ADMET in silico modelling: towards prediction paradise?

Nature Reviews Drug Discovery - Tập 2 Số 3 - Trang 192-204 - 2003
H. Van De Waterbeemd1, Eric Gifford2
1Pfizer Global Research & Development, PDM, Sandwich, UK
2Pfizer Global Research & Development, Discovery Research Informatics, Ann Arbor, USA

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Tài liệu tham khảo

Kennedy, T. Managing the drug discovery/development interface. Drug Disc. Today 2, 436–444 (1997).

Van de Waterbeemd, H. High-throughput and in silico techniques in drug metabolism and pharmacokinetics. Curr. Opin. Drug Disc. Dev. 5, 33–43 (2002).

Sadowski, J. & Kubinyi, H. A scoring scheme for discriminating between drugs and nondrugs. J. Med. Chem. 41, 3325–3329 (1998).

Anzali, S., Barnickel, G., Cezanne, B., Krug, M., Filimonov, D. & Poroikov, V. Discriminating between drugs and nondrugs by prediction of activity spectra for substances (PASS). J. Med. Chem. 44, 2432–2437 (2001).

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. Del. Revs. 23, 3–25 (1997). Paper introducing Lipinski's rule-of–5.

Lipinski, C. A. Drug-like properties and the causes of poor solubility and poor permeability. J. Pharmacol. Toxicol. Methods 44, 235–249 (2001).

Van de Waterbeemd, H., Smith, D. A., Beaumont, K. & Walker, D. K. Property-based design: Optimisation of drug absorption and pharmacokinetics. J. Med. Chem. 44, 1313–1333 (2001).

Johnson, M. A. & Maggiora, G. M. Concepts and Applications of Molecular Similarity Analysis (Wiley Interscience, New York, 1990).

Raevsky, O. A., Trepalin, S. V., Trepalina, H. P., Gerasimenko, V. A. & Raevskaja, O. E. SLIPPER-2001 —Software for predicting molecular properties on the basis of physicochemical descriptors and structural similarity. J. Chem. Inf. Comput. Sci. 42, 540–549 (2002)

Janssen, D. The power of prediction. Drug Disc. 38–40 (January 2002).

Carlson, T. J. & Segall, M. D. Predictive, computational models of ADME properties. Curr. Drug Disc. 34–36 (March 2002)

Podlogar, B. L., Muegge, I. & Brice, L. J. Computational methods to estimate drug development parameters. Curr. Opin. Drug Disc. Dev. 4, 102–109 (2001).

Ekins, S., Waller, C. L., Swaan, P. W., Cruciani, G., Wrighton, S. A. & Wikel, J. H. Progress in predicting human ADME parameters in silico. J. Pharmacol. Toxicol. Methods 44, 251–272 (2001). Excellent summary of in silico ADME and tables with references to data sets for permeability/absorption, brain penetration, P-gp, clearance and bioavailability.

Ekins, S. & Wrighton, S. A. Application of in silico approaches to predicting drug–drug interactions. J. Pharmacol. Toxicol. Methods 45, 65–69 (2001).

Rodrigues, A. D., Winchell, G. A. & Dobrinska, M. R. Use of in vitro drug metabolism data to evaluate metabolic drug–drug interactions in man: the need for quantitative databases. J. Clin. Pharmacol. 41, 368–373 (2001)

Bachman, K. A. & Ghosh, R. The use of in vitro methods to predict in vivo pharmacokinetics and drug interactions. Curr. Drug Metab. 2, 299–314 (2001).

Beresford, A. P., Selick, H. E. & Tarbit, M. H. The emerging importance of predictive ADME simulation in drug discovery. Drug Disc. Today 7, 109–116 (2002).

Guner, O. (ed). Pharmacophore Perception, Development and Use in Drug Design (IUL Biotechnology Series, 2000)

Van de Waterbeemd, H. & Rose, S. In The Practice of Medicinal Chemistry 2nd (ed Wermuth, L. G.) 1367–1385 (Academic Press, 2003).

Todeschini, R. & Consonni, V. Handbook of Molecular Descriptors (Wiley–VCH, Weinheim, 2000).

Cruciani, G., Crivori, P., Carrupt, P. A. & Testa, B. Molecular fields in quantitative structure–permeation relationships: The VolSurf approach. Theochem. 503, 17–30 (2000)

Buchwald, P. & Bodor, N. Computer-aided drug design: the role of quantitative structure–property, structure–activity and structure–metabolism relationships (QSPR, QSAR, QSMR). Drug Future 27, 577–588 (2002).

Van de Waterbeemd, H., Smith, D. A. & Jones, B. C. Lipophilicity in PK design: methyl, ethyl, futile. J. Comput. Aid. Mol. Des. 15, 273–286 (2001).

Van de Waterbeemd, H. In Pharmacokinetic Challenges in Drug Discovery (Eds) 213–234 (Ernst-Schering Research Foundation Workshop Series No. 37, Springer, 2001).

Walther, B., Vis, P. & Taylor, A. In: Lipophilicity in Drug Action and Toxicology (eds Pliska. V., Testa, B., Van de Waterbeemd, H.) 253–261 (VCH, Weinheim, 1996).

Bevan, C. D. & Lloyd, R. S. A high-throughput screening method for the determination of aqueous drug solubility using laser nephelometry in microtiter plates. Anal. Chem. 72, 1781–1787 (2000).

Jorgensen, W. L. & Duffy, E. M. Prediction of drug solubility from structure. Adv. Drug Del. Rev. 54, 355–366 (2002).

Bergstrom, C. A. S., Norinder, U., Luthman, K. & Artursson, P. Experimental and computational screening models for prediction of aqueous drug solubility. Pharm. Res. 19, 182–188 (2002).

Hilal, S. H., Karickhoff, S. W. & Carreira, L. A. A rigorous test for SPARC's chemical reactivity models: estimation of more than 4300 ionization pKas. Quant. Struct.-Act. Relat. 14, 348–355 (1995).

Raevsky, O. A., Fetisov, V. I., Trepalina, E. P., McFarland, J. W. & Schaper, K. -J. Quantitative estimation of drug absorption in humans for passively transported compounds on the basis of their physico-chemical parameters. Quant. Struct.-Act. Relat. 19, 366–374 (2000).

Stenberg, P., Norinder, U., Luthman, K. & Artursson, P. Experimental and computational screening models for the prediction of intestinal drug absorption. J. Med. Chem. 44, 1927–1937 (2001).

Ertl, P., Rohde, B. & Selzer, P. Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties. J. Med. Chem. 43, 3714–3717 (2000).

Kulkarni, A., Yi, H. & Hopfinger, A. J. Predicting Caco-2 cell permeation coefficients of organic molecules using membrane-interaction QSAR analysis. J. Chem. Inf. Comput. Sci. 42, 331–342 (2002).

Cummins, C. L., Jacobsen, W. & Benet, L. Z. Unmasking the dynamic interplay between intestinal P-glycoprotein and CYP3A4. J. Pharmacol. Exp. Ther. 300, 1036–1045 (2002).

Gumbleton, M. & Audus, K. L. Progress and limitations in the use of in vitro cell cultures to serve as a permeability screen for the blood-brain barrier. J. Pharm. Sci, 90, 1681–1698 (2001).

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. 90, 749–784 (2001). Key reference for oral absorption data of 169 compounds.

Van de Waterbeemd, H. Quantitative structure-absorption relationships. In: Pharmacokinetic Optimization in Drug Research: Biological, Physicochemical and Computational Strategies, Testa, B., Van de Waterbeemd, H., Folkers, G. & Guy, R. (Eds), Verlag HCA, Zurich (2001), pp. 499–511.

Norinder, U. & Österberg, T. Theoretical calculation and prediction of drug transport processes using simple parameters and partial least squares projections to latent structures (PLS) statistics. The use of electrotopological state indices. J. Pharm. Sci. 90, 1076–1085 (2001).

Yu, L. X., Gatlin, L. & Amidon, G. L. Predicting oral drug absorption in humans. Drugs Pharm. Sci. 102 (Transport Processes in Pharmaceutical Systems), 377–409 (2000).

Ho, N. F. H., Raub, T. J., Burton, P. S., Barsuhn, C. L., Adson, A., Audus, K. L. & Borchardt, R. T. Quantitative approaches to delineate passive transport mechanisms in cell culture monolayers. Drugs Pharm. Sci. 102 (Transport Processes in Pharmaceutical Systems), 219–316 (2000).

Agatonovic-Kustrin, S., Beresford, R. & Yusof, A. P. M. Theoretically-derived molecular descriptors important in human intestinal absorption. J. Pharm. Biomed. Anal. 25, 227–237 (2001).

Fu, X. C., Liang, W. Q. & Yu, Q. S. Correlation of drug absorption with molecular charge distribution. Pharmazie 56, 267–268 (2001).

Agoram, B., Woltosz, W. S. & Bolger, M. B. Predicting the impact of physiological and biochemical processes on oral drug bioavailability. Adv. Drug Del. Rev. 90, S41–S67 (2001).

Norris, D. A., Leesman, G. D., Sinko, P. J. & Grass, G. M. Development of predictive pharmacokinetic simulation models for drug discovery. J. Contr. Rel. 65, 55–62 (2000).

Parrott, N. & Lavé, T. Prediction of intestinal absorption: comparative assessment of GastroPlus and iDEA. Eur. J. Pharm. Sci. 17, 51–61 (2002)

Veber, D. F., Johnson, S. R., Cheng, H. Y., Smith, B. R., Ward, K. W. & Kopple, K. D. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45, 2615–2623 (2002)

Yoshida, F. & Topliss, J. G. QSAR model for drug human oral bioavailability, J. Med. Chem. 43, 2575–2585 (2000). Good reference for data on bioavailability of more than 200 compounds.

Andrews, C. W., Bennett, L. & Yu, L. X. Predicting human oral bioavailability of a compound: development of a novel quantitative structure-bioavailability relationship. Pharm. Res. 17, 639–644 (2000)

Bains, W., Gilbert, R., Sviridenko, L., Gascon, J. -M., Scoffin, R., Birchall, K., Harvey, I. & Caldwell, J. Evolutionary computational methods to predict oral bioavailability QSPRs. Curr. Opin. Drug Disc. Dev. 5, 44–51 (2002)

Pintore, M., Van de Waterbeemd, H., Piclin, N. & Chrétien, J. R., Prediction of oral bioavailability by adaptive fuzzy partitioning, Eur. J. Med. Chem. (in the press).

Mandagere, A. K., Thompson, T. N. & Hwang, K. K. A graphical model for estimating oral bioavailability of drugs in humans and other species from their Caco-2 permeability and in vitro liver enzyme metabolic stability rates. J. Med. Chem. 45, 304–311 (2002)

De Lange, E. C. M. & Danhof, M. Considerations in the use of cerebrospinal fluid pharmacokinetics to predict brain target concentrations in the clinical setting. Clin. Pharmacokinet. 41, 691–703 (2002).

Van de Waterbeemd, H., Camenisch, G., Folkers, G., Chretien, J. R. & Raevsky O. A. Estimation of blood-brain barrier crossing of drugs using molecular size and shape, and H-bonding descriptors. J. Drug Target. 6, 151–165 (1998). Discussion of the critical physicochemical properties required for brain penetration.

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. 88, 815–821 (1999).

Feher, M., Sourial, E. & Schmidt, J. M. A simple model for the prediction of blood-brain partitioning. Int. J. Pharmaceut. 201, 239–247 (2000).

Crivori, P., Cruciani, G., Carrupt, P. A. & Testa, B. Predicting blood-brain barrier permeation from three-dimensional molecular structure. J. Med. Chem. 43, 2204–2216 (2000).

Ooms, F., Weber, P., Carrupt, P. A. & Testa, B. A simple model to predict blood-brain barrier permeation from 3D molecular fields. Biochim. Biophys. Acta 1587, 118–125 (2002).

Kaznessis, Y. N., Snow, M. E. & Blankley, C. J. Prediction of blood-brain partitioning using Monte Carlo simulations of molecules in water. J. Comput. Aid. Mol. Des. 15, 697–708 (2001).

Rose, K., Hall, L. H. & Kier, L. B. Modeling blood-brain barrier partitioning using the electrotopological state. J. Chem. Inf. Comput. Sci. 42, 651–666 (2002).

Abraham, M. H. & Platts, J. A. Physicochemical factors that influence brain uptake. Blood-Brain Barrier Drug Delivery CNS, 9–32 (2000).

Ayrton, A. & Morgan, P. The role of transport proteins in drug absorption, distribution and excretion. Xenobiotica 31, 469–497 (2001).

Van Asperen, J., Mayer, U., Van Tellingen, O. & Beijnen, J. H. The functional role of P-glycoprotein in the blood-brain barrier. J. Pharm. Sci. 86, 881–884 (1997).

Wiese, M. & Pajeva, I. K. Structure-activity relationships of multidrug resistance reversers. Curr. Med. Chem. 8, 685–713 (2001).

Penzotti, J. E., Lamb, M. L., Evensen, E. & Grootenhuis, P. D. J. A computational ensemble pharmacophore model for identifying substrates of P-glycoprotein. J. Med. Chem. 45, 1737–1740 (2002).

Seelig, A. A general pattern for substrate recognition by P-glycoprotein. Eur. J. Biochem. 251, 252–261 (1998).

Seelig, A. & Landwojtowicz, E. Structure-activity relationship of P-glycoprotein substrates and modifiers. Eur. J. Pharm. Sci. 12, 31–40 (2000).

Seelig, A., Blatter, X. L. & Wohnsland, F. Substrate recognition by p-glycoprotein and the multidrug resistance-associated protein MRP1: a comparison. Int. J. Clin. Pharmacol. Ther. 38, 111–121 (2000).

Österberg, Th. & Norinder, U. Theoretical calculation and prediction of P-glycoprotein-interacting drugs using MolSurf parametrization and PLS statistics. Eur. J. Pharm. Sci. 10, 295–303 (2000).

Pajeva, I. K. & Wiese, M. Human P-glycoprotein pseudoreceptor modeling: 3D-QSAR study on thioxanthene type multidrug resistance modulators. Quant. Struct. Act. Relat. 20, 130–138 (2001).

Pajeva, I. K. & Wiese, M. Pharmacophore model of drugs involved in P-glycoprotein multidrug resistance: Explanation of structural variety (Hypothesis). J. Med. Chem. 45, 5671–5686 (2002).

Ekins, S., Kim, R. B., Leake, B. F., Dantzig, A. H., Schuetz, E. G., Lan, L. -B., Yasuda, K., Shepard, R. L., Winter, M. A., Scheutz, J. D., Wikel, J. H. & Wrighton, S. A. Three-dimensional quantitative structure-activity relationships of inhibitors of P-glycoprotein. Mol. Pharmacol. 61, 964–973 (2002).

Goh, L. -B., Spears, K. J., Yao, D., Ayrton, A., Morgan, P., Wolf, C. R. & Friedberg, T. Endogenous drug transporters in in vitro and in vivo models for the prediction of drug disposition in man. Biochem. Pharmacol. 64, 1569–1578 (2002).

Zhang, E. Y., Phelps, M. A., Cheng C., Ekins, S. & Swaan, P. W. Modeling of active transport systems. Adv. Drug Del. Revs. 54, 329–354 (2002)

Pugh, W. J., Degim, I. T. & Hadgraft, J. Epidermal permeability-penetrant structure relationships. 4. QSAR of permeant diffusion across human stratum corneum in terms of molecular weight, H-bonding and electronic charge. Int. J. Pharm. 197, 203–211 (2000).

Ghafourian, T. & Fooladi, S. The effect of structural QSAR parameters on skin penetration. Int. J. Pharm. 217, 1–11 (2001).

Smith, D. A., Van de Waterbeemd, H. & Walker, D. K. Pharmacokinetics and Metabolism in Drug Design, (Wiley–VCH, Weinheim, Germany, 2001).

Colmenarejo, G., Alvarez-Pedraglio, A. & Lavandera, J. -L. Chemoinformatic models to predict binding affinities to human serum albumin. J. Med. Chem. 44, 4370–4378 (2001).

Saiakhov, R. D., Stefan, L. R. & Klopman, G. Multiple computer-automated structure evaluation model of the plasma protein binding affinity of diverse drugs. Perspec. Drug Disc. Des. 19, 133–155 (2000).

Kratochwil, N. A., Huber, W., Müller, F., Kansy, M. & Gerber, P. Predicting plasmas protein binding of drugs: A new approach. Biochem. Pharmacol. 64, 1355–1374 (2002).

Lombardo, F., Obach, R. S., Shalaeva, M. Y. & Gao, F. Prediction of volume of distribution values in humans for neutral and basic drugs using physicochemical measurements and plasma protein binding data. J. Med. Chem. 45, 2867–2876 (2002).

Van de Graaf, P. H., Nilsson, J., Van Schaick, E. A. & Danhof, M. Multivariate quantitative structure-pharmacokinetic relationships (QSPKR) analysis of adenosine A1 receptor agonists in rat. J. Pharm. Sci. 88, 306–312 (1999).

Schneider, G., Coassolo, P. & Lavé, T. Combining in vitro and in vivo pharmacokinetic data for prediction of hepatic drug clearance in humans by artificial neural networks and multivariate statistical techniques. J. Med. Chem. 42, 5072–5076 (1999).

Qui–ones, C., Caceres, J., Stud, M. & Martinez, A. Prediction of drug half-life values of antihistamines based on the CODES/neural network model. Quant. Struct.-Act. Relat. 19, 448–454 (2000).

Poulin, P. & Theil, F. P. A priori prediction of tissue: plasma partition coefficients of drugs to facilitate the use of physiologically-based pharmacokinetic models in drug discovery. J. Pharm. Sci. 89, 16–35 (2000).

Poulin, P., Schoenlein, K. & Theil, F. P. Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs. J. Pharm. Sci. 90, 436–447 (2001).

Ekins, S., Bravi, G., Binkley, S., Gillespie, J. S., Ring, B. J., Wikel, J. H. & Wrighton, S. A. Three- and four-dimensional quantitative structure-activity relationship (3D/4D-QSAR) analyses of CYP2C9 inhibitors. Drug Metab. Dispos. 28, 994–1002 (2000).

De Groot, M., Ackland, M. J., Horne, V. A., Alex, A. & Jones, B. C. Novel approach to predicting P450-mediated drug metabolism: Development of a combined protein and pharmacophore model for CYP2D6. J. Med. Chem. 42, 1515–1524 (1999).

Ekins, S., De Groot, M. & Jones, J. P. Pharmacophore and three-dimensional quantitative structure-activity relationship methods for modelling cytochrome P450 active sites. Drug Metab. Dispos. 29, 936–944 (2001).

Zuegge, J., Fechner, U., Roche, O., Parratt, N. J., Engkvist, O. & Schneider, G. A fast virtual screening filter for cytochrome P450 3A4 inhibition liability of compound libraries, Quant. Struct. Act. Relat. 21, 249–256 (2002).

Higgins, L., Korzekwa, K. R., Rao, S., Shou, M. & Jones, J. P. An assessment of the reaction energetics for cytochrome P450-mediated reactions. Arch Biochem Biophys 385, 220–230 (2001).

Langowski, J. & Long, A. Computer systems for the prediction of xenobiotic metabolism. Adv. Drug Delivery Rev. 54, 407–415 (2002).

Ehrhardt, P. W. (Ed): Drug Metabolism Databases and High–Throughput Testing During Drug Design and Development (IUPAC, Blackwell Science, Malden, Massachussetts, 1999).

Richard, A. M. & Benigni R. AI and SAR approaches for predicting chemical carcinogenicity: survey and status report. SAR and QSAR Environ. Res. 13, 1–19 (2002).

Greene, N., Computer systems for the prediction of toxicity: an update. Adv. Drug Deliv. Rev. 54, 417–431 (2002).

Durham, S. K. & Pearl, G. M. Computational methods to predict drug safety liabilities. Drug Disc. Dev. 4, 110–115 (2001).

Roche, O., Trube, G., Zuegge, J., Pflimlin, P., Alanine, A. & Schneider, G. A virtual screening method for prediction of the hERG potassium channel liability of compound libraries. ChemBioChem 3, 455–459 (2002).

Cavalli, A., Poluzzi, E., De Ponti, F. & Recanatini, M. Toward a pharmacophore for drugs inducing long QT syndrome: Insights from a CoMFA study of HERG K+ channel blockers. J. Med. Chem, 45, 3844–3853 (2002).

Fischer, H., Kansy, M., Potthast, M. & Csato, M. Prediction of in vitro phospholipidosis of drugs by means of their amphiphilic properties. In: Rational Approaches to Drug Design (eds. Höltje, H.-D. & Sippl, W.) 286–289 (Prous Science, Barcelona, 2001).

Bonnabry, P., Sievering, J., Leemann, Th. & Dayer, P. Quantitative drug interactions prediction system (Q-DIPS). Clin. Pharmacokinet. 40, 631–640 (2001)

Willson, T. M. & Kliewer, S. A. PXR, CAR and drug metabolism. Nature Rev. Drug Disc. 1, 259–266 (2002).

Farr–Jones, S., Computational methods to predict ADME/tox properties for drug discovery, Decision Resources Inc. November 28 (2001).

Clark, D. E. & Grootenhuis, P. D. J. Progress in computational methods for the prediction of ADMET properties. Curr. Opin. Drug Disc. Dev. 5, 382–390 (2002).

Smith, D. A. Hello Drug Discovery, I am from Insilico, take me to your president. Drug Disc. Today 7, 1080–1081 (2002).

Anderson, R. J. 20/20 Vision: A brave new world of drug development. Curr. Drug Disc. 25–29 (August 2002).

Sietsema, W. K. The absolute oral bioavailability of selected drugs. Int. J. Clin. Pharmacol. Ther. Toxicol. 27, 179–211 (1989).

Thummel, K. E. & Shen, D. D. In Goodman & Gilman's The Pharmacological Basis of Therapeutics (eds Hardman, J. G. & Limbird, L. E.) 1917–2023 (McGraw-Hill, New York, 2001). Good reference for pharmacokinetic data of over 300 marketed drugs.

Sakaeda, T., Okamura, N., Nagata, S., Yagami, T., Horinouchi, M., Okumura, K., Yamashita, F. & Hashida M. Molecular and pharmacokinetic properties of 222 commercially available oral drugs in humans. Biol. Pharm. Bull. 24, 935–940 (2001).

Van de Waterbeemd, H. & De Groot, M., Can the Internet help to meet the challenges in ADME and e-ADME? SAR QSAR Environ. Res. 13, 391–401 (2002).