E. Muratov 1, E. Varlamova 2, A. Artemenko 2, D. Fourches 1, V. Kuz'min 2, A. Tropsha 1

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1 E. Muratov 1, E. Varlamova 2, A. Artemenko 2, D. Fourches 1, V. Kuz'min 2, A. Tropsha 1 1 University of orth Carolina, Chapel Hill, C, UA; 2 A.V. Bogatsky Physical-Chemical Institute AU, dessa, Ukraine;

2 2 Importance of studying the mixtures The importance of mixtures and forecasting their environmental impact has been recognized by the U Environmental Protection Agency (EPA) in special guidelines devoted to health risk assessment of chemical mixtures: Guidelines for the Health Risk Assessment of Chemical Mixtures, U EPA, Washington, upplementary Guidance for Conducting Health Risk Assessment of Chemical Mixtures, U EPA, Washington, 2000 To adequately respond to the challenges posed by such complex exposure scenarios, it is the declared intention of cumulative risk assessment in the UA to develop approaches that allow evaluations of the effects of multiple chemicals: Via multiple routes, Multiple time frames, Giving rise to multiple adverse health outcomes. tate of the Art Report on Mixture Toxicity, /2007/485103/ETU/D.1, University of London, London, UK, 2009

3 3 Why to use mixtures Dual combination of drugs might overcome the disadvantages of monotherapy. ynergistic combinations of inhibitors could achieve the same antiviral effect at lower concentrations than those required if drugs were used alone. Combining drugs could also restrain the resistance phenomenon and increase the selectivity ratio. Combining drugs may sometimes reveal antagonistic interactions, side effects and complications.

4 4 Growing interest in mixtures in medicinal chemistry literature 30 # of publications Year Queried using Thomson Reuters Web of cience v.4

5 5 Difficulty of modeling the mixtures The investigation and prediction of mixture toxicity represent one of most challenging problems in environmental chemical risk assessment. Zhang et al. Chemosphere 2007, 67, Two main problems of QAR analysis of mixtures are: (i) lack of data concerning the action or property of mixtures, and (ii) adequate description of any mixture by the system of (structural) parameters. Muratov et al. Mol. Inf. 2012, 31, 202

6 6 Data sources PubChem: ~ complexes, mixtures and salts ChEMBL: 356 mixtures CI database: ~300 mixtures DTP AID Antiviral creen database: ~200 mixtures Thomson Reuters Integrity: 1248 mixtures mall et al. at Chem Bio 2011: 555 mixtures prisiu et al Mol Info 2012: 261 mixtures Ajmani et al. QAR Comb ci 2008: 271 mixtures Ajmani et al. Mol Info 2010: 411 mixtures

7 7 Classification of Methods for Mixtures Representation bvious: mixture activity = activity1*a+activity2*b, where a+b=1 (o QAR analysis is needed) (Lozitsky et al, 1995) Additive: mixture descriptors = descriptors1*a+descriptors2*b, where a+b=1 (Vasil ev et al, 2004) Divide et impera: dataset is divided on small subsets with one constant component (Kuz min et al, 2010, in preparation) Conglomeratic: mixture is represented as one molecule via pseudo-bond (Varnek et al, 2007) Double: mixture is represented as two molecules (Kuz min et al 2009)

8 8 Classification of Existing Mixture Descriptors Descriptiveness Low High Applicability High Low Integral additive descriptors Descriptors based on partition coefficient for mixtures Fragment non-additive descriptors Integral non-additive descriptors

9 9 Integral mixture descriptors Integral additive descriptors Integral non-additive descriptors MD=X 1 D 1 +X 2 D 2 Descriptors: Dragon, Cerius2 Quantum chemical parameters obtained for mixtures with 1:1 composition. Mixture descriptors reflected intermolecular interactions between the components of mixture: MD=X 1 X 2 f(d 1,D 2 ) where MD is the mixture descriptor, X 1 and X 2 are the mole fractions of the first and second components in the mixture, D 1 and D 2 are the descriptors of the first and second components

10 10 Fragment non-additive descriptors MD = X 1 D 1 + X 2 D 2 ; X 1 D 1+2 MD = X 1 D 1 + X 2 D 2 ; X 1 D 1 -X 2 D 2 H Descriptors: irm (1) (2) H H + H 2 H 2 H Descriptors: IIDA, Dragon, etc. IIDA Fragmentation Traditional simplexes D 1 and D 2 for components 1 and 2 taken separately H H H Mixture simplexes D 1+2 for components 1 and 2 taken together H EQUECE (I) AUGMETED ATM (II) Atoms and Bonds =C--C; C--C; C-; C(-C)(-)(=)(-C=C)(--C)(--C) =C-; C= Atoms CC; CC; C; C; C(C)()()(CC)(C)(C) C Bonds C(-)(-)(=)(- =)(- -)(- -)

11 11 Proper External Validation of QAR models of mixtures The conventional external cross-validation procedure, when the points (compounds) are randomly placed in the external set (or fold)is unacceptable because it leads to an over-optimistic estimation of the predictive power of the developed models. Depending on the initial data and potential application of developed models, three different strategies of external validation could be used: (i) "points out (similar to conventional validation) (ii) "mixtures out (iii) "compounds out Muratov et al. Mol. Inf. 2012, 31, 202

12 12 Mixtures out Filling of missing cells in the initial data (mixtures) matrix, i.e., prediction of the investigated property for mixtures with unknown activity created by pure compounds from the modeling set Cpds c01 c02 c03 c04 c05 c06 c07 c08 c09 c10 c c c c c c c c c c

13 13 Compounds out prediction of the investigated property for mixtures formed by novel pure compound(s) absent in the modeling set Cpds c01 c02 c03 c04 c05 c06 c07 c08 c09 c10 c c c c c c c c c c

14 14 Everything out Training set Compounds out Everything out Muratov, E. et al. Mol. Info. 2014, submitted

15 15 elected QAR studies of Mixtures Authors/ Year Ajmani et al Muratov et al Ajmani et al olov ev et al prisiu et al prisiu et al Investigated activity Infinite dilution activity coefficients Anti-Polio activity Excess molar volume ormal T boiling point T Bubble point T Bubble point umber of compounds/ mixtures 0/411 8/146 0/271 0/176 67/ /94 67/ /94 Descriptors Cerius irm Cerius IIDA irm, IIDA Dragon, ChemAxon, Inductive descriptors

16 16 Double 2.5D Models of Chiral rganophosphates R + R P C 2 H 5 F CH 3 H 5 C 2 H 3 C P R F + P P R F P P R P P H 3 C R F P H 3 C P R R + Achiral + Achiral Kuz min et al. QAR Comb ci. 2009, 28, 664

17 17 implex descriptors for mixtures H (A) H H + (B) H 2 H 2 H Traditional simplexes D(A) for component A taken separately Mixture simplexes D(A+B) for components A and B taken together Traditional simplexes D(B) for component B taken separately H H H H Muratov, E. et al. Mol. Info. 2012, 31, 202. Descriptors of mixture n A *D(A) + n B *D(B) n A *D(A+B) n A and n B are molar fractions of components A and B; n A < n B

18 18 ome applications of irm for mixtures QAR of poliovirus inhibition Muratov et al. truct. Chem. 2013, DI /s Prediction of new more potent drug combinations H H 3 C CH 3 Enviroxime H 2 + Me Me Me Pleconaril F F F QPR of boiling temperature prisiu et al. Mol. Inf. 2012, 31, Predicted (T, K) bserved (T, K) Filling the matrix of mixtures, prediction of mixtures containing new compounds

19 19 implex descriptors for materials E D A B A D C Traditional simplexes for material constituents taken separately D(A), D(B), etc. Ba 2 Ca 2 Cu 3 Hg 8 = A 2 BCD 2 E Mixture simplexes for material constituents taken separately D(A+B), D(A+B+C), etc. Cu Ca Hg Ba Cu Cu Ba Hg Hg Ba Ca Descriptors of material n A *D(A) + n B *D(B) + n A *D(A+B) + n B *D(B+C+E) +... n A, n B, etc., are stoichiometric fractions of constituents A, B,...; n A < n B <... Isayev et al in preparation

20 20 Modified implex descriptors for materials rti 3 BaZr 3 E Dd A B Pp Dd Traditional simplexes for material constituents taken separately D(A), D(B), etc. Pp C Bb A Pp C D Dd A Pp C Dd A BbDdPp 3 Mixture simplexes for material constituents taken separately D(A+B), D(A+B+C), etc. Descriptors of material n A *D(A) + n B *D(B) + n A *D(A+B) + n B *D(B+C+E) +... n A, n B, etc., are stoichiometric fractions of constituents A, B,...; n A < n B <...

21 21 Predictive QMPR* Modeling *QMPR = Quantitative Material tructure Activity Modeling Y superconductivity (Tc, K) 163 materials with Tc = 2-133K; irm descriptors + Random Forest; 2 models continuous and binary classification with Tc = 20K cut-off Q 2 ext = 0.69 CCR = 88%

22 22 Interpretation of developed QMPR* models Increase of superconductivity; Indifferent; Decrease of superconductivity

23 23 Modeling of Drug-Drug interactions* The most frequently occurring clinically significant drug-drug interactions of ~400 common drugs (inhibition of cytochrome P450 enzymes responsible for their metabolism. CYP Matrix (drugs) Total # of data points # of actives 1A2 34x34 fully filled C9 58x58 fully filled D6 72x72 fully filled A4 237x237 fully filled In collaboration with Dr A. Zaharov (CI, Fort Detrick, UA) Dr A. Lagunin (IBMC, Moscow, Russia) Zaharov et al. In preparation CCR Results of consensus modeling are presented: irm_rf + QA_RBF 1A2 2C9 2D6 3A4 In Progress

24 24 Conclusions: 1 Growing interest in mixtures creates an emerging need for new theoretical approaches employing QAR modeling The biggest problem in the field is the lack of data. However, we expect rapid growth of large, publicly available databases in the next several years. The adequate description of any mixture by the system of (structural) parameters is another main problem. We address this issue by using non-additive mixture descriptors that are (i) based on structural features of both constituents and the mixture as a whole and (ii) sensitive to interaction effects.

25 25 Conclusions: 2 The latest trends of conventional QAR analysis, i.e., careful collection and understanding of the data, thorough data curation, rigorous internal and external validation, and application of developed models for virtual screening of large databases, which are almost absent in current mixture studies, will significantly improve the quality of mixture QAR models. External validation of QAR models for mixtures is less straightforward than in traditional QAR. The conventional external cross-validation procedure leads to over-optimistic estimation of the models predictive power. The compounds out and everything out" strategies are recommended for the estimation of error of prediction for the mixtures created by one or both new compounds.

26 26 Acknowledgement: ofia: Acad. Angel Galabov; Dr. Lubomira ikolaeva. trasbourg: Prof. Alexandre Varnek; Dr. Gilles Marcou. Dr. Ioanna prisiu CI: Dr. Alexey Zaharov dessa: Dr. Pavel Polishchuk Chapel Hill: Dr. Alexander Isayev. Duke: Prof. tephano Curtalolo; Dr. Kevin Rasch. Moscow: Dr. Alexey Lagunin Prof. Vladimir Poroikov