Quality relationships, QSAR

As the uses of toxicological-based quantitative structure-activity relationships (QSARs) move into the arenas of priority setting, risk assessment, and chemical classification and labeling the demands for a better understanding of the foundations of these QSARs are increasing. Specifically, issues of quality, transparency, domain identification, and validation have been recognized as topics of particular interest (Schultz and Cronin, 2003). 

A9.5.2.4.1 For organic substances experimentally derived high-quality Kow values, or values which are evaluated in reviews and assigned as the recommended values , are preferred over other determinations of Kow. When no experimental data of high quality are available, validated Quantitative Structure Activity Relationships (QSARs) for log Kow may be used in the classification process. Such validated QSARs may be used without modification to the agreed criteria if they are restricted to chemicals for which their applicability is well characterized. For substances like strong acids and bases, substances which react with the eluent, or surface-active substances, a QSAR estimated value of Kow or an estimate based on individual -octanol and water solubilities should be provided instead of an analytical determination of Kow (EEC A.8., 1992 OECD 117, 1989). Measurements should be taken on ionizable substances in their non-ionized form (free acid or free base) only by using an appropriate buffer with pH below pK for free acid or above the pK for free base. 

The ubiquitous involvement of these materials in chirality induction for the purposes of assaying small molecules, determining enantiomeric purities, quality control, and quantitative structure-activity relationships (QSAR) is reviewed later in this article. The detector that is common to all these applications is CD. 

In addition to the biochemistry introduced in this chapter, a great deal of emphasis is placed on the determination of the activity of a compound by an analysis of its structure. Quantitative structure-activity relationships (QSAR), used judiciously, have the ability to help set testing priorities and identify potentially toxic materials in mixtures. Heavily reliant upon the quality of the toxicity data discussed in Chapter 4, these methods use sophisticated statistical techniques or analysis of interaction of a toxicant with the receptor to estimate toxicity. A method that uses structure-activity relationships coupled with availability and an assumed additive model for toxicity is presented to estimate the risk due to polyaromatic hydrocarbons (PAHs). 

Three major approaches to the prediction of aqueous solubility of organic chemicals using Quantitative Structure Activity Relationship (QSAR) techniques arc reviewed. The rationale behind six QSAR models derived from these three approaches, and the quality of their fit to the experimental data are summarized. Their utility and predictive ability are examined and compared on a common basis. Three of the models employed octanol-water partition coefficient as the primary descriptor, while two others used the solvatochromic parameters. The sixth model utilized a combination of connectivity indexes and a modified polarizability parameter. Considering the case of usage, predictive ability, and the range of applicability, the model derived from the connectivity- polarizability approach appears to have greater utility value. 

The bioaccumulation of a substance into an organism is not an adverse effect hazard in itself. Bioconcentration and bioaccumulation may lead to an increase in body burden which may cause toxic effects due to direct and/or indirect exposure. Bioaccumulative substances characterized by high persistence and toxicity, negligible metabolism and a log ATow between 5 and 8 may represent a concern when widely dispersed in the environment. The potential of a substance to bioaccumulate is primarily related to its lipophilicity. A surrogate measure of this quality is the n-octanol - water partition coefficient (/fow), which is correlated with bioconcentration potential. Therefore, /fow values are normally used as predictors in quantitative structure - activity relationships (QSARs) for bioconcentration factors (BCFs) of organic non-polar substances. 

Probably the best known field in chemistry in which modeling is applied almost mandatorily are quantitative structure-activity relationship (QSAR) studies (see Environmental Chemistry QSAR Quality Control, Data Analysis and Quantitative Structure-Activity Relationships in Drug... 

Regulatory QSAR models are more demanding because of their relationship with the law, which introduces requirements, some internal to the QSAR model process, others external. Internally the model needs a high level of quality control. Externally, the model has to comply with, and be suited for, the regulatory use. 

Odor and taste quality can be mapped by multidimensional scaling (MDS) techniques. Physicochemical parameters can be related to these maps by a variety of mathematical methods including multiple regression, canonical correlation, and partial least squares. These approaches to studying QSAR (quantitative structure-activity relationships) in the chemical senses, along with procedures developed by the pharmaceutical industry, may ultimately be useful in designing flavor compounds by computer. 

It is not yet possible to design a molecule with specific odor (or taste) characteristics because the relations between sensory properties of flavor compounds and their molecular properties are not well understood. As a consequence, the development of compounds with desired flavor qualities has had to rely on relatively tedious synthetic approaches. Recent advances, however, in computer-based methods developed by the pharmaceutical industry to study QSAR (quantitative structure-activity relationships) may ultimately be helpful in the rational design of new flavor-structures with predictable sensory attributes. Results from QSAR studies may also provide insight into the mechanism of the molecule-receptor interaction. 

The search for relationships among the dynamic and equilibrium properties of related series of compounds has been a paradigm of chemists for many years. The discovery of such unifying principles and predictive relationships has practical benefits. Numerous relationships exist among the structural characteristics, physicochemical properties, and/or biological qualities of classes of related compounds. Perhaps the best-known attribute relationships are the correlations between reaction rate constants and equilibrium constants for related reactions commonly known as linear tree-energy relationships (LFERs). The LFER concept led to the broader concepts of QSARs, which seek to predict the environmental fate of related compounds based on correlations between their bioactivity or physicochemical properties and structural features. For example, therapeutic response, environmental fate, and toxicity of organic compounds have been correlated with... 

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