MODELS FOR CYTOCHROME P 450 : A PREDICTIVE ELECTRONIC MODEL FOR AROMATIC OXIDATION AND HYDROGEN ATOM ABSTRACTION

Experimental observations suggest that electronic characteristics play a role in the rates of substrate oxidation for cytochrome P450 enzymes. For example, the tendency for oxidation of a certain functional group generally follows the relative stability of the radicals that are formed (e.g., N-dealkylation > O-dealkylation > 2° carbon oxidation > 1° carbon oxidation). In addition, results show that useful correlations between the rates of product formation can be developed using electronic models. In this article, we attempt to determine whether a combined computational model for aromatic and aliphatic hydroxylation can be developed. Toward this goal, we used a combination of experimental data and semiempirical molecular orbital calculations to predicted activation energies for aromatic and aliphatic hydroxylation. The resulting model extends the predictive capacity of our previous aliphatic hydroxylation model to include the second most important group of oxidations, aromatic hydroxylation. The combined model can account for about 83% of the variance in the data for the 20 compounds in the training set and has an error of about 0.7 kcal/mol. The P450 enzymes are a superfamily of monooxygenases involved in the metabolism of both exogenous and endogenous compounds. Ironically, these enzymes play a central role in both the prevention and induction of chemical toxicities and carcinogenicity. Although most P450 oxidations of xenobiotics result in detoxification, occasionally a more toxic intermediate is formed. In fact, many ultimate toxins and carcinogens are formed by the bioactivation of less reactive compounds, and many bioactivation reactions are mediated by the P450 enzymes. Often bioactivation reactions are in competition with detoxification pathways for the same substrate. Since these enzymes play such a central role in both detoxification and bioactivation, predictive models for cytochrome P450 catalysis will be useful tools for evaluating of the potential risks of environmental exposures. One of the most pertinent but difficult problems in risk assessment is translating bench results and mechanistic information into a form that can be used. This article outlines steps toward the development of computational models from laboratory data into a form that can be used for risk assessments that accurately reflect experimental results. For example, these models now more completely describe all positions of metabolism for nitriles and should be more complete in predicting the toxicity related to nitrile metabolism. These semiempirical computational models blend experimental data and computational chemistry in such a way as to provide a consistent prediction of the bioactivation rates for a broad spectrum of compounds. In particular, these models can be used to predict xenobiotic metabolism by the P450 enzyme family, including the bioactivation of compounds to toxins and carcinogens. Models such as those presented here for P450-mediated reactions can also play a role in drug design. Tools that predict regioselectivity can be used to assess the pharmacokinetics of drugs before synthesis, saving time and money in the drug development process. These types of tools, either alone or in combination with homology or pharmacophore models, can also provide for the design of better drugs, with higher compliance and fewer toxic side-effects. However, P450 enzymes are difficult to model by the standard methods used for most drug targets, which are based mainly on predicting binding affinities related to steric features, since accurate results depend upon the prediction of both the electronic and steric features of the enzymes. This is different from many other enzyme systems since P450 has the need to metabolize a vast array of xenobiotics, which makes it impractical to have one enzyme for each compound or even each class of compounds. Therefore, although most cellular functions tend to be very specific, xenobiotic metabolism requires enzymes with diverse substrate specificities. The cytochromes P450 have apparently assumed much of this role. The required diversity is accomplished by families and subfamilies of enzymes with generally broad substrate specificities, a very reactive oxygenating species and a broad regioselectivity. Thus, for many reactions, the electronic features of the substrate are all that are required to predict regioselectivity (Grogan et al., 1992; Harris et al., 1992; de Groot et al., 1995, 1999; Yin et al., 1995). We have reported a rapid electronic model for the prediction of regioselectivity in P450-mediated hydrogen atom abstraction mechanisms (Korzekwa et al., 1990a). The methods depend only on the calculation of the AM1 ground-state energies for the parent compound This work was supported by National Institute of Environmental Health Sciences Grant ES09122. 1 Abbreviations used are: P450, cytochrome P450; AM1, Austin model-1; PNR, p-nitrosophenoxy radical. Address correspondence to: Jeffrey P. Jones, Department of Chemistry, Washington State University, Pullman, Washington, 99164. E-mail: jpj@wsu.edu 0090-9556/02/3001-7–12$3.00 DRUG METABOLISM AND DISPOSITION Vol. 30, No. 1 Copyright © 2002 by The American Society for Pharmacology and Experimental Therapeutics 564/951813 DMD 30:7–12, 2002 Printed in U.S.A. 7 at A PE T Jornals on O cber 3, 2017 dm d.aspurnals. org D ow nladed from