Noncongeneric

The following sections summarize the pharmacophore and QSAR modeling of the cq-AR subtype affinity and selectivity through the employment of noncongeneric series of compounds. 

The availability of in vitro binding affinity data on the cloned oq-ARs allowed for the building of subtype specific QSAR models based on noncongeneric series of ligands. 

Early studies were based on 13 noncongeneric molecular series of oqA-AR antagonists [65]. The considered series included compounds 1, 3, 9-11, 14, 19 and 32 in Scheme 8.1 and compounds 43 and 45 in Scheme 8.2. Compounds 9 and 32 were considered in both the enantiomeric forms, whereas compound 45, corynanthine, was considered also in its diastereomeric form yohimbine [55]. The natural agonist, norepinephrine, was also considered, for comparative analysis [55],... 

In this noncongeneric molecular series, variations in the native oqA-adrenoceptor binding affinity values did not correspond to any significant and/or organized variation in the values of the geometric and the computed MO reactivity descriptors. 

P. Buchwald, N. Bodor, Quantitative Structure-Metabolism Relationships Steric and Nonsteric Effects in the Enzymatic Hydrolysis of Noncongeneric Carboxylic Esters , J. Med. Chem. 1999, 42, 5160-5168. 

Benigni, R. and Richard, A.M., QSARs of mutagens and carcinogens two case studies illustrating problems in the construction of models for noncongeneric chemicals, Mutation Research, 37(1), 29-46, 1996. 

Benigni, R., Andreoli, C., Cotta-Ramusino, M., Giogi, R, and Gallo, G., The electronic properties of carcinogens and their role in SAR studies of noncongeneric chemicals, Toxicity Modeling, 1, 157-167, 1995. 

Visualization of the OMAP is useful to judge the additional information introduced as each new compound is added (Fig. 3.32). Computationally, it is much more efficient to treat the set of noncongeneric compounds simultaneously (111,399), as we shall see, but reassuring when identical results are obtained if one uses the sequential procedure introducing each molecule in turn, where intermediate results may be visually verified. The use of computer graphics to confirm intermediate processing of data in convenient display modes becomes increasingly more important as the individual computations and numbers of molecules under consideration increase. 

Cocchi, M., Menziani, M.C., Fanelli, F. and De Benedetti, P.G. (1995). Theoretical Quantitative Structure-Activity Relationship Analysis of Congeneric and Noncongeneric Alpha(l) Adrenoceptor Antagonists A Chemometrie Study. J.Mol.Struct(Theochem), 331, 79-93. 

Hirono, S., Nakagome, I., Hirano, H., Yoshii, F. and Moriguchi, I. (1994). Noncongeneric Structure Pharmacokinetic Property Correlation Studies Using Fuzzy Adaptive Least Squares Volume of Distribution. Bio.Pharm.Bull., 17,686-690. 

The prediction of BP for noncongeneric diverse sets of compounds is more interesting and is still a challenging problem. Despite the wide availability of reasonably high-quality experimental BP data, there are just a few methods developed using thousands of molecules. 

With the advent of powerful computers and easy access to them, and the introduction of expert systems, artificial intelligence, and neural networks in QSAR, radically different models designed from noncongeneric large sets of chemicals have been proposed. No attempts are made to design a model that is easily interpretable in terms of MOA. The main objective of the present models is to provide powerful simulators with a wide domain of application for predicting the toxicity of any kind of molecule. 

TABLE 23.4 Selected QSAR Models Derived from Large Noncongeneric Data Sets... 

Devillers J, Domine D. A noncongeneric model for predicting toxicity of organic molecules to Vibrio fischeri. SAR QSAR Environ Res 1999 10 61-70. 

Poroikov, V. V, Rilimonov, D. A., Borodina, Y. V., Lagunin, A. A., Kos, A. Robustness of biological activity spectra predicting by computer program PASS for noncongeneric sets of chemical compounds. J. Chem. Inform. Comput. Sci. 2000, 40, 1349—1355. 

Hirono, S., Nakagome, L, Hirano, H., Matsushita, Y, Yoshi, E, and Moriguchi, 1. Noncongeneric structure-pharmacokinetic property correlation studies using fuzzy adaptive least-squares Oral bioavailability. Biol. Pharm. Bull. 17 306-309, 1994. 

TOPS-MODE. 2. Neurotoxicity of a noncongeneric series of solvents. SAR ej QSAR Environ. Res., 12, 445-459. 

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