Quantitative property-activity relationships

Hirokawa S, Imasaka T, Imasaka T. Chlorine substitution pattern, molecular electronic properties, and the nature of the ligand-receptor interaction Quantitative property-activity relationships of polychlorinated dibenzofurans. Chem Res Toxicol 2005 18 232-8. 

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,). 

When the property being described is a physical property, such as the boiling point, this is referred to as a quantitative structure-property relationship (QSPR). When the property being described is a type of biological activity, such as drug activity, this is referred to as a quantitative structure-activity relationship (QSAR). Our discussion will first address QSPR. All the points covered in the QSPR section are also applicable to QSAR, which is discussed next. 

PW91 (Perdew, Wang 1991) a gradient corrected DFT method QCI (quadratic conhguration interaction) a correlated ah initio method QMC (quantum Monte Carlo) an explicitly correlated ah initio method QM/MM a technique in which orbital-based calculations and molecular mechanics calculations are combined into one calculation QSAR (quantitative structure-activity relationship) a technique for computing chemical properties, particularly as applied to biological activity QSPR (quantitative structure-property relationship) a technique for computing chemical properties... 

Quantitative Structure—Activity Relationships. Many quantitative stmcture—activity relationship (QSAR) studies of progestins have appeared in the Hterature and an extensive review of this work is available (174). QSAR studies attempt to correlate electronic, steric, and/or hydrophobic properties to progestational activity or receptor binding affinity. A review focusing on the problems associated with QSAR of steroids has been pubUshed (175). 

Increasing attention has been paid to the generation of quantitative stmcture—activity relationships in which the effects of molecular substitution on pharmacologic activity can be interpreted in terms of the physicochemical properties of the substituents. These approaches are based on the extrathermodynamic analysis of substituent effects (36) ... 

BT Luke. Evolutionary programming applied to the development of quantitative structure-activity relationships and quantitative structure-property relationships. J Chem Inf Comput Sci 34(6) I279-1287, 1994. 

In 1868 two Scottish scientists, Crum Brown and Fraser [4] recognized that a relation exists between the physiological action of a substance and its chemical composition and constitution. That recognition was in effect the birth of the science that has come to be known as quantitative structure-activity relationship (QSAR) studies a QSAR is a mathematical equation that relates a biological or other property to structural and/or physicochemical properties of a series of (usually) related compounds. Shortly afterwards, Richardson [5] showed that the narcotic effect of primary aliphatic alcohols varied with their molecular weight, and in 1893 Richet [6] observed that the toxicities of a variety of simple polar chemicals such as alcohols, ethers, and ketones were inversely correlated with their aqueous solubilities. Probably the best known of the very early work in the field was that of Overton [7] and Meyer [8], who found that the narcotic effect of simple chemicals increased with their oil-water partition coefficient and postulated that this reflected the partitioning of a chemical between the aqueous exobiophase and a lipophilic receptor. This, as it turned out, was most prescient, for about 70% of published QSARs contain a term relating to partition coefficient [9]. 

Lipophilicity is the measure of the partitioning of a compound between a lipidic and an aqueous phase [1]. The terms lipophilicity and hydrophobicity are often used inconsistently in the literature. Lipophilicity encodes most of the intramolecular forces that can take place between a solute and a solvent. Hydrophobicity is a consequence of attractive forces between nonpolar groups and thereby is a component of lipophilicity [2]. Lipophilicity is one of the most informative physicochemical properties in medicinal chemistry and since long successfully used in quantitative structure-activity relationship (QSAR) studies. Its... 

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. 

The prediction of the properties of molecules from a knowledge of their structure (quantitative structure-property relationships [QSPRs] or quantitative structure-activity relationships [QSARs]). ANNs can be used to determine QSPRs or QSARs from experimental data and, hence, predict the properties of a molecule, such as its toxicity in humans, from its structure. 

Although traditional octanol/water distribution coefficients are still widely used in quantitative structure-activity relationships (QSAR) and in ADME/ pharmacokinetic (PK) studies, alternatives have been proposed. To cover the variability in biophysical characteristics of different membrane types, a set of four solvents has been suggested - sometimes called the critical quartet [49-51], The 1,2-dichloroethane (DCE)/water system has been promoted as a good alternative to alkane/water due to its far better dissolution properties [50, 51], but it may be used only rarely due to its carcinogenic properties. 

Quantitative Structure-Activity Relationship studies search for a relationship between the activity/toxicity of chemicals and the numerical representation of their structure and/or features. The overall task is not easy. For instance, several environmental properties are relatively easy to model, but some toxicity endpoints are quite difficult, because the toxicity is the result of many processes, involving different mechanisms. Toxicity data are also affected by experimental errors and their availability is limited because experiments are expensive. A 3D-QSAR model reflects the characteristics of... 

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