Quantitative structure–pharmacokinetic/pharmacodynamic relationships
Tài liệu tham khảo
Abdel-Rahman, 2004, The integration of pharmacokinetics and pharmacodynamics: understanding dose–response, Annu. Rev. Pharmacol. Toxicol., 44, 111, 10.1146/annurev.pharmtox.44.101802.121347
Hansch, 1995
Van de Waterbeemd, 1986
Hansch, 1964, Rho–sigma–pi analysis. A method for the correlation of biological activity and chemical structure, J. Am. Chem. Soc., 86, 1616, 10.1021/ja01062a035
Smith, 2001
Meyer, 1899, Zur Theories der Alkoholnarkose. 1. Welche Eigenschaft der Anasthetica bedingt ihre narkotische Wirkung?, Arch. Exp. Pathol. Pharmakol., 42, 109, 10.1007/BF01834479
Overton, 1897, Uber die osmotischen Eigenschaften der Zelle in ihrer Bedeutung fur die Toxikologie und Pharmakologie, Z. Phys. Chem., 22, 189, 10.1515/zpch-1897-2220
Free, 1964, A mathematical contribution to structure–activity studies, J. Med. Chem., 53, 395, 10.1021/jm00334a001
Fujita, 1971, Structure–activity study of phenethylamines as substrates of biosynthetic enzymes of sympathetic transmitters, J. Med. Chem., 14, 148, 10.1021/jm00284a016
Hansch, 1972, Linear relationships between lipophilic character and biological activity of drugs, J. Pharm. Sci., 61, 1, 10.1002/jps.2600610102
Collander, 1951, Partition of organic compounds between higher alcohols and water, Acta Chem. Scand., 5, 774, 10.3891/acta.chem.scand.05-0774
Hansch, 1973, Lipophilic character and biological activity of drugs. II. The parabolic case, J. Pharm. Sci., 62, 1, 10.1002/jps.2600620102
Higuchi, 1970, Thermodynamic analysis of structure–activity relationships of drugs: prediction of optimal structure, J. Pharm. Sci., 59, 1376, 10.1002/jps.2600591003
McFarland, 1970, On the parabolic relationship between drug potency and hydrophobicity, J. Med. Chem., 13, 1192, 10.1021/jm00300a040
Kubinyi, 1976, Quantitative structure–activity relationships. IV. Non-linear dependence of biological activity on hydrophobic character: a new model, Arzneim.-Forsch., 26, 1991
Kubinyi, 1978, Drug partitioning: relationships between forward and reverse rate constants and partition coefficient, J. Pharm. Sci., 67, 262, 10.1002/jps.2600670237
Balaz, 1992, A time hierarchy-based model for kinetics of drug disposition and its use in quantitative structure–activity relationships, J. Pharm. Sci., 81, 849, 10.1002/jps.2600810902
Balaz, 1996, Kinetics of subcellular distribution of multiply ionizable compounds: a mathematical description and its use in QSAR, J. Theor. Biol., 178, 7, 10.1006/jtbi.1996.0002
Dvorsky, 1997, Kinetics of subcellular distribution of compounds in simple biosystems and its use in QSAR, J. Theor. Biol., 185, 213, 10.1006/jtbi.1996.0308
Buchwald, 2005, General linearized biexponential model for QSAR data showing bilinear-type distribution, J. Pharm. Sci., 94, 2355, 10.1002/jps.20438
Aarons, 1982, Parabolic structure–activity relationships: a simple pharmacokinetic model, J. Pharm. Pharmacol., 34, 746, 10.1111/j.2042-7158.1982.tb06217.x
Karelson, 2000
El Tayar, 1991, Partitioning of solutes in different solvent systems: the contribution of hydrogen-bonding capacity and polarity, J. Pharm. Sci., 80, 590, 10.1002/jps.2600800619
Meylan, 1995, Atom/fragment contribution method for estimating octanol–water partition coefficients, J. Pharm. Sci., 84, 83, 10.1002/jps.2600840120
Taskinen, 2003, Prediction of physicochemical properties based on neural network modeling, Adv. Drug Deliv. Rev., 55, 1163, 10.1016/S0169-409X(03)00117-0
Tetko, 2004, Application of ALOGPS to predict 1-octanol/water distribution coefficients, logP, and logD, of AstraZeneca in-house database, J. Pharm. Sci., 93, 3103, 10.1002/jps.20217
Testa, 1996, Lipophilicity in molecular modeling, Pharm. Res., 13, 335, 10.1023/A:1016024005429
Kier, 1971, vol. 10
Taft, 1952, Polar and steric substituent constants for aliphatic and o-benzoate groups from rates of esterification and hydrolysis of esters, J. Am. Chem. Soc., 74, 3120, 10.1021/ja01132a049
Connolly, 1983, Solvent-accessible surfaces of proteins and nucleic acids, Science, 221, 709, 10.1126/science.6879170
Kier, 1976
Geladi, 1986, Partial least-squares regression: a tutorial, Anal. Chim. Acta, 185, 1, 10.1016/0003-2670(86)80028-9
Cramer, 1988, Comparative molecular field analysis (CoMFA). 1. Effect of shape on binding of steroids to carrier proteins, J. Am. Chem. Soc., 110, 5959, 10.1021/ja00226a005
Klebe, 1994, Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity, J. Med. Chem., 37, 4130, 10.1021/jm00050a010
Erb, 1993, Introduction to backpropagation neural network computation, Pharm. Res., 10, 165, 10.1023/A:1018966222807
Agatonovic-Kustrin, 2000, Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research, J. Pharm. Biomed. Anal., 22, 717, 10.1016/S0731-7085(99)00272-1
Winkler, 2004, Neural networks as robust tools in drug lead discovery and development, Mol. Biotechnol., 27, 139, 10.1385/MB:27:2:139
Bailer-Jones, 1998, A recurrent neural network for modelling dynamical systems, Netw. Comput. Neural Syst., 9, 531, 10.1088/0954-898X/9/4/008
Neal, 1996
Burden, 2001, Quantitative structure–activity relationship studies using Gaussian processes, J. Chem. Inf. Comput. Sci., 41, 830, 10.1021/ci000459c
Stouch, 2003, In silico ADME/Tox: why models fail, J. Comput. Aided Mol. Des., 17, 83, 10.1023/A:1025358319677
Seydel, 1981, Quantitative structure–pharmacokinetic relationships and drug design, Pharmacol. Ther., 15, 131, 10.1016/0163-7258(81)90040-1
Austel, 1983, Absorption, distribution, and metabolism of drugs, 437
Mayer, 1985, Development of quantitative structure–pharmacokinetic relationships, Environ. Health Perspect., 61, 295, 10.2307/3430080
Ekins, 2000, Progress in predicting human ADME parameters in silico, J. Pharmacol. Toxicol. Methods, 44, 251, 10.1016/S1056-8719(00)00109-X
van de Waterbeemd, 2003, ADMET in silico modelling: towards prediction paradise?, Nat. Rev. Drug Discov., 2, 192, 10.1038/nrd1032
Yamashita, 2004, In silico approaches for predicting ADME properties of drugs, Drug Metab. Pharmacokinet., 19, 327, 10.2133/dmpk.19.327
Martinez, 2002, A mechanistic approach to understanding the factors affecting drug absorption: a review of fundamentals, J. Clin. Pharmacol., 42, 620, 10.1177/00970002042006005
Cummins, 2002, Unmasking the dynamic interplay between intestinal P-glycoprotein and CYP3A4, J. Pharmacol. Exp. Ther., 300, 1036, 10.1124/jpet.300.3.1036
Amidon, 1995, A theoretical basis for a biopharmaceutic drug classification: the correlation of in vitro drug product dissolution and in vivo bioavailability, Pharm. Res., 12, 413, 10.1023/A:1016212804288
Wu, 2005, Predicting drug disposition via application of BCS: transport/absorption/elimination interplay and development of a biopharmaceutics drug disposition classification system, Pharm. Res., 22, 11, 10.1007/s11095-004-9004-4
Wagner, 1965, Blood levels of drug at the equilibrium state after multiple dosing, Nature, 207, 1301, 10.1038/2071301a0
Jusko, 1992, Guidelines for collection and analysis of pharmacokinetic data
Watari, 1988, Prediction of hepatic first-pass metabolism and plasma levels following intravenous and oral administration of barbiturates in the rabbit based on quantitative structure–pharmacokinetic relationships, J. Pharmacokinet. Biopharm., 16, 279, 10.1007/BF01062138
Wagner, 1971
Kubinyi, 1979, Lipophilicity and biological activity. Drug transport and drug distribution in model systems and in biological systems, Arzneimittelforschung, 29, 1067
Bermejo, 1999, Validation of a biophysical drug absorption model by the PATQSAR system, J. Pharm. Sci., 88, 398, 10.1021/js980370+
Camenisch, 1996, Review of theoretical passive drug absorption models: historical background, recent developments and limitations, Pharm. Acta Helv., 71, 309, 10.1016/S0031-6865(96)00031-3
Camenisch, 1998, Shapes of membrane permeability–lipophilicity curves: extension of theoretical models with an aqueous pore pathway, Eur. J. Pharm. Sci., 6, 325, 10.1016/S0928-0987(98)00033-5
Dressman, 1985, Absorption potential: estimating the fraction absorbed for orally administered compounds, J. Pharm. Sci., 74, 588, 10.1002/jps.2600740523
Lipinski, 1997, Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Deliv. Rev., 23, 3, 10.1016/S0169-409X(96)00423-1
Palm, 1997, Polar molecular surface properties predict the intestinal absorption of drugs in humans, Pharm. Res., 14, 568, 10.1023/A:1012188625088
Clark, 1999, Rapid calculation of polar molecular surface area and its application to the prediction of transport phenomena. 1. Prediction of intestinal absorption, J. Pharm. Sci., 88, 807, 10.1021/js9804011
Veber, 2002, Molecular properties that influence the oral bioavailability of drug candidates, J. Med. Chem., 45, 2615, 10.1021/jm020017n
Kelder, 1999, Polar molecular surface as a dominating determinant for oral absorption and brain penetration of drugs, Pharm. Res., 16, 1514, 10.1023/A:1015040217741
Johnson, 2006, Recent progress in the computational prediction of aqueous solubility and absorption, AAPS J, 8, E27, 10.1208/aapsj080104
Stenberg, 1999, Prediction of the intestinal absorption of endothelin receptor antagonists using three theoretical methods of increasing complexity, Pharm. Res., 16, 1520, 10.1023/A:1015092201811
Liu, 2005, The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine, J. Comput. Aided Mol. Des., 19, 33, 10.1007/s10822-005-0095-8
Hirono, 1994, Non-congeneric structure–pharmacokinetic property correlation studies using fuzzy adaptive least-squares: oral bioavailability, Biol. Pharm. Bull., 17, 306, 10.1248/bpb.17.306
Andrews, 2000, Predicting human oral bioavailability of a compound: development of a novel quantitative structure–bioavailability relationship, Pharm. Res., 17, 639, 10.1023/A:1007556711109
Turner, 2004, Bioavailability prediction based on molecular structure for a diverse series of drugs, Pharm. Res., 21, 68, 10.1023/B:PHAM.0000012154.09631.26
Pintore, 2003, Prediction of oral bioavailability by adaptive fuzzy partitioning, Eur. J. Med. Chem., 38, 427, 10.1016/S0223-5234(03)00052-7
Yoshida, 2000, QSAR model for drug human oral bioavailability, J. Med. Chem., 43, 2575, 10.1021/jm0000564
Zmuidinavicius, 2003, Classification structure–activity relations (C-SAR) in prediction of human intestinal absorption, J. Pharm. Sci., 92, 621, 10.1002/jps.10321
MacKichan, 1992, Influence of protein binding and use of unbound (free) drug concentrations
Wilkinson, 1983, Plasma and tissue binding considerations in drug disposition, Drug Metab. Rev., 14, 427, 10.3109/03602538308991396
Ebling, 1986, 6 alpha-Methylprednisolone and 6 alpha-methylprednisone plasma protein binding in humans and rabbits, J. Pharm. Sci., 75, 760, 10.1002/jps.2600750807
Bohl, 1992, Theoretical investigations on steroid structure and quantitative structure–activity relationships, 91
Toon, 1983, Structure–pharmacokinetic relationships among the barbiturates in the rat, J. Pharmacol. Exp. Ther., 225, 752
Van der Graaf, 1999, Multivariate quantitative structure–pharmacokinetic relationships (QSPKR) analysis of adenosine A1 receptor agonists in rat, J. Pharm. Sci., 88, 306, 10.1021/js980294a
Mager, 2002, Quantitative structure–pharmacokinetic/pharmacodynamic relationships of corticosteroids in man, J. Pharm. Sci., 91, 2441, 10.1002/jps.10231
Colmenarejo, 2001, Cheminformatic models to predict binding affinities to human serum albumin, J. Med. Chem., 44, 4370, 10.1021/jm010960b
Gobburu, 1995, Quantitative structure–pharmacokinetic relationships (QSPR) of beta blockers derived using neural networks, J. Pharm. Sci., 84, 862, 10.1002/jps.2600840715
Turner, 2004, Pharmacokinetic parameter prediction from drug structure using artificial neural networks, Int. J. Pharm., 270, 209, 10.1016/j.ijpharm.2003.10.011
Yap, 2005, Quantitative structure–pharmacokinetic relationships for drug distribution properties by using general regression neural network, J. Pharm. Sci., 94, 153, 10.1002/jps.20232
Gillette, 1971, Factors affecting drug metabolism, Ann. N. Y. Acad. Sci., 179, 43, 10.1111/j.1749-6632.1971.tb46890.x
Lin, 1987, Protein binding as a primary determinant of the clinical pharmacokinetic properties of non-steroidal anti-inflammatory drugs, Clin. Pharmacokinet., 12, 402, 10.2165/00003088-198712060-00002
Balant-Gorgia, 1993, Pharmacokinetic optimisation of the treatment of psychosis, Clin. Pharmacokinet., 25, 217, 10.2165/00003088-199325030-00005
Hinderling, 1988, Drug distribution in the body: in vitro prediction and physiological interpretation, Prog. Pharmacol., 6, 1
Davis, 2000, Robust assessment of statistical significance in the use of unbound/intrinsic pharmacokinetic parameters in quantitative structure–pharmacokinetic relationships with lipophilicity, Drug Metab. Dispos., 28, 103
Herman, 1994, Quantitative structure–pharmacokinetic relationships for systemic drug distribution kinetics not confined to a congeneric series, J. Pharm. Sci., 83, 423, 10.1002/jps.2600830332
Hirono, 1994, Non-congeneric structure–pharmacokinetic property correlation studies using fuzzy adaptive least-squares: volume of distribution, Biol. Pharm. Bull., 17, 686, 10.1248/bpb.17.686
Lombardo, 2002, 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, 10.1021/jm0200409
Oie, 1979, Effect of altered plasma protein binding on apparent volume of distribution, J. Pharm. Sci., 68, 1203, 10.1002/jps.2600680948
Ghafourian, 2004, Quantitative structure–pharmacokinetic relationship modeling: apparent volume of distribution, J. Pharm. Pharmacol., 56, 339, 10.1211/0022357022890
Wajima, 2003, Prediction of human pharmacokinetics from animal data and molecular structural parameters using multivariate regression analysis: volume of distribution at steady state, J. Pharm. Pharmacol., 55, 939, 10.1211/0022357021477
Ng, 2004, Quantitative structure–pharmacokinetic parameters relationships (QSPKR) analysis of antimicrobial agents in humans using simulated annealing k-nearest neighbor and partial least-square analysis methods, J. Pharm. Sci., 93, 2535, 10.1002/jps.20117
Turner, 2003, Multiple pharmacokinetic parameter prediction for a series of cephalosporins, J. Pharm. Sci., 92, 552, 10.1002/jps.10314
Ritschel, 1995, Application of neural networks for the prediction of human pharmacokinetic parameters, Methods Find. Exp. Clin. Pharmacol., 17, 629
Gleeson, 2006, In silico human and rat Vss quantitative structure–activity relationship models, J. Med. Chem., 49, 1953, 10.1021/jm0510070
Hinderling, 1984, Quantitative relationships between structure and pharmacokinetics of beta-adrenoceptor blocking agents in man, J. Pharmacokinet. Biopharm., 12, 263, 10.1007/BF01061721
Gibaldi, 1982
Hall, 1984, Relationship between renal clearance, protein binding and urine flow for digitoxin, a compound of low clearance in the isolated perfused rat kidney, J. Pharmacol. Exp. Ther., 228, 174
Wesson, 1954, A theoretical analysis of urea excretion by the mammalian kidney, Am. J. Physiol., 179, 364, 10.1152/ajplegacy.1954.179.2.364
Mayer, 1988, Relationship between lipophilicity and tubular reabsorption for a series of 5-alkyl-5-ethylbarbituric acids in the isolated perfused rat kidney preparation, J. Pharm. Sci., 77, 359, 10.1002/jps.2600770416
Saville, 1992, Models of hepatic drug elimination, Drug Metab. Rev., 24, 49, 10.3109/03602539208996290
Rowland, 1973, Clearance concepts in pharmacokinetics, J. Pharmacokinet. Biopharm., 1, 123, 10.1007/BF01059626
Pang, 1977, Hepatic clearance of drugs. I. Theoretical considerations of a “well-stirred” model and a “parallel-tube” model. Influence of hepatic blood flow, plasma and red cell binding, and the hepatocellular enzymatic activity on hepatic drug clearance, J. Pharmacokinet. Biopharm., 5, 625, 10.1007/BF01059688
Houston, 1994, Utility of in vitro drug metabolism data in predicting in vivo metabolic clearance, Biochem. Pharmacol., 47, 1469, 10.1016/0006-2952(94)90520-7
Schneider, 1999, 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, 10.1021/jm991030j
Lewis, 2002, Structure–activity relationship for human cytochrome P450 substrates and inhibitors, Drug Metab. Rev., 34, 69, 10.1081/DMR-120001391
Hansch, 2004, QSAR of cytochrome P450, Drug Metab. Rev., 36, 105, 10.1081/DMR-120028428
Ekins, 2000, Three-dimensional quantitative structure activity relationship computational approaches for prediction of human in vitro intrinsic clearance, J. Pharmacol. Exp. Ther., 295, 463
Balakin, 2004, Quantitative structure–metabolism relationship modeling of metabolic N-dealkylation reaction rates, Drug Metab. Dispos., 32, 1111, 10.1124/dmd.104.000364
Balakin, 2004, Kohonen maps for prediction of binding to human cytochrome P450 3A4, Drug Metab. Dispos., 32, 1183, 10.1124/dmd.104.000356
Ekins, 2001, Pharmacophore and three-dimensional quantitative structure activity relationship methods for modeling cytochrome P450 active sites, Drug Metab. Dispos., 29, 936
Dedrick, 1970, Interspecies correlation of plasma concentration history of methotrexate (NSC-740), Cancer Chemother. Rep., 54, 95
Boxenbaum, 1983, Interspecies pharmacokinetic scaling and the Dedrick plots, Am. J. Physiol., 245, R768
West, 1997, A general model for the origin of allometric scaling laws in biology, Science, 276, 122, 10.1126/science.276.5309.122
West, 1999, The fourth dimension of life: fractal geometry and allometric scaling of organisms, Science, 284, 1677, 10.1126/science.284.5420.1677
Wajima, 2002, Prediction of human clearance from animal data and molecular structural parameters using multivariate regression analysis, J. Pharm. Sci., 91, 2489, 10.1002/jps.10242
Wajima, 2003, Prediction of human pharmacokinetics from animal data and molecular structural parameters using multivariate regression analysis: oral clearance, J. Pharm. Sci., 92, 2427, 10.1002/jps.10510
Yap, 2005, Quantitative structure–pharmacokinetic relationships for drug clearance by using statistical learning methods, J. Mol. Graph. Model., 24, 383, 10.1016/j.jmgm.2005.10.004
Fouchecourt, 2001, Quantitative structure–pharmacokinetic relationship modelling, Sci. Total Environ., 274, 125, 10.1016/S0048-9697(01)00743-4
Fouchecourt, 1999, Quantitative relationship between steady-state blood concentrations and structural features of aliphatic hydrocarbons, Toxicol. Lett., 110, 177, 10.1016/S0378-4274(99)00155-1
Khor, 2000, Pharmacokinetics, pharmacodynamics, allometry, and dose selection of rPSGL-Ig for phase I trial, J. Pharmacol. Exp. Ther., 293, 618
Wajima, 2004, Prediction of human pharmacokinetic profile in animal scale up based on normalizing time course profiles, J. Pharm. Sci., 93, 1890, 10.1002/jps.20099
Gobburu, 1996, Artificial neural networks as a novel approach to integrated pharmacokinetic–pharmacodynamic analysis, J. Pharm. Sci., 85, 505, 10.1021/js950433d
Chow, 1997, Application of neural networks to population pharmacokinetic data analysis, J. Pharm. Sci., 86, 840, 10.1021/js9604016
Gaweda, 2003, Pharmacodynamic population analysis in chronic renal failure using artificial neural networks—a comparative study, Neural Netw., 16, 841, 10.1016/S0893-6080(03)00084-4
Urquidi-Macdonald, 2004, Abciximab pharmacodynamic model with neural networks used to integrate sources of patient variability, Clin. Pharmacol. Ther., 75, 60, 10.1016/j.clpt.2003.09.008
Veng-Pedersen, 1993, Application of neural networks to pharmacodynamics, J. Pharm. Sci., 82, 918, 10.1002/jps.2600820910
Elman, 1990, Finding structure in time, Cogn. Sci., 14, 179, 10.1207/s15516709cog1402_1
Mager, 2006, Quantitative structure pharmacokinetic relationships (QSPKR) using Bayesian neural networks
Bonate, 2000, Prospective allometric scaling: does the emperor have clothes?, J. Clin. Pharmacol., 40, 335, 10.1177/00912700022009017
Yu, 1999, A compartmental absorption and transit model for estimating oral drug absorption, Int. J. Pharm., 186, 119, 10.1016/S0378-5173(99)00147-7
Sinko, 1991, Predicting fraction dose absorbed in humans using a macroscopic mass balance approach, Pharm. Res., 8, 979, 10.1023/A:1015892621261
Usansky, 2005, Estimating human drug oral absorption kinetics from Caco-2 permeability using an absorption–disposition model: model development and evaluation and derivation of analytical solutions for k(a) and F(a), J. Pharmacol. Exp. Ther., 314, 391, 10.1124/jpet.104.076182
Willmann, 2004, A physiological model for the estimation of the fraction dose absorbed in humans, J. Med. Chem., 47, 4022, 10.1021/jm030999b
Gerlowski, 1983, Physiologically based pharmacokinetic modeling: principles and applications, J. Pharm. Sci., 72, 1103, 10.1002/jps.2600721003
Nestorov, 2003, Whole body pharmacokinetic models, Clin. Pharmacokinet., 42, 883, 10.2165/00003088-200342100-00002
D'Souza, 1988, Physiological pharmacokinetic models: some aspects of theory, practice and potential, Toxicol. Ind. Health, 4, 151, 10.1177/074823378800400202
Brown, 1997, Physiological parameter values for physiologically based pharmacokinetic models, Toxicol. Ind. Health, 13, 407, 10.1177/074823379701300401
Xu, 2003, Physiologically-based pharmacokinetics and molecular pharmacodynamics of 17-(allylamino)-17-demethoxygeldanamycin and its active metabolite in tumor-bearing mice, J. Pharmacokinet. Pharmacodyn., 30, 185, 10.1023/A:1025542026488
Grass, 2002, Physiologically-based pharmacokinetic simulation modelling, Adv. Drug Deliv. Rev., 54, 433, 10.1016/S0169-409X(02)00013-3
Theil, 2003, Utility of physiologically based pharmacokinetic models to drug development and rational drug discovery candidate selection, Toxicol. Lett., 138, 29, 10.1016/S0378-4274(02)00374-0
Blakey, 1997, Quantitative structure–pharmacokinetics relationships: I. Development of a whole-body physiologically based model to characterize changes in pharmacokinetics across a homologous series of barbiturates in the rat, J. Pharmacokinet. Biopharm., 25, 277, 10.1023/A:1025771608474
Nestorov, 1998, Quantitative structure–pharmacokinetics relationships: II. A mechanistically based model to evaluate the relationship between tissue distribution parameters and compound lipophilicity, J. Pharmacokinet. Biopharm., 26, 521, 10.1023/A:1023221116200
Nestorov, 1999, Empirical versus mechanistic modelling: comparison of an artificial neural network to a mechanistically based model for quantitative structure pharmacokinetic relationships of a homologous series of barbiturates, AAPS PharmSci, 1, E17, 10.1208/ps010417
DeJongh, 1997, A quantitative property–property relationship (QPPR) approach to estimate in vitro tissue–blood partition coefficients of organic chemicals in rats and humans, Arch. Toxicol., 72, 17, 10.1007/s002040050463
Poulin, 2000, 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, 10.1002/(SICI)1520-6017(200001)89:1<16::AID-JPS3>3.0.CO;2-E
Poulin, 2001, Prediction of adipose tissue: plasma partition coefficients for structurally unrelated drugs, J. Pharm. Sci., 90, 436, 10.1002/1520-6017(200104)90:4<436::AID-JPS1002>3.0.CO;2-P
Poulin, 2002, Prediction of pharmacokinetics prior to in vivo studies. II. Generic physiologically based pharmacokinetic models of drug disposition, J. Pharm. Sci., 91, 1358, 10.1002/jps.10128
Poulin, 2002, Prediction of pharmacokinetics prior to in vivo studies. 1. Mechanism-based prediction of volume of distribution, J. Pharm. Sci., 91, 129, 10.1002/jps.10005
Luttringer, 2003, Physiologically based pharmacokinetic (PBPK) modeling of disposition of epiroprim in humans, J. Pharm. Sci., 92, 1990, 10.1002/jps.10461
Parrott, 2005, An evaluation of the utility of physiologically based models of pharmacokinetics in early drug discovery, J. Pharm. Sci., 94, 2327, 10.1002/jps.20419
Jones, 2006, A novel strategy for physiologically based predictions of human pharmacokinetics, Clin. Pharmacokinet., 45, 511, 10.2165/00003088-200645050-00006
Liu, 2005, Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines, J. Comput. Aided Mol. Des., 19, 499, 10.1007/s10822-005-9003-5
Gueorguieva, 2004, Fuzzy simulation of pharmacokinetic models: case study of whole body physiologically based model of diazepam, J. Pharmacokinet. Pharmacodyn., 31, 185, 10.1023/B:JOPA.0000039564.35602.78
Roy, 2004, Physicochemical properties of neuromuscular blocking agents and their impact on the pharmacokinetic–pharmacodynamic relationship, Br. J. Anaesth., 93, 241, 10.1093/bja/aeh181
Yang, 1998, Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling, Environ. Health Perspect., 106, 1385, 10.1289/ehp.98106s61385
Levy, 1998, Impact of pharmacodynamic variability on drug delivery(1), Adv. Drug Deliv. Rev., 33, 201, 10.1016/S0169-409X(98)00028-3
Mager, 2003, Diversity of mechanism-based pharmacodynamic models, Drug Metab. Dispos., 31, 510, 10.1124/dmd.31.5.510
Dayneka, 1993, Comparison of four basic models of indirect pharmacodynamic responses, J. Pharmacokinet. Biopharm., 21, 457, 10.1007/BF01061691
Rohatagi, 1996, Mathematical modeling of cortisol circadian rhythm and cortisol suppression, Eur. J. Pharm. Sci., 4, 341, 10.1016/S0928-0987(96)00174-1
Derendorf, 1991, Pharmacokinetics and oral bioavailability of hydrocortisone, J. Clin. Pharmacol., 31, 473, 10.1002/j.1552-4604.1991.tb01906.x
Derendorf, 1993, Receptor-based pharmacokinetic–pharmacodynamic analysis of corticosteroids, J. Clin. Pharmacol., 33, 115, 10.1002/j.1552-4604.1993.tb03930.x
Jusko, 2001, Relationship of dose- and time-dependent corticosteroid responses to receptor turnover, 95
Wolff, 1978, Nature of steroid–glucocorticoid receptor interactions: thermodynamic analysis of the binding reaction, Biochemistry, 17, 3201, 10.1021/bi00609a005
Ramakrishnan, 2002, Fifth-generation model for corticosteroid pharmacodynamics: application to steady-state receptor down-regulation and enzyme induction patterns during seven-day continuous infusion of methylprednisolone in rats, J. Pharmacokinet. Pharmacodyn., 29, 1, 10.1023/A:1015765201129
Mager, 2003, Integrated QSPR—pharmacodynamic model of genomic effects of several corticosteroids, J. Pharm. Sci., 92, 881, 10.1002/jps.10343
Sun, 1998, Fourth-generation model for corticosteroid pharmacodynamics: a model for methylprednisolone effects on receptor/gene-mediated glucocorticoid receptor down-regulation and tyrosine aminotransferase induction in rat liver, J. Pharmacokinet. Biopharm., 26, 289, 10.1023/A:1020746822634
Jusko, 1994, Fifteen years of operation of a high-performance liquid chromatographic assay for prednisolone, cortisol and prednisone in plasma, J. Chromatogr., B Biomed. Appl., 658, 47, 10.1016/0378-4347(94)00218-5
Diamondstone, 1966, Assay of tyrosine aminotransferase activity by conversion of p-hydroxy-phenylpyruvate to p-hydroxybenzaldehyde, Anal. Biochem., 16, 395, 10.1016/0003-2697(66)90220-X
Nichols, 1989, Second generation model for prednisolone pharmacodynamics in the rat, J. Pharmacokinet. Biopharm., 17, 209, 10.1007/BF01059029
Nichols, 1990, Receptor-mediated prednisolone pharmacodynamics in rats: model verification using a dose-sparing regimen, J. Pharmacokinet. Biopharm., 18, 189, 10.1007/BF01062199
Yao, 2006, Modeling circadian rhythms of glucocorticoid receptor and glutamine synthetase expression in rat skeletal muscle, Pharm. Res., 23, 670, 10.1007/s11095-005-9608-3
Mahmood, 2005, Pharmacokinetic and pharmacodynamic considerations in the development of therapeutic proteins, Clin. Pharmacokinet., 44, 331, 10.2165/00003088-200544040-00001
Lobo, 2004, Antibody pharmacokinetics and pharmacodynamics, J. Pharm. Sci., 93, 2645, 10.1002/jps.20178