Literature DB >> 10514276

A novel approach to predicting P450 mediated drug metabolism. CYP2D6 catalyzed N-dealkylation reactions and qualitative metabolite predictions using a combined protein and pharmacophore model for CYP2D6.

M J de Groot1, M J Ackland, V A Horne, A A Alex, B C Jones.   

Abstract

A combined protein and pharmacophore model for cytochrome P450 2D6 (CYP2D6) has been extended with a second pharmacophore in order to explain CYP2D6 catalyzed N-dealkylation reactions. A group of 14 experimentally verified N-dealkylation reactions form the basis of this second pharmacophore. The combined model can now accommodate both the usual hydroxylation and O-demethylation reactions catalyzed by CYP2D6, as well as the less common N-dealkylation reactions. The combined model now contains 72 metabolic pathways catalyzed by CYP2D6 in 51 substrates. The model was then used to predict the involvement of CYP2D6 in the metabolism of a "test set" of seven compounds. Molecular orbital calculations were used to suggest energetically favorable sites of metabolism, which were then examined using modeling techniques. The combined model correctly predicted 6 of the 8 observed metabolites. For the well-established CYP2D6 metabolic routes, the predictive value of the current combined protein and pharmacophore model is good. Except for the highly unusual metabolism of procainamide and ritonavir, the known metabolites not included in the development of the model were all predicted by the current model. Two possible metabolites have been predicted by the current model, which have not been detected experimentally. In these cases, the model may be able to guide experiments. P450 models, like the one presented here, have wide applications in the drug design process which will contribute to the prediction and elimination of polymorphic metabolism and drug-drug interactions.

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Year:  1999        PMID: 10514276     DOI: 10.1021/jm991058v

Source DB:  PubMed          Journal:  J Med Chem        ISSN: 0022-2623            Impact factor:   7.446


  22 in total

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2.  A virtual high throughput screen for high affinity cytochrome P450cam substrates. Implications for in silico prediction of drug metabolism.

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3.  New methods in predictive metabolism.

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Review 4.  Do drug metabolism and pharmacokinetic departments make any contribution to drug discovery?

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Review 5.  New methods in predictive metabolism.

Authors:  Scott Boyer; Ismael Zamora
Journal:  Mol Divers       Date:  2002       Impact factor: 2.943

Review 6.  Pharmacophore-based discovery of ligands for drug transporters.

Authors:  Cheng Chang; Sean Ekins; Praveen Bahadduri; Peter W Swaan
Journal:  Adv Drug Deliv Rev       Date:  2006-09-26       Impact factor: 15.470

7.  Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models.

Authors:  M Paul Gleeson; Andrew M Davis; Kamaldeep K Chohan; Stuart W Paine; Scott Boyer; Claire L Gavaghan; Catrin Hasselgren Arnby; Cecilia Kankkonen; Nan Albertson
Journal:  J Comput Aided Mol Des       Date:  2007-11-22       Impact factor: 3.686

Review 8.  Modeling kinetics of subcellular disposition of chemicals.

Authors:  Stefan Balaz
Journal:  Chem Rev       Date:  2009-05       Impact factor: 60.622

Review 9.  Scaffold-hopping as a strategy to address metabolic liabilities of aromatic compounds.

Authors:  Phillip R Lazzara; Terry W Moore
Journal:  RSC Med Chem       Date:  2019-12-16

10.  Linker Variation and Structure-Activity Relationship Analyses of Carboxylic Acid-based Small Molecule STAT3 Inhibitors.

Authors:  Francisco Lopez-Tapia; Christine Brotherton-Pleiss; Peibin Yue; Heide Murakami; Ana Carolina Costa Araujo; Bruna Reis Dos Santos; Erin Ichinotsubo; Anna Rabkin; Raj Shah; Megan Lantz; Suzie Chen; Marcus A Tius; James Turkson
Journal:  ACS Med Chem Lett       Date:  2018-02-16       Impact factor: 4.345

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