In Silico Approaches to Predict DDIs

This chapter will briefly describe in silico methodologies for the prediction of drug–drug interactions (DDIs) and highlight the broad application of computational tools to study DDIs. This chapter outlines the main methodologies currently applied including QSAR modeling, pharmacophore modeling, docking, and the combination of in silico and experimental approaches. There is an emphasis on cytochrome P450 and how in silico models are used in current drug discovery efforts to reduce the risk of DDIs. The discussion of the limitations associated with the various approaches as well as future aspects of DDI modeling and simulation can give researchers helpful guidance to this useful and growing area.

[1]  Marcel J. de Groot,et al.  Designing better drugs: predicting cytochrome P450 metabolism. , 2006 .

[2]  M. Gottesman,et al.  Multidrug resistance in cancer: role of ATP–dependent transporters , 2002, Nature Reviews Cancer.

[3]  L. Kier Molecular Orbital Theory In Drug Research , 1971 .

[4]  D. Lewis,et al.  Molecular Modeling and Quantitative Structure–Activity Relationship of Substrates and Inhibitors of Drug Metabolism Enzymes , 2007 .

[5]  Barry C Jones,et al.  Development of a combined protein and pharmacophore model for cytochrome P450 2C9. , 2002, Journal of medicinal chemistry.

[6]  Garrett M Morris,et al.  Using AutoDock for Ligand‐Receptor Docking , 2008, Current protocols in bioinformatics.

[7]  Christel A. S. Bergström,et al.  Structural requirements for drug inhibition of the liver specific human organic cation transport protein 1. , 2008, Journal of medicinal chemistry.

[8]  Robert J Riley,et al.  Mechanism-based inhibition of cytochrome P450 enzymes: an evaluation of early decision making in vitro approaches and drug-drug interaction prediction methods. , 2009, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[9]  Y. Z. Chen,et al.  In Silico Prediction of Pregnane X Receptor Activators by Machine Learning Approache , 2007, Molecular Pharmacology.

[10]  P. Meier,et al.  The superfamily of organic anion transporting polypeptides. , 2003, Biochimica et biophysica acta.

[11]  Allan M. Ferguson,et al.  EVA: A new theoretically based molecular descriptor for use in QSAR/QSPR analysis , 1997, J. Comput. Aided Mol. Des..

[12]  David S. Wishart,et al.  Improving Early Drug Discovery through ADME Modelling , 2007 .

[13]  W. Trager,et al.  Mechanism-based inactivation of cytochrome P450 3A4 by L-754,394. , 2000, Biochemistry.

[14]  Virginie Nahoum,et al.  Discovery of a Highly Active Ligand of Human Pregnane X Receptor: A Case Study from Pharmacophore Modeling and Virtual Screening to “In Vivo” Biological Activity , 2007, Molecular Pharmacology.

[15]  Rieko Arimoto,et al.  Development of CYP3A4 Inhibition Models: Comparisons of Machine-Learning Techniques and Molecular Descriptors , 2005, Journal of biomolecular screening.

[16]  Frank E. Blaney,et al.  Crystal Structure of Human Cytochrome P450 2D6* , 2005, Journal of Biological Chemistry.

[17]  D. Lewis,et al.  Quantitative structure-activity relationships (QSARs) in inhibitors of various cytochromes P450: The importance of compound lipophilicity , 2007, Journal of enzyme inhibition and medicinal chemistry.

[18]  D. Lewis,et al.  QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIPS (QSARs) WITHIN CYTOCHROMES P450 2B (CYP2B) SUBFAMILY ENZYMES: THE IMPORTANCE OF LIPOPHILICITY FOR BINDING AND METABOLISM , 2006, Drug metabolism and drug interactions.

[19]  Sean Ekins,et al.  In vitro and pharmacophore insights into CYP3A enzymes. , 2003, Trends in pharmacological sciences.

[20]  Berith F. Jensen,et al.  In silico prediction of cytochrome P450 2D6 and 3A4 inhibition using Gaussian kernel weighted k-nearest neighbor and extended connectivity fingerprints, including structural fragment analysis of inhibitors versus noninhibitors. , 2007, Journal of medicinal chemistry.

[21]  Jaina Mistry,et al.  A rapid computational filter for cytochrome P450 1A2 inhibition potential of compound libraries. , 2005, Journal of medicinal chemistry.

[22]  Daniel P Vercauteren,et al.  Recursive partitioning for the prediction of cytochromes P450 2D6 and 1A2 inhibition: importance of the quality of the dataset. , 2006, Journal of medicinal chemistry.

[23]  Yongjun Wang,et al.  Considerations and recent advances in QSAR models for cytochrome P450-mediated drug metabolism prediction , 2008, J. Comput. Aided Mol. Des..

[24]  T. Sjögren,et al.  Structural basis for ligand promiscuity in cytochrome P450 3A4 , 2006, Proceedings of the National Academy of Sciences.

[25]  C. Jefcoate,et al.  Measurement of substrate and inhibitor binding to microsomal cytochrome P-450 by optical-difference spectroscopy. , 1978, Methods in enzymology.

[26]  C David Stout,et al.  Adaptations for the Oxidation of Polycyclic Aromatic Hydrocarbons Exhibited by the Structure of Human P450 1A2*♦ , 2007, Journal of Biological Chemistry.

[27]  Sean Ekins,et al.  A Comprehensive in Vitro and in Silico Analysis of Antibiotics That Activate Pregnane X Receptor and Induce CYP3A4 in Liver and Intestine , 2008, Drug Metabolism and Disposition.

[28]  E. Scott,et al.  Structures of Human Cytochrome P-450 2E1 , 2008, Journal of Biological Chemistry.

[29]  G. Schneider,et al.  A Virtual Screening Filter for Identification of Cytochrome P450 2C9 (CYP2C9) Inhibitors , 2007 .

[30]  Jeremy N. Harvey,et al.  QM/MM modeling of benzene hydroxylation in human cytochrome P450 2C9. , 2008, The journal of physical chemistry. A.

[31]  B Testa,et al.  In silico pharmacology for drug discovery: applications to targets and beyond , 2007, British journal of pharmacology.

[32]  F. Sanz,et al.  Quinolone antibacterial agents: relationship between structure and in vitro inhibition of the human cytochrome P450 isoform CYP1A2. , 1993, Molecular pharmacology.

[33]  Thierry Langer,et al.  Pharmacophore Modeling and in Silico Screening for New P450 19 (Aromatase) Inhibitors , 2006, J. Chem. Inf. Model..

[34]  Bernd Beck,et al.  Multivariate modeling of cytochrome P450 3A4 inhibition. , 2005, European journal of pharmaceutical sciences : official journal of the European Federation for Pharmaceutical Sciences.

[35]  Eric F. Johnson,et al.  The Structure of Human Microsomal Cytochrome P450 3A4 Determined by X-ray Crystallography to 2.05-Å Resolution* , 2004, Journal of Biological Chemistry.

[36]  Sean Ekins,et al.  Computational Approaches That Predict Metabolic Intermediate Complex Formation with CYP3A4 (+b5) , 2007, Drug Metabolism and Disposition.

[37]  Caroline A. Lee,et al.  Drug–Drug Interactions Mediated Through P‐Glycoprotein: Clinical Relevance and In Vitro–In Vivo Correlation Using Digoxin as a Probe Drug , 2009, Clinical pharmacology and therapeutics.

[38]  Sean Ekins,et al.  Modeling of active transport systems. , 2002, Advanced drug delivery reviews.

[39]  Chris de Graaf,et al.  Cytochrome p450 in silico: an integrative modeling approach. , 2005, Journal of medicinal chemistry.

[40]  S. Pickett,et al.  GRid-INdependent descriptors (GRIND): a novel class of alignment-independent three-dimensional molecular descriptors. , 2000, Journal of medicinal chemistry.

[41]  Anders Karlén,et al.  Conformer- and alignment-independent model for predicting structurally diverse competitive CYP2C9 inhibitors. , 2004, Journal of medicinal chemistry.

[42]  R. Zauhar,et al.  Rapid Classification of CYP3A4 Inhibition Potential Using Support Vector Machine Approach , 2007 .

[43]  Eric F. Johnson,et al.  The Structure of Human Cytochrome P450 2C9 Complexed with Flurbiprofen at 2.0-Å Resolution* , 2004, Journal of Biological Chemistry.

[44]  Xiaodong Zhang,et al.  Synthetic inhibitors of cytochrome P-450 2A6: inhibitory activity, difference spectra, mechanism of inhibition, and protein cocrystallization. , 2006, Journal of medicinal chemistry.

[45]  Bin Wang,et al.  An In Silico Method for Screening Nicotine Derivatives as Cytochrome P450 2A6 Selective Inhibitors Based on Kernel Partial Least Squares , 2007, International Journal of Molecular Sciences.

[46]  Gianpaolo Bravi,et al.  Application of MS‐WHIM Descriptors: 1. Introduction of New Molecular Surface Properties and 2. Prediction of Binding Affinity Data , 2000 .

[47]  D S Goodsell,et al.  Automated docking of flexible ligands: Applications of autodock , 1996, Journal of molecular recognition : JMR.

[48]  P. Labute A widely applicable set of descriptors. , 2000, Journal of molecular graphics & modelling.

[49]  A. Sangamwar,et al.  Exploring CYP1A1 as anticancer target: homology modeling and in silico inhibitor design , 2008, Journal of molecular modeling.

[50]  Scott Boyer,et al.  Generation of in-silico cytochrome P450 1A2, 2C9, 2C19, 2D6, and 3A4 inhibition QSAR models , 2007, J. Comput. Aided Mol. Des..

[51]  S. Ekins,et al.  Three- and four-dimensional quantitative structure activity relationship analyses of cytochrome P-450 3A4 inhibitors. , 1999, The Journal of pharmacology and experimental therapeutics.

[52]  Ling Yang,et al.  An in silico approach for screening flavonoids as P-glycoprotein inhibitors based on a Bayesian-regularized neural network , 2005, J. Comput. Aided Mol. Des..

[53]  S. Ekins,et al.  Three- and four-dimensional-quantitative structure activity relationship (3D/4D-QSAR) analyses of CYP2C9 inhibitors. , 2000, Drug metabolism and disposition: the biological fate of chemicals.

[54]  Nikhil S. Ketkar,et al.  High confidence predictions of drug-drug interactions: predicting affinities for cytochrome P450 2C9 with multiple computational methods. , 2008, Journal of medicinal chemistry.

[55]  S. O'Brien,et al.  Greater than the sum of its parts: combining models for useful ADMET prediction. , 2005, Journal of medicinal chemistry.

[56]  Sean Ekins,et al.  Machine learning methods and docking for predicting human pregnane X receptor activation. , 2008, Chemical research in toxicology.

[57]  Ismael Zamora,et al.  CYP2C9 structure-metabolism relationships: substrates, inhibitors, and metabolites. , 2007, Journal of medicinal chemistry.

[58]  Maurice Dickins,et al.  Quantitative structure–activity relationships (QSARs) in CYP3A4 inhibitors: The importance of lipophilic character and hydrogen bonding , 2006, Journal of enzyme inhibition and medicinal chemistry.

[59]  Jose Cosme,et al.  Crystal Structures of Human Cytochrome P450 3A4 Bound to Metyrapone and Progesterone , 2004, Science.

[60]  Sean Ekins,et al.  Development of Computational Models for Enzymes, Transporters, Channels, and Receptors Relevant to ADME/Tox , 2004 .

[61]  Osman F. Güner,et al.  Pharmacophore perception, development, and use in drug design , 2000 .

[62]  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.

[63]  D. A. Smith,et al.  The Adaptive In Combo Strategy , 2007 .

[64]  Robyn Ayscue,et al.  Use of simple docking methods to screen a virtual library for heteroactivators of cytochrome P450 2C9. , 2007, Journal of medicinal chemistry.

[65]  S. Ekins,et al.  Present and future in vitro approaches for drug metabolism. , 2000, Journal of pharmacological and toxicological methods.

[66]  M. Gilson,et al.  Ligand configurational entropy and protein binding , 2007, Proceedings of the National Academy of Sciences.

[67]  Jozef Hritz,et al.  Impact of plasticity and flexibility on docking results for cytochrome P450 2D6: a combined approach of molecular dynamics and ligand docking. , 2008, Journal of medicinal chemistry.

[68]  Maykel Pérez González,et al.  Applications of 2D descriptors in drug design: a DRAGON tale. , 2008, Current topics in medicinal chemistry.

[69]  L. Benet,et al.  Effect of OATP1B Transporter Inhibition on the Pharmacokinetics of Atorvastatin in Healthy Volunteers , 2007, Clinical pharmacology and therapeutics.

[70]  R. Obach,et al.  Drug–Drug Interactions: Screening for Liability and Assessment of Risk , 2009 .

[71]  Bernd Beck,et al.  A support vector machine approach to classify human cytochrome P450 3A4 inhibitors , 2005, J. Comput. Aided Mol. Des..

[72]  A. M. Doweyko,et al.  4.23 – Three-Dimensional Quantitative Structure–Activity Relationship: The State of the Art , 2007 .

[73]  Laszlo Urban,et al.  Evaluation of Fluorescence- and Mass Spectrometry—Based CYP Inhibition Assays for Use in Drug Discovery , 2008, Journal of biomolecular screening.

[74]  Jan M. Kriegl,et al.  Chapter 5 Linear Quantitative Structure–Activity Relationships for the Interaction of Small Molecules with Human Cytochrome P450 Isoenzymes , 2007 .

[75]  M. Neves,et al.  Combining Computational and Biochemical Studies for a Rationale on the Anti‐Aromatase Activity of Natural Polyphenols , 2007, ChemMedChem.

[76]  Z R Li,et al.  Machine learning approaches for predicting compounds that interact with therapeutic and ADMET related proteins. , 2007, Journal of pharmaceutical sciences.

[77]  Alex Phipps,et al.  Application of CYP3A4 in vitro data to predict clinical drug-drug interactions; predictions of compounds as objects of interaction. , 2008, British journal of clinical pharmacology.

[78]  Chris Oostenbrink,et al.  Computational prediction of drug binding and rationalisation of selectivity towards cytochromes P450 , 2008 .

[79]  Narayanan Surendran,et al.  Implementation of an ADME enabling selection and visualization tool for drug discovery. , 2004, Journal of pharmaceutical sciences.

[80]  Jarl E. S. Wikberg,et al.  Generalized Proteochemometric Model of Multiple Cytochrome P450 Enzymes and Their Inhibitors , 2008, J. Chem. Inf. Model..

[81]  Jiunn H. Lin,et al.  Transporter-mediated drug interactions: clinical implications and in vitro assessment , 2007, Expert opinion on drug metabolism & toxicology.

[82]  D. J. Triggle,et al.  Comprehensive medicinal chemistry II , 2006 .

[83]  Roberto Todeschini,et al.  MS-WHIM, new 3D theoretical descriptors derived from molecular surface properties: A comparative 3D QSAR study in a series of steroids , 1997, J. Comput. Aided Mol. Des..

[84]  J. Leeder,et al.  CYP3A4-Mediated Carbamazepine (CBZ) Metabolism: Formation of a Covalent CBZ-CYP3A4 Adduct and Alteration of the Enzyme Kinetic Profile , 2008, Drug Metabolism and Disposition.

[85]  Ismael Zamora,et al.  Combining pharmacophore and protein modeling to predict CYP450 inhibitors and substrates. , 2002, Methods in enzymology.

[86]  Per B. Brockhoff,et al.  In silico prediction of cytochrome P450 inhibitors , 2006 .

[87]  Jose Cosme,et al.  Crystal structure of human cytochrome P450 2C9 with bound warfarin , 2003, Nature.

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

[89]  Jeffrey P. Jones,et al.  Cytochrome P450 2C9 type II binding studies on quinoline-4-carboxamide analogues. , 2008, Journal of medicinal chemistry.

[90]  Stewart B Kirton,et al.  Prediction of binding modes for ligands in the cytochromes P450 and other heme‐containing proteins , 2005, Proteins.