Using a homology model of cytochrome P450 2D6 to predict substrate site of metabolism

Unwalla, Rayomand J.; Cross, Jason B.; Salaniwal, Sumeet; Shilling, Adam D.; Leung, Louis; Kao, John Y.; Humblet, Christine

doi:10.1007/s10822-010-9336-6

Cited by 38 publications

(31 citation statements)

References 68 publications

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“…Predictions were deemed correct in cases where a known SOM is located within a 4.5 Å distance from the catalytic center among the five highestranked poses. This was found to be correct for 85% of the 16 investigated compounds [316]. This study points out the common observation that, due to considerable conformational rearrangements of the protein upon ligand binding, P450 apo structures generally yield inferior performance compared to holo structures.…”

Section: Metabolite Predictionsupporting

confidence: 64%

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

Gleeson¹,

Modi²,

Bender³

et al. 2012

CPD

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The percentage of failures in late pharmaceutical development due to toxicity has increased dramatically over the last decade or so, resulting in increased demand for new methods to rapidly and reliably predict the toxicity of compounds. In this review we discuss the challenges involved in both the building of in silico models on toxicology endpoints and their practical use in decision making. In particular, we will reflect upon the predictive strength of a number of different in silico models for a range of different endpoints, different approaches used to generate the models or rules, and limitations of the methods and the data used in model generation. Given that there exists no unique definition of a 'good' model, we will furthermore highlight the need to balance model complexity/interpretability with predictability, particularly in light of OECD/REACH guidelines. Special emphasis is put on the data and methods used to generate the in silico toxicology models, and their strengths and weaknesses are discussed. Switching to the applied side, we next review a number of toxicity endpoints, discussing the methods available to predict them and their general level of predictability (which very much depends on the endpoint considered). We conclude that, while in silico toxicology is a valuable tool to drug discovery scientists, much still needs to be done to, firstly, understand more completely the biological mechanisms for toxicity and, secondly, to generate more rapid in vitro models to screen compounds. With this biological understanding, and additional data available, our ability to generate more predictive in silico models should significantly improve in the future.

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Section: Metabolite Predictionsupporting

confidence: 64%

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

Gleeson¹,

Modi²,

Bender³

et al. 2012

CPD

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“…8A-E). It has been reported that Phe120, Glu216, Asp301, Phe481 and Phe483 are key amino acid residues for substrate interaction within in the active site of CYP2D6 (McLaughlin et al, 2005;Unwalla et al, 2010). Alanine substitution of Phe120 had only a minor effect on the inhibition and binding activity, whereas alanine substitution of Glu216 and Asp301 played a significant role in binding to CYP2D6 active site in productive orientation.…”

Section: Molecular Docking Studiesmentioning

confidence: 99%

An evaluation of the CYP2D6 and CYP3A4 inhibition potential of metoprolol metabolites and their contribution to drug–drug and drug–herb interaction by LC‐ESI/MS/MS

Borkar

Bhandi

Dubey

et al. 2016

Biomedical Chromatography

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The aim of the present study was to evaluate the contribution of metabolites to drug-drug interaction and drug-herb interaction using the inhibition of CYP2D6 and CYP3A4 by metoprolol (MET) and its metabolites. The peak concentrations of unbound plasma concentration of MET, α-hydroxy metoprolol (HM), O-desmethyl metoprolol (ODM) and N-desisopropyl metoprolol (DIM) were 90.37 ± 2.69, 33.32 ± 1.92, 16.93 ± 1.70 and 7.96 ± 0.94 ng/mL, respectively. The metabolites identified, HM and ODM, had a ratio of metabolic area under the concentration-time curve (AUC) to parent AUC of ≥0.25 when either total or unbound concentration of metabolite was considered. In vitro CYP2D6 and CYP3A4 inhibition by MET, HM and ODM study revealed that MET, HM and ODM were not inhibitors of CYP3A4-catalyzed midazolam metabolism and CYP2D6-catalyzed dextromethorphan metabolism. However, DIM only met the criteria of >10% of the total drug related material and <25% of the parent using unbound concentrations. If CYP inhibition testing is solely based on metabolite exposure, DIM metabolite would probably not be considered. However, the present study has demonstrated that DIM contributes significantly to in vitro drug-drug interaction. Copyright © 2016 John Wiley & Sons, Ltd.

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“…For more than 75% of the substrate, the X-ray structure fails to rationalize the metabolism in terms of the binding modes [45]. Similar results were observed in a comparison of another homology model with the X-ray structure [46]. One of the substrates that were successfully docked into the X-ray structure was 3,4-methylenedioxy-N-ethylamphetamine.…”

Section: Protein Flexibilitymentioning

confidence: 55%

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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Using a homology model of cytochrome P450 2D6 to predict substrate site of metabolism

Cited by 38 publications

References 68 publications

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

The Challenges Involved in Modeling Toxicity Data In Silico: A Review

An evaluation of the CYP2D6 and CYP3A4 inhibition potential of metoprolol metabolites and their contribution to drug–drug and drug–herb interaction by LC‐ESI/MS/MS

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

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