Predicting Drug Metabolism by Cytochrome P450 2C9: Comparison with the 2D6 and 3A4 Isoforms

Rydberg, Patrik; Olsen, Lars

doi:10.1002/cmdc.201200160

Cited by 52 publications

(57 citation statements)

References 51 publications

(128 reference statements)

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“…Substrates for certain CYP forms have common functional groups that interact with particular amino acid residues in the active site; thus, these residues position the substrate into a certain binding conformation in the active site. For these substrates, the intramolecular distance of each potential SOM to the functional group can be taken into account in form-specific scoring functions (Rydberg and Olsen, 2011, 2012). …”

Section: Modeling Of Cypsmentioning

confidence: 99%

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

et al. 2015

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The adverse effects to humans and environment of only few chemicals are well known. Absorption, distribution, metabolism, and excretion (ADME) are the steps of pharmaco/toxicokinetics that determine the internal dose of chemicals to which the organism is exposed. Of all the xenobiotic-metabolizing enzymes, the cytochrome P450 (CYP) enzymes are the most important due to their abundance and versatility. Reactions catalyzed by CYPs usually turn xenobiotics to harmless and excretable metabolites, but sometimes an innocuous xenobiotic is transformed into a toxic metabolite. Data on ADME and toxicity properties of compounds are increasingly generated using in vitro and modeling (in silico) tools. Both physics-based and empirical modeling approaches are used. Numerous ligand-based and target-based as well as combined modeling methods have been employed to evaluate determinants of CYP ligand binding as well as predicting sites of metabolism and inhibition characteristics of test molecules. In silico prediction of CYP–ligand interactions have made crucial contributions in understanding (1) determinants of CYP ligand binding recognition and affinity; (2) prediction of likely metabolites from substrates; (3) prediction of inhibitors and their inhibition potency. Truly predictive models of toxic outcomes cannot be created without incorporating metabolic characteristics; in silico methods help producing such information and filling gaps in experimentally derived data. Currently modeling methods are not mature enough to replace standard in vitro and in vivo approaches, but they are already used as an important component in risk assessment of drugs and other chemicals.

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Section: Modeling Of Cypsmentioning

confidence: 99%

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

et al. 2015

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“…The resultant score is referred to as a SMARTCyp score and is used to rank potential SoMs. Recently, a new version of the SMARTCyp program, version 2.4.2, was released with parameters specific to CYP2C9 ligands(31). …”

Section: Resultsmentioning

confidence: 99%

Combining Structure- and Ligand-Based Approaches to Improve Site of Metabolism Prediction in CYP2C9 Substrates

et al. 2014

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Purpose Predicting atoms in a potential drug compound that are susceptible to oxidation by cytochrome P450 (CYP) enzymes is of great interest to the pharmaceutical community. We aimed to develop a computational approach combining ligand- and structure-based design principles to accurately predict sites of metabolism (SoMs) in a series of CYP2C9 substrates. Methods We employed the reactivity model, SMARTCyp, ensemble docking, and pseudo-receptor modeling based on quantitative structure-activity relationships (QSAR) to account for influences of both the inherent reactivity of each atom and the physical structure of the CYP2C9 binding site. Results We tested ligand-based prediction alone (i.e. SMARTCyp), structure-based prediction alone (i.e. AutoDock Vina docking), the linear combination of the SMARTCYP and docking scores, and finally a pseudo-receptor QSAR model based on the docked compounds in combination with SMARTCyp. We found that by using the latter combined approach we were able to accurately predict 88% and 96% of the true SoMs, within the top-1 and top-2 predictions, respectively. Conclusions We have outlined a novel combination approach for accurately predicting SoMs in CYP2C9 ligands. We believe that this method may be applied to other CYP2C9 ligands as well as to other CYP systems.

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“…If the intrinsic reactivity is such that a reaction is extremely unlikely, then an optimal orientation in the active site will not improve this. Interestingly, a very simple approach such as SMARTCyp (see Chapter 11), which is based solely on the intrinsic reactivity of potential SoMs, performs very well to predict the SoM of substrates of CYP1A2 and 3A4, but an orientational penalty needs to be included for isoforms 2C9 and 2D6 [11][12][13]. Ideally, a structure-based approach should be combined with an estimate of the intrinsic reactivity, which is the key idea behind approaches such as MetaSite [7], MLite [14], or IDSite [10].…”

Section: å Rulementioning

confidence: 99%

“…For the CYP isoforms 2D6 and 2C9, the SMARTCyp approach seems to indicate that the orientation due to the protein does play a significant role [12,13]. Before the availability of any X-ray structures, a homology model was described for CYP2D6 based on the rabbit CYP2C5 structure [36].…”

Section: Prediction Of Binding Posesmentioning

confidence: 99%

Structure‐Based Methods for Predicting the Sites and Products of Metabolism

Oostenbrink

2014

Methods and Principles in Medicinal Chemistry

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IntroductionCytochrome P450s (CYPs) are heme-containing enzymes that can be found in virtually all organisms. In humans, the CYP superfamily constitutes the most important enzymes in drug metabolism (see Chapters 4-8).Various computational approaches to study CYP metabolism have been reported over the years [1][2][3][4], and depending on the question that is being addressed, different methods may be more or less appropriate. For instance, if the actual reaction mechanism of a particular substrate is to be studied, a quantum mechanical description explicitly describing the electrons that are responsible for bonds being broken and formed is required [5]. On the other hand, a classification of a large number of compounds in terms of their likelihood to show an interaction with a CYP may rather be performed by application of cheminformatics machine learning tools [6]. In almost all cases, the versatility of the enzymes, the diversity of the substrates and inhibitors, and the malleability of the active sites pose additional challenges to the computational approach at hand. This chapter focuses on structure-based approaches to study the metabolism of substrates by CYP enzymes. Rather than addressing the actual enzymatic reactions, as in Chapters 6 and 11, we will focus here on the preferred orientations of substrates in the active site of CYP enzymes as a crucial first step in the prediction of metabolism. Using mostly examples from our own work, we will highlight some of the possibilities and challenges in structure-based predictions of sites of metabolism (SoMs). 6 Å RuleThe basic concept behind structure-based prediction of metabolism by CYP enzymes is extremely simple. The crucial cofactor of the enzymes is a heme 243 Drug Metabolism Prediction, First Edition. Edited by Johannes Kirchmair.

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Predicting Drug Metabolism by Cytochrome P450 2C9: Comparison with the 2D6 and 3A4 Isoforms

Cited by 52 publications

References 51 publications

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

Modeling of interactions between xenobiotics and cytochrome P450 (CYP) enzymes

Combining Structure- and Ligand-Based Approaches to Improve Site of Metabolism Prediction in CYP2C9 Substrates

Structure‐Based Methods for Predicting the Sites and Products of Metabolism

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