Validation of Model of Cytochrome P450 2D6: An in Silico Tool for Predicting Metabolism and Inhibition Carol A. Kemp, Jack U. Flanagan, Annamaria J. van.

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Presentation transcript:

Validation of Model of Cytochrome P450 2D6: An in Silico Tool for Predicting Metabolism and Inhibition Carol A. Kemp, Jack U. Flanagan, Annamaria J. van Eldik, Jean-Didier Mare´chal, C. Roland Wolf, Gordon C. K. Roberts,§ Mark J. I. Paine & Michael J. Sutcliffe J. Med. Chem. 2004

Cytochrome P450 (Cyp450) Group of oxidative enzymes Exits in all lineages Membrane protein (ER, mitochondria) Metabolite thousands of endogenous and exogenous compounds

Importance of Cyp 2D6 Oxidation of >50 drugsInhibited by drugs Cytochrome P450 2D6 Analgesics (pain killers) Beta Blockers (cardiovascular diseases) Quinidine (heart rhythm disturbance) fluoxertine (depression)

Research Goals Previous work: HM + docking position metabolism site above heme Typical (basic nitrogen) substrates Screening a database for CYP2D6 inhibitors Can 3D method improve over 2D approach Asses model accuracy

Comparative Modeling of 2D6 FSSP = Fold classification & Secondary Structure Alignment (DALI) Bacterial P450Mammalian P450

Model Validation Does a sequence fit a structure ? 1.Buried area 2.% side chain buried with polar atoms 3.Secondary structure Errat non covalently pairs interactions ( CC, CN, CO, NN, NO, OO ) 9 residue sliding window

Screened Available Databases Ekins ( 21 compounds ) Strobl (30 compounds ) Docking Software:GOLD 2.0 Genetic algorithm Full ligand flexibility partial protein flexibility Energy functions partly based on conformational and non-bonded contact information from the CSD 12 ring systems r 2 = ring system r 2 = 0.36

Creating an Additional Dataset NCI database (compounds tested for treating cancer) Weight ~ Ekins & Strobl datasets < 4 Ring Systems Availability 33 Compunds Basic Nitrogen & Aromatic Group

Consistency with known inhibition measurements Cyp450 2D6 Small Molecule AMMC demethylase Inhibition AMMC Ekins / Strobl

Predicting inhibition using Docking Cutoffs: IC 50 < 10 µM = inhibitor -30 kJ/mol = predicted inhibitor Predictions: 13 correct 7 false positives

Questions for discussion 1.Is the method applicable for large scale database scanning ? (~7 min CPU on a one processor Silicon Graphics R14) 2.Can substrate affinity be predicted with the same accuracy ? 3.Are positions reliable enough for predicting drug-drug interactions ?

Thank you for your attention