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I. Lunga et al./Chem.J.Mold. 2008, 3 (1), 89-93AcknowledgmentsThanks are due to the financial support from INTAS (The International Association for the Promotion of Cooperationwith Scientists from the New Independent States). Also the authors are grateful to Dr. Assunta Napolitanafrom the Department of Pharmaceutical Sciences, University of Salerno for MS measurements.References[1] Zhang, R. S.; Ye, Y. P.; Shen, Y. M.; Liang, H. L. Tetrahedron 2000, 56, 3875–3879.[2] Mellado, G. G.; Zubia, E.; Ortega, M. J.; Lopez-Gonzalez, P. J. Steroids 2004, 69, 291–299.[3] Halaweish, F. T.; Huntimer, E.; Khalil, A. T. Phytochem. Anal. 2004, 15, 189–194.[4] Yin, J.; Kouda, K.; Tezuka, Y.; Tran, Q. L.; Miyahara, T.; Chen, Y. J.; Kadota, S. J. Nat. Prod. 2003, 66,646–650.[5] Liu, H. W.; Xiong, Z.; Li, F.; Qu, G. X.; Kobayashi, H.; Yao, X. S. Chem. Pharm. Bull. 2003, 51, 1089–1091.[6] Deepak, D.; Srivastav, S.; Khare, A. In Herz, W., Kirby, G. W., Moore, R. E., Strglich, W., Tamm, Ch., Eds.;Progress in the chemistry of organic natural products; Springer: New York, 1997, 71, pp 169–325.[7] Gutsu, E.V.; Kintya, P.K.; Lazur’evskii, G.V. Khimia Prirodnykh Soedinenii 1987, 2, 242-246.[8] Imai, S.; Fujioka, S.; Murata, E.; Goto, M.; Kawasaki, T.; Yamauchi, T. Takeda Kenkyusho Nenpo 1967, 26,76-83.[9] Murakami, K.; Saijo, R.; Nohara, T.; Tomimatsu, T. Yakugaku Zasshi 1981, 101 (3), 275-279.[10] Shvets, S. A.; Naibi, A. M.; Kintya, P. K. Khimiya Prirodnykh Soedinenii 1995, 2, 247-252.93
Chemistry Journal of Moldova. N.Gorinchoy General, et al./Chem.J.Mold. Industrial and Ecological 2008, 3 (1), Chemistry. 94-104 2008, 3 (1), 94-104TOXICOPHORES AND QUANTITATIVE STRUCTURE -TOXICITYRELATIONSHIPS FOR SOME ENVIRONMENTAL POLLUTANTSN. N. Gorinchoy a , I. Ya. Ogurtsov a , A. Tihonovschi a , I. Balan a , I. B. Bersuker a,b ,A. Marenich b and J.Boggs baInstitute of Chemistry, Academy of Sciences of Moldova, Academiei str. 3, MD 2028 Kishinev, Republic of MoldovabDepartment of Chemistry & Biochemistry, University of Texas at Austin, USA*E-mail: ngorinchoy@yahoo.com; Phone 373 22 739649Abstract: The electron-conformational (EC) method is employed to reveal the toxicophore and to predictaquatic toxicity quantitatively using as a training set a series of 51 compounds that have aquatic toxicity to fish.By performing conformational analysis (optimization of geometries of the low-energy conformers by the PM3method) and electronic structure calculations (by ab initio method corrected within the SM54/PM3 solvatationmodel), the Electron-Conformational Matrix of Congruity (ECMC) was constructed for each conformation of thesecompounds. The toxicophore defined as the EC sub-matrix of activity (ECSA), a sub-matrix with matrix elementscommon to all the active compounds under consideration within minimal tolerances, is determined by an iterativeprocedure of comparison of their ECMC’s, gradually minimizing the tolerances. Starting with only the four mosttoxic compounds, their ECSA (toxicophore) was found to consists of a 4x4 matrix (four sites with certain electronicand topologic characteristics) which was shown to be present in 17 most active compounds. A structure-toxicitycorrelation between three toxicophore parameters and the activities of these 17 compounds with R 2 =0.94 was found.It is shown that the same toxicophore with larger tolerances satisfies the compounds with les activity, thus explicitlydemonstrating how the activity is controlled by the tolerances quantitatively and which atoms (sites) are most flexiblein this respect. This allows for getting slightly different toxicophores for different levels of activity. For some activecompounds that have no toxicophore a bimolecular mechanism of activity is suggested. Distinguished from otherQSAR methods, no arbitrary descriptors and no statistics are involved in this EC structure-activity investigation.Keywords: aquatic toxicity, electron-conformational method, QSARAbbreviations: Tph –toxicophoreEC – electron-conformationalECMC – electron-conformational matrix of congruityECSA – electron-conformational sub-matrix of activityQSAR – quantitative structure-activity relationshipsIntroductionMany chemicals (organic environmental pollutants) and drugs, in addition to their useful properties, possessdifferent types of toxicity to environmental living organisms. The QSAR methodology is usually used to revealrelationships between the chemical structure of the compound and the toxicity under consideration in order to predictthe latter in new chemicals. The main problem in this approach is to choose the molecular features (descriptors) thatproperly represent the possible interaction of the toxicant with the bioreceptor to produce the toxicity, and to correlatethe descriptors with the toxicity by means of some regression relationships. There are many monographs, review articlesand original works devoted to this problem (see e.g. [1-3] and references therein). One of the latest versions of theQSAR approach to this problem is the so-called support vector machine (SVM) model [4, 5] in which the hypersurfacein multi- dimensional space of the descriptors separates two classes of chemicals, toxic and not toxic. A rule to classifychemicals into four classes was proposed by Verhaar et al. [6].In all these approaches to QSAR problems there is a common shortcoming: the choice of descriptors is notdirectly based on first principles, meaning it is arbitrary, and their weight is evaluated by means of statistical comparisonwith the activities. Since such a set of descriptors is necessarily incomplete and they are non-orthogonal to each otherwith unknown overlap, the comparison with activities may lead to chance correlations, some (or all) descriptors beingthus artifacts with no physical meaning implied in their initial choice (see also [7]).In the present paper we explore some problems of chemical toxicity, more precisely aquatic toxicity to fish, usingthe electron-conformational (EC) method of pharmacophore identification and quantitative bioactivity predictions whichis free from the above shortcomings (see the reviews of this method in [8], and also in [1], p. 455; an earlier less accuratequalitative version of this method was reviewed in [9]). Distinguished from the traditional QSAR approaches, the ECmethod does not employ arbitrary descriptors and statistics in its evaluation. Instead one (a unique) descriptor based onfirst principles is used, the electronic structure and topology of the molecule, calculated by means of quantum-chemicalmethods and presented in computer friendly digital-matrix form (see below). The comparison of these matrices with the94
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