Congeneric series

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. 

The hydrophobic constant r is a measure of the contribution of a substituent X to the lipophilidty of compound R-X compared with R-H. The constant representing the solvent/solvent system, analogously to Hammett s p constant for the reaction type, was arbitrarily set to 1 for octanol/water and thus does not appear in Eq. (7). The lipophilidty constant ti allows the estimation of log P values for congeneric series of compounds with various substituents (see Eq. (8)). 

Their advantages are that they are simple to use and are transparent that is, the descriptors that best model the biological activity can be seen and— hopefully—understood. Their disadvantages are that they work best when restricted to congeneric series of compounds, they assume that the biological activity is a rectilinear function of each descriptor, and they can suffer from a high risk of chance correlations, especially when a large pool of descriptors is used. 

A key requirement of QSAR is that the compounds used in the modeling and prediction processes should have the same mechanism of action, and for this reason most QSAR studies are made with congeneric series of compounds. However, if a diverse set of compounds can reasonably be assumed to have the same mechanism of action, QSAR modeling can justihably be carried out. For example, Dearden et al. [43] developed a QSAR for the ratio of brain levels of 22 very diverse drugs in the wild-type mouse and the P-glycoprotein knockout mouse (R+/ ) ... 

However, from our point of view, there remains a lack of sufficiently precise and reliable methods to compute thermodynamic water solubility. The majority of methods work only for congeneric series of compounds, and many have not been developed to function in areas of pharmaceutical research using drug-like molecules. Most of the methods do not use the three-dimensional structure of the compounds, while some depend on previous knowledge of certain experimental properties of the compounds of interest. Moreover, all of the methods are dependent upon the quality of solubility values in the training set used to develop the model indeed, this latter point is a critical limitation that has a major influence on solubility estimations. 

Shiu, W.-Y., Gobas, F. A. P. C., Mackay, D. (1987) Physical-chemical properties of three congeneric series of chlorinated aromatic hydrocarbons. In QSAR in Environmental Toxicology II. Kaiser, K. L. E., Ed., pp. 347-362, D. Reidel Publishing, Dordrecht, The Netherlands. 

We concluded that phenoxy, benzyl, benzoyl and phenyl substituents were all members of a congeneric series of substituents to which simpler members, say fluorine or a methyl group also belonged. In other words, for these simple benzyl esters, no bridging atom was required for insecticidal activity. 

Pearlman, D.A. (2005) Evaluating the molecular mechanics Poisson-Boltzmann surface area free energy method using a congeneric series of ligands to p38 MAP kinase, foumal of Medicinal Chemistry, 48, 7796-7807. 

Sophisticated drug-likeness models are normally used across a congeneric series of compounds to solve a specific problem in lead optimization. On the other hand, empirical rules are frequently used in lead finding. 

Molecules are characterized by potential hydrogen bonding, polar, hydrophobic, and electrostatic interactions in 3D space, using 3D molecular fields. Techniques such as Comparative Molecular Field Analysis (CoMFA), which considers the 3D distribution of electrostatic and steric fields, have been applied to congeneric series of enzyme substrates or inhibitors generating 3D QSAR equations. Most examples of such applications are to modeling CYP substrate and inhibitor specificity and these have been extensively reviewed in the literature (Ekins et al., 2000 2001 Ter Laak and Vermeulen, 2001 Ter Laak et al., 2002). 

One approach to developing QSARs has been the use of congeneric series of chemicals. While it is easy in the case of a congeneric series to identify the chemical domain, the congeneric series-derived QSAR is of little predictive value precisely because of the narrow structural domain on which they are based (Kaiser et al., 1999). Even within homologous series, efforts such as selecting derivatives with markedly different substitutents can be made to optimize molecular diversity and thus the domain. 

In addition, they found a good correlation with first-order simple molecular connectivity, but only for a congeneric series of aliphatic alcohols. They also investigated the approach of Magee (1991) in developing a mechanism-based QSAR, using hydrogen bond donation, molar refractivity, and components of log Kow this approach resulted in a correlation no better than that with log K0J (n = 49, R2 = 0.812, s = 0.271, F = 37.1). 

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 ... 

Lipinski 2000). Alternatively, the assumption might be valid that congeneric series behave similar in multiple mechanism assays, so that a local model for this series is primarily capturing the main trend of this series. 

Quantitative Structure Activity Relationship (QSAR) is a method that makes predictions by the quantitative description of molecular properties with the use of descriptors of the chemical structure (Dearden 2003). This means QSAR models describe the quantitative or calculated relationship between a chemical structure and their biological activity (e.g. toxicity) with the help of chemical descriptors that are generated from the molecular structure (Durham and Pearl 2001). This relationship is described in from of a mathematical equation (e.g. log 1/C = a tt + b a +. .. + const). QSAR models generally show better predictivity if all compounds of a dataset involved in the prediction are derived from a congeneric series of compounds, that means they should all act by the same mechanism of action, since the physico-chemical and structural descriptors used in the QSAR reflect the same mechanism of action. Sometimes it is difficult to determine the mechanism of action, so series of compounds involved in a QSAR model are often restricted to a given chemical class in the hope that this will ensure a single mechanism of action (Dearden 2003). 

Most published QSAR models have been developed from congeneric series. But with the recent use of large, diverse chemical libraries, there is an increasing interest for QSAR models for a heterogeneous collection of compounds, a non-congeneric series of compounds. It is often possible to develop QSAR models for non-congeneric series to be used as classi-... 

One restriction of QSAR is that in most cases a good predictivity of a model is limited to a congeneric series or a specific class of compounds, which... 

ADAPT Pattern recognition Cluster analysis Uses QSAR descriptors of molecular structure Limited to congeneric series of chemicals... 

In addition to log P, many other structural parameters have been found to relate to toxicity,209-214 in particular, those involving molecular dimensions and features of electronic structure, especially those pertaining to frontier orbitals. Although QSARs provide strong correlations for congeneric series of chemicals, they are not always applicable to diverse structures. 

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