The Hansch Approach

The Hansch approach is one of the most widely used methods for analysing structure-activity relationships when quantitative data are available. It is named after the founder of modern QSAR, Corwin Hansch (Hansch and Fujita, 1964), who suggested that the biological activity of a molecule was a function of its electronic, steric and hydrophobic properties the last most often being represented by the partition coefficient (P) between water and octanol (equation 2). 

A set of well-chosen compounds is one that contains sufficient structural variation to permit the uncovering of a relationship between molecular structure and the activity of interest. It is also important that the series of compounds exhibits varied levels of potency. If a SAR is formulated for a set of very similar compounds, both in terms of activity and structure, the correlation is likely to be very high, but its ability to predict the activity of materials outside this range is likely to be very poor. 

The last was used by Boelens and Punter (1978) to quantify the odour quality of 16 muguet-smelling materials. These data were then used to derive equation (3), which related the odour similarity (OS) to molecular weight (MW) and the Kier connectivity index (A ). The concept of molecular connectivity was introduced by Randic (1975) and further elaborated by Kier and Hall (1976). It involves the calculation of numerical indices which describe the topology of a molecule. The Kier 

Based on this work, Boelens designed 4-[4-(l-hydroxy-1-methylethyl)-cyclohexylidenyl]butanal, (27), which, when made, did indeed have excellent olfactive properties. Thus, the activity of compound (27) had been correctly predicted and Boelens s model further substantiated. However, if the prediction had been incorrect it should not, as is quite often the case, be ignored and classified as an exception to the rule . It is important that the reasons for the poor prediction are investigated. Outliers or compounds that exhibit unique biological activity can often provide vital clues about the structural requirements for that biological activity. Thus, the development of a SAR is an iterative process, with the information acquired from new materials being used to refine the model. 

Boelens has also used this approach to derive QSAR equations for musk, jasmine, fruit and bitter almond odorants (Boelens, 1976 Boelens and Punter, 1978 Boelens et al., 1983). In the case of bitter almond and musk, he concluded that hydrophobic and steric parameters were important. For the jasmine materials, he found that molecular connectivity indices were useful parameters. Molecular connectivity indices were also used by their inventors, Kier et al. (1977), to analyse anosmia to fatty acids and the odour similarities of ethereal, floral and benzaldehyde-like odorants. Dearden (1994) also developed a QSAR equation relating the odour similarity of bitter almond odorants to two connectivity indices. Greenberg (1979) found that the odour intensity of a series of homologous compounds was correlated to their hydrophobic properties and not to steric or polar properties, while Rossiter (1996b) found that the fruitiness of aliphatic esters was related to steric hindrance of the ester group and either molecular length or log P. 

1) As a threshold concentration, e.g. the minimum concentration of the compound in the air detected by the nose. 

2) As a concentration required to produce the same odour strength as a given dilution of a standard compound. 

3) As odour intensity ratings, which could in fact be as simple as assigning 0 to an odourless compound, 1 to a compound that has a weak odour, 2 to a compound of moderate intensity and 3 to a very strong smelling compound. 

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This section includes veterinary applications. The antiviral, bactericidal, and antimicrobial applications of 2-aminothiazoles and 2-imino-4-thiazolines are summarized in Table VI-7. They show a marked anti-trichonomicidal activity, which has even been quantitatively measured by the Hansch approach (797). The antiparasitic action of these compounds has been investigated for some compounds and is summarized in Table VI-8 interesting results were obtained with aminotrozal (1348). 

P.N. Craig, Comparison of the Hansch and Free-Wilson approaches to structure-activity correlation, In Biological Correlations — The Hansch Approach (R.F. Gould, Ed.). Advances in Chemistry Series, No. 114. American Chemical Society, Washington DC, 1972, pp. 115-129. P.N. Craig, Interdependence between physical parameters and selection of substituent groups for correlation studies. J. Med. Chem., 14 (1971) 680-684. 

The information contained in karma s knowledge bases is based upon quantitative structure-activity relationships (QSAR), kinetic data, and structural chemistry. The combination of QSAR and kinetic data allows for the study of enzyme-ligand interactions. The Hansch approach to QSAR, based on a set of congeners, states ... 

Five new pyridazinones were synthesized with substitutions in the two-position of the phenyl ring as given in Table II. Using the Hansch approach, correlations were made between the experimentally determined 18 2/18 3 ratios and the values and a,a values taken out of the data collection of Hansch and Leo (15) (Table II and Equations 1-3). The hydrophobic parameter is derived from the 1-octanol/water partition coefficient and o is the Hammett electronic parameter. 

To achieve these various best fits, statistical methods are employed. A regression analysis of the effects of various substituents on a molecule using the Hansch approach... 

Other problems with the Hansch method are that biological systems are often too crude as models for its application, or the electronic effects operative in a drug molecule are not sufficiently understood or precise. Finally, the method requires considerable time and expense, even in the hands of an expert. Difficulties notwithstanding, the Hansch approach took both chemists and pharmacologists out of the dark age of pure empiricism and allowed them to consider simultaneously the effects of a large number of variables of drug activity—a feat unattainable with classical methods. 

The Free-Wilson method of deriving quantitative structure-activity-relationships 101 uses implicit representations of physico-chemical properties and there are also numerous examples where indicator variables have been successfully included in the Hansch approach. 

In recent years, quantitative procedures have been developed for the structure-activity correlation studies of biologically active compounds. Among them, the Hansch approach has been most widely and effectively used and sometimes successfully applied to design novel compounds of potent activity. 

When the Hansch approach was initiated, the correlation of the biological activity (1/C C is the equieffective concentration or dose) with structural parameters was analyzed using Eq. 1,... 

Here k f k2f and kare microscopic ionization constants defined in section 4.6. From this it was possible to calculate and plot variation in Dnoh with pH for pentazocine as shown in Figure 2. The fourth partition coefficient was obtained by structural group contribution to the measured partition coefficient of a reference compound (morphine). This was done following the Hansch approach. 

Several SARs and QSARs have been derived from data on antineoplastic activity as well as for MDR-reversing activity. The Hansch approach, Free-Wilson, and neural network analysis have been applied. The importance of lipophilicity, molar refractiv-ity (MR), and charge for the description of activity is common to all derived relations. 

These efforts were guided by the study of quantitative structure-activity relationships (QSAR) following the Hansch approach. In this method linear free-energy related and other electronic, hydrophobic, and steric substituent constants are used for a quantitative analysis of the possible ways in which substituents may modulate bioactivity in a congeneric series. In the QSAR studies of benzoylphenyl ureas the electronic Hammett a-constants and the hydro-phobic Hansch n-constants were used. To measure the steric influences, steric substituent constants of a new type (B1,B2,B3,B4, and L) were applied which had recently been introduced by us and which give improved correlations in comparison with the steric Es constants used in the literature hitherto (21, 22). The constants B- toBj are measures of the widths of substituents in four rectangular directions. The L-constant accounts for the length of a substituent ... 

Based on the earlier work of Meyer and Overton, who showed that the narcotic effect of anesthetics was related to their oil/water partition coefficients, Hansch and his co-workers have demonstrated unequivocally the importance of hydrophobic parameters such as log P (where P is, usually, the octanol/water partition coefficient) in QSAR analysis.28 The so-called classical QSAR approach, pioneered by Hansch, involves stepwise multiple regression analysis (MRA) in the generation of activity correlations with structural descriptors, such as physicochemical parameters (log P, molar refractivity, etc.) or substituent constants such as ir, a, and Es (where these represent hydrophobic, electronic, and steric effects, respectively). The Hansch approach has been very successful in accurately predicting effects in many biological systems, some of which have been subsequently rationalized by inspection of the three-dimensional structures of receptor proteins.28 The use of log P (and its associated substituent parameter, tr) is very important in toxicity,29-32 as well as in other forms of bioactivity, because of the role of hydrophobicity in molecular transport across cell membranes and other biological barriers. 

Gould, R.G.E. Biological Correlations - The Hansch Approach. Advances in Chemistry, 1972,114, The American Chemical Society. 

In Biological Correlations—The Hansch Approach Van Valkenburg, W. Advances in Chemistry American Chemical Society Washington, DC, 1974. 

The background supporting the versatility of the Hansch approach is that it is in fact an extrathermodynamic approach to drug action. The information on the structure-activity relationship of various kinds of drugs is expressed as equations which provide a convenient way for comparative studies with simpler and similar model reactions to elucidate the mechanism of overall drug action without microscopic knowledge of complex processes occurring in vivo. 

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