Quantitative structural model

The aliphatic alicyclic hydrocarbon precursor is not well recognized as the major aliphatic component in dissolved humic substances, although it was previously postulated to occur (II). This precursor might arise from terpenoid hydrocarbon lipids, but the data presented in this chapter favor polyunsaturated lipid precursors that are oxidatively coupled and cyclized by free-radical mechanisms (20). Degradative studies have not identified this aliphatic component in recognizable fragments. The quantitative, structural-model approach presented here combines the results of 13C NMR, NMR,... 

The combination of the latter two studies allowed a quantitative structural model for the microemulsions to be presented. 

As results obtained in Refs. [34, 39] have shown, the behavior of cross-linked pol5nners is just slightly different from the above-described one for linear PC and PAr. However, further progress in this field is quite difficult due to, at least, two reasons excessive overestimation of the chemical crosslinks role and the quantitative structural model absence. In the Ref [39] the yielding mechanism of cross-linked polymer has been offered, based on the application of the cluster model and the latest developments in the deformable solid body S5meigetics field [40] on the example of two already above-mentioned epoxy polymers of amine (EP-1) and anhydrazide (EP-2) curing type. 

The authors of Ref. [21] supposed, that in orientational drawing process of poly(metyl methacrylate) (PMMA) the following structure changes occur the transition to more equilibrium structure owing to molecular package improvement and internal stresses relaxation. The quantitative structural model absence not allows the authors of Ref [21] to give direct proofs of their suppositions. In Ref [22] such treatment was fulfilled on the example of extruded amorphous polyarylates DV and DF-10 with the cluster model of polymers amorphous state structure using [12, 23],... 

At present analysis of relations between molecular characteristics, supramolecular (suprasegmental) structure parameters and properties of crosslinked polymers is carried out, as a rule, on the qualitative level [27]. It is connected with the complexity of the structure of spatial networks and the quantitative structural model for absence of these polymers [93, 130]. Therefore receiving quantitative relations between the mentioned parameters is an important goal of polymer physics, which is necessary for prediction of the properties of crosslinked polymers. The authors [130] solved this problem by the application of a number of physical concepts synergetics of deformable bodies [47], fractal analysis [92, 93] and the cluster model of the amorphous state structure of polymers [5, 6]. 

The authors of paper [76] showed the distinction of micro- and macroexpansion in amorphous polymers and explained it by a certain degree of ordering of chain macromolecules. In other words, the authors [76] found interconnection of thermal expansion and supramolecular structure for a number of amorphous polymers. However, the quantitative structural model for absence of the amorphous state does not allow similar interconnection details to be more precise. Therefore the authors [77] carried out the study of interconnection for amorphous epoxy polymers EP-1 and EP-2 of thermal expansion and structure, for the description of which the cluster model [8, 9] was used. 

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). 

The final physical properties of thermoset polymers depend primarily on the network structure that is developed during cure. Development of improved thermosets has been hampered by the lack of quantitative relationships between polymer variables and final physical properties. The development of a mathematical relationship between formulation and final cure properties is a formidable task requiring detailed characterization of the polymer components, an understanding of the cure chemistry and a model of the cure kinetics, determination of cure process variables (air temperature, heat transfer etc.), a relationship between cure chemistry and network structure, and the existence of a network structure parameter that correlates with physical properties. The lack of availability of easy-to-use network structure models which are applicable to the complex crosslinking systems typical of "real-world" thermosets makes it difficult to develop such correlations. 

These pharmacophore techniques are different in format from the traditional pharmacophore definitions. They can not be easily visualized and mapped to the molecular structures rather, they are encoded as keys or topological/topographical descriptors. Nonetheless, they capture the same idea as the classic pharmacophore concept. Furthermore, this formalism is quite useful in building quantitative predictive models that can be used to classify and predict biological activities. 

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. 

Balakin KV, Ekins S, Bugrim A, Ivanenkov YA, Korolev D, Nikolsky Y, et al. Quantitative structure-metabolism relationship modeling of the metabolic V-dealkylation rates. Drug Metab Dispos 2004 32 1111-20. 

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. 

Benigni R, Richard AM. Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity. Methods 1998 14 264-76. 

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. 

Fouchecourt MO, Beliveau M, Krishnan K. Quantitative structure-pharmacokinetic relationship modelling. Sci Total Environ 2001 274 125-35. 

Blakey GE, Nestorov lA, Arundel PA, Aarons LJ, Rowland M. Quantitative structure-pharmacokinetics relationships I. Development of a whole-body physiologically based model to characterize changes in pharmacokinetics across a homologous series of barbiturates in the rat. J Pharmacokinet Biopharm 1997 Jun 25(3) 277-312. Erratum in J Pharmacokinet Biopharm 1998 Feb 26(l) 131. 

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. 

An alternative viewpoint for structure-activity investigations is to utilize quantitative models as probes into the mechanism of action of the set of compounds being studied. In this case it is most useful if the molecular descriptors are explicitly meaningful in terms of chemical reactivity or physiological behavior, e.g., distribution of the compound in an organism (see Table II). In a previous symposium, (18), we described our application of this approach toward the development of a quantitative structure-potency expression, equation 1,... 

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. 

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). 

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. 

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