Predicting ADME properties in drug discovery

doi:10.1017/CBO9780511730412.013

11 - Predicting ADME properties in drug discovery

from PART II - COMPUTATIONAL CHEMISTRY METHODOLOGY

Published online by Cambridge University Press: 06 July 2010

William J. Egan

Edited by

Kenneth M. Merz, Jr ,

Dagmar Ringe and

Charles H. Reynolds

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Kenneth M. Merz, Jr: Affiliation:
University of Florida
Dagmar Ringe: Affiliation:
Brandeis University, Massachusetts
Charles H. Reynolds: Affiliation:
Johnson & Johnson Pharmaceutical Research & Development

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Summary

INTRODUCTION

Drug discovery is an extremely risky business. Practically every molecule ever made in a drug discovery research project will be a failure. It is estimated that for every ten research projects producing molecules of high-enough quality to begin clinical testing in man, 10,000 to 20,000 molecules will need to be synthesized. Of those ten clinical candidates, nine will fail, leaving just one new drug in the end. In short, the pharmaceutical industry has a failure rate on the order of 99.99%. These many failures do not come cheaply. The cost of developing a new drug is estimated to be between $500 million and $2 billion, depending on the indication and company.

As Dr. Arthur Patchett of Merck said, “Current, major stumbling blocks in drug development are often the clumsy, empirical, and time-consuming efforts required to go from an exquisitely potent in vitro inhibitor to one with good bioavailability and an adequate duration of action. This is the unglamorous part of drug development but often separates highly successful ventures from those which lag behind them.” Medicinal chemists commonly synthesize potent molecules only to find out later they have poor exposure in vivo and thus poor efficacy.

The broad term exposure can be broken down into its component factors: absorption, distribution, metabolism, and excretion, which are commonly known as ADME. Solubility is also very important and tends to be implicitly included in discussions of ADME.

Type: Chapter
Information: Drug Design
Structure- and Ligand-Based Approaches
, pp. 165 - 178

DOI: https://doi.org/10.1017/CBO9780511730412.013 [Opens in a new window]

Publisher: Cambridge University Press

Print publication year: 2010

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References

Mervis, J.Productivity counts – but the definition is key. Science 2005, 309, 726.Google Scholar

Kola, I.; Landis, J.Opinion: Can the pharmaceutical industry reduce attrition rates?Nature Rev. Drug Disc. 2004, 3, 711.Google Scholar

Adams, C.; Brantner, V.Estimating the cost of new drug development: is it really 802 million dollars?Health Affairs 2006, 25, 420.Google Scholar

Patchett, A.Excursions in drug discovery. J. Med. Chem. 1993, 36, 2051.Google Scholar

Hamming, R.Numerical Methods for Scientists and Engineers. New York: McGraw-Hill; 1962.

Lipinski, C. A.; Lombardo, F.; Dominy, B. W.; Feeney, P. J.Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 1997, 23, 3.Google Scholar

Wenlock, M. C.; Austin, R. P.; Barton, P.; Davis, A. M.; Leeson, P. D.A comparison of physiochemical property profiles of development and marketed oral drugs. J. Med. Chem. 2003, 46, 1250.Google Scholar

Vieth, M.; Siegel, M. G.; Higgs, R. E.; Watson, I. A.; Robertson, D. H.; Savin, K. A.; Durst, G. L.; Hipskind, P. A.Characteristic physical properties and structural fragments of marketed oral drugs. J. Med. Chem. 2004, 47, 224.Google Scholar

Leeson, P. D.; Davis, A. M.Time-related differences in the physical property profiles of oral drugs. J. Med. Chem. 2004, 47, 6338.Google Scholar

Proudfoot, J. R.The evolution of synthetic oral drug properties. Bioorg. Med. Chem. Lett. 2005, 15, 1087.Google Scholar

Vieth, M.; Sutherland, J.Dependence of molecular properties on proteomic family for marketed oral drugs. J. Med. Chem. 2006, 49, 3451.Google Scholar

Morphy, R.The influence of target family and functional activity on the physicochemical properties of pre-clinical compounds. J. Med. Chem. 2006, 49, 2969.Google Scholar

O'Shea, R.; Moser, H. E.Physicochemical properties of antibacterial compounds: implications for drug discovery. J. Med. Chem. 2008, 51, 2871.Google Scholar

Gleeson, M. P.Generation of a set of simple, interpretable ADMET rules of thumb. J. Med. Chem. 2008, 51, 817.Google Scholar

Bhal, S. K.; Kassam, K.; Peirson, I. G.; Pearl, G. M.The Rule of Five revisited: applying log D in place of log P in drug-likeness filters. Mol. Pharm. 2007, 4, 556.Google Scholar

Faller, B.; Wang, J.; Zimmerlin, A.; Bell, L.; Hamon, J.; Whitebread, S.; Azzaoui, K.; Bojanic, D.; Urban, L.High-throughput in vitro profiling assays: lessons learnt from experiences at Novartis. Expert Opin. Drug Metab. Tox. 2006, 2, 823.Google Scholar

Johnson, S. R.; Zheng, W.Recent progress in the computational prediction of aqueous solubility and absorption. AAPS J. 2006, 8, E27.Google Scholar

Balakin, K. V.; Savchuk, N. P.; Tetko, I. V.In silico approaches to prediction of aqueous and DMSO solubility of drug-like compounds: trends, problems and solutions. Curr. Med. Chem. 2006, 13, 223.Google Scholar

Leach, A. G.; Jones, H. D.; Cosgrove, D. A.; Kenny, P. W.; Ruston, L.; MacFaul, P.; Wood, J. M.; Colclough, N.; Law, B.Matched molecular pairs as a guide in the optimization of pharmaceutical properties; a study of aqueous solubility, plasma protein binding and oral exposure. J. Med. Chem. 2006, 49, 6672.Google Scholar

Lamanna, C.; Bellini, M.; Padova, A.; Westerberg, G.; Maccari, L.Straightforward recursive partitioning model for discarding insoluble compounds in the drug discovery process. J. Med. Chem. 2008, 51, 2891.Google Scholar

Huuskonen, J.Estimation of aqueous solubility for a diverse set of organic compounds based on molecular topology. J. Chem. Inf. Comp. Sci. 2000, 40, 773.Google Scholar

Yan, A.; Gasteiger, J.Prediction of aqueous solubility of organic compounds by topological descriptors. QSAR Combin. Sci. 2003, 22, 821.Google Scholar

Yan, A.; Gasteiger, J.; Krug, M.; Anzali, S.Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods. J. Comput. Aided Mol. Des. 2004, 18, 75.Google Scholar

Delaney, J. S.Predicting aqueous solubility from structure. Drug Discov. Today 2005, 10, 289.Google Scholar

Lipinski, C. A.Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Meth. 2000, 44, 235.Google Scholar

Goeller, A. H.; Hennemann, M.; Keldenich, J.; Clark, T.In silico prediction of buffer solubility based on quantum-mechanical and HQSAR- and topology-based descriptors. J. Chem. Inf. Model. 2006, 46, 648.Google Scholar

Schwaighofer, A.; Schroeter, T.; Mika, S.; Laub, J.; Laak, A.; Suelzle, D.; Ganzer, U.; Heinrich, N.; Mueller, K.-R.Accurate solubility prediction with error bars for electrolytes: a machine learning approach. J. Chem. Inf. Model. 2007, 47, 407.Google Scholar

Obrezanova, O.; Csanyi, G.; Gola, J. M. R.; Segall, M. D.Gaussian processes: a method for automatic QSAR modeling of ADME properties. J. Chem. Inf. Model. 2007, 47, 1847.Google Scholar

Obrezanova, O.; Gola, J. M. R.; Champness, E. J.; Segall, M. D.Automatic QSAR modeling of ADME properties: blood-brain barrier penetration and aqueous solubility. J. Comput. Aided Mol. Des. 2008, 22, 431.Google Scholar

Balakin, K. V.; Ivanenkov, Y. A.; Skorenko, A. V.; Nikolsky, Y. V.; Savchuk, N. P.; Ivashchenko, A. A.In silico estimation of DMSO solubility of organic compounds for bioscreening. J. Biomolec. Screen. 2004, 9, 22.Google Scholar

Lu, J.; Bakken, G. A. Building classification models for DMSO solubility: comparison of five methods. 228th ACS National Meeting, Philadelphia, PA, United States, August 22–26, 2004, CINF-045.

Johnson, S. R.; Chen, X. Q.; Murphy, D.; Gudmundsson, O. A.Computational model for the prediction of aqueous solubility that includes crystal packing, intrinsic solubility, and ionization effects. Mol. Pharm. 2007, 4, 513.Google Scholar

Egan, W. J.; Lauri, G.Prediction of intestinal permeability. Adv. Drug Deliv. Rev. 2002, 54, 273.Google Scholar

Egan, W. J.; Merz, K. M.; Baldwin, J. J.Prediction of drug absorption using multivariate statistics. J. Med. Chem. 2000, 43, 3867.Google Scholar

Zhao, Y. H.; Le, J.; Abraham, M. H.; Hersey, A.; Eddershaw, P. J.; Luscombe, C. N.; Boutina, D.; Beck, G.; Sherborne, B.; Cooper, I.; Platts, J. A.Evaluation of human intestinal absorption data and subsequent derivation of a quantitative structure-activity relationship (QSAR) with the Abraham descriptors. J. Pharm. Sci. 2001, 90, 749.Google Scholar

Zhao, Y. H.; Abraham, M. H.; Hersey, A.; Luscombe, C. N.Quantitative relationship between rat intestinal absorption and Abraham descriptors. Eur. J. Med. Chem. 2003, 38, 939.Google Scholar

Bai, J. P. F.; Utis, A.; Crippen, G.; He, H. D.; Fischer, V.; Tullman, R.; Yin, H. Q.; Hsu, C. P.; Jing, Hwang, K. K.Use of classification regression tree in predicting oral absorption in humans. J. Chem. Inf. Comp. Sci. 2004, 44, 2061.Google Scholar

Jones, R.; Connolly, P. C.; Klamt, A.; Diedenhofen, M.Use of surface charges from DFT calculations to predict intestinal absorption. J. Chem. Inf. Model. 2005, 45, 1337.Google Scholar

Rezai, T.; Bock, J. E.; Zhou, M. V.; Kalyanaraman, C.; Lokey, R. S.; Jacobson, M. P.Conformational flexibility, internal hydrogen bonding, and passive membrane permeability: successful in silico prediction of the relative permeabilities of cyclic peptides. J. Am. Chem. Soc. 2006, 128, 14073.Google Scholar

Rezai, T.; Yu, B.; Millhauser, G. L.; Jacobson, M. P.; Lokey, R. S.Testing the conformational hypothesis of passive membrane permeability using synthetic cyclic peptide diastereomers. J. Am. Chem. Soc. 2006, 128, 2510.Google Scholar

Stoner, C. L.; Troutman, M.; Gao, H.; Johnson, K.; Stankovic, C.; Brodfuehrer, J.; Gifford, E.; Chang, M.Moving in silico screening into practice: a minimalist approach to guide permeability screening!!Lett. Drug Design Discov. 2006, 3, 575.Google Scholar

Clark, D. E.Computational prediction of blood-brain barrier permeation. Ann. Rep. Med. Chem. 2005, 40, 403.Google Scholar

Kelder, J.; Grootenhuis, P. D.; J. Bayada, D. M.; Delbressine, L. P. C.; Ploemen, J. P.Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs. Pharm. Res. 1999, 16, 1514.Google Scholar

Clark, D. E.Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 2. Prediction of blood-brain barrier penetration. J. Pharm. Sci. 1999, 88, 815.Google Scholar

Lobell, M.; Molnar, L.; Keseru, G. M.Recent advances in the prediction of blood-brain partitioning from molecular structure. J. Pharm. Sci. 2003, 92, 360.Google Scholar

Gerebtzoff, G.; Seelig, A.In silico prediction of blood-brain barrier permeation using the calculated molecular cross-sectional area as main parameter. J. Chem. Inf. Model. 2006, 46, 2638.Google Scholar

Abraham, M. H.; Ibrahim, A.; Zhao, Y.; Acree, W. E.A data base for partition of volatile organic compounds and drugs from blood/plasma/serum to brain, and an LFER analysis of the data. J. Pharm. Sci. 2006, 95, 2091.Google Scholar

Zhao, Y. H.; Abraham, M. H.; Ibrahim, A.; Fish, P. V.; Cole, S.; Lewis, M. L.; Groot, M. J.; Reynolds, D. P.Predicting penetration across the blood-brain barrier from simple descriptors and fragmentation schemes. J. Chem. Inf. Model. 2007, 47, 170.Google Scholar

Pardridge, W. M.Log(BB), PS products and in silico models of drug brain penetration. Drug Disc. Today 2004, 9, 392.Google Scholar

Liu, X.; Tu, M.; Kelly, R. S.; Chen, C.; Smith, B. J.Development of a computational approach to predict blood-brain barrier permeability. Drug Metab. Dispos. 2004, 32, 132.Google Scholar

Abraham, M. H.The factors that influence permeation across the blood-brain barrier. Eur. J. Med. Chem. 2004, 39, 235.Google Scholar

Lin, J. H.; Yamazaki, M.Role of P-glycoprotein in pharmacokinetics: clinical implications. Clin. Pharmacokinet. 2003, 42, 59.Google Scholar

Raub, T. J.P-glycoprotein recognition of substrates and circumvention through rational drug design. Mol. Pharm. 2006, 3, 3.Google Scholar

Varma, M. V. S.; Sateesh, K.; Panchagnula, R.Functional role of P-glycoprotein in limiting intestinal absorption of drugs: contribution of passive permeability to P-glycoprotein mediated efflux transport. Mol. Pharm. 2005, 2, 12.Google Scholar

Gombar, V. K.; Polli, J. W.; Humphreys, J. E.; Wring, S. A.; Serabjit-Singh, C. S.Predicting P-glycoprotein substrates by a quantitative structure-activity relationship model. J. Pharm. Sci. 2004, 93, 957.Google Scholar

Cabrera, M. A.; Gonzalez, I.; Fernandez, C.; Navarro, C.; Bermejo, M.A topological substructural approach for the prediction of P-glycoprotein substrates. J. Pharm. Sci. 2006, 95, 589.Google Scholar

Lima, P.; Golbraikh, A.; Oloff, S.; Xiao, Y.; Tropsha, A.Combinatorial QSAR modeling of P-glycoprotein substrates. J. Chem. Inf. Model. 2006, 46, 1245.Google Scholar

Seelig, A.A general pattern for substrate recognition by P-glycoprotein. Eur. J. Biochem. 1998, 251, 252.Google Scholar

Pajeva, I. K.; Wiese, M.Pharmacophore model of drugs involved in P-glycoprotein multidrug resistance: explanation of structural variety (hypothesis). J. Med. Chem. 2002, 45, 5671.Google Scholar

Vandevuer, S.; Bambeke, F.; Tulkens, P. M.; Prevost, M.Predicting the three-dimensional structure of human P-glycoprotein in absence of ATP by computational techniques embodying crosslinking data: insight into the mechanism of ligand migration and binding sites. Proteins 2006, 63, 466.Google Scholar

Cianchetta, G.; Singleton, R. W.; Zhang, M.; Wildgoose, M.; Giesing, D.; Fravolini, A.; Cruciani, G.; Vaz, R.A pharmacophore hypothesis for P-glycoprotein substrate recognition using GRIND-based 3D-QSAR. J. Med. Chem. 2005, 48, 2927.Google Scholar

Crivori, P.; Reinach, B.; Pezzetta, D.; Poggesi, I.Computational models for identifying potential P-glycoprotein substrates and inhibitors. Mol. Pharm. 2006, 3, 33.Google Scholar

Mente, S. R.; Lombardo, F.A recursive-partitioning model for blood-brain barrier permeation. J. Comput. Aided Mol. Design 2005, 19, 465.Google Scholar

Garg, P.; Verma, J.In silico prediction of blood brain barrier permeability: an artificial neural network model. J. Chem. Inf. Model. 2006, 46, 289.Google Scholar

Waterbeemd, H.; Smith, D. A.; Jones, B. C.Lipophilicity in PK design: methyl, ethyl, futile. J. Comput. Aided Mol. Des. 2001, 15, 273.Google Scholar

Yamazaki, K., Kanaoka, M.Computational prediction of the plasma protein-binding percent of diverse pharmaceutical compounds. J. Pharm. Sci. 2004, 93, 1480.Google Scholar

Kratochwil, N. A.; Huber, W.; Mueller, F.; Kansy, M.; Gerber, P. R.Predicting plasma protein binding of drugs – revisited. Curr. Opin. Drug Disc. Devel. 2004, 7, 507.Google Scholar

Kratochwil, N. A.; Huber, W.; Muller, F.; Kansy, M.; Gerber, P. R.Predicting plasma protein binding of drugs: a new approach. Biochem. Pharmacol. 2002, 64, 1355.Google Scholar

Gleeson, M. P.Plasma protein binding affinity and its relationship to molecular structure: an in-silico analysis. J. Med. Chem. 2007, 50, 101.Google Scholar

Votano, J. R.; Parham, M.; Hall, L. M.; Hall, L. H.; Kier, L. B.; Oloff, S.; Tropsha, A.QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation. J. Med. Chem. 2006, 49, 7169.Google Scholar

Liu, J.; Yang, L.; Li, Y.; Pan, D.; Hopfinger, A. J.Constructing plasma protein binding model based on a combination of cluster analysis and 4D-fingerprint molecular similarity analyses. Bioorg. Med. Chem. 2006, 14, 611.Google Scholar

Estrada, E.; Uriarte, E.; Molina, E.; Simon-Manso, Y.; Milne, G. W. A.An integrated in silico analysis of drug-binding to human serum albumin. J. Chem. Inf. Model. 2006, 46, 2709.Google Scholar

Ghuman, J.; Zunszain, P. A.; Petitpas, I.; Bhattacharya, A. A.; Otagiri, M.; Curry, S.Structural basis of the drug-binding specificity of human serum albumin. J. Mol. Biol. 2005, 353, 38.Google Scholar

Rodgers, S. L.; Davis, A. M.; Tomkinson, N. P.; Waterbeemd, H.QSAR modeling using automatically updating correction libraries: application to a human plasma protein binding model. J. Chem. Inf. Model. 2007, 47, 2401.Google Scholar

Zhang, H.; Zhang, Y.Convenient nonlinear model for predicting the tissue/blood partition coefficients of seven human tissues of neutral, acidic, and basic structurally diverse compounds. J. Med. Chem. 2006, 49, 5815.Google Scholar

Gleeson, M. P.; Waters, N. J.; Paine, S. W.; Davis, A. M.In silico human and rat Vss quantitative structure-activity relationship models. J. Med. Chem. 2006, 49, 1953.Google Scholar

Lombardo, F.; Obach, R. S.; DiCapua, F.; Bakken, G. A.; Lu, J.; Potter, D. M.; Gao, F.; Miller, M. D.; Zhang, Y.A hybrid mixture discriminant analysis-random forest computational model for the prediction of volume of distribution of drugs in human. J. Med. Chem. 2006, 49, 2262.Google Scholar

Hirom, P. C.; Millburn, P.; Smith, R. L.; Williams, R. T.Species variations in the threshold molecular-weight factor for the biliary excretion of organic anions. Biochem. J. 1972, 129, 1071.Google Scholar

Hirom, P. C.; Millburn, P.; Smith, R. L.Bile and urine as complementary pathways for the excretion of foreign organic compounds. Xenobiotica 1976, 6, 55.Google Scholar

Smith, D. A.Physicochemical properties in drug metabolism and pharmacokinetics. In: Computer-Assisted Lead Finding and Optimization: Current Tools for Medicinal Chemistry, Waterbeemd, H.; Testa, B.; Folkers, G.; Eds. Weinheim: Wiley-VCH; 1997, 267.

Doddareddy, M. R.; Cho, Y. S.; Koh, H. Y.; Kim, D. H.; Pae, A. N.In silico renal clearance model using classical Volsurf approach. J. Chem. Inf. Model. 2006, 46, 1312.Google Scholar

Yap, C. W.; Li, Z. R.; Chen, Y. Z.Quantitative structure-pharmacokinetic relationships for drug clearance by using statistical learning methods. J. Mol. Graph. Model. 2006, 24, 383.Google Scholar

Borodina, Y.; Rudik, A.; Filimonov, D.; Kharchevnikova, N.; Dmitriev, A.; Blinova, V.; Poroikov, V.A new statistical approach to predicting aromatic hydroxylation sites. Comparison with model-based approaches. J. Chem. Inf. Comp. Sci. 2004, 44, 1998.Google Scholar

Boyer, S.; Arnby, C.; Hasselgren, C.; Carlsson, L.; Smith, J.; Stein, V.; Glen, R. C.Reaction site mapping of xenobiotic biotransformations. J. Chem. Inf. Model. 2007, 47, 583.Google Scholar

Singh, S. B.; Shen, L. Q.; Walker, M. J.; Sheridan, R. P.A model for predicting likely sites of CYP3A4-mediated metabolism on drug-like molecules. J. Med. Chem. 2003, 46, 1330.Google Scholar

Lewin, J. L.; Cramer, C. J.Rapid quantum mechanical models for the computational estimation of C-H bond dissociation energies as a measure of metabolic stability. Mol. Pharm. 2004, 1, 128.Google Scholar

Beck, M. E.Do Fukui function maxima relate to sites of metabolism? A critical case study. J. Chem. Inf. Model. 2005, 45, 273.Google Scholar

Olsen, L.; Rydberg, P.; Rod, T. H.; Ryde, U.Prediction of activation energies for hydrogen abstraction by Cytochrome P450. J. Med. Chem. 2006, 49, 6489.Google Scholar

Shen, M.; Xiao, Y.; Golbraikh, A.; Gombar, V. K.; Tropsha, A.Development and validation of k-nearest-neighbor QSPR models of metabolic stability of drug candidates. J. Med. Chem. 2003, 46, 3013.Google Scholar

Crivori, P.; Zamora, I.; Speed, B.; Orrenius, C.; Poggesi, I.Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates. J. Comput. Aided Mol. Des. 2004, 18, 155.Google Scholar

Wang, Y. H.; Li, Y.; Li, Y. H.; Yang, S. L.; Yang, L.Modeling Km values using electrotopological state: substrates for cytochrome P450 3A4-mediated metabolism. Bioorg. Med. Chem. Lett. 2005, 15, 4076.Google Scholar

Lee, P. H.; Cucurull-Sanchez, L.; Lu, J.; Du, Y. J.Development of in silico models for human liver microsomal stability. J. Comput. Aided Mol. Des. 2007, 21, 665.Google Scholar

Schwaighofer, A.; Schroeter, T.; Mika, S.; Hansen, K.; Laak, A.; Lienau, P.; Reichel, A.; Heinrich, N.; Mueller, K. R.A probabilistic approach to classifying metabolic stability. J. Chem. Inf. Model. 2008, 48, 785.Google Scholar

Lewis, D. F. V.; Ito, Y.; Goldfarb, P. S.Structural modeling of the human drug-metabolizing cytochromes P 450. Curr. Med. Chem. 2006, 13, 2645.Google Scholar

Lewis, D. F. V.; Lake, B. G.; Dickins, M.; Goldfarb, P. S.Homology modelling of CYP3A4 from the CYP2C5 crystallographic template: analysis of typical CYP3A4 substrate interactions. Xenobiotica 2004, 34, 549.Google Scholar

Tanaka, T.; Okuda, T.; Yamamoto, Y.Characterization of the CYP3A4 active site by homology modeling. Chem. Pharm. Bull. 2004, 52, 830.Google Scholar

Park, H.; Lee, S.; Suh, J.Structural and dynamical basis of broad substrate specificity, catalytic mechanism, and inhibition of cytochrome P 450 3A4. J. Am. Chem. Soc. 2005, 127, 13634.Google Scholar

Yano, J. K.; Wester, M. R.; Schoch, G. A.; Griffin, K. J.; Stout, C. D.; Johnson, E. F.The structure of human microsomal Cytochrome P450 3A4 determined by x-ray crystallography to 2.05-.ANG. resolution. J. Biol. Chem. 2004, 279, 38091.Google Scholar

Williams, P. A; Cosme, J.; Vinkovic, D. M.; Ward, A.; Angove, H. C.; Day, P. J.; Vonrhein, C.; Tickle, I. J.; Jhoti, H.Crystal structures of human cytochrome P450 3A4 bound to metyrapone and progesterone. Science 2004, 305, 683.Google Scholar

Ekroos, M.; Sjoegren, T.Structural basis for ligand promiscuity in cytochrome P 450 3A4. Proc. National Acad. Sci. U.S.A. 2006, 103, 13682.Google Scholar

Cruciani, G.; Carosati, E.; Boeck, B.; Ethirajulu, K.; Mackie, C.; Howe, T.; Vianello, R.MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist. J. Med. Chem. 2005, 48, 6970.Google Scholar

Zhou, D.; Afzelius, L.; Grimm, S. W.; Andersson, T. B.; Zauhar, R. J.; Zamora, I.Comparison of methods for the prediction of the metabolic sites for CYP3A4-mediated metabolic reactions. Drug Metab. Dispos. 2006, 34, 976.Google Scholar

Kjellander, B.; Masimirembwa, C. M.; Zamora, I.Exploration of enzyme-ligand interactions in CYP2D6 & 3A4 homology models and crystal structures using a novel computational approach. J. Chem. Inf. Model. 2007, 47, 1234.Google Scholar

Caron, G.; Ermondi, G.; Testa, B.Predicting the oxidative metabolism of statins: an application of the MetaSite algorithm. Pharm. Res. 2007, 24, 480.Google Scholar

Ahlstroem, M. A.; Ridderstrom, M.; Zamora, I.; Luthman, K.CYP2C9 structure-metabolism relationships: optimizing the metabolic stability of COX-2 inhibitors. J. Med. Chem. 2007, 50, 4444.Google Scholar

Ahlstroem, M. A.; Ridderstrom, M.; Zamora, I.CYP2C9 structure-metabolism relationships: substrates, inhibitors, and metabolites. J. Med. Chem. 2007, 50, 5382.Google Scholar

Sykes, M. J.; McKinnon, R. A.; Miners, J. O.Prediction of metabolism by cytochrome P450 2C9: alignment and docking studies of a validated database of substrates. J. Med. Chem. 2008, 51, 780.Google Scholar

Sheridan, R. P.; Korzekwa, K. R.; Torres, R. A.; Walker, M. J.Empirical regioselectivity models for human cytochromes P450 3A4, 2D6, and 2C9. J. Med. Chem. 2007, 50, 3173.Google Scholar

Oh, W. S.; Kim, D. N.; Jung, J.; Cho, K. H.; No, K. T.New combined model for the prediction of regioselectivity in cytochrome P450/3A4 mediated metabolism. J. Chem. Inf. Model. 2008, 48, 591.Google Scholar

Terfloth, L.; Bienfait, B.; Gasteiger, J.Ligand-based models for the isoform specificity of cytochrome P450 3A4, 2D6, and 2C9 substrates. J. Chem. Inf. Model. 2007, 47, 1688.Google Scholar

Delisle, R. K.; Lowrie, J. F.; Hobbs, D. W.; Diller, D. J.Computational ADME/Tox modeling: aiding understanding and enhancing decision making in drug design. Curr. Comput. Aided Drug Des. 2005, 1, 325.Google Scholar

Stoner, C. L.; Gifford, E.; Stankovic, C.; Lepsy, C. S.; Brodfuehrer, J.; Prasad, J.; Surendran, N.Implementation of an ADME enabling selection and visualization tool for drug discovery. J. Pharm. Sci. 2004, 93, 1131.Google Scholar

Lobell, M.; Hendrix, M.; Hinzen, B.; Keldenich, J.; Meier, H.; Schmeck, C.; Schohe-Loop, R.; Wunberg, T.; Hillisch, A.In silico ADMET traffic lights as a tool for the prioritization of HTS hits. ChemMedChem 2006, 1, 1229.Google Scholar

Segall, M. D.; Beresford, A. P.; Gola, J. M. R.; Hawksley, D.; Tarbit, M. H.Focus on success: using a probabilistic approach to achieve an optimal balance of compound properties in drug discovery. Expert Opin. Drug Metab. Toxicol. 2006, 2, 325.Google Scholar

Appell, K.; Baldwin, J. J.; Egan, W. J.Combinatorial chemistry and high-throughput screening in drug discovery and development. In: Handbook of Modern Pharmaceutical Analysis, Ahuja, S; Scypinski, S.; Eds. San Diego, Academic Press; 2001, 23.

Biller, S. A.; Custer, L.; Dickinson, K. E.; Durham, S. K.; Gavai, A. V.; Hamann, L. G.; Josephs, J. L.; Moulin, F.; Pearl, G. M.; Flint, O. P.; Sanders, M.; Tymiak, A. A.; Vaz, R.The challenge of quality in candidate optimization. In: Biotechnology: Pharmaceutical Aspects, 1(Pharmaceutical Profiling in Drug Discovery for Lead Selection), Arlington, AAPS Press, 2004, 413.

Egan, W. J.; Walters, W. P.; Murcko, M. A.Guiding molecules towards drug-likeness. Curr. Opin. Drug Discov. Devel. 2002, 5, 540.Google Scholar