1 / 12

Validation of Model of Cytochrome P450 2D6: An in Silico Tool for Predicting Metabolism and Inhibition

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.

nitsa
Download Presentation

Validation of Model of Cytochrome P450 2D6: An in Silico Tool for Predicting Metabolism and Inhibition

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Validation of Model of Cytochrome P450 2D6: An in Silico Tool for PredictingMetabolism 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

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

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

  4. 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

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

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

  7. Screened Available Databases Ekins ( 21 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 Strobl (30 compounds ) 1 ring system r2 = 0.36 12 ring systems r2 = 0.56

  8. 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

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

  10. Predicting inhibition using Docking Cutoffs: IC50 < 10 µM = inhibitor -30 kJ/mol = predicted inhibitor Predictions: 13 correct 7 false positives

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

  12. Thank you for your attention

More Related