 70.9 (C-1), 50.8
(C-7), 32.5 (C-8), 31.8 (C-4 or C-5), 31.5 (C-4 or C-5), 23.6 (C-9) ppm.
15N NMR: δ = −58.7, −50.2, 106.7 ppm. 195Pt NMR: δ = 3459 ppm. IR
(ATR): ν = 3269 br, 3058 br (νN−H); 2958 br; 1697 m, 1632 m, 1571 s
(νCO); 1483 m; 1441 s, 1339 w, 1319 m, 1255 m, 1195 m, 1106 s, 997
w cm−1. ESI MS (positive): m/z 694.0 [M + Na+]+, 671.0 [M + H+]+.
ESI MS (negative): m/z 669.9 [M − H+]−, 705.8 [M + Cl−]−. Anal.
(C20H36N4O9Pt·0.5H2O) C, H, N.
Crystallographic Structure Determination. Yellow crystals of 7,
suitable for X-ray data collection, were obtained after slow evaporation
of a MeOH/EtOAc solution. X-ray diffraction measurement was per-
formed on a Bruker X8 APEXII CCD diffractometer. A single crystal was
positioned at 40 mm from the detector, and 1638 frames were measured,
each for 15 s over 1° scan width. The data were processed using SAINT
software.31 Crystal data, data collection parameters, and structure
refinement details are given in Table S10 (Supporting Information).
The structure was solved by direct methods and refined by full-matrix
least-squares techniques. Non-H atoms were refined with anisotropic
displacement parameters. H atoms were inserted in calculated positions
and refined with a riding model. The isotropic thermal parameters were
estimated to be 1.2 times the values of the equivalent isotropic thermal
parameters of the atoms to which hydrogens were bonded. Structure
solution was achieved with SHELXS-97 and refinement with SHELXL-
97,32 and graphics were produced with ORTEP-3.33
Cytotoxicity Assays. CH1 (ovarian carcinoma, human) cells were a
gift from Lloyd R. Kelland (CRC Centre for Cancer Therapeutics,
Institute of Cancer Research, Sutton, U.K.). SW480 (colon carcinoma,
human) cells were kindly provided by Brigitte Marian (Institute of
Cancer Research, Department of Medicine I, Medical University of
Vienna, Austria). Cells were grown in 75 cm2 culture flasks (Iwaki/Asahi
Technoglass) as adherent monolayer cultures in complete medium,
i.e., minimal essential medium (MEM) supplemented with 10% heat-
inactivated fetal bovine serum, 1 mM sodium pyruvate, 4 mM
L-glutamine, and 1% v/v nonessential amino acids (from 100× ready-
to-use stock) (all purchased from Sigma-Aldrich) without antibiotics.
Cultures were maintained at 37 °C in a humidified atmosphere
containing 5% CO2 and 95% air. Cytotoxicity in the cell lines
mentioned above was determined by the colorimetric MTT assay
QSAR Analysis. QSAR Data Set. The pIC50 = log(1/IC50) values,
used to develop the QSAR models, were taken from the MTT assays,
described in refs 14 and 18−20 for complexes 1−47 and 51−53 and in
the present paper for complexes 48−50. The cytototoxicity data in CH1
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dx.doi.org/10.1021/jm3016427 | J. Med. Chem. 2013, 56, 330−344

Journal of Medicinal Chemistry
Article
and SW480 cells for all investigated compounds in comparison with the
clinically approved platinum-based drugs cisplatin, carboplatin,
oxaliplatin, and nedaplatin are summarized in Table 1.
Notes
The authors declare no competing financial interest.
By use of QM calculations, the following descriptors were extracted:
molar volume (Vm), dipole moment (μ), polarizability (α), charge on
the Pt atom (q(Pt)), energy of the HOMO and LUMO (and the corre-
sponding HOMO−LUMO gap), SASA, vertical and adiabatic solvation
energies (Es and Es′), vertical gas-phase ionization energies (Ei) and
electron affinities (Eea), and vertical and adiabatic oxidation (Eis) and
reduction (Eeas and Eeas′) potentials in the water solvent model. In
addition molecular weight (MW), number of H-bonds donors (Hdon),
numbers of H-bonds acceptors (Hacc), and presence/absence of
carboxylic groups (COOH) in the axial ligands as constitutional molecular
descriptors were used. The values of the used descriptors in the present
study are summarized in Tables S3 and S4 (Supporting Information).
Chemometric Methods and Statistics. QSAR analysis was
performed with the QSAR program41 developed by the Ponder group
ACKNOWLEDGMENTS
■
H.P.V. is thankful for financial support from the University of
Vienna, Austria, within the doctoral program Initiativkolleg
Functional Molecules IKI041-N. The authors are indebted to the
FFGResearch and Technology Development, the FWF
(Austrian Science Fund, Grant P20683-N19), and COST D39.
This work was supported by grants from the Danish Center for
Scientific Computation and the Danish Natural Science Research
Council. We also thank Mahsa S. Adib-Razavi for performing the
MTT tests for compounds 48−50.
ABBREVIATIONS USED
and Schrodinger Strike 2.0 for Maestro application.42 Standard multiple
■
̈
linear regression (MLR) and principal component analysis (PCA)
methods were used to analyze the data, and simulated annealing was
employed to identify the best combinations of descriptors. All de-
scriptors were centered and autoscaled prior to analysis.
FDA, Food and Drug Administration; ESP, electrostatic
potential; SAR, structure−activity relationship; QSAR, quanti-
tative structure−activity relationship; QSPR, quantitative
structure−property relationship; DFT, density functional
theory; MLR, multiple linear regression; MM, molecular
mechanics; log Po/w, logarithm of partition coefficient between
n-octanol and water; Ep, redox potential; NPA, natural
population analysis; PC, principal component; PCA, principal
component analysis; RSD, relative standard deviation; CDI, 1,1′-
carbonyldiimidazole
The robustness of the models and their predictivity were evaluated
through R2, Q2 (R2 of cross-validated predictions, using the leave-one-
out procedure (LOOP) or leave-two-out procedure (LTOP)), AAR
(average absolute error), and rms (root mean squared error). The actual
predictive capability of every model was checked with external validation
by splitting the data set into training and predictive sets. Five different
ways of partitioning the data into training and predictive data sets were
used in order to test the robustness of the QSAR model. In each case the
training set encompassed 75% of the data while the remaining 25% was
selected as (a) random, including 14 compounds representing every
subtype, (b) including cisplatin and its bis(ethylamine) analogue
derivatives (complexes 1−5, counting all conformers for 2 and 21−26),
(c) including most of the ethylenediamine analogues (complexes 6−
19), (d) including most of the carboplatin analogues (27−40), and (e)
including oxaliplatin, nedaplatin, and the other part of the carboplatin
analogues (complexes 41−53, counting both conformers of 50). By use
of the models derived from the training sets, the pIC50 values in the
predictive sets were calculated and R2 predictive, AAS, and RMS were
measured.
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ASSOCIATED CONTENT
* Supporting Information
■
S
Figures with ORTEP view of 7, chemical structures of the model
systems used in the reduction studies, dependency of the QSAR
model properties from the descriptors number, loading plots
derived from the PCA of the CH1 cells four variable model, and
superposition of the optimized conformers of complexes 2 and
50; tables with comparison of calculated and experimental
geometrical parameters, calculated descriptors, and additional
statistical data for chosen QSAR models and their external
validation, dependency of the QSAR model properties from the
input IC50 values used for the inactive compounds, elemental
analysis data, structure refinement details for 7; X-ray crystallo-
graphic data in CIF format. This material is available free of
charge via the Internet at http://pubs.acs.org. Crystallographic
data have been deposited with the Cambridge Crystallographic
Data Center with number CCDC 909001. Copies of data can be
obtained, free of charge, on application to CCDC, 12 Union
Road, Cambridge CB2 1EZ, U.K. (deposit@ccdc.com.ac.uk).
AUTHOR INFORMATION
Corresponding Author
■
*For F.J.: phone, +45-87155908; e-mail, frj@chem.au.dk. For
M.G.: phone, +43-1-4277-52600; fax, +43-1-4277-52680; e-mail,
markus.galanski@univie.ac.at.
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