NANOPARTICLES:NANOPARTICLES: UNUSUAL QSAR FOR UNUSUAL UNUSUAL QSAR FOR UNUSUAL STRUCTURESTRUCTURE

Novoselska Natalia

Bakhtiyor Rasulev, Agnieszka Gajewicz, Tomasz Puzyn, Jerzy Leszczynski, Kuzmin Viktor

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RECENT NANO-QSAR STUDIESRECENT 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. …..7. N.Novoselska et. al, 2D – nanoQSAR models for predict the

cytotoxicity of metal oxides nanoparticles. NanoScale, not yet issued

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

• 2D-simplexes descriptors

3Kuz’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

IS THE SIRMS APPROACH IS THE SIRMS APPROACH APPLICAPABLE FOR NANOPARTICLES’ APPLICAPABLE FOR NANOPARTICLES’ DESCRIPTION?DESCRIPTION?

1. Analysis of efficiency SiRMS: solubility of C[60] and C[70] derivatives in chlorobenzene

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P. Troshin et al. Material Solubility-Photovoltaic Performance Relationship in the Design of Novel Fullerene Derivatives for Bulk Heterojunction Solar Cells

Advanced Functional Materials, 200919, 5, 779–788

1. Analysis of efficiency SiRMS: solubility of C[60] and C[70] derivatives in chlorobenzeneA. Toropov et. alCORAL: QSPR models for solubility of [C60] and [C70] fullerene derivativesMolecular Diversity, 2011, 5, 249-256

R2 = 0.90S = 12.5 (mg/mL)

R2 (consensus) = 0.98S = 2.5 (mg/mL)

Our results:

CH3O

*

O*

O*CH3

CH3O *

O

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2. Analysis of efficiency SiRMS: fullerene-based HIV-1 PR inhibitors

H. Tzoupis et. al, Binding of novel fullerene inhibitors to HIV-1 protease; J. Comput. Aided Mol. Des., 2011, 25, 959–976

A. Toropov et. al, SMILES-Based Optimal Descriptors: QSAR Analysis of Fullerene-Based HIV-1 PR Inhibitors by Means of Balance of Correlations; J. Comp. Chem, 2010, 31, 381–392

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 = 0.5-0.99S = 0.127-0.352

R2 = 0.76-0.97S = 0.271-0.681

CoMFA:R2 = 0.98Q2

= 0.61S = 0.154

CoMSIA:R2 = 0.99Q2

= 0.79S = 0.137

R2(consensus) = 0.98

S = 0.14

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UNUSUAL QSAR… OH, REALLY?UNUSUAL QSAR… OH, REALLY?

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LDM: LIQUID DROP MODELLDM: LIQUID DROP MODEL

31

4

3

AN

Mrw

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:

M

N A

- molecular mass of molecule,

- mass density,

- Avogadro constant.

Smirnov B M.

Processes involving clusters and small particles in a buffer gas. Phys. Usp. 2011, 54, 691–721

3. Superconductivity critical temperatures of inorganic nanoparticles

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Compound TcZnS 195ZnSe 75ZnTe 52CdS 200CdSe 80CdTe 60GaN 415GaP 95GaAs 130GaSb 60InN 315InP 65InAs 44

Diagram of relative influence (%) on critical temperatures

R2 (consensus) = 0.83S = 0.3

4. Comparative QSAR analysis of toxic effects of metal oxide nanoparticles

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Compound HaCaT cells,log(1/EC50)

E. Coli,log(1/EC50)

Size, nm Aggregation size, nm

Al2O3 2.49 1.85 44 372Bi2O3 2.82 2.5 90 2029CoO 3.51 2.83 100 257

Cr2O3 2.51 2.3 60 617Fe2O3 2.29 2.05 32 298In2O3 2.81 2.92 30 224La2O3 2.87 2.87 46 673NiO 3.45 2.49 30 291

Sb2O3 2.64 2.31 20 223SiO2 2.2 2.12 150 640SnO2 2.01 2.67 15 810TiO2 1.74 1.76 46 265V2O3 3.14 2.24 15 1307WO3 - 2.56 50 180Y2O3 2.87 2.21 38 1223ZnO 3.45 3.32 71 189ZrO2 2.15 2.02 47 661

Compound HaCaT cells,log(1/EC50)

E. Coli,log(1/EC50)

Al2O3 2.49 1.85Bi2O3 2.82 2.5CoO 3.51 2.83

Cr2O3 2.51 2.3Fe2O3 2.29 2.05In2O3 2.81 2.92La2O3 2.87 2.87NiO 3.45 2.49

Sb2O3 2.64 2.31SiO2 2.2 2.12SnO2 2.01 2.67TiO2 1.74 1.76V2O3 3.14 2.24WO3 - 2.56Y2O3 2.87 2.21ZnO 3.45 3.32ZrO2 2.15 2.02

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LDM: LIQUID DROP MODELLDM: LIQUID DROP MODEL

31

4

3

AN

Mrw

rw

rn

0

3

31

4

nF

F

F

1in volume molecules

molecules surface

particle single of size

aggregate of sizeparameter n Aggregatio

rmCI 2)(

rZCPP 2)(

(CPP) - reflects the relative importance of covalent interactions relative to ionic during metal-ligand binding:

(CI) - reflects the energy of the metal ion during electrostatic interactions with a ligand:

METAL-LIGAND BINDING METAL-LIGAND BINDING CHARACTERISTICSCHARACTERISTICS

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

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  HaCaT cells(17 compounds)

E.Coli(16 compounds)

R2 (training set) 0.96 0.93

S (training set) 0.10 0.13

R2 (test set) 0.92 0.78

S (test set) 0.12 0.32

4. Comparative QSAR analysis of toxic effects of metal oxide nanoparticles

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Diagram of relative influence (%) on toxicity to HaCaT cells

Diagram of relative influence (%) on toxicity to E.Coli

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4. Comparative QSAR analysis of toxic effects of metal oxide nanoparticles

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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.

THANK YOU FOR THANK YOU FOR YOUR ATTENTION!YOUR ATTENTION!

ACKNOWLEDGEMENTSA.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

SiRMS+

LDM

Simple combination

Recalculation

LDM: LIQUID DROP MODELLDM: LIQUID DROP MODEL

31

4

3

AN

Mrw

rw

rn

0

3

31

4

nF

F

F

1in volume molecules

molecules surface

particle single of size

aggregate of sizeparameter n Aggregatio

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CLASSIFICATIONCLASSIFICATION OF OF NANOPARTICLESNANOPARTICLES

2. Analysis of efficiency SiRMS: fullerene-based HIV-1 PR inhibitors

ClH

Physical-Chemical

Charges

Lipophilicity

Polarizability

Volume

etc

Molecular Field

Descriptoral

Electrostatic

Steric

Informational

Simplex Representation of Molecular Structure

!4)!4(!

nn

N

Forest is a set of classification or regression trees (T).

nn

OOB YYYYR 222 )(/)(1

)1/()( 2 nYYSEn

nYYMAEn

/||

The major criterion for estimation of the predictive ability of the RF models and model selection is the value of R2

OOB. Coefficient of

determination for OOB set:

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.

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.

n

iiYn 1

1Consensus =