PT  - JOURNAL ARTICLE
AU  - Sean Ekins
AU  - Gianpaolo Bravi
AU  - Barbara J. Ring
AU  - Todd A. Gillespie
AU  - Jennifer S. Gillespie
AU  - Mark Vandenbranden
AU  - Steven A. Wrighton
AU  - James H. Wikel
TI  - Three-Dimensional Quantitative Structure Activity Relationship Analyses of Substrates for CYP2B6
DP  - 1999 Jan 01
TA  - Journal of Pharmacology and Experimental Therapeutics
PG  - 21--29
VI  - 288
IP  - 1
4099  - http://jpet.aspetjournals.org/content/288/1/21.short
4100  - http://jpet.aspetjournals.org/content/288/1/21.full
SO  - J Pharmacol Exp Ther1999 Jan 01; 288
AB  - To begin to build an understanding of the interactions of CYP2B6 with substrates, two different 3-dimensional quantitative structure activity relationship (3D-QSAR) models were constructed using 16 substrates of B-lymphoblastoid expressed CYP2B6. A pharmacophore model was built using the program Catalyst, which was compared with a partial least-squares (PLS) model using molecular surface-weighted holistic invariant molecular (MS-WHIM) descriptors. The Catalyst model yielded a 3-dimensional model of the common structural features of CYP2B6 substrates, whereas PLS MS-WHIM generated a model based on statistical analyses of molecular descriptors for size and shape of the substrate. The pharmacophore model obtained with Catalyst consisted of three hydrophobes and one hydrogen bond acceptor region. The cross-validated PLS MS-WHIM model gave a good q2 value of 0.607. Size, positive electrostatic potential, hydrogen bonding acceptor capacity, and hydrophobicity were found to be the most relevant descriptors for the model. These models were then used to predict the Km (apparent) values of a test set of structurally diverse substrates for CYP2B6 not included in the model building, specifically lidocaine, amitriptyline, bupropion, arteether, and verapamil. Overall, both 3D-QSAR methods yielded satisfactory Km (apparent) value predictions for the majority of the molecules in the test set. However, PLS MS-WHIM was unable to reliably predict theKm (apparent) value for verapamil, whereas Catalyst did not predict the Km (apparent)value for lidocaine. In both of these cases the residual of theKm (apparent) value was greater than one log unit. The strengths and limitations of both of these 3D-QSAR approaches are discussed. The American Society for Pharmacology and Experimental Therapeutics