Quantitative structure-activity relationships QSARs models

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]. 

In a study by Andersson et al. [30], the possibilities to use quantitative structure-activity relationship (QSAR) models to predict physical chemical and ecotoxico-logical properties of approximately 200 different plastic additives have been assessed. Physical chemical properties were predicted with the U.S. Environmental Protection Agency Estimation Program Interface (EPI) Suite, Version 3.20. Aquatic ecotoxicity data were calculated by QSAR models in the Toxicity Estimation Software Tool (T.E.S.T.), version 3.3, from U.S. Environmental Protection Agency, as described by Rahmberg et al. [31]. To evaluate the applicability of the QSAR-based characterization factors, they were compared to experiment-based characterization factors for the same substances taken from the USEtox organics database [32], This was done for 39 plastic additives for which experiment-based characterization factors were already available. 

Among the possible alternative methods, in vitro assay (for ATMs) and quantitative structure-activity relationships (QSARs) models (for ANTMs) are the most applied approaches in the toxicological and ecotoxicological evaluation of chemicals profiles, even in the field of environmental research and risk assessment. 

Although the above methodologies proved to be very successful in identifying active kinase inhibitors, they utilized "generic" kinase models and did not address selectivity issues. An interesting recent report has attempted to create quantitative structure-activity relationship (QSAR) models based on data sets of compounds tested against multiple kinases [33]. 

C-H and N-H bond dissociation energies (BDEs) of various five- and six-membered ring aromatic compounds (including 1,2,5-oxadiazole) were calculated using composite ab initio CBS-Q, G3, and G3B3 methods. It was found that all these composite ab initio methods provided very similar BDEs, despite the fact that different geometries and different procedures in the extrapolation to complete incorporation of electron correlation and complete basis set limit were used. A good quantitive structure-activity relationship (QSAR) model for the C-H BDEs of aromatic compounds... 

The basic paradigm underlying the field of research broadly referred to as quantitative structure-activity relationship (QSAR) modeling is that the structure of the chemical determines its activity ... 

A hierarchical approach. In Quantitative Structure-Activity Relationship (QSAR) Models of Mutagens and Carcinogens, Benigni, R., Ed., CRC Press, Boca Raton, FL, 2003, pp. 207-234. 

Basak, S. C., Mills, D., Hawkins, D. M., Kraker, J. J. Quantitative structure-activity relationship (QSAR) modeling of human blood air partitioning with proper statistical methods and validation. Chem. Biodivers., accepted. 

The validity of a model is always limited to a certain domain in the parameter space. For example, if a quantitative structure-activity relationships (QSAR) model is specified for nonpolar organic chemicals in the log range from 2 to 6 and has a molecular weight below 700, then an application to substances outside this range is an improper extrapolation. Note that the parameter space may be difficult to discern for example, combinations of low values for one variable and high values for another could constitute an extrapolation if such combinations had been missing in the validation or specification of the model. Exceedence of model boundaries introduces additional uncertainty at best, but can also lead to completely incorrect outcomes. 

For halogenated aromatic hydrocarbons like polychlorinated biphenyls (PCBs), polychlorinated dibenzofurans (PCDFs), and polychlorinated dibenzo-p-dioxins (PCDDs) the binding to the aryl hydrocarbon (Ah) receptor regulates their toxicity [89]. The Ah receptor controls the induction of one of the cytochrome P450 enzymes in the liver. Toxic responses such as thymic atrophy, iveight loss, immu-notoxicity and acute lethality are associated ivith the relative affinity of PCBs, PCDFs and PCDDs for the Ah receptor [89]. The quantitative structure-activity relationship (QSAR) models predicting the affinity of the halogenated aromatic hydrocarbons ivith the Ah receptor describe the electron acceptor capability as well as the hydrophobicity and polarizability of the chemicals [89[. 

Quantitative structure-activity relationship (QSAR) models for kinetic rate constants and molecular descriptors, such as dipole moment, EHOMO, ELUMO/... 

Clearly, molecular structure influences the reaction kinetics of organic compounds during their photocatalytic oxidation. This relationship between degradability and molecular structure may be described using quantitative structure-activity relationship (QSAR) models. QSAR models can be developed to predict kinetic rate constants for organic compounds with similar chemical structures. The following section discusses QSAR models developed by Tang and Hendrix (1998) as well as those developed by other researchers. 

The dissolution rate of a drug is usually determined experimentally. However, there is an abundance of theoretical and quantitative structure-activity relationship (QSAR) models (Grant and Higuchi, 1990) that provide estimates of the dissolution rates. The mass of the soJidfe (ny given time can be deLned as... 

The quantitative structure-activity relationship (QSAR) model is by definition a model. Any model, such as animal model (also called in vivo) or in vitro model, is a system that applies to a specific situation, and thus, it is useful to study, evaluate, or assess a more complex system, which cannot be used experimentally for investigation. Thus, any model is a simplification of the target object of the study, and the model is useful for this or not depending on its purpose. It is also possible to imagine a series of models, each addressing one or more features of the more complex system. 

Table 10.1 Compilation of representative Quantitative Structure-Activity Relationships (QSAR) models for nitro-aromatic compounds...

Quantitative structure-activity relationship (QSAR) models have proven their utility, from both the pharmaceutical and toxicological perspectives, for the identification of chemicals that might interact with ER. While their primary function in the pharmaceutical enterprise is lead discovery and optimization for high-affinity ER ligands, QSAR models can play an essential role in toxicology as a priority-setting tool for risk assessment. 

As will be seen, the large amount of quantitative structure-activity relationship (QSAR) modeling that has been carried out for soil sorption has almost exclusively involved nonionic organic compounds. For strongly ionizing and inorganic chemicals, no QSARs are available. However, Bintein and Devillers (1994) developed a soil sorption QSAR that incorporated correction factors for ionization of weak acids and bases. 

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