Molecular modelling of CYP2B6 based on homology with the CYP2C5 crystal structure: analysis of enzyme-substrate interactions

Quantitative structure-activity relationships (QSARs) within cytochromes P450 2B (CYP2B) subfamily enzymes: the importance of lipophilicity for binding and metabolism

The results of qualitative structure-activity relationship (QSAR) analysis are reported for several series of compounds which act as substrates for mammalian CYP2B subfamily enzymes, together with a homologous series of aliphatic primary amines which are known to inhibit CYP2B enzymes. It is found that the compound lipophilicity in the form of the log P value (where P is the octanol/water partition coefficient) is related, either linearly or quadratically, to equilibrium constants of inhibition (Ki), binding (Ks) or metabolism (Km) depending on the series of compounds in question. In some instances, the difference between frontier orbital energy levels (deltaE) also features in several of the log P expressions with biological activity. Also present in a small number of correlations are parameters which are likely to be related to logP: namely, Rm, which is the partitioning factor derived from thin layer chromatography (TLC) retention times, and also the compound molecular weight (Mr). All of these three parameters ((log P, Rm and Mr) are thought to be related to the compound's ability to desolvate the P450 active site when they bind to the enzyme. Although the linear relationships between lipophilicity and CYP2B-related activity point to a major role for desolvation of the enzyme binding site in the overall interaction, it is noted that there may be an optimal log P value displayed by preferred substrates as shown by parabolic relationships with this lipophilic parameter. In addition, there is a remarkable similarity in the coefficients for the log P term of any QSAR expression, which suggests that the hydrophobicity of CYP2B active sites may be broadly equivalent between the various mammalian species.

Utility of homology models in the drug discovery process

Advances in bioinformatics and protein modeling algorithms, in addition to the enormous increase in experimental protein structure information, have aided in the generation of databases that comprise homology models of a significant portion of known genomic protein sequences. Currently, 3D structure information can be generated for up to 56% of all known proteins. However, there is considerable controversy concerning the real value of homology models for drug design. This review provides an overview of the latest developments in this area and includes selected examples of successful applications of the homology modeling technique to pharmaceutically relevant questions. In addition, the strengths and limitations of the application of homology models during all phases of the drug discovery process are discussed.