Nanoparticles: unusual QSAR for unusual structure

1. Nanoparticles: unusual QSAR for unusual structure Novoselska Natalia Bakhtiyor Rasulev, Agnieszka Gajewicz, Tomasz Puzyn, Jerzy Leszczynski, Kuzmin Viktor

2. Recent Nano-QSAR studies H. Tzoupis et. al, Binding of novel fullerene inhibitors to HIV-1 protease. J. Comput.Aided Mol. Des., 2011, 25, 959–976 A. Toropova et. al. CORAL: QSPR models for solubility of [C60] and [C70] fullerene derivatives. Molecular Diversity, 2011, 5, 249-256 T. Puzyn, et. al. Using nano-QSAR to predict the cytotoxicity of metal oxide. Nature Nanotechnology, 2011, 6, 175-178 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 K. Muzino et. al, Antimicrobial Photodynamic Therapy with Functionalized Fullerenes:Quantitative Structure-activity Relationships. J NanomedicNanotechnol., 2011, 2, 175-17 ….. N.Novoselska et. al, 2D – nanoQSAR models for predict the cytotoxicity of metal oxides nanoparticles. NanoScale, not yet issued

3. Is the SiRMS approach applicapable for nanoparticles’ description? • 2D-simplexes descriptors 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

4. 1. Analysis of efficiency SiRMS: solubility of C[60] and C[70] derivatives in chlorobenzene P. Troshinet al. Material Solubility-Photovoltaic Performance Relationship in the Design of Novel Fullerene Derivatives for Bulk Heterojunction Solar Cells Advanced Functional Materials, 2009 19, 5, 779–788

5. 1. Analysis of efficiency SiRMS: solubility of C[60] and C[70] derivatives inchlorobenzene 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) R2 (consensus) = 0.98 S= 2.5 (mg/mL)

6. 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 CoMFA: R2 = 0.98 Q2= 0.61 S = 0.154 CoMSIA: R2= 0.99 Q2= 0.79 S = 0.137 R2(consensus) = 0.98 S = 0.14 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 = 0.5-0.99 S = 0.127-0.352 R2 = 0.76-0.97 S= 0.271-0.681

7. Unusual QSAR… Oh, really?

8. 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: - 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

9. 3. Superconductivity critical temperatures of inorganicnanoparticles R2 (consensus)= 0.83 S= 0.3 Diagram of relative influence (%) on critical temperatures

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

11. LDM: Liquid Drop Model

12. Metal-ligand Binding Characteristics (CI) - reflects the energy of the metal ion during electrostatic interactions with a ligand: (CPP) - reflects the relative importance of covalent interactions relative to ionic during metal-ligand binding: 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

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

14. 4. Comparative QSAR analysis of toxic effects of metal oxide nanoparticles Diagram of relative influence (%) on toxicity to HaCaT cells Diagram of relative influence (%) on toxicity toE.Coli

15. 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.

16. Thank you for your attention! AcknowledgementsA.V.BogatskiPhysico-Chemical Institute NAS of UkraineKuzmin ViktorInterdisciplinary Center for NanotoxicityBakhtiyorRasulev, Jerzy LeszczynskiUniversity of GdanskAgnieszkaGajewicz, Tomasz Puzyn

17. LDM: Liquid Drop Model SiRMS+ LDM Simple combination Recalculation

18. Classificationof nanoparticles

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

20. Simplex Representation of Molecular Structure Electrostatic Steric Informational Charges Lipophilicity Polarizability Volume etc Molecular Field Physical-Chemical Descriptoral

21. Random Forestmethod implemented in CF program (http://qsar4u.com) was used for the development of QSPR models at the 2D level of representation of molecular structure. Forestis aset ofclassificationorregression trees (T). The major criterion for estimationof the predictive ability of the RF models and model selectionis the value of R2OOB. 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. R2testfor test set is calculated similar to R2OOB.

22. Consensus =