Novel Application of 2D and 3D-Similarity Searches To Identify Substrates among Cytochrome P450 2C9, 2D6, and 3A4

Cytochrome P450 (CYP450) is a class of enzymes where the substrate identification is particularly important to know. It would help medicinal chemists to design drugs with lower side effects due to drug-drug interactions and to extensive genetic polymorphism. Herein, we discuss the application of the 2D and 3D-similarity searches in identifying reference structures with higher capacity to retrieve substrates of three important CYP enzymes (CYP2C9, CYP2D6, and CYP3A4). On the basis of the complementarities of multiple reference structures selected by different similarity search methods, we proposed the fusion of their individual Tanimoto scores into a consensus Tanimoto score (T(consensus)). Using this new score, true positive rates of 63% (CYP2C9) and 81% (CYP2D6) were achieved with false positive rates of 4% for the CYP2C9-CYP2D6 data set. Extended similarity searches were carried out on a validation data set, and the results showed that by using the T(consensus) score, not only the area of a ROC graph increased, but also more substrates were recovered at the beginning of a ranked list.

[1]  Thierry Langer,et al.  Development and validation of an in silico P450 profiler based on pharmacophore models. , 2006, Current drug discovery technologies.

[2]  Gabriele Cruciani,et al.  Predicting drug metabolism: a site of metabolism prediction tool applied to the cytochrome P450 2C9. , 2003, Journal of medicinal chemistry.

[3]  L. Wienkers,et al.  Predicting in vivo drug interactions from in vitro drug discovery data , 2005, Nature Reviews Drug Discovery.

[4]  Donglu Zhang,et al.  Role of Drug Metabolism in Drug Development , 2007 .

[5]  R. Sheridan,et al.  Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9. , 2007, Journal of medicinal chemistry.

[6]  Johann Gasteiger,et al.  Ligand-Based Models for the Isoform Specificity of Cytochrome P450 3A4, 2D6, and 2C9 Substrates , 2007, J. Chem. Inf. Model..

[7]  Ajay N. Jain,et al.  Parameter estimation for scoring protein-ligand interactions using negative training data. , 2006, Journal of medicinal chemistry.

[8]  Slobodan Petar Rendic.  Summary of information on human CYP enzymes: human P450 metabolism data , 2002, Drug metabolism reviews.

[9]  Pierre Acklin,et al.  Similarity Metrics for Ligands Reflecting the Similarity of the Target Proteins , 2003, J. Chem. Inf. Comput. Sci..

[10]  A. Alex,et al.  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. , 1999, Journal of medicinal chemistry.

[11]  Valerie J Gillet,et al.  A Comparison of Field-Based Similarity Searching Methods: CatShape, FBSS, and ROCS , 2008, J. Chem. Inf. Model..

[12]  Robert P Sheridan,et al.  Why do we need so many chemical similarity search methods? , 2002, Drug discovery today.

[13]  Tudor I. Oprea,et al.  Virtual screening applications: a study of ligand-based methods and different structure representations in four different scenarios , 2007, J. Comput. Aided Mol. Des..

[14]  Yu Zong Chen,et al.  Prediction of Cytochrome P450 3A4, 2D6, and 2C9 Inhibitors and Substrates by Using Support Vector Machines , 2005, J. Chem. Inf. Model..

[15]  William Stafford Noble,et al.  Matrix2png: a utility for visualizing matrix data , 2003, Bioinform..

[16]  Jonas Boström,et al.  Assessing the performance of OMEGA with respect to retrieving bioactive conformations. , 2003, Journal of molecular graphics & modelling.

[17]  P. Willett,et al.  Promoting Access to White Rose Research Papers Similarity-based Virtual Screening Using 2d Fingerprints , 2022 .

[18]  I. Kola,et al.  Can the pharmaceutical industry reduce attrition rates? , 2004, Nature Reviews Drug Discovery.

[19]  S. Ekins,et al.  Three-Dimensional-Quantitative Structure Activity Relationship Analysis of Cytochrome P-450 3 A 4 Substrates , 1999 .

[20]  J. Tillement,et al.  5.02 – Clinical Pharmacokinetic Criteria for Drug Research , 2007 .

[21]  M. Sutcliffe,et al.  Insights into drug metabolism by cytochromes P450 from modelling studies of CYP2D6‐drug interactions , 2008, British journal of pharmacology.

[22]  Jérôme Hert,et al.  Comparison of Fingerprint-Based Methods for Virtual Screening Using Multiple Bioactive Reference Structures , 2004, J. Chem. Inf. Model..

[23]  Christopher I. Bayly,et al.  Evaluating Virtual Screening Methods: Good and Bad Metrics for the "Early Recognition" Problem , 2007, J. Chem. Inf. Model..

[24]  Florian Nigsch,et al.  How To Winnow Actives from Inactives: Introducing Molecular Orthogonal Sparse Bigrams (MOSBs) and Multiclass Winnow , 2008, J. Chem. Inf. Model..

[25]  Thierry Kogej,et al.  Multifingerprint Based Similarity Searches for Targeted Class Compound Selection , 2006, J. Chem. Inf. Model..

[26]  Anthony Nicholls,et al.  What do we know and when do we know it? , 2008, J. Comput. Aided Mol. Des..

[27]  Chris Oostenbrink,et al.  Catalytic site prediction and virtual screening of cytochrome P450 2D6 substrates by consideration of water and rescoring in automated docking. , 2006, Journal of medicinal chemistry.

[28]  Markus A Lill,et al.  Prediction of Small‐Molecule Binding to Cytochrome P450 3A4: Flexible Docking Combined with Multidimensional QSAR , 2006, ChemMedChem.

[29]  Robert P. Sheridan,et al.  Comparison of Topological, Shape, and Docking Methods in Virtual Screening. , 2007 .

[30]  Thierry Langer,et al.  Fast and Efficient in Silico 3D Screening: Toward Maximum Computational Efficiency of Pharmacophore-Based and Shape-Based Approaches , 2007, J. Chem. Inf. Model..

[31]  R. Sheridan,et al.  A model for predicting likely sites of CYP3A4-mediated metabolism on drug-like molecules. , 2003, Journal of medicinal chemistry.

[32]  Ismael Zamora,et al.  The Molecular Basis of CYP2D6-Mediated N-Dealkylation: Balance between Metabolic Clearance Routes and Enzyme Inhibition , 2008, Drug Metabolism and Disposition.