Quantitative property-solubility relationship

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]. 

There is a continuing effort to extend the long-established concept of quantitative-structure-activity-relationships (QSARs) to quantitative-structure-property relationships (QSPRs) to compute all relevant environmental physical-chemical properties (such as aqueous solubility, vapor pressure, octanol-water partition coefficient, Henry s law constant, bioconcentration factor (BCF), sorption coefficient and environmental reaction rate constants from molecular structure). 

A quantitative analysis of the structure-retention relationship can be derived by using the relative solubility of solutes in water. One parameter is the partition coefficient, log P, of the analyte measured as the octanol-water partition distribution. In early work, reversed-phase liquid chromatography was used to measure log P values for drug design. Log P values were later used to predict the retention times in reversed-phase liquid chromatography.The calculation of the molecular properties can be performed with the aid of computational chemical calculations. In this chapter, examples of these quantitative structure-retention relationships are described. 

There are several properties of a chemical that are related to exposure potential or overall reactivity for which structure-based predictive models are available. The relevant properties discussed here are bioaccumulation, oral, dermal, and inhalation bioavailability and reactivity. These prediction methods are based on a combination of in vitro assays and quantitative structure-activity relationships (QSARs) [3]. QSARs are simple, usually linear, mathematical models that use chemical structure descriptors to predict first-order physicochemical properties, such as water solubility. Other, similar models can then be constructed that use the first-order physicochemical properties to predict more complex properties, including those of interest here. Chemical descriptors are properties that can be calculated directly from a chemical structure graph and can include abstract quantities, such as connectivity indices, or more intuitive properties, such as dipole moment or total surface area. QSAR models are parameterized using training data from sets of chemicals for which both structure and chemical properties are known, and are validated against other (independent) sets of chemicals. 

A diverse collection of quantitative property-water solubility relationships (QPWSR) is available in the literature. These QPWSR differ in their solubility representation (Cw, Sw, Xw), spectrum of independent variables, and applicability with respect to structure and physical state (liquid or solid). The following types of QPWSR are considered ... 

Quantitative Property-S T) Relationship Dickhut et al. [67] developed a QP-5VV(7 )R based on experimental mole fraction solubilities for alkylbenzenes, PAHs, PCBs, chlorinated dibenzofuranes and p-dioxins, and alkyl- and halo-substituted naphthalenes and p-terphenyls in the range 4 to 40°C ... 

Many hydrophobic molecules such as vitamin A, vitamin D and steroid hormones play vital roles in a variety of cellular processes. Because of the low solubility of these molecules in water, it has been difficult to measure the binding properties of the site-directed mutants of the proteins that interact with these hydrophobic ligands such as cellular retinoic acid binding proteins (CRABPs) (Zhang et al. 1992 Chen et al. 1995). This has greatly hampered the studies of the quantitative structure-function relationships of these important proteins. 

Abstract In sensor and microfluidic applications, the need is to have an adequate solvent resistance of polymers to prevent degradation of the substrate surface upon deposition of sensor formilations, to prevent contamination of the solvent-containing sensor formulations or contamination of organic liquid reactions in microfluidic channels. Unfortunately, no comprehensive quantitative reference solubility data of unstressed copolymers is available to date. In this study, we evaluate solvent-resistance of several polycarbonate copolymers prepared from the reaction of hydroqui-none (HQ), resorcinol (RS), and bisphenol A (BPA). Our high-throughput polymer evaluation approach permitted the construction of detailed solvent-resistance maps, the development of quantitative structure-property relationships for BPA-HQ-RS copolymers and provided new knowledge for the further development of the polymeric sensor and microfluidic components. 

The simplest methodology is two-dimensional (2D) quantitative structure-activity relationships (QSAR), in which calculated descriptors of molecules are related to an end point of interest via a mathematical relationship to estimate a numerical or categorical value for that end point. The mathematical relationship is fitted to a training set of compounds for which data for the end point has been measured experimentally. New molecules can then be described with the descriptors used in the model and their end point values predicted. 2D QSAR methods can be used to predict the interaction of compoimds with protein targets or antitargets and are widely used for prediction of physicochemical and ADME properties, such as hpophilicity, solubility, hiunan intestinal absorption, and blood-brain barrier penetration [18]. An excellent review of the strategies and pitfalls of 2D QSAR has been published by Lewis and Wood [19]. 

Hydrogen Bonding 1 Hydrogen Bonding 2 Molecular Surfaces and Solubility Quantitative Structure-Activity Relationships in Drug Design Quantitative Structure-Property Relationships (QSPR). 

The history of quantitative structure-activity relationships dates back to the last century, when Crum-Brown and Fraser in 1865 postulated that there ought to be a relationship between physiological activities <1> and chemical structures C. Later, Richet correlated toxicities with aqueous solubility. Around 1900, Meyer and Overton found linear relationships between the narcotic potencies of organic compounds and their partitioning behavior. In the mid-1930s, Hammett defined a reaction constant p to describe the reactivity of aromatic systems R, expressed by rate constants k (or equilibrium constants K) and a parameter o to describe the electronic properties of aromatic substituents X (1 equation 1) (see Linear Free Energy Relationships (LFER)) ... 

Graph Theory in Chemistry Molecular Surfaces and Solubility Neural Networks in Chemistry Partial Least Squares Projections to Latent Structures (PLS) in Chemistry Quantitative Structure-Activity Relationships in Drug Design Quantitative Structure-Property Relationships (QSPR) Shape Analysis Structural Similarity Measures for Database Searching Topological Methods in Chemical Structure and Bonding,... 

In this respect, the in silico prediction of the thermodynamic mixing behavior of different polymer-drug/excipient mixtures is of central interest. A common approach to cope with this problem is the calculation of the solubility parameters according to Hildebrand or Hansen [9-12], which is standard in the development of polymer mixtures [13]. The use of highly developed force fields as the basis of any MD simulation software enables the calculation of solubility parameters with accuracy comparable to those measured experimentally by inverse gas chromatography [14], and an increasing number of other statistical quantitative property relationships between simulated and experimental values are established [15-18]. 

Two approaches to quantify/fQ, i.e., to establish a quantitative relationship between the structural features of a compoimd and its properties, are described in this section quantitative structure-property relationships (QSPR) and linear free energy relationships (LFER) cf. Section 3.4.2.2). The LFER approach is important for historical reasons because it contributed the first attempt to predict the property of a compound from an analysis of its structure. LFERs can be established only for congeneric series of compounds, i.e., sets of compounds that share the same skeleton and only have variations in the substituents attached to this skeleton. As examples of a QSPR approach, currently available methods for the prediction of the octanol/water partition coefficient, log P, and of aqueous solubility, log S, of organic compoimds are described in Section 10.1.4 and Section 10.15, respectively. 

Liu, R., So, S.-S. Development of quantitative structure-property relationship models for early ADME evaluation in drug discovery. 1. Aqueous solubility./. Chem. Inf. Comp. Set. 2001, 41, 1633-1639. 

S. Prediction of aqueous solubility of organic compounds using a quantitative structure-property relationship. J. Pharm. Sd. 2002, 91,1838-1852. 

Solubility parameters can be a useful guide to solvent selection, but precise quantitative relationships between solvent properties and extraction rates are not yet possible [37]. As an illustrative example we mention extraction of Irganox 1010 from PP [37]. Freeze-ground PP was extracted at 120 °C with 2-propanol,... 

Dunnivant, F. M., Elzerman, A. W., Jurs, P. C., Hansen, M. N. (1992) Quantitative structure-property relationships for aqueous solubilities and Henry s law constants of polychlorinated biphenyls. Environ. Sci. Technol. 26, 1567-1573. 

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