NANOPARTICLES:
UNUSUAL QSAR FOR UNUSUAL
STRUCTURE
Novoselska Natalia
Bakhtiyor Rasulev, Agnieszka Gajewicz, Tomasz Puzyn,
Jerzy Leszczynski, Kuzmin Viktor
RECENT NANO-QSAR STUDIES
1. H. Tzoupis et. al, Binding of novel fullerene inhibitors to HIV-1
protease. J. Comput. Aided Mol. Des., 2011, 25, 959–976
2. A. Toropova et. al. CORAL: QSPR models for solubility of [C60] and
[C70] fullerene derivatives. Molecular Diversity, 2011, 5, 249-256
3. T. Puzyn, et. al. Using nano-QSAR to predict the cytotoxicity of metal
oxide. Nature Nanotechnology, 2011, 6, 175-178
4. A. Toropov et. al, InChI-based optimal descriptors: QSAR analysis of
fullerene[C60]-based HIV-1 PR inhibitors by correlation balance. Eur.
J. of Med. Chem., 2010, 45, 1387–1394
5. K. Muzino et. al, Antimicrobial Photodynamic Therapy with
Functionalized Fullerenes:Quantitative Structure-activity
Relationships. J Nanomedic Nanotechnol., 2011, 2, 175-17
6. …..
2
7. N.Novoselska et. al, 2D – nanoQSAR models for predict the
cytotoxicity of metal oxides nanoparticles. NanoScale, not yet issued
IS THE SIRMS APPROACH APPLICAPABLE FOR
• 2D-simplexes descriptors
NANOPARTICLES’ DESCRIPTION?
Differentiation by type, charge, refraction, donor/acceptor of
hydrogen bond, lipophilicity
Lipophilicity was calculated by additive scheme (XLogP) [Renxiao
Wang, Ying Fu, Luhua Lai, J.Chem. Inf. Comput. Sci., 37 (1997)]
Integral characteristics: XLogP, Rf, AW, En
Kuz’min V.E. et al. Virtual screening and molecular design based on hierarchical QSAR technology. //
Challenges and Advances in Computational Chemistry and Physics, 2010, 8, 127-176
3
1. Analysis of efficiency SiRMS:
solubility of C[60] and C[70] derivatives in chlorobenzene
P. Troshin et al.
Material SolubilityPhotovoltaic Performance
Relationship in the Design
of Novel Fullerene
Derivatives for Bulk
Heterojunction Solar Cells
Advanced Functional
Materials, 2009
19, 5, 779–788
4
1. Analysis of efficiency SiRMS:
solubility of C[60] and C[70] derivatives in chlorobenzene
A. Toropov et. al
CORAL: QSPR models for solubility of [C60]
and [C70] fullerene derivatives
Molecular Diversity, 2011, 5, 249-256
*
Our results:
R2
= 0.90
S = 12.5 (mg/mL)
O
R2 (consensus)
= 0.98
S = 2.5 (mg/mL)
H3C
O
*
O
H3C
*
O
*
CH3
O
13
33
5
5
44
Electronegativity
Partial charges
Lipophilicity
Polarization
Atom's individuality
S, mg/mL (calculated)
140
120
100
80
60
40
20
5
0
0
20
40
60
80
S, mg/mL (Exp)
100
120
2. Analysis of efficiency SiRMS:
fullerene-based HIV-1 PR inhibitors
CoMFA:
R2 = 0.98
Q2 = 0.61
S = 0.154
CoMSIA:
R2 = 0.99
Q2 = 0.79
S = 0.137
A. Toropov et. al, SMILES-Based Optimal
Descriptors: QSAR Analysis of FullereneBased HIV-1 PR Inhibitors by Means of
Balance of Correlations; J. Comp. Chem,
2010, 31, 381–392
R2 = 0.5-0.99
S = 0.127-0.352
-1.5
-8
-7
-6
-5
-4
-3
pEC50 (Calc)
H. Tzoupis et. al, Binding of novel fullerene
inhibitors to HIV-1 protease; J. Comput.
Aided Mol. Des., 2011, 25, 959–976
-2.5
-2
-3.5
-4.5
-5.5
-6.5
-7.5
pEC50 (Exp)
R2(consensus) = 0.98
S = 0.14
A. Toropov et. al, InChI-based optimal
descriptors: QSAR analysis of fullerene[C60]based HIV-1 PR inhibitors by correlation
balance Eur. J. of Med. Chem., 2010, 45,
1387–1394
R2 = 0.76-0.97
S = 0.271-0.681
6
UNUSUAL QSAR… OH, REALLY?
7
LDM: LIQUID DROP MODEL
In a liquid drop model, nanoparticle is represented as the spherical
drop, which elementary particles are densely packed, and density of
cluster is equal to mass density. In this model the minimum radius of
interactions between elementary particles in cluster is described by
Wigner-Seitz radius:
1
 3M  3


rw  

 4 N A 
- molecular mass of molecule,
M

- mass density,
NA
- Avogadro constant.
Smirnov B M.
8
Processes involving clusters and small particles
in a buffer gas. Phys. Usp. 2011, 54, 691–721
3. Superconductivity critical temperatures of inorganic
nanoparticles
Tc
195
75
52
200
80
60
415
95
130
60
315
65
44
Predicted values of log(Tc)
Compound
ZnS
ZnSe
ZnTe
CdS
CdSe
CdTe
GaN
GaP
GaAs
GaSb
InN
InP
InAs
2.9
2.7
2.5
2.3
R2 (consensus) = 0.83
S = 0.3
2.1
1.9
1.7
1.5
1.5
1.7
1.9
2.1
2.3
2.5
Observed values of log(Tc)
34
E gap
2.7
2.9
13
Electronegativity
Wiegner-Zeits radius
Diagram of relative influence (%) on
critical temperatures
53
9
4. Comparative QSAR analysis of toxic effects of metal oxide
nanoparticles
Compound
Al2O3
Bi2O3
CoO
Cr2O3
Fe2O3
In2O3
La2O3
NiO
Sb2O3
SiO2
SnO2
TiO2
V2O3
WO3
Y2O3
ZnO
ZrO2
HaCaT cells,
log(1/EC50)
2.49
2.82
3.51
2.51
2.29
2.81
2.87
3.45
2.64
2.2
2.01
1.74
3.14
2.87
3.45
2.15
E. Coli,
log(1/EC50)
1.85
2.5
2.83
2.3
2.05
2.92
2.87
2.49
2.31
2.12
2.67
1.76
2.24
2.56
2.21
3.32
2.02
Size, nm
44
90
100
60
32
30
46
30
20
150
15
46
15
50
38
71
47
Aggregation
size, nm
372
2029
257
617
298
224
673
291
223
640
810
265
1307
180
1223
189
661
10
LDM: LIQUID DROP MODEL
1
 3M  3


rw  

 4 N A 
F  4n
3
 r0 

n  
 rw 
 13
surfacem olecules
F

m oleculesin volum e 1  F
Aggregatio n parameter 
size of aggregate
size of single particle
11
M ETAL- LIGAND B INDING C HARACTERISTICS
(CI) - reflects the energy of the metal ion during electrostatic
interactions with a ligand:
(CI )   2
mr
(CPP) - reflects the relative importance of covalent
interactions relative to ionic during metal-ligand binding:
(CPP)  Z 2 r
M.C. Newman, et al .
Using metal–ligand binding characteristics to predict metal
toxicity: quantitative ion character–activity relationships
(QICARs). Environ. Health Persp., 1998, 106, 1419–1425
12
4. Comparative QSAR analysis of toxic effects of metal oxide
nanoparticles
3.5
Observed values of log(1/EC50)
Observed values of log(1/EC50)
3.3
3.1
2.9
2.7
2.5
2.3
2.1
1.9
1.7
3.3
3.1
2.9
2.7
2.5
2.3
2.1
1.9
1.7
1.7
2.2
2.7
3.2
1.7
2.2
2.7
3.2
Observed values of log(1/EC50)
Observed values of log(1/EC50)
R2 (training set)
HaCaT cells
(17 compounds)
0.96
E.Coli
(16 compounds)
0.93
S (training set)
0.10
0.13
R2 (test set)
0.92
0.78
S (test set)
0.12
0.32
13
4. Comparative QSAR analysis of toxic effects of metal oxide
nanoparticles
32.2
43
8
31
32
9.6
15.2
29
Van-der-Waals interactions
Van-der-Waals interactions
electronegativity
electronegativity
covalent index
cation polarizing power
LDM-based
LDM-based
Diagram of relative influence (%) on
toxicity to HaCaT cells
Diagram of relative influence (%) on
toxicity to E.Coli
14
It was shown that SiRMS descriptors (in case of
fullerenes) and combination of LDM-based descriptors
with SiRMS (in case of inorganic nanoparticles) can be
helpful for QSAR investigation of different properties of
nanomaterials.
15
THANK YOU FOR
YOUR ATTENTION!
ACKNOWLEDGEMENTS
A.V.BOGATSKI PHYSICO-CHEMICAL INSTITUTE
NAS OF UKRAINE
KUZMIN VIKTOR
INTERDISCIPLINARY CENTER FOR NANOTOXICITY
BAKHTIYOR RASULEV, JERZY LESZCZYNSKI
UNIVERSITY OF GDANSK
AGNIESZKA GAJEWICZ, TOMASZ PUZYN
LDM: LIQUID DROP MODEL
1
 3M  3


rw  

 4 N A 
F  4n
3
 r0 

n  
 rw 
 13
surfacem olecules
m oleculesin volum e

F
1 F
Aggregatio n parameter 
size of aggregate
size of single particle
Simple
combination
SiRMS
+
LDM
Recalculation
CLASSIFICATION OF NANOPARTICLES
18
2. Analysis of efficiency SiRMS:
fullerene-based HIV-1 PR inhibitors
Simplex Representation of Molecular
Structure
Electrostatic
Steric
Charges
Informational
Lipophilicity
Polarizability
Volume
PhysicalChemical
H
Descriptoral
Cl
Molecular Field
etc
N
n!
(n  4)!4!
Random
Forest
method implemented in CF program
(http://qsar4u.com) was used for the development of QSPR models at the
2D level of representation of molecular structure.
Forest is a set of classification or regression trees (T).
The major criterion for estimation of the predictive ability of the RF
models and model selection is the value of R2OOB. Coefficient of
determination for OOB set:
2
ROOB
 1   (Y  Y ) 2 /  (Y  Y ) 2
n
n
Determination coefficient for test set (R2test), standard error (SE) and
mean absolute error (MAE) are also characteristics of the models. R2test for
test set is calculated similar to R2OOB.
SE 
2
(
Y

Y
)
/(n  1)

n
MAE   | Y  Y | / n
n
1 n
Consensus =

n i 1 Y i