Quantitative Structure Activity model

We have developed a quantitative structure-activity model for the variations in potency among the nitrosamines and, more recently, a related model for the variation in target organ for a smaller set of nitrosamines. We are currently developing a model for interspecies variation in susceptibility toward carcinogenic nitrosamines. The model for organ selectivity requires terms for the parent nitrosamine as well as for the hypothesized metabolites while the model for potency variations contains terms only for the unmetabolized parent compound. 

There appears now to be ample evidence that the variations in carcinogenicity among the nitrosamines are systematically and rationally related to structure and that several Indices of carcinogenic potency can be used as indices of biological response for the generation of quantitative structure-activity models (11-17). 

Singer, J.A. and Purcell, W.P. (1967). Relationships Among Current Quantitative Structure-Activity Models. J.Med.Chem., 10,1000-1002. 

Often it is possible to replace a toxicity test with an alternative methodology, especially when cellular or mechanistic studies are undertaken. Tissue in laboratory culture, microorganisms, or lower invertebrates can also be used in place of whole animal studies. In the case of screening tests, there now exists a broad variety of quantitative structure-activity models that can predict and... 

Quantitative structure-activity models for acids and bases are much more complex than models for neutral compounds. Whereas the neutral forms of acids and bases are more or less lipophilic, their charged forms, anions of acids or protonated bases, are more hydrophilic by about three to four orders of magnitude. Correspondingly, many acids and bases can easily penetrate lipid phases in their neutral form (provided they are not too polar), whereas the charged forms are water-soluble. 

It may seem that the various structure-activity models and parameters are not truly so independent as they are presented here. Certainly this suspicion is justified. Recently, Singer and Purcell evaluated the interrelationships among the quantitative structure—activity models and illustrated their similarities (125). Also, the parameters used in these models can not be totally independent of one another. One merely attempts to find those parameters which alone or in combination best describe the biological activity. In view of this, Leo et al. have reported a comparison of the parameters currently used in studies of this type (86). 

The r value is commonly employed to quantify the degree of association between predicted values (from either a physics-based or empirical model) and observed values from eqn (9.1). The endpoints could be as diverse as estimates of affinity from 3-dimensional protein-ligand complexes, to estimates of solubility from a quantitative structure-activity model. The coefficient of variation (r ) expresses the fraction of the variation in the observed values that is explained by the predicted values, or more generally the fraction of the variation in the y-data that is explained by the x-data. 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

Furthermore, QSPR models for the prediction of free-energy based properties that are based on multilinear regression analysis are often referred to as LFER models, especially, in the wide field of quantitative structure-activity relationships (QSAR). 

The fundamental assumption of SAR and QSAR (Structure-Activity Relationships and Quantitative Structure-Activity Relationships) is that the activity of a compound is related to its structural and/or physicochemical properties. In a classic article Corwin Hansch formulated Eq. (15) as a linear frcc-cncrgy related model for the biological activity (e.g.. toxicity) of a group of congeneric chemicals [37, in which the inverse of C, the concentration effect of the toxicant, is related to a hy-drophobidty term, FI, an electronic term, a (the Hammett substituent constant). Stcric terms can be added to this equation (typically Taft s steric parameter, E,). 

Quantitative Structure—Activity Relationships (QSAR). Quantitative Stmcture—Activity Relationships (QSAR) is the name given to a broad spectmm of modeling methods which attempt to relate the biological activities of molecules to specific stmctural features, and do so in a quantitative manner (see Enzyme INHIBITORS). The method has been extensively appHed. The concepts involved in QSAR studies and a brief overview of the methodology and appHcations are given here. 

Among others, 11 was included in a series of drugs to study quantitative structure-activity relationships (96KFZ(6)29, 98MI7, 99BMC2437). A statistically significant CoMFA model was developed for describing the... 

Risperidone (11) was also included among a a 1-adrenergic receptor antagonists to study a quantitative structure-activity relationship (99BMC2437). A pharmacophore model for atypical antipsychotics, including 11, was established (00MI41). An increased plasma level of 11 and 9-hydroxyrisperidone (12) was observed in combination with paroxetine (01 MI 13). The effect of vanlafaxine on the pharmacokinetics of 11 was reported (99MI13). 

Ekins S, De Groot MJ, Jones JP. Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome P450 active sites. Drug Metab Dispos 2001 29 936-44. 

Smith PA, Sorich MJ, McKinnon RA, Miners JO. Pharmacophore and quantitative structure-activity relationship modeling complementary approaches for the rationalization and prediction of UDP-glucuronosyltransferase 1A4 substrate selectivity. J Med Chem 2003 46 1617-26. 

Wang YW, Liu HX, Zhao CY, Liu HX, Cai ZW, Jiang GB. Quantitative structure-activity relationship models for prediction of the toxicity of polybrominated diphenyl ether congeners. Environ Sci Technol 2005 39 4961-6. 

Enslein K, Gombar VK, Blake BW, Maibach HI, Hostynek JJ, Sigman CC et al. A quantitative structure-activity relationships model for the dermal sensitization guinea pig maximization assay. Food Chem Toxicol 1997 35 1091-8. 

We recently reported a structure-activity model for variations In target organs (12) and are currently examining the possible application of the quantitative structure-activity approach to the problem of specles-to-specles differences In susceptibility toward nltrosamlne carcinogenesis (19). These two topics will be discussed In the remainder of this presentation. 

The Danish EPA has developed an advisory list for self-classification of dangerous substances including 20 624 substances. The substances have been identified by means of QSAR models (Quantitative Structure-Activity Relationship) as having acute oral toxicity, sensitization, mutagenicity, carcinogenicity, and/or danger to the aquatic environment. 

More recently (2006) we performed and reported quantitative structure-activity relationship (QSAR) modeling of the same compounds based on their atomic linear indices, for finding fimctions that discriminate between the tyrosinase inhibitor compounds and inactive ones [50]. Discriminant models have been applied and globally good classifications of 93.51 and 92.46% were observed for nonstochastic and stochastic hnear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67 and 89.44% [50]. In addition to this, these fitted models have also been employed in the screening of new cycloartane compounds isolated from herbal plants. Good behavior was observed between the theoretical and experimental results. These results provide a tool that can be used in the identification of new tyrosinase inhibitor compounds [50]. 

Ghose, A. K., Crippen, G. M. Atomic physicochemical parameters for three-dimensional strucmre directed quantitative structure-activity relationships. II. modeling dispersive and hydrophobic interactions. 7. Chem. Inf. Comp. Sci. 1987, 27, 21-35. 

QSAR models Quantitative Structure-Activity Relationship-statistical models that relate biological activity to features of a molecule... 

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