Regioselectivity Prediction of CYP1A2-Mediated Phase I Metabolism

Jung, Jihoon; Kim, Nam Doo; Kim, Su Yeon; Choi, Inhee; Cho, Kwang Hwi; Oh, Won Seok; Kim, Doo Nam; No, Kyoung Tai

doi:10.1021/ci800001m

Cited by 39 publications

(43 citation statements)

References 39 publications

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“…Molecular modeling methods have been used over the years to study and predict P450 active site accessibility (Harris, 2004), ligand binding affinity (de Groot et al, 2004;Vasanthanathan et al, 2009b), site of metabolism (Cruciani et al, 2005;Vasanthanathan et al, 2009a), selectivity and specificity (Terfloth et al, 2007;Jung et al, 2008), toxicity (Hansch et al, 2004), virtual screening, and rule-based methods for P450-mediated metabolic reactions (Rydberg et al, 2009).…”

mentioning

confidence: 99%

Computational Prediction of Binding Affinity for CYP1A2-Ligand Complexes Using Empirical Free Energy Calculations

Poongavanam¹,

Olsen²,

Jørgensen³

et al. 2010

Drug Metab Dispos

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ABSTRACT:Predicting binding affinities for receptor-ligand complexes is still one of the challenging processes in computational structurebased ligand design. Many computational methods have been developed to achieve this goal, such as docking and scoring methods, the linear interaction energy (LIE) method, and methods based on statistical mechanics. In the present investigation, we started from an LIE model to predict the binding free energy of structurally diverse compounds of cytochrome P450 1A2 ligands, one of the important human metabolizing isoforms of the cytochrome P450 family. The data set includes both substrates and inhibitors. It appears that the electrostatic contribution to the binding free energy becomes negligible in this particular protein and a simple empirical model was derived, based on a training set of eight compounds. The root mean square error for the training set was 3.7 kJ/mol. Subsequent application of the model to an external test set gives an error of 2.1 kJ/mol, which is remarkably good, considering the simplicity of the model. The structures of the proteinligand interactions are further analyzed, again demonstrating the large versatility and plasticity of the cytochrome P450 active site.The cytochromes P450 (P450s) are ubiquitous heme-containing enzymes, which play a vital role in the detoxification of xenobiotics and procarcinogen activation. This family shares a common structural feature of the enzymatic active site, including a planar ferric-protoporphyrin IX heme complex, in which the iron is attached through a cysteine link to the protein.The metabolism of a drug is commonly a process of converting a hydrophobic compound into a hydrophilic one by introducing hydrophilic moieties, very often hydroxyl groups, to the parent compounds. More than 50 human isoforms of P450s have been characterized and only seven of them are responsible for Ͼ90% of metabolism of currently marketed drugs (Williams et al., 2004;Nelson, 2009). The most important isoforms are CYP1A2, CYP2C8, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4. Some of the P450 isoforms also show polymorphism (e.g., CYP1A2, CYP2C9, and CYP2D6), which leads to a poor pharmacokinetic profile. Early prediction of pharmacokinetic properties such as absorption, distribution, metabolism, elimination, and toxicity of new compounds in drug discovery has become essential these days (Dimasi, 2001). This has been achieved by in vivo, in vitro, and in silico methods and should lead to a reduction in failure rates in the drug development process (van de Waterbeemd and Gifford, 2003;Baranczewski et al., 2006;Afzelius et al., 2007;Stjernschantz et al., 2008).Cytochrome P450 1A2 is an inducible member of the P450 superfamily. It is induced by some polycyclic aromatic hydrocarbons and heterocyclic amines, some of which are found in cigarette smoke and charred food (Fontana et al., 1999). Activation and overexpression of this isoform are linked with high risk of various cancers (Seow et al., 2001;Jernström et al., 2008). The importance of CYP1A2 in...

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mentioning

confidence: 99%

Computational Prediction of Binding Affinity for CYP1A2-Ligand Complexes Using Empirical Free Energy Calculations

Poongavanam¹,

Olsen²,

Jørgensen³

et al. 2010

Drug Metab Dispos

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“…Furthermore, the proton-iron distances between the reaction site of NBCeN and the heme of CYP1A1 (3.78 Å) were much shorter than those in CYP1A2 (5.37 Å), suggesting that NBCeN - O -dechloroethylation could occur more readily in CYP1A1. Given that the catalytically reactive distance from the proton-iron of CYP450 is typically within 5.5 Å, 27 NBCeN - O -dechloroethylation is hardly likely to occur in CYP1A2. These results are concordant with the experimental results, which displayed that CYP1A1 but not CYP1A2, can catalyze NBCeN - O -dechloroethylation with a high catalytic efficacy.…”

Section: Resultsmentioning

confidence: 99%

A practical strategy to design and develop an isoform-specific fluorescent probe for a target enzyme: CYP1A1 as a case study

et al. 2017

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“…The optimized model gave 76% correct predictions for the 25 compounds of the test set when considering the two highest-ranked atom positions for each molecule. This approach is related to a further method developed by this research consortium, 76 which also employed the semi-empirical (AM1 60 ) method developed by Korzekwa et al and Jones et al for reactivity estimation, in this case for CYP1A2. AutoDock was used to orientate the ligands in the binding site to examine potential SOM and to estimate binding free energies.…”

Section: Predicting Sites Of Metabolismmentioning

confidence: 99%

Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms

Kirchmair

Williamson

Tyzack

et al. 2012

J. Chem. Inf. Model.

257

219

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Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure–activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein–ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

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Regioselectivity Prediction of CYP1A2-Mediated Phase I Metabolism

Cited by 39 publications

References 39 publications

Computational Prediction of Binding Affinity for CYP1A2-Ligand Complexes Using Empirical Free Energy Calculations

Computational Prediction of Binding Affinity for CYP1A2-Ligand Complexes Using Empirical Free Energy Calculations

A practical strategy to design and develop an isoform-specific fluorescent probe for a target enzyme: CYP1A1 as a case study

Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms

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