4.6 Article

Mechanism and structure-reactivity relationships for aromatic hydroxylation by cytochrome P450

Journal

ORGANIC & BIOMOLECULAR CHEMISTRY
Volume 2, Issue 20, Pages 2998-3005

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/b410729b

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Cytochrome P450 enzymes play a central role in drug metabolism, and models of their mechanism could contribute significantly to pharmaceutical research and development of new drugs. The mechanism of cytochrome P450 mediated hydroxylation of aromatics and the effects of substituents on reactivity have been investigated using B3LYP density functional theory computations in a realistic porphyrin model system. Two different orientations of substrate approach for addition of Compound I to benzene, and also possible subsequent rearrangement pathways have been explored. The rate-limiting Compound I addition to an aromatic carbon atom proceeds on the doublet potential energy surface via a transition state with mixed radical and cationic character. Subsequent formation of epoxide, ketone and phenol products is shown to occur with low barriers, especially starting from a cation-like rather than a radical-like tetrahedral adduct of Compound I with benzene. Effects of ring substituents were explored by calculating the activation barriers for Compound I addition in the meta and para-position for a range of monosubstituted benzenes and for more complex polysubstituted benzenes. Two structure-reactivity relationships including 8 and 10 different substituted benzenes have been determined using (i) experimentally derived Hammett sigma-constants and (ii) a theoretical scale based on bond dissociation energies of hydroxyl adducts of the substrates, respectively. In both cases a dual-parameter approach that employs a combination of radical and cationic electronic descriptors gave good relationships with correlation coefficients R-2 of 0.96 and 0.82, respectively. These relationships can be extended to predict the reactivity of other substituted aromatics, and thus can potentially be used in predictive drug metabolism models.

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