Classical QSAR

So far, SAR studies for P-gp have been performed on the basis of classical QSAR principles which were designed for transporters or receptors, which naturally bind one specific substrate from an aqueous environment. The assumptions made are that (i) the modeled conformation is the bioactive one (ii) the binding site and/or mode is the same for all modeled compounds (iii) interactions between the drug and the binding site are mainly due to enthalpic processes (e.g., van der Waals interactions) and (iv) solvent or membrane effects are negligible (cf. Ref. [35]). 

Quantitative Structure-Activity Relationship (QSAR) approach was first developed by Cros (1863) and Brown and Fraser (1868). In the 1960s, C. Hansch, T. Fujita, S. M. Free Jr. and J. W. Wilson started what is now considered to be classical QSAR. A series of powerful advanced computer tools have now been introduced, increasing the capacity of QSAR. 

As a possible alternative to in vitro metabolism studies, QSAR and molecular modelling may play an increasing role. Quantitative stracture-pharmacokinetic relationships (QSPR) have been studied for nearly three decades [42,45-52]. These are often based on classical QSAR approaches based on multiple linear regression. In its most simple form, the relationship between PK properties and lipophilicity has been discussed by various workers in the field [36, 49, 50]. 

Classical QSAR will continue to play its part in the optimization and selection of drug candidates. A fundamental difficulty with classical (property-based) QSAR is an over-reliance on the relevance of hydrophobicity, electrostatic and simple bulk steric effects as determinants of relative potency. We know that conformation is crucially important, but this is ignored in the classical approaches. The need for a structure-based QSAR method which also incorporates conformational flexibility might be met by development of a neural network (Livingstone and Salt, 1992 So and Richards, 1992) or machine learning program (King et al., 1992). 

The criticisms in the previous paragraphs lead to a question If Hansch analysis is of such questionable value, then why has an entire chapter of this textbook been devoted to the subject Despite the fading utility of classical QSAR methods such as Hansch analysis, the logic behind Hansch analysis is invaluable to medicinal chemistry. Synthetic chemists in the pharmaceutical industry intuitively consider the ideas used to construct Hansch equations. Ideas such as electronics, sterics, and lipophilicity underlie traditional SAR approaches in the laboratory. Critical analysis of activity data and emphasis on seeking holes in R-group selection are also fundamental to successful SAR on a lead. Through the study of Hansch analysis, all these crucial ideas are presented in a rational framework that helps demonstrate their relevance. Just as importantly, Hansch analysis provides the foundation for the next generation of QSAR comparative molecular field analysis. 

Comparative molecular field analysis (CoMFA) is a modern, powerful extension of the classical QSAR methods that were developed in the 1960s.14 While Hansch analysis is simple to understand and fairly easy for any medicinal chemist to perform, CoMFA requires specialized software and an understanding of statistics. Since CoMFA is outside the experience of most synthetic chemists, pharmaceutical companies have dedicated computational chemistry groups to handle advanced QSAR tasks. 

Hansch analysis and other classical QSAR approaches evaluate the QSAR model based on the correlation of rows of compounds with known activities (dependent variables) to columns of parameters (independent variables). For this reason, classical QSAR is sometimes called 2D QSAR. CoMFA is an example of 3D QSAR because lead analogues are modeled and analyzed in a virtual three-dimensional space. The value of both methods ultimately hinges on how well experimental and calculated activities correlate (Figure 12.2) and how well the model predicts the activity of compounds not included in the training set. 

To validate the equilibrated receptor, its potency to predict free energies of binding (AGpiej) is examined. Therefore, classical QSAR methods such as cross-validation via leave-X%-out analyses and/or prediction of activity for an external set of compounds (test set) are accomplished. Since all QSAR models are typically constructed to predict properties of new or even virtual molecules, model validation with an external test set reflects reality best (unbiased or biased random selection of training set and test set ligands is supported by the software). 

The chemical information can be expressed in a large number of ways. Classical QSAR models used physico-chemical experimental values measured in... 

The third fundamental component in the QSAR model is the mathematical algorithms. Many methods have been used, and in the last years, there has been an increase of the methods, and hence, quite probably this trend will continue, introducing many other methods [4—6]. Classical QSAR methods, used decades ago, were simple linear relationships. Corwin Hansch has been a pioneer of these methods [2]. An example can be the linear relationship between the fish toxicity and the partition coefficient between octanol and water, called Kow [3]. Kow, and its logarithm, called log P, is still the most popular chemical descriptor used in QSAR models for fish toxicity, and it is the base of software programs used by the US Environmental Protection Agency for fish toxicity [11]. The theoretical assumptions for the use of log P are that (1) octanol mimics the lipophylic component of the fish cell, and (2) the toxic effect is due to the adsorption of the chemical substance into the cell. 

Classical QSAR models were focused on a rather small number of compounds. The basic assumption was that a certain model is specific for a certain chemical class, sharing a common skeleton. Modifications of some simple chemical features were reflected in the property. Thus, for instance, a series of linear alcohols present aquatic toxicity, which varies in a rather linear way with the chain length [3],... 

The REACH legislation stimulated the discussion about QSAR models. Some models are specific for a very focused chemical class. This is quite typical of the academic models of the classical QSAR studies. Commercial and public models are more typically suitable for a large variety of chemical compounds, even if in some cases, they are composed by a certain number of sub-models, each specific for a certain chemical class. 

Model building. Classical QSAR analysis is based on two premises first, that biological activity of compounds and differences in potency are a function of... 

The classical QSAR methodology started 1964 with the publications of Hansch and Fujita (1964) and Free and Wilson (1964) and the statement of Hansch (1969) resulted from a proposal by Fujita. They proposed to combine several physiochemical parameters (tt, a), also called descriptors, in a quantitative model. This Hansch-type analysis is very flexible and describes many different kinds of biological activities, e.g. in vitro data such as enzyme inhibition (Kubinyi 2002) ... 

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. 

D descriptors are a property of the connectivity of a molecule, and hence contain little information about stereochemistry or local shape. Against that, they are easy to compute, store and manipulate. 2D descriptors have been a powerful weapon in setting up classical QSARs, perhaps because they can implicitly encode 3D features within a homologous series. 

Classical QSAR In the classical QSAR approach, pioneered by Hansch and Leo, biological properties are usually correlated with a set of descriptors using equations similar to Eq. [17]. 

Like classical QSAR, this de novo approach assumes that substituent effects are additive and constant. BA is the biological activity is the jth substituent, which carries a value 1 if present, 0 if absent. The term Uj represents the contribution of the jth substituent to biological activity and p i s the overall average activity. The summation of all activity contributions at each position must equal zero. The series of linear equations that are formulated are solved by linear regression analysis. It is necessary for each substituent to appear more than once at a position in different combinations with substituents at other positions. 

Partial Least Squares regression (PLS) is usually performed on a - data matrix to search for a correlation between the thousands of CoMFA descriptors and biological response. However, usually after - variable selection, the PLS model is transformed into and presented as a multiple regression equation to allow comparison with classical QSAR models. 

MFTA often gives models that are comparable in quality of description and prediction to models based on the widely used classical QSAR methods and 3D approaches. 

The term classical QSAR is often used to denote the - Hansch analysis, -> Free-Wilson analysis, -> Linear Free Energy Relationships (LFER) and -> Linear Solvation Energy Relationships (LSER), i.e. those SRC approaches developed between 1960 and 1980 that can be considered the beginning of the modern QSAR/QSPR methods. 

Horwell, D.C., Howson, W., Higginbottom, M., Naylor, D., Ratcliffe, G.S. and Williams, S. (1995). Quantitative Structure-Activity Relationships (QSARs) of N-Terminus Fragments of NKl Tachykinin Antagonists A Comparison of Classical QSARs and Three-Dimensional QSARs from Similarity Matrices. J.Med.Chem.,38, 4454-4462. 

Kim, K.H. (1995b). Comparison of Classical QSAR and Comparative Molecular Field Analysis Toward Lateral Validations. In Classical and Three-Dimensional QSAR in Agrochemistry (Hansch, C. and Fujita, T, eds.), American Chemical Society, Washington (DC), pp. 302-317. 

Topliss, J.G. (1993). Some Qbservation on Classical QSAR. Persp.Drug Disc.Des., 1, 253-268. 

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